From 79402f56ea99826838298226cb2a89b2e2885519 Mon Sep 17 00:00:00 2001 From: Julian Giordani Date: Tue, 31 Oct 2023 18:07:35 +1100 Subject: [PATCH] V2.15.1b (#676) * Updating development version to 2.13-dev * Release guidelines update * Add Changes to Changelog (#604) * Remoev UWGeodynamics installation from dockerfiles * Petsc 3.17 compatibility and removing duplicate examples python scripts * Add petsc variants to conda recipe * Add python 3.10 conda builds to actions. * Change PETSC and MPICH versions * Fix MPICH complaining... * Getting Dockerfiles ready: Ubuntu 22.04LTS and python 3.10. Note that this will fail for now because of h5py not being compatible. PR is on its way. * Update README * Update README * Transfer UWGeo documentation to Underworld * Remove python3-numpy-dbg for now * Fix Documentation Links (UWGeodynamics) Fix visualisation imports * Fix imports (UWGeodynamics) * Change Depth documentation toctree * UWGeodynamics Merging * Run tests * Move tests * H5py 3.7 is released, Use it in docker. * Typo in Petsc Dockerfile * Getting UWGeo internals to use uw2 mpi4py interface (#612) * Getting UWGeo internals to use uw2 mpi4py interface * Update CHANGES.md * ask per 2.13 release (#615) 2.13 release Co-authored-by: Romain Beucher * Update _version.py * Fix for analytic ID, which changed during cmake update. Ganalytics should now be right * Typo fix for 3.10 * Dockerfile improvement - should be a single layer command * safer #define when not using an official release of petsc * Update PetscOptionsBegin() to be dev compatible. see https://gitlab.com/petsc/petsc/-/blob/f3ed01cd9ab452072c1a44e935347389f67d72c6/doc/docs/changes/dev.rst * Dockerfile updates. * Adding mpi dockerfiles - only openmpi for now, mpich coming soon. * Removing `base` image for now. * Resultant UW image is ~2.4Gb rather than 3Gb. * Add some documentation * Activate UWGeo testing * Updates github actions for Dockerfile change * Update CI.yml Fix typo * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Updating versions of github actions * Fixing petsc image name * Rebuild UW is the dockerfile changes * Add checks * Forgot github action variable * checks variable is working or not? * Can't get underworld check in github action to work ???!!! * Fix? * Typo fix * Update CI.yml debug * Update CI.yml always! * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Including vim in find docker image * Better way to look for h5 units. Previous h5py interface was causing erros on the c level. * Updates to build docker * Enable tests round 1 * Update CI.yml * Update CI.yml * Renmae UWGeo test, they take a while, might reintroduce later * Revert "Renmae UWGeo test, they take a while, might reintroduce later" This reverts commit d8a2268af752d7e43bb3a4c6f100b662c014f70a. * Smarter way to disable UWgeo tests for CI * Typo fix * Typo #2 * opss, fixes broken units check, thanks tests ;) * Typo #3 * type #4 * Enable multi arch build !!! * Update for dockerfiles and macos * Rename docker images * Update Dockerfile * Update Dockerfile new docker image names * Update Dockerfile new docker image names * CHAGES.md and checkin docker-builder.sh for manual docker image creation * docs/development/docker/docker-builder.sh * Updates for conda - still testing * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Testing conda-forge connection to github actions * Making petsc version a build-arg for the dockerfiles. * Typo and adding UW_VERSION to github actions too. * Update CI.yml Can't resolve tag in `container` of github actions!!! * Disable arm build for underworld image - easier for now. * Dockerfile go * Work please * Dockerfile madness * Pin down psutil as a dependency was updating it - breaking jupyter-lab * Updates * psutil set to 5.9.3 for github action builds * Touch Lavavu * Dockerfile touch ups * remove psutil confusion * setuptools 65.6.0 has an error, so use 65.5.1 * update lavavu * fix * Fix typo in docker label * v2.14 branch upto with dev development (#640) * Updating development version to 2.13-dev * Release guidelines update * Add Changes to Changelog (#604) * Remoev UWGeodynamics installation from dockerfiles * Petsc 3.17 compatibility and removing duplicate examples python scripts * Add petsc variants to conda recipe * Add python 3.10 conda builds to actions. * Change PETSC and MPICH versions * Fix MPICH complaining... * Getting Dockerfiles ready: Ubuntu 22.04LTS and python 3.10. Note that this will fail for now because of h5py not being compatible. PR is on its way. * Update README * Update README * Transfer UWGeo documentation to Underworld * Remove python3-numpy-dbg for now * Fix Documentation Links (UWGeodynamics) Fix visualisation imports * Fix imports (UWGeodynamics) * Change Depth documentation toctree * UWGeodynamics Merging * Run tests * Move tests * H5py 3.7 is released, Use it in docker. * Typo in Petsc Dockerfile * Getting UWGeo internals to use uw2 mpi4py interface (#612) * Getting UWGeo internals to use uw2 mpi4py interface * Update CHANGES.md * ask per 2.13 release (#615) 2.13 release Co-authored-by: Romain Beucher * Update _version.py * Fix for analytic ID, which changed during cmake update. Ganalytics should now be right * Typo fix for 3.10 * Dockerfile improvement - should be a single layer command * safer #define when not using an official release of petsc * Update PetscOptionsBegin() to be dev compatible. see https://gitlab.com/petsc/petsc/-/blob/f3ed01cd9ab452072c1a44e935347389f67d72c6/doc/docs/changes/dev.rst * Petsc 3.18 updates Petsc error handling updates only. Co-authored-by: Romain Beucher * Add build-args for openmpi Docker creation * Dockerfile updates (#1) * Dockerfile updates. * Adding mpi dockerfiles - only openmpi for now, mpich coming soon. * Removing `base` image for now. * Resultant UW image is ~2.4Gb rather than 3Gb. * Add some documentation * Activate UWGeo testing * Updates github actions for Dockerfile change * Update CI.yml Fix typo * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Updating versions of github actions * Fixing petsc image name * Rebuild UW is the dockerfile changes * Add checks * Forgot github action variable * checks variable is working or not? * Can't get underworld check in github action to work ???!!! * Fix? * Typo fix * Update CI.yml debug * Update CI.yml always! * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Including vim in find docker image * Better way to look for h5 units. Previous h5py interface was causing erros on the c level. * Updates to build docker * Enable tests round 1 * Update CI.yml * Update CI.yml * Renmae UWGeo test, they take a while, might reintroduce later * Revert "Renmae UWGeo test, they take a while, might reintroduce later" This reverts commit d8a2268af752d7e43bb3a4c6f100b662c014f70a. * Smarter way to disable UWgeo tests for CI * Typo fix * Typo #2 * opss, fixes broken units check, thanks tests ;) * Typo #3 * type #4 * Enable multi arch build !!! * Update for dockerfiles and macos * Rename docker images * Update Dockerfile * Update Dockerfile new docker image names * Update Dockerfile new docker image names * CHAGES.md and checkin docker-builder.sh for manual docker image creation * docs/development/docker/docker-builder.sh * Updates for conda - still testing * Update CI.yml * Update CI.yml * Update CI.yml * Update CI.yml * Testing conda-forge connection to github actions * Making petsc version a build-arg for the dockerfiles. * Typo and adding UW_VERSION to github actions too. * Update CI.yml Can't resolve tag in `container` of github actions!!! * Disable arm build for underworld image - easier for now. * Dockerfile go * Work please * Dockerfile madness * Pin down psutil as a dependency was updating it - breaking jupyter-lab * Updates * psutil set to 5.9.3 for github action builds * Touch Lavavu * Dockerfile touch ups * remove psutil confusion * setuptools 65.6.0 has an error, so use 65.5.1 * update lavavu * fix * Fix typo in docker label * v2.14 branch upto with dev development (#640) * Updating development version to 2.13-dev * Release guidelines update * Add Changes to Changelog (#604) * Remoev UWGeodynamics installation from dockerfiles * Petsc 3.17 compatibility and removing duplicate examples python scripts * Add petsc variants to conda recipe * Add python 3.10 conda builds to actions. * Change PETSC and MPICH versions * Fix MPICH complaining... * Getting Dockerfiles ready: Ubuntu 22.04LTS and python 3.10. Note that this will fail for now because of h5py not being compatible. PR is on its way. * Update README * Update README * Transfer UWGeo documentation to Underworld * Remove python3-numpy-dbg for now * Fix Documentation Links (UWGeodynamics) Fix visualisation imports * Fix imports (UWGeodynamics) * Change Depth documentation toctree * UWGeodynamics Merging * Run tests * Move tests * H5py 3.7 is released, Use it in docker. * Typo in Petsc Dockerfile * Getting UWGeo internals to use uw2 mpi4py interface (#612) * Getting UWGeo internals to use uw2 mpi4py interface * Update CHANGES.md * ask per 2.13 release (#615) 2.13 release Co-authored-by: Romain Beucher * Update _version.py * Fix for analytic ID, which changed during cmake update. Ganalytics should now be right * Typo fix for 3.10 * Dockerfile improvement - should be a single layer command * safer #define when not using an official release of petsc * Update PetscOptionsBegin() to be dev compatible. see https://gitlab.com/petsc/petsc/-/blob/f3ed01cd9ab452072c1a44e935347389f67d72c6/doc/docs/changes/dev.rst * Petsc 3.18 updates Petsc error handling updates only. Co-authored-by: Romain Beucher * Add build-args for openmpi Docker creation Co-authored-by: Julian Giordani Co-authored-by: Romain Beucher * Enable the default args * disable petsc in github workflows * Update to underworldcode docker hub * update for 2.14 release (#642) * Images will push to underworldcode docker hub now * Change to kick off underworld2 image build * fix spacing in version file * Update CHANGES.md * Adding mpich Dockerfile * Disable github actions build of petsc and uw * Adding ARG smarts to petsc and UW image. Now MPI_IMAGE for petsc/Dockerfile and PETSC_IMAGE for uw2/Dockerfile are options that can we used to specify the exact docker image name. This will be used by github actions in future. * Update CI.yml * Update CI.yml Fix indenting of yaml * Update CI.yml * workdispatch only for openmpi/mpich and lavavu * Update CI.yml Adding MPICH_VERSION as an env var * Clean up docker-builder script * Smarts for docker-builder.sh * Include 'temperatureDot' checkpointing for UWGeo models by default * Adding pragmatric to petsc * cleanup of underworld2 dockerfile * Update version and disable conda for conda-forge staging process * Adding a fix for swarmvariable reloading. Thanks Arijit! * Removing unused imports * Disable openmpi. * Will delete this conda directory once the conda-forge feedstock is accepted. * Update for 2.14.2 * Fixes for temperatureDot checkpoint/restarting. * Petsc Dockerfile updates for h5py * Fixing the np.int deprecation issue. fixes #652 * Fixing some test. nbmark and matplotlib are needed for the tests. * Fixing/Shortening some models so they pass CI * removing ipynb version * Fixing all test in ./doc/test/* to not require matplotlib * update workflow with the new script name * Updating python dependencies. * Swig is a build dependency * Numpy version reducing to 1.20.3 for setonix * Fixes for checkpointing/restart with freeSurface's in UWGeo * Fix for #657 * Added path check in utils/_io.py * 1st cut adding Ben Knight's 3D surface veclocity code. Test model to come * Upload 3D SP example * Adding setonix install guide * starting for new release uw2.15 * Update surfaceProcesses.py Update timeField to record time of deposition of sediment in SP functions * Update surfaceProcesses.py Fix typo in 3D surface. Also reset time of the air material to the model time * Update surfaceProcesses.py fixed typo in func * updating the setonix jobs runners * Update surfaceProcesses.py Simplify 2D velocity and diffusivity SP functions * Update surfaceProcesses.py Didn't create object to bcast surface function to * Update surfaceProcesses Revert back to MPI gather of data over evaluate_global Update the examples * Adding melt dynamic heating to the advection diffusion solver see issue #669 * docker utils updates * Adding some final fixes to the dockerfile scripts * Update CHANGES.md * Remove obsolete glucifer bits * Update dockerfile Badlands install and CHANGES.md * Update CI for docker builds * Fixing the velocitySurface 2D and 3D implementation to handle restarts. Changes also include: * Renaming the surface tracers to prevent unexpected overwrites * The surfaceProcess class saving the passive tracer key to use during initialisation and solve. This is to make the code not save an instance of the pt to the swarm incase the pt is swapped in/out of Model memory. And helps with code readability. * Get the latest release of badlands from pypi --------- Co-authored-by: Romain Beucher Co-authored-by: Julian Giordani Co-authored-by: Ben Knight <55677727+bknight1@users.noreply.github.com> --- .github/workflows/CI.yml | 85 +- CHANGES.md | 15 + LICENSE.md | 2 +- conda/conda_build_config.yaml | 10 +- conda/meta.yaml | 2 +- .../examples/1_08_ViscoElasticHalfSpace.ipynb | 45 +- ...iscoelastoplasticity-in-simple-shear.ipynb | 8 +- ..._Kaus2010_Free_Surface_Stabilization.ipynb | 10 +- ...rial_6_1_sedimentation_erosion_rates.ipynb | 332 +- .../Tutorial_6_2_diffusive_surface.ipynb | 331 +- ...al_6_3_3Dsedimentation_erosion_rates.ipynb | 517 +++ docs/development/docker/docker-builder.sh | 23 +- docs/development/docker/petsc/Dockerfile | 10 +- .../development/docker/underworld2/Dockerfile | 38 +- docs/development/release_guidelines.md | 3 +- docs/examples/03_BlankenbachBenchmark.ipynb | 29 +- docs/install_guides/nci_gadi/sample.pbs | 2 +- docs/install_guides/setonix_baremetal.sh | 30 + docs/pytests/test_UWGeo_examples.py | 6 +- docs/pytests/test_UWGeo_user_guide.py | 6 +- docs/pytests/test_examples.py | 7 +- docs/pytests/test_user_guide.py | 6 +- docs/pytests/{test.py => tests.py} | 9 +- .../Analytic Soln Convergence Tests.ipynb | 3697 ++++++++++++++++- docs/test/SteadyState.ipynb | 663 --- docs/test/SteadyState.py | 16 +- docs/test/UWGeodynamics/image_tests.py | 4 +- docs/test/func_debug_messages.py | 12 +- docs/test/image_tests.py | 6 +- pyproject.toml | 2 +- setup.py | 25 +- underworld/UWGeodynamics/_melt.py | 4 +- underworld/UWGeodynamics/_model.py | 53 +- underworld/UWGeodynamics/_rcParams.py | 1 + underworld/UWGeodynamics/_utils.py | 3 +- underworld/UWGeodynamics/surfaceProcesses.py | 998 ++++- underworld/_version.py | 2 +- underworld/container/_indexset.py | 2 +- underworld/swarm/_swarm.py | 32 +- underworld/swarm/_swarmvariable.py | 5 +- underworld/systems/_advectiondiffusion.py | 6 +- underworld/utils/_io.py | 5 + 42 files changed, 5299 insertions(+), 1763 deletions(-) create mode 100644 docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb create mode 100644 docs/install_guides/setonix_baremetal.sh rename docs/pytests/{test.py => tests.py} (76%) delete mode 100644 docs/test/SteadyState.ipynb diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 8c89da6c7..32692438f 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -6,8 +6,8 @@ on: workflow_dispatch: env: - PETSC_VERSION: 3.18.1 - UW_VERSION: 2.14.0 + PETSC_VERSION: 3.19.4 + UW_VERSION: 2.15.0 OMPI_VERSION: 4.1.4 MPICH_VERSION: 3.4.3 @@ -282,51 +282,52 @@ jobs: - name: Run Tests run: | cd docs - pytest -vvv pytests/test.py pytests/test_examples.py pytests/test_user_guide.py + pytest -vvv pytests/tests.py pytests/test_examples.py pytests/test_user_guide.py #cd test #cp -rf ../UWGeodynamics/examples UWGeodynamics/. #cp -rf ../UWGeodynamics/tutorials UWGeodynamics/. #cp -rf ../UWGeodynamics/benchmarks UWGeodynamics/. #pytest -vvv UWGeodynamics - - conda_build: - name: Conda Build (Python ${{matrix.python-version}} ${{ matrix.os }}) - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - os: ["ubuntu-latest", "macos-latest"] - python-version: ["3.8", "3.9", "3.10"] - steps: - - uses: actions/checkout@v3 - - uses: conda-incubator/setup-miniconda@v2 - with: - auto-update-conda: true - python-version: ${{ matrix.python-version }} - - - name: Config Conda - shell: bash -l {0} - run: | - conda install --channel conda-forge conda-build anaconda-client conda-verify - conda config --add channels conda-forge - conda config --add channels underworldcode - conda config --set anaconda_upload no - anaconda logout - - - name: Config Conda For Upload - if: github.event_name == 'release' - shell: bash -l {0} - run: conda config --set anaconda_upload yes - - - name: Upload new Packages - if: github.event_name == 'release' - shell: bash -l {0} - run: | - conda info - anaconda login --hostname github-actions-${{ matrix.os }}-$RANDOM --username ${{ secrets.ANACONDA_USERNAME }} --password ${{ secrets.ANACONDA_PASSWORD }} - conda-build --channel conda-forge --user geo-down-under conda - anaconda logout + # + # conda_build: + # name: Conda Build (Python ${{matrix.python-version}} ${{ matrix.os }}) + # runs-on: ${{ matrix.os }} + # strategy: + # fail-fast: false + # matrix: + # os: ["ubuntu-latest", "macos-latest"] + # python-version: ["3.9", "3.10"] + # steps: + # - uses: actions/checkout@v3 + # - uses: conda-incubator/setup-miniconda@v2 + # with: + # auto-update-conda: true + # python-version: ${{ matrix.python-version }} + # + # - name: Config Conda + # shell: bash -l {0} + # run: | + # conda install --channel conda-forge conda-build anaconda-client conda-verify + # conda config --add channels conda-forge + # conda config --add channels underworldcode + # conda config --set anaconda_upload no + # anaconda logout + # + # - name: Config Conda For Upload + # if: github.event_name == 'release' + # shell: bash -l {0} + # run: conda config --set anaconda_upload yes + # + # - name: Upload new Packages + # if: github.event_name == 'release' + # shell: bash -l {0} + # run: | + # conda info + # anaconda login --hostname github-actions-${{ matrix.os }}-$RANDOM --username ${{ secrets.ANACONDA_USERNAME }} --password ${{ secrets.ANACONDA_PASSWORD }} + # conda-build --channel conda-forge --user geo-down-under conda + # anaconda logout + # pypi: runs-on: ${{ matrix.os }} @@ -336,7 +337,7 @@ jobs: strategy: matrix: os: ["ubuntu-latest", "macos-latest"] - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.9", "3.10"] steps: - uses: actions/checkout@v3 - name: Set up Python diff --git a/CHANGES.md b/CHANGES.md index 7eec9a2aa..fa95f61a6 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,6 +1,21 @@ CHANGES: Underworld2 ======================= +Release 2.15.0 [2023-04-19] +--------------------------- +New: + * Move to Petsc-3.19.4 + * New 3D free surface implementation. (Not fully tested). + * new install guides for Gadi and setonix. + +Changes: + +Fixes: + * UWGeodynamics - add dynamic heating back into the advection diffusion solver, + https://github.com/underworldcode/underworld2/issues/669 + * Using updated Badlands-2.2.3 without license issue. + + Release 2.14 [2022-11-29] --------------------------- New: diff --git a/LICENSE.md b/LICENSE.md index 37132a214..d528e76d2 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -16,7 +16,7 @@ Underworld has been in development since 2003. It has always been released under ### Copyright holders -Copyright Australian National University, 2020-2022 +Copyright Australian National University, 2020-2023 Copyright Melbourne University, 2014-2021 Copyright Monash University, 2003-2021 Copyright VPAC, 2003-2009 diff --git a/conda/conda_build_config.yaml b/conda/conda_build_config.yaml index 12c214671..937456d8d 100644 --- a/conda/conda_build_config.yaml +++ b/conda/conda_build_config.yaml @@ -1,5 +1,5 @@ - mpi: - - mpich - - openmpi [linux] - petsc: - - 3.18.1 +mpi: + - mpich + #- openmpi [linux] +petsc: + - 3.18.1 diff --git a/conda/meta.yaml b/conda/meta.yaml index 52688def7..538291545 100644 --- a/conda/meta.yaml +++ b/conda/meta.yaml @@ -1,6 +1,6 @@ {% set name = "underworld" %} {% set version = "2.14.0" %} -{% set sha256 = "fdc6c7ae1034b5bd6159b465ae44d75a19ca9bb44021e9d16c9eafb6bced2e12" %} +{% set sha256 = "a9323209b0e36743bc953bf3b3d8e0a84d5e4e7f1911a1e78d1404a0ade6337d" %} {% set build = 0 %} package: diff --git a/docs/UWGeodynamics/examples/1_08_ViscoElasticHalfSpace.ipynb b/docs/UWGeodynamics/examples/1_08_ViscoElasticHalfSpace.ipynb index b2186a562..c95c92032 100644 --- a/docs/UWGeodynamics/examples/1_08_ViscoElasticHalfSpace.ipynb +++ b/docs/UWGeodynamics/examples/1_08_ViscoElasticHalfSpace.ipynb @@ -509,13 +509,7 @@ "Step: 102 Model Time: 125465.2 year dt: 1230.1 year (2021-02-19 11:00:02)\n", "Step: 103 Model Time: 126695.2 year dt: 1230.1 year (2021-02-19 11:00:03)\n", "Step: 104 Model Time: 127925.3 year dt: 1230.1 year (2021-02-19 11:00:04)\n", - "Step: 105 Model Time: 129155.3 year dt: 1230.1 year (2021-02-19 11:00:05)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Step: 105 Model Time: 129155.3 year dt: 1230.1 year (2021-02-19 11:00:05)\n", "Step: 106 Model Time: 130385.4 year dt: 1230.1 year (2021-02-19 11:00:06)\n", "Step: 107 Model Time: 131615.4 year dt: 1230.1 year (2021-02-19 11:00:07)\n", "Step: 108 Model Time: 132845.5 year dt: 1230.1 year (2021-02-19 11:00:08)\n", @@ -623,13 +617,7 @@ "Step: 210 Model Time: 258310.6 year dt: 1230.1 year (2021-02-19 11:01:41)\n", "Step: 211 Model Time: 259540.7 year dt: 1230.1 year (2021-02-19 11:01:42)\n", "Step: 212 Model Time: 260770.7 year dt: 1230.1 year (2021-02-19 11:01:43)\n", - "Step: 213 Model Time: 262000.8 year dt: 1230.1 year (2021-02-19 11:01:44)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Step: 213 Model Time: 262000.8 year dt: 1230.1 year (2021-02-19 11:01:44)\n", "Step: 214 Model Time: 263230.8 year dt: 1230.1 year (2021-02-19 11:01:45)\n", "Step: 215 Model Time: 264460.9 year dt: 1230.1 year (2021-02-19 11:01:46)\n", "Step: 216 Model Time: 265690.9 year dt: 1230.1 year (2021-02-19 11:01:47)\n", @@ -737,13 +725,7 @@ "Step: 318 Model Time: 391156.1 year dt: 1230.1 year (2021-02-19 11:03:19)\n", "Step: 319 Model Time: 392386.1 year dt: 1230.1 year (2021-02-19 11:03:20)\n", "Step: 320 Model Time: 393616.2 year dt: 1230.1 year (2021-02-19 11:03:20)\n", - "Step: 321 Model Time: 394846.2 year dt: 1230.1 year (2021-02-19 11:03:21)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Step: 321 Model Time: 394846.2 year dt: 1230.1 year (2021-02-19 11:03:21)\n", "Step: 322 Model Time: 396076.3 year dt: 1230.1 year (2021-02-19 11:03:22)\n", "Step: 323 Model Time: 397306.3 year dt: 1230.1 year (2021-02-19 11:03:23)\n", "Step: 324 Model Time: 398536.4 year dt: 1230.1 year (2021-02-19 11:03:24)\n", @@ -851,13 +833,7 @@ "Step: 426 Model Time: 524001.5 year dt: 1230.1 year (2021-02-19 11:04:53)\n", "Step: 427 Model Time: 525231.6 year dt: 1230.1 year (2021-02-19 11:04:54)\n", "Step: 428 Model Time: 526461.6 year dt: 1230.1 year (2021-02-19 11:04:55)\n", - "Step: 429 Model Time: 527691.7 year dt: 1230.1 year (2021-02-19 11:04:55)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Step: 429 Model Time: 527691.7 year dt: 1230.1 year (2021-02-19 11:04:55)\n", "Step: 430 Model Time: 528921.7 year dt: 1230.1 year (2021-02-19 11:04:56)\n", "Step: 431 Model Time: 530151.8 year dt: 1230.1 year (2021-02-19 11:04:57)\n", "Step: 432 Model Time: 531381.8 year dt: 1230.1 year (2021-02-19 11:04:58)\n", @@ -943,7 +919,7 @@ } ], "source": [ - "Model.run_for(duration=tMax, dt=1e-2*t_relax)" + "Model.run_for(duration=tMax/10., dt=1e-2*t_relax)" ] }, { @@ -1014,6 +990,13 @@ "ax.legend(loc='best')\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1032,9 +1015,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.1" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/UWGeodynamics/examples/1_10_Viscoelastoplasticity-in-simple-shear.ipynb b/docs/UWGeodynamics/examples/1_10_Viscoelastoplasticity-in-simple-shear.ipynb index b5708382d..1fd6515d8 100644 --- a/docs/UWGeodynamics/examples/1_10_Viscoelastoplasticity-in-simple-shear.ipynb +++ b/docs/UWGeodynamics/examples/1_10_Viscoelastoplasticity-in-simple-shear.ipynb @@ -329,7 +329,7 @@ " previousStress_xy.append(\n", " Model._previousStressField[2].evaluate(Model.swarm)[0])\n", " totalStress_xy.append( \n", - " Model._stressFn[2].evaluate(Model.swarm)[0])\n" + " Model._stressFn[2].evaluate(Model.swarm)[0][0])\n" ] }, { @@ -491,7 +491,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -505,9 +505,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/UWGeodynamics/examples/1_28_Kaus2010_Free_Surface_Stabilization.ipynb b/docs/UWGeodynamics/examples/1_28_Kaus2010_Free_Surface_Stabilization.ipynb index 896e2ffd8..613519a77 100644 --- a/docs/UWGeodynamics/examples/1_28_Kaus2010_Free_Surface_Stabilization.ipynb +++ b/docs/UWGeodynamics/examples/1_28_Kaus2010_Free_Surface_Stabilization.ipynb @@ -510,7 +510,9 @@ } ], "source": [ - "Model.run_for(nstep=1000, checkpoint_interval=1, dt=5000*u.year)" + "steps = 1000\n", + "test_steps = 20\n", + "Model.run_for(nstep=test_steps, checkpoint_interval=1, dt=5000*u.year)" ] }, { @@ -546,9 +548,9 @@ ], "metadata": { "kernelspec": { - "display_name": "uw2_venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "uw2_venv" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -560,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb b/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb index 2462d6615..a5745b8f7 100644 --- a/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb +++ b/docs/UWGeodynamics/tutorials/Tutorial_6_1_sedimentation_erosion_rates.ipynb @@ -9,17 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded rc file /opt/venv/lib/python3.7/site-packages/UWGeodynamics/uwgeo-data/uwgeodynamicsrc\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import UWGeodynamics as GEO\n", "from underworld import visualisation as vis" @@ -27,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,22 +186,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", @@ -226,20 +205,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_temperatureBCs(top=293.15 * u.degK, \n", " bottom=1603.15 * u.degK, \n", @@ -255,20 +223,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_velocityBCs(left=[-2.5 * u.centimeter / u.year, None],\n", " right=[2.5 * u.centimeter / u.year, None],\n", @@ -285,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -352,22 +309,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projPlasticStrain)\n", @@ -376,22 +320,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Points(Model.swarm, Model.materialField, fn_size=3.0)\n", @@ -400,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -409,22 +340,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projViscosityField, logScale=True)\n", @@ -433,155 +351,24 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { - "scrolled": false + "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running with UWGeodynamics version 2.10.2\n", - "Options: -Q22_pc_type uw -ksp_type bsscr -pc_type none -ksp_k2_type NULL -rescale_equations False -remove_constant_pressure_null_space False -change_backsolve False -change_A11rhspresolve False -restore_K False -A11_ksp_type fgmres -A11_ksp_rtol 1e-06 -scr_ksp_type fgmres -scr_ksp_rtol 1e-05\n", - "SP total time: 41551.76 years, timestep: 6925.29 years, No. of its: 6\n", - "Step: 1 Model Time: 41551.8 year dt: 41551.8 year (2021-09-20 05:16:09)\n", - "SP total time: 41769.64 years, timestep: 6961.61 years, No. of its: 6\n", - "Step: 2 Model Time: 83321.4 year dt: 41769.6 year (2021-09-20 05:16:22)\n", - "SP total time: 41794.6 years, timestep: 6965.77 years, No. of its: 6\n", - "Step: 3 Model Time: 125116.0 year dt: 41794.6 year (2021-09-20 05:16:36)\n", - "SP total time: 41647.84 years, timestep: 6941.31 years, No. of its: 6\n", - "Step: 4 Model Time: 166763.8 year dt: 41647.8 year (2021-09-20 05:16:50)\n", - "SP total time: 41734.73 years, timestep: 6955.79 years, No. of its: 6\n", - "Step: 5 Model Time: 208498.6 year dt: 41734.7 year (2021-09-20 05:17:04)\n", - "SP total time: 41313.64 years, timestep: 6885.61 years, No. of its: 6\n", - "Step: 6 Model Time: 249812.2 year dt: 41313.6 year (2021-09-20 05:17:17)\n", - "SP total time: 41635.16 years, timestep: 6939.19 years, No. of its: 6\n", - "Step: 7 Model Time: 291447.4 year dt: 41635.2 year (2021-09-20 05:17:31)\n", - "SP total time: 41652.58 years, timestep: 6942.1 years, No. of its: 6\n", - "Step: 8 Model Time: 333100.0 year dt: 41652.6 year (2021-09-20 05:17:46)\n", - "SP total time: 41638.11 years, timestep: 6939.68 years, No. of its: 6\n", - "Step: 9 Model Time: 374738.1 year dt: 41638.1 year (2021-09-20 05:18:00)\n", - "SP total time: 41716.94 years, timestep: 6952.82 years, No. of its: 6\n", - "Step: 10 Model Time: 416455.0 year dt: 41716.9 year (2021-09-20 05:18:14)\n", - "SP total time: 41643.61 years, timestep: 6940.6 years, No. of its: 6\n", - "Step: 11 Model Time: 458098.6 year dt: 41643.6 year (2021-09-20 05:18:28)\n", - "SP total time: 41912.97 years, timestep: 6985.49 years, No. of its: 6\n", - "Step: 12 Model Time: 500011.6 year dt: 41913.0 year (2021-09-20 05:18:43)\n", - "SP total time: 41816.75 years, timestep: 6969.46 years, No. of its: 6\n", - "Step: 13 Model Time: 541828.3 year dt: 41816.8 year (2021-09-20 05:18:57)\n", - "SP total time: 42059.28 years, timestep: 7009.88 years, No. of its: 6\n", - "Step: 14 Model Time: 583887.6 year dt: 42059.3 year (2021-09-20 05:19:12)\n", - "SP total time: 41912.71 years, timestep: 6985.45 years, No. of its: 6\n", - "Step: 15 Model Time: 625800.3 year dt: 41912.7 year (2021-09-20 05:19:26)\n", - "SP total time: 41941.67 years, timestep: 6990.28 years, No. of its: 6\n", - "Step: 16 Model Time: 667742.0 year dt: 41941.7 year (2021-09-20 05:19:41)\n", - "SP total time: 41876.74 years, timestep: 6979.46 years, No. of its: 6\n", - "Step: 17 Model Time: 709618.7 year dt: 41876.7 year (2021-09-20 05:19:56)\n", - "SP total time: 42120.81 years, timestep: 7020.13 years, No. of its: 6\n", - "Step: 18 Model Time: 751739.5 year dt: 42120.8 year (2021-09-20 05:20:10)\n", - "SP total time: 42163.63 years, timestep: 7027.27 years, No. of its: 6\n", - "Step: 19 Model Time: 793903.2 year dt: 42163.6 year (2021-09-20 05:20:24)\n", - "SP total time: 41429.42 years, timestep: 6904.9 years, No. of its: 6\n", - "Step: 20 Model Time: 835332.6 year dt: 41429.4 year (2021-09-20 05:20:48)\n", - "SP total time: 41663.61 years, timestep: 6943.94 years, No. of its: 6\n", - "Step: 21 Model Time: 876996.2 year dt: 41663.6 year (2021-09-20 05:21:03)\n", - "SP total time: 41913.18 years, timestep: 6985.53 years, No. of its: 6\n", - "Step: 22 Model Time: 918909.4 year dt: 41913.2 year (2021-09-20 05:21:17)\n", - "SP total time: 41909.0 years, timestep: 6984.83 years, No. of its: 6\n", - "Step: 23 Model Time: 960818.4 year dt: 41909.0 year (2021-09-20 05:21:31)\n", - "SP total time: 39181.62 years, timestep: 7836.32 years, No. of its: 5\n", - "Step: 24 Model Time: 1.0 megayear dt: 39181.6 year (2021-09-20 05:22:16)\n", - "SP total time: 42258.84 years, timestep: 7043.14 years, No. of its: 6\n", - "Step: 25 Model Time: 1.0 megayear dt: 42258.8 year (2021-09-20 05:22:30)\n", - "SP total time: 41625.9 years, timestep: 6937.65 years, No. of its: 6\n", - "Step: 26 Model Time: 1.1 megayear dt: 41625.9 year (2021-09-20 05:22:59)\n", - "SP total time: 41690.95 years, timestep: 6948.49 years, No. of its: 6\n", - "Step: 27 Model Time: 1.1 megayear dt: 41690.9 year (2021-09-20 05:23:14)\n", - "SP total time: 42427.25 years, timestep: 7071.21 years, No. of its: 6\n", - "Step: 28 Model Time: 1.2 megayear dt: 42427.2 year (2021-09-20 05:23:31)\n", - "SP total time: 42650.37 years, timestep: 7108.39 years, No. of its: 6\n", - "Step: 29 Model Time: 1.2 megayear dt: 42650.4 year (2021-09-20 05:23:46)\n", - "SP total time: 42651.8 years, timestep: 7108.63 years, No. of its: 6\n", - "Step: 30 Model Time: 1.3 megayear dt: 42651.8 year (2021-09-20 05:24:01)\n", - "SP total time: 42329.86 years, timestep: 7054.98 years, No. of its: 6\n", - "Step: 31 Model Time: 1.3 megayear dt: 42329.9 year (2021-09-20 05:24:16)\n", - "SP total time: 42385.22 years, timestep: 7064.2 years, No. of its: 6\n", - "Step: 32 Model Time: 1.3 megayear dt: 42385.2 year (2021-09-20 05:24:31)\n", - "SP total time: 42458.07 years, timestep: 7076.34 years, No. of its: 6\n", - "Step: 33 Model Time: 1.4 megayear dt: 42458.1 year (2021-09-20 05:24:46)\n", - "SP total time: 42677.27 years, timestep: 7112.88 years, No. of its: 6\n", - "Step: 34 Model Time: 1.4 megayear dt: 42677.3 year (2021-09-20 05:25:01)\n", - "SP total time: 42919.47 years, timestep: 7153.25 years, No. of its: 6\n", - "Step: 35 Model Time: 1.5 megayear dt: 42919.5 year (2021-09-20 05:25:16)\n", - "SP total time: 43317.52 years, timestep: 7219.59 years, No. of its: 6\n", - "Step: 36 Model Time: 1.5 megayear dt: 43317.5 year (2021-09-20 05:25:31)\n", - "SP total time: 43134.31 years, timestep: 7189.05 years, No. of its: 6\n", - "Step: 37 Model Time: 1.6 megayear dt: 43134.3 year (2021-09-20 05:25:46)\n", - "SP total time: 42758.75 years, timestep: 7126.46 years, No. of its: 6\n", - "Step: 38 Model Time: 1.6 megayear dt: 42758.7 year (2021-09-20 05:26:02)\n", - "SP total time: 42871.85 years, timestep: 7145.31 years, No. of its: 6\n", - "Step: 39 Model Time: 1.6 megayear dt: 42871.9 year (2021-09-20 05:26:21)\n", - "SP total time: 44194.59 years, timestep: 7365.77 years, No. of its: 6\n", - "Step: 40 Model Time: 1.7 megayear dt: 44194.6 year (2021-09-20 05:26:43)\n", - "SP total time: 44520.91 years, timestep: 7420.15 years, No. of its: 6\n", - "Step: 41 Model Time: 1.7 megayear dt: 44520.9 year (2021-09-20 05:26:59)\n", - "SP total time: 44389.94 years, timestep: 7398.32 years, No. of its: 6\n", - "Step: 42 Model Time: 1.8 megayear dt: 44389.9 year (2021-09-20 05:27:15)\n", - "SP total time: 44142.17 years, timestep: 7357.03 years, No. of its: 6\n", - "Step: 43 Model Time: 1.8 megayear dt: 44142.2 year (2021-09-20 05:27:32)\n", - "SP total time: 44062.9 years, timestep: 7343.82 years, No. of its: 6\n", - "Step: 44 Model Time: 1.9 megayear dt: 44062.9 year (2021-09-20 05:27:49)\n", - "SP total time: 44105.35 years, timestep: 7350.89 years, No. of its: 6\n", - "Step: 45 Model Time: 1.9 megayear dt: 44105.3 year (2021-09-20 05:28:05)\n", - "SP total time: 44375.27 years, timestep: 7395.88 years, No. of its: 6\n", - "Step: 46 Model Time: 1.9 megayear dt: 44375.3 year (2021-09-20 05:28:22)\n", - "SP total time: 45323.12 years, timestep: 6474.73 years, No. of its: 7\n", - "Step: 47 Model Time: 2.0 megayear dt: 45323.1 year (2021-09-20 05:29:05)\n", - "SP total time: 6728.33 years, timestep: 6728.33 years, No. of its: 1\n", - "Step: 48 Model Time: 2.0 megayear dt: 6728.3 year (2021-09-20 05:29:52)\n", - "SP total time: 10000.0 years, timestep: 5000.0 years, No. of its: 2\n", - "Step: 49 Model Time: 2.0 megayear dt: 10000.0 year (2021-09-20 05:30:10)\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.run_for(2.01 * u.megayear, checkpoint_interval=1.*u.megayears)" + "outputs": [], + "source": [ + "Model.run_for(duration=2.01 * u.megayear, checkpoint_interval=0.1*u.megayears)" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", "### output tracers\n", - "Fig.Points(Model.surfacetracers_tracers, pointSize=1.0)\n", + "Fig.Points(Model.surface_tracers, pointSize=1.0)\n", "\n", "# for line in lines:\n", "# Fig.Points(line, pointSize=2.0)\n", @@ -591,22 +378,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.strainRate_2ndInvariant, logScale=True)\n", @@ -623,7 +397,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -637,9 +411,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb b/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb index 8a121f68a..63bd50eee 100644 --- a/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb +++ b/docs/UWGeodynamics/tutorials/Tutorial_6_2_diffusive_surface.ipynb @@ -9,17 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loaded rc file /opt/venv/lib/python3.7/site-packages/UWGeodynamics/uwgeo-data/uwgeodynamicsrc\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import UWGeodynamics as GEO\n", "from underworld import visualisation as vis" @@ -27,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -194,22 +186,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", @@ -226,20 +205,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_temperatureBCs(top=293.15 * u.degK, \n", " bottom=1603.15 * u.degK, \n", @@ -255,20 +223,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Model.set_velocityBCs(left=[-2.5 * u.centimeter / u.year, None],\n", " right=[2.5 * u.centimeter / u.year, None],\n", @@ -285,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -351,22 +308,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projPlasticStrain)\n", @@ -375,22 +319,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Points(Model.swarm, Model.materialField, fn_size=3.0)\n", @@ -399,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -408,22 +339,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.projViscosityField, logScale=True)\n", @@ -432,155 +350,25 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { - "scrolled": false + "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running with UWGeodynamics version 2.10.2\n", - "Options: -Q22_pc_type uw -ksp_type bsscr -pc_type none -ksp_k2_type NULL -rescale_equations False -remove_constant_pressure_null_space False -change_backsolve False -change_A11rhspresolve False -restore_K False -A11_ksp_type fgmres -A11_ksp_rtol 1e-06 -scr_ksp_type fgmres -scr_ksp_rtol 1e-05\n", - "SP total time: 41606.92 years, timestep: 201.0 years, No. of its: 207\n", - "Step: 1 Model Time: 41606.9 year dt: 41606.9 year (2021-09-20 04:56:45)\n", - "SP total time: 41652.4 years, timestep: 200.25 years, No. of its: 208\n", - "Step: 2 Model Time: 83259.3 year dt: 41652.4 year (2021-09-20 04:56:59)\n", - "SP total time: 41665.53 years, timestep: 200.32 years, No. of its: 208\n", - "Step: 3 Model Time: 124924.8 year dt: 41665.5 year (2021-09-20 04:57:14)\n", - "SP total time: 41598.51 years, timestep: 200.96 years, No. of its: 207\n", - "Step: 4 Model Time: 166523.4 year dt: 41598.5 year (2021-09-20 04:57:30)\n", - "SP total time: 41589.54 years, timestep: 200.92 years, No. of its: 207\n", - "Step: 5 Model Time: 208112.9 year dt: 41589.5 year (2021-09-20 04:57:45)\n", - "SP total time: 41514.71 years, timestep: 200.55 years, No. of its: 207\n", - "Step: 6 Model Time: 249627.6 year dt: 41514.7 year (2021-09-20 04:58:00)\n", - "SP total time: 41767.65 years, timestep: 200.81 years, No. of its: 208\n", - "Step: 7 Model Time: 291395.3 year dt: 41767.7 year (2021-09-20 04:58:15)\n", - "SP total time: 41699.84 years, timestep: 200.48 years, No. of its: 208\n", - "Step: 8 Model Time: 333095.1 year dt: 41699.8 year (2021-09-20 04:58:29)\n", - "SP total time: 41633.23 years, timestep: 200.16 years, No. of its: 208\n", - "Step: 9 Model Time: 374728.3 year dt: 41633.2 year (2021-09-20 04:58:42)\n", - "SP total time: 41609.6 years, timestep: 201.01 years, No. of its: 207\n", - "Step: 10 Model Time: 416337.9 year dt: 41609.6 year (2021-09-20 04:58:56)\n", - "SP total time: 41642.12 years, timestep: 200.2 years, No. of its: 208\n", - "Step: 11 Model Time: 457980.0 year dt: 41642.1 year (2021-09-20 04:59:09)\n", - "SP total time: 41849.32 years, timestep: 200.24 years, No. of its: 209\n", - "Step: 12 Model Time: 499829.4 year dt: 41849.3 year (2021-09-20 04:59:23)\n", - "SP total time: 41818.0 years, timestep: 201.05 years, No. of its: 208\n", - "Step: 13 Model Time: 541647.4 year dt: 41818.0 year (2021-09-20 04:59:37)\n", - "SP total time: 42021.98 years, timestep: 201.06 years, No. of its: 209\n", - "Step: 14 Model Time: 583669.3 year dt: 42022.0 year (2021-09-20 04:59:59)\n", - "SP total time: 42020.64 years, timestep: 201.06 years, No. of its: 209\n", - "Step: 15 Model Time: 625690.0 year dt: 42020.6 year (2021-09-20 05:00:14)\n", - "SP total time: 41600.45 years, timestep: 200.97 years, No. of its: 207\n", - "Step: 16 Model Time: 667290.4 year dt: 41600.4 year (2021-09-20 05:00:30)\n", - "SP total time: 41641.41 years, timestep: 200.2 years, No. of its: 208\n", - "Step: 17 Model Time: 708931.8 year dt: 41641.4 year (2021-09-20 05:00:44)\n", - "SP total time: 41740.4 years, timestep: 200.67 years, No. of its: 208\n", - "Step: 18 Model Time: 750672.2 year dt: 41740.4 year (2021-09-20 05:00:59)\n", - "SP total time: 41974.9 years, timestep: 200.84 years, No. of its: 209\n", - "Step: 19 Model Time: 792647.1 year dt: 41974.9 year (2021-09-20 05:01:13)\n", - "SP total time: 41639.26 years, timestep: 200.19 years, No. of its: 208\n", - "Step: 20 Model Time: 834286.4 year dt: 41639.3 year (2021-09-20 05:01:29)\n", - "SP total time: 41839.98 years, timestep: 200.19 years, No. of its: 209\n", - "Step: 21 Model Time: 876126.4 year dt: 41840.0 year (2021-09-20 05:01:43)\n", - "SP total time: 41826.91 years, timestep: 201.09 years, No. of its: 208\n", - "Step: 22 Model Time: 917953.3 year dt: 41826.9 year (2021-09-20 05:01:59)\n", - "SP total time: 41709.76 years, timestep: 200.53 years, No. of its: 208\n", - "Step: 23 Model Time: 959663.0 year dt: 41709.8 year (2021-09-20 05:02:14)\n", - "SP total time: 40336.95 years, timestep: 200.68 years, No. of its: 201\n", - "Step: 24 Model Time: 1.0 megayear dt: 40337.0 year (2021-09-20 05:03:00)\n", - "SP total time: 42574.8 years, timestep: 200.82 years, No. of its: 212\n", - "Step: 25 Model Time: 1.0 megayear dt: 42574.8 year (2021-09-20 05:03:19)\n", - "SP total time: 41958.72 years, timestep: 200.76 years, No. of its: 209\n", - "Step: 26 Model Time: 1.1 megayear dt: 41958.7 year (2021-09-20 05:03:35)\n", - "SP total time: 42161.09 years, timestep: 200.77 years, No. of its: 210\n", - "Step: 27 Model Time: 1.1 megayear dt: 42161.1 year (2021-09-20 05:03:49)\n", - "SP total time: 42592.15 years, timestep: 200.91 years, No. of its: 212\n", - "Step: 28 Model Time: 1.2 megayear dt: 42592.2 year (2021-09-20 05:04:08)\n", - "SP total time: 42948.93 years, timestep: 200.7 years, No. of its: 214\n", - "Step: 29 Model Time: 1.2 megayear dt: 42948.9 year (2021-09-20 05:04:27)\n", - "SP total time: 42689.88 years, timestep: 200.42 years, No. of its: 213\n", - "Step: 30 Model Time: 1.3 megayear dt: 42689.9 year (2021-09-20 05:04:41)\n", - "SP total time: 42439.84 years, timestep: 200.19 years, No. of its: 212\n", - "Step: 31 Model Time: 1.3 megayear dt: 42439.8 year (2021-09-20 05:04:58)\n", - "SP total time: 42223.57 years, timestep: 201.06 years, No. of its: 210\n", - "Step: 32 Model Time: 1.3 megayear dt: 42223.6 year (2021-09-20 05:05:13)\n", - "SP total time: 42445.07 years, timestep: 200.21 years, No. of its: 212\n", - "Step: 33 Model Time: 1.4 megayear dt: 42445.1 year (2021-09-20 05:05:29)\n", - "SP total time: 42402.16 years, timestep: 200.96 years, No. of its: 211\n", - "Step: 34 Model Time: 1.4 megayear dt: 42402.2 year (2021-09-20 05:05:47)\n", - "SP total time: 42636.96 years, timestep: 200.17 years, No. of its: 213\n", - "Step: 35 Model Time: 1.5 megayear dt: 42637.0 year (2021-09-20 05:06:02)\n", - "SP total time: 42715.45 years, timestep: 200.54 years, No. of its: 213\n", - "Step: 36 Model Time: 1.5 megayear dt: 42715.4 year (2021-09-20 05:06:16)\n", - "SP total time: 42415.54 years, timestep: 201.02 years, No. of its: 211\n", - "Step: 37 Model Time: 1.6 megayear dt: 42415.5 year (2021-09-20 05:06:30)\n", - "SP total time: 42029.29 years, timestep: 201.1 years, No. of its: 209\n", - "Step: 38 Model Time: 1.6 megayear dt: 42029.3 year (2021-09-20 05:06:57)\n", - "SP total time: 42709.76 years, timestep: 200.52 years, No. of its: 213\n", - "Step: 39 Model Time: 1.6 megayear dt: 42709.8 year (2021-09-20 05:07:19)\n", - "SP total time: 43780.35 years, timestep: 200.83 years, No. of its: 218\n", - "Step: 40 Model Time: 1.7 megayear dt: 43780.3 year (2021-09-20 05:07:42)\n", - "SP total time: 43875.98 years, timestep: 200.35 years, No. of its: 219\n", - "Step: 41 Model Time: 1.7 megayear dt: 43876.0 year (2021-09-20 05:07:59)\n", - "SP total time: 43888.99 years, timestep: 200.41 years, No. of its: 219\n", - "Step: 42 Model Time: 1.8 megayear dt: 43889.0 year (2021-09-20 05:08:14)\n", - "SP total time: 43810.35 years, timestep: 200.96 years, No. of its: 218\n", - "Step: 43 Model Time: 1.8 megayear dt: 43810.4 year (2021-09-20 05:08:29)\n", - "SP total time: 44702.28 years, timestep: 200.46 years, No. of its: 223\n", - "Step: 44 Model Time: 1.9 megayear dt: 44702.3 year (2021-09-20 05:08:58)\n", - "SP total time: 44941.05 years, timestep: 200.63 years, No. of its: 224\n", - "Step: 45 Model Time: 1.9 megayear dt: 44941.0 year (2021-09-20 05:09:13)\n", - "SP total time: 45042.24 years, timestep: 201.08 years, No. of its: 224\n", - "Step: 46 Model Time: 1.9 megayear dt: 45042.2 year (2021-09-20 05:09:28)\n", - "SP total time: 45250.95 years, timestep: 201.12 years, No. of its: 225\n", - "Step: 47 Model Time: 2.0 megayear dt: 45250.9 year (2021-09-20 05:09:45)\n", - "SP total time: 7764.61 years, timestep: 199.09 years, No. of its: 39\n", - "Step: 48 Model Time: 2.0 megayear dt: 7764.6 year (2021-09-20 05:10:28)\n", - "SP total time: 10000.0 years, timestep: 200.0 years, No. of its: 50\n", - "Step: 49 Model Time: 2.0 megayear dt: 10000.0 year (2021-09-20 05:10:45)\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Model.run_for(2.01 * u.megayear, checkpoint_interval=1.*u.megayears)" + "outputs": [], + "source": [ + "Model.run_for(duration=2.01 * u.megayear, checkpoint_interval=0.1*u.megayears)" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from underworld import visualisation as vis\n", "Fig = vis.Figure(figsize=(1200,400))\n", "# ### output tracers\n", - "Fig.Points(Model.surfacetracers_tracers, pointSize=3.0)\n", + "Fig.Points(Model.surface_tracers, pointSize=3.0)\n", "\n", "# for line in lines:\n", "# Fig.Points(line, pointSize=2.0)\n", @@ -590,22 +378,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "Fig = vis.Figure(figsize=(1200,400))\n", "Fig.Surface(Model.mesh, Model.strainRate_2ndInvariant, logScale=True)\n", @@ -622,7 +397,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -636,9 +411,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb b/docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb new file mode 100644 index 000000000..9212b5850 --- /dev/null +++ b/docs/UWGeodynamics/tutorials/Tutorial_6_3_3Dsedimentation_erosion_rates.ipynb @@ -0,0 +1,517 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 6: Simple Surface Processes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from underworld import UWGeodynamics as GEO\n", + "from underworld import visualisation as vis\n", + "\n", + "import underworld.function as fn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "u = GEO.UnitRegistry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Characteristic values of the system\n", + "half_rate = 1.8 * u.centimeter / u.year\n", + "model_length = 360e3 * u.meter\n", + "model_height = 120e3 * u.meter\n", + "refViscosity = 1e24 * u.pascal * u.second\n", + "surfaceTemp = 273.15 * u.degK\n", + "baseModelTemp = 1603.15 * u.degK\n", + "bodyforce = 3300 * u.kilogram / u.metre**3 * 9.81 * u.meter / u.second**2\n", + "\n", + "KL = model_length\n", + "Kt = KL / half_rate\n", + "KM = bodyforce * KL**2 * Kt**2\n", + "KT = (baseModelTemp - surfaceTemp)\n", + "\n", + "GEO.scaling_coefficients[\"[length]\"] = KL\n", + "GEO.scaling_coefficients[\"[time]\"] = Kt\n", + "GEO.scaling_coefficients[\"[mass]\"]= KM\n", + "GEO.scaling_coefficients[\"[temperature]\"] = KT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "Model = GEO.Model(elementRes=(16, 16, 16), \n", + " minCoord=(0. * u.kilometer, 0. * u.kilometer, -110. * u.kilometer), \n", + " maxCoord=(120. * u.kilometer, 120. * u.kilometer, 10. * u.kilometer), \n", + " gravity=(0.0, 0.0, -9.81 * u.meter / u.second**2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "Model.outputDir=\"outputs_tutorial6.3_velSP_3D\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.diffusivity = 1e-6 * u.metre**2 / u.second \n", + "Model.capacity = 1000. * u.joule / (u.kelvin * u.kilogram)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air = Model.add_material(name=\"Air\", shape=GEO.shapes.Layer3D(top=Model.top, bottom=0.0 * u.kilometer))\n", + "# stickyAir = Model.add_material(name=\"StickyAir\", shape=GEO.shapes.Layer2D(top=air.bottom, bottom= 0.0 * u.kilometer))\n", + "uppercrust = Model.add_material(name=\"UppperCrust\", shape=GEO.shapes.Layer3D(top=air.bottom, bottom=-35.0 * u.kilometer))\n", + "mantleLithosphere = Model.add_material(name=\"MantleLithosphere\", shape=GEO.shapes.Layer3D(top=uppercrust.bottom, bottom=-100.0 * u.kilometer))\n", + "mantle = Model.add_material(name=\"Mantle\", shape=GEO.shapes.Layer3D(top=mantleLithosphere.bottom, bottom=Model.bottom))\n", + "sediment = Model.add_material(name=\"Sediment\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air.diffusivity = 1.0e-6 * u.metre**2 / u.second\n", + "air.capacity = 100. * u.joule / (u.kelvin * u.kilogram)\n", + "\n", + "# stickyAir.diffusivity = 1.0e-6 * u.metre**2 / u.second\n", + "# stickyAir.capacity = 100. * u.joule / (u.kelvin * u.kilogram)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air.density = 1. * u.kilogram / u.metre**3\n", + "# stickyAir.density = 1. * u.kilogram / u.metre**3\n", + "uppercrust.density = GEO.LinearDensity(reference_density=2620. * u.kilogram / u.metre**3)\n", + "mantleLithosphere.density = GEO.LinearDensity(reference_density=3370. * u.kilogram / u.metre**3)\n", + "mantle.density = GEO.LinearDensity(reference_density=3370. * u.kilogram / u.metre**3)\n", + "sediment.density = GEO.LinearDensity(reference_density=2300. * u.kilogram / u.metre**3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uppercrust.radiogenicHeatProd = 0.7 * u.microwatt / u.meter**3\n", + "sediment.radiogenicHeatProd = 0.7 * u.microwatt / u.meter**3\n", + "mantleLithosphere.radiogenicHeatProd = 0.02 * u.microwatt / u.meter**3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rh = GEO.ViscousCreepRegistry()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "air.viscosity = 1e19 * u.pascal * u.second\n", + "# stickyAir.viscosity = 1e20 * u.pascal * u.second\n", + "uppercrust.viscosity = 1 * rh.Wet_Quartz_Dislocation_Gleason_and_Tullis_1995\n", + "mantleLithosphere.viscosity = rh.Dry_Olivine_Dislocation_Karato_and_Wu_1993\n", + "mantle.viscosity = 0.2 * rh.Dry_Olivine_Dislocation_Karato_and_Wu_1993\n", + "sediment.viscosity = rh.Wet_Quartz_Dislocation_Gleason_and_Tullis_1995" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plasticity = GEO.DruckerPrager(cohesion=20.0 * u.megapascal,\n", + " cohesionAfterSoftening=20 * u.megapascal,\n", + " frictionCoefficient=0.12,\n", + " frictionAfterSoftening=0.02,\n", + " epsilon1=0.5,\n", + " epsilon2=1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uppercrust.plasticity = plasticity\n", + "mantleLithosphere.plasticity = plasticity\n", + "mantle.plasticity = plasticity\n", + "sediment.plasticity = plasticity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temperature Boundary Condition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.set_temperatureBCs(top=293.15 * u.degK, \n", + " bottom=1603.15 * u.degK, \n", + " materials=[(mantle, 1603.15 * u.degK), (air, 293.15 * u.degK)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Velocity Boundary Conditions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vel = 2.5 * u.centimeter / u.year\n", + "\n", + "\n", + "\n", + "vol_out = 2*(vel*(air.top - air.bottom)*Model.maxCoord[1]).to_base_units()\n", + "vol_out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Velocity at surface to replace air that gets removed at sides\n", + "vel_in = vol_out / (Model.maxCoord[0] * Model.maxCoord[1])\n", + "vel_in.to_base_units()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.set_velocityBCs(left = [-vel, None, None],\n", + " right=[vel, None, None],\n", + " front=[None, 0.0, None], back=[None, 0.0, None],\n", + " top = [None, None, -1*vel_in],\n", + " bottom = [None, None, None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial Damage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def gaussian(xx, centre, width):\n", + " return ( np.exp( -(xx - centre)**2 / width ))\n", + "\n", + "maxDamage = 0.7\n", + "Model.plasticStrain.data[:] = 0.\n", + "Model.plasticStrain.data[:] = maxDamage * np.random.rand(*Model.plasticStrain.data.shape[:])\n", + "Model.plasticStrain.data[:,0] *= gaussian(Model.swarm.particleCoordinates.data[:,0], (GEO.nd(Model.maxCoord[0] - Model.minCoord[0])) / 2.0, GEO.nd(5.0 * u.kilometer))\n", + "Model.plasticStrain.data[:,0] *= gaussian(Model.swarm.particleCoordinates.data[:,2], GEO.nd(-35. * u.kilometer) , GEO.nd(5.0 * u.kilometer))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if GEO.nProcs == 1:\n", + " Fig = vis.Figure(resolution=(1200,600))\n", + " Fig.Surface(Model.mesh, Model.plasticStrain, cullface=False, opacity=0.5)\n", + " Fig.window()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if GEO.nProcs == 1:\n", + " Fig = vis.Figure(resolution=(1200,600))\n", + " Fig.Surface(Model.mesh, Model.materialField, cullface=False, opacity=0.5)\n", + " Fig.window()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### x and y coordinates for the surface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "x = np.linspace(Model.minCoord[0], Model.maxCoord[0], 4*(Model.mesh.elementRes[0]+1))\n", + "y = np.linspace(Model.minCoord[1], Model.maxCoord[1], 4*(Model.mesh.elementRes[1]+1))\n", + "\n", + "xi, yi = np.meshgrid(x, y)\n", + "\n", + "coords = np.zeros(shape=(xi.flatten().shape[0], 3))\n", + "coords[:,0] = xi.flatten()\n", + "coords[:,1] = yi.flatten()\n", + "coords[:,2] = np.zeros_like(coords[:,0]) ### or any array with same shape as x and y coords with the initial height\n", + "\n", + "### add back in the dim\n", + "coords = coords * u.kilometer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Include erosion and sedimentation rates in model runs\n", + "\n", + "A branching condition is used to create erosion and sedimentation rates that can vary across the domain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ve_conditions = fn.branching.conditional([((Model.y >= GEO.nd(Model.maxCoord[1])/2.), GEO.nd(2.5 * u.millimeter/u.year)),\n", + " (True, GEO.nd(0.0 * u.millimeter/u.year))])\n", + "\n", + "vs_conditions = fn.branching.conditional([((Model.y >= GEO.nd(Model.maxCoord[1])/2.), GEO.nd(2.5 * u.millimeter/u.year)),\n", + " (True, GEO.nd(0.0 * u.millimeter/u.year))])\n", + "\n", + "Model.surfaceProcesses = GEO.surfaceProcesses.velocitySurface_3D(airIndex = air.index,\n", + " sedimentIndex= sediment.index,\n", + " surfaceArray = coords, ### grid with surface points (x, y, z)\n", + " vs_condition = vs_conditions, ### sedimentation rate at each grid point\n", + " ve_condition = ve_conditions, ### erosion rate at each grid point\n", + " surfaceElevation=air.bottom)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from underworld import visualisation as vis\n", + "Fig = vis.Figure(figsize=(1200,400))\n", + "Fig.Points(Model.surface_tracers, Model.surface_tracers.ve, fn_size=5)\n", + "Fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from underworld import visualisation as vis\n", + "Fig = vis.Figure(figsize=(1200,400))\n", + "Fig.Points(Model.surface_tracers, Model.surface_tracers.vs, fn_size=5)\n", + "Fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model.init_model(temperature=\"steady-state\", pressure=\"lithostatic\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "GEO.rcParams['initial.nonlinear.min.iterations'] = 1\n", + "GEO.rcParams['nonlinear.min.iterations'] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Model.solver.set_inner_method(\"mumps\")\n", + "# Model.solver.set_penalty(1e6)\n", + "GEO.rcParams[\"initial.nonlinear.tolerance\"] = 1e-2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "Model.run_for(duration=0.51 * u.megayear, checkpoint_interval=0.5*u.megayears)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if GEO.size == 1:\n", + " import matplotlib.pyplot as plt\n", + " \n", + " \n", + " surface = GEO.dim(Model.surface_tracers.data, u.kilometer)\n", + "\n", + " scatter = plt.scatter(surface[:,0], surface[:,1], c=surface[:,2], s=10)\n", + "\n", + " cbar = plt.colorbar(scatter)\n", + " \n", + " cbar.set_label('Topo [km]')\n", + " \n", + " \n", + " plt.xlabel('x [km]')\n", + " plt.xlabel('y [km]')\n", + " \n", + " plt.show()\n", + " \n", + " \n", + " plt.plot()\n", + " \n", + " profile1 = surface[surface[:,1].m == np.unique(surface.m[:,0])[20]]\n", + " profile2 = surface[surface[:,1].m == np.unique(surface.m[:,0])[-20]]\n", + " \n", + " plt.plot(profile1[:,0], profile1[:,2], label = 'No SP') \n", + " \n", + " plt.plot(profile2[:,0], profile2[:,2], label = 'SP')\n", + " \n", + " plt.xlabel('x [km]')\n", + " \n", + " plt.ylabel('Topo [km]')\n", + " \n", + " plt.legend()\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/development/docker/docker-builder.sh b/docs/development/docker/docker-builder.sh index 0a15e8f47..2e2f01e7f 100755 --- a/docs/development/docker/docker-builder.sh +++ b/docs/development/docker/docker-builder.sh @@ -13,24 +13,27 @@ ARCH=$(uname -m) echo "Will build docker image locally for architecture type: $ARCH" echo "************************************************************\n" +# Get the ubuntu image +docker pull ubuntu:22.04 + ## The mpi and lavavu images should be automatically made via github actions -#docker build . --pull -f ./docs/development/docker/mpi/Dockerfile.openmpi -t underworldcode/openmpi:4.1.4-$ARCH -#docker build . --pull -f ./docs/development/docker/lavavu/Dockerfile -t underworldcode/lavavu:$ARCH +docker build . -f ./docs/development/docker/mpi/Dockerfile.openmpi -t underworldcode/openmpi:4.1.4-$ARCH +docker build . -f ./docs/development/docker/lavavu/Dockerfile -t underworldcode/lavavu:$ARCH -docker build . --pull \ +docker build . \ -f ./docs/development/docker/petsc/Dockerfile \ - --build-arg MPI_IMAGE="underworldcode/openmpi:4.1.4" \ - -t underworldcode/petsc:3.18.1-$ARCH + --build-arg MPI_IMAGE="underworldcode/openmpi:4.1.4-$ARCH" \ + -t underworldcode/petsc:3.19.4-$ARCH -# don't use pull here as we want the petsc image above +## don't use pull here as we want the petsc image above docker build . \ - --build-arg PETSC_IMAGE="underworldcode/petsc:3.18.1-$ARCH" \ + --build-arg PETSC_IMAGE="underworldcode/petsc:3.19.4-$ARCH" \ -f ./docs/development/docker/underworld2/Dockerfile \ - -t underworldcode/underworld2:2.14.0b-$ARCH + -t underworldcode/underworld2:2.15.0b-$ARCH -docker push underworldcode/petsc:3.18.1-$ARCH -docker push underworldcode/underworld2:2.14.0b-$ARCH +#docker push underworldcode/petsc:3.19.4-$ARCH +#docker push underworldcode/underworld2:2.15.0b-$ARCH #### if updates for both arm64 and x86_64 build manifest, ie # docker manifest create underworldcode/petsc:3.18.1 \ diff --git a/docs/development/docker/petsc/Dockerfile b/docs/development/docker/petsc/Dockerfile index a06481c7c..ff827a91b 100644 --- a/docs/development/docker/petsc/Dockerfile +++ b/docs/development/docker/petsc/Dockerfile @@ -52,7 +52,8 @@ RUN apt-get update -qq \ # build and open virtual environment RUN python3 -m venv $PYOPT \ && chmod ugo+rwx $PYOPT \ -&& pip3 install -U setuptools +&& pip3 install -U setuptools \ + wheel FROM runtime as build @@ -79,12 +80,12 @@ RUN apt-get update -qq \ && rm -rf /var/lib/apt/lists/* RUN pip3 install --no-cache-dir \ - cython \ + cython==0.29.36 \ numpy \ mpi4py # get petsc -ARG PETSC_VERSION="3.18.1" +ARG PETSC_VERSION="3.19.4" RUN mkdir -p /tmp/src WORKDIR /tmp/src RUN wget http://ftp.mcs.anl.gov/pub/petsc/release-snapshots/petsc-lite-${PETSC_VERSION}.tar.gz --no-check-certificate \ @@ -107,6 +108,7 @@ RUN PETSC_DIR=`pwd` ./configure --with-debugging=0 --prefix=/usr/local \ --download-hypre=1 \ --download-scalapack=1 \ --download-superlu_dist=1 \ + --download-pragmatic=1 \ --download-ctetgen \ --download-eigen \ --download-superlu=1 \ @@ -117,7 +119,7 @@ RUN PETSC_DIR=`pwd` ./configure --with-debugging=0 --prefix=/usr/local \ && rm -rf /usr/local/share/petsc # install h5py with MPI enabled -RUN CC=h5pcc HDF5_MPI="ON" HDF5_DIR=${PETSC_DIR} pip3 install --no-cache-dir --no-binary=h5py h5py \ +RUN CC=mpicc HDF5_MPI="ON" HDF5_DIR=${PETSC_DIR} pip3 install --no-cache-dir --no-build-isolation --no-binary=h5py h5py \ && pip install --no-cache-dir jupyterlab # record builder stage packages used diff --git a/docs/development/docker/underworld2/Dockerfile b/docs/development/docker/underworld2/Dockerfile index 09f2bbc5e..38533ddc1 100644 --- a/docs/development/docker/underworld2/Dockerfile +++ b/docs/development/docker/underworld2/Dockerfile @@ -17,7 +17,7 @@ # Used for github actions on the underworld repo # Must go before the 1st FROM see # https://docs.docker.com/engine/reference/builder/#understand-how-arg-and-from-interact -ARG PETSC_IMAGE="underworldcode/petsc:3.18.1" +ARG PETSC_IMAGE="underworldcode/petsc:3.19.4" # 'petsc-image' will be used later on in build stage COPY command FROM ${PETSC_IMAGE} as petsc-image @@ -94,7 +94,7 @@ RUN apt-get update -qq \ # Remove this for future versions # setuptools=65.6.0 has a unfixed error, so forcing version -RUN pip3 install setuptools==65.5.1 --force-reinstall --no-cache \ +RUN pip3 install setuptools --force-reinstall --no-cache \ && pip3 install --no-cache-dir \ matplotlib \ scipy \ @@ -127,10 +127,11 @@ WORKDIR /tmp COPY --chown=$NB_USER:users . /tmp/underworld2 WORKDIR /tmp/underworld2 RUN pip3 install -vvv . -RUN pip3 install setuptools==65.5.1 --force-reinstall --no-cache-dir \ +RUN pip3 install setuptools --force-reinstall --no-cache-dir \ && pip3 install --no-cache-dir \ git+https://github.com/drufat/triangle.git \ - badlands + badlands==2.2.4 \ + jupyter_contrib_nbextensions RUN pip3 freeze >/opt/requirements.txt # Record manually install apt packages. @@ -143,20 +144,25 @@ FROM runtime as final COPY --from=build --chown=$NB_USER:users /opt /opt COPY --from=build --chown=$NB_USER:users /usr/local /usr/local -# Copy in examples, tests, etc. -COPY --chown=jovyan:users ./docs/examples $NB_HOME/Underworld/examples -COPY --chown=jovyan:users ./docs/cheatsheet $NB_HOME/Underworld/cheatsheet -COPY --chown=jovyan:users ./docs/user_guide $NB_HOME/Underworld/user_guide -COPY --chown=jovyan:users ./docs/test $NB_HOME/Underworld/test -COPY --chown=jovyan:users ./docs/UWGeodynamics/examples $NB_HOME/Underworld/UWGeodynamics/examples -COPY --chown=jovyan:users ./docs/UWGeodynamics/benchmarks $NB_HOME/Underworld/UWGeodynamics/benchmarks -COPY --chown=jovyan:users ./docs/UWGeodynamics/tutorials $NB_HOME/Underworld/UWGeodynamics/tutorials -RUN mkdir -p $NB_HOME/workspace \ -&& chown jovyan:users /home/jovyan/workspace -RUN jupyter serverextension enable --sys-prefix jupyter_server_proxy +# must make directory before COPY into it for permissions to work (!!!) +RUN mkdir -p $NB_HOME/workspace $NB_HOME/Underworld/UWGeodynamics \ +&& chown $NB_USER:users -R $NB_HOME \ +&& jupyter serverextension enable --sys-prefix jupyter_server_proxy + +#Copy in examples, tests, etc. +COPY --chown=$NB_USER:users ./docs/examples $NB_HOME/Underworld/examples +COPY --chown=$NB_USER:users ./docs/cheatsheet $NB_HOME/Underworld/cheatsheet +COPY --chown=$NB_USER:users ./docs/user_guide $NB_HOME/Underworld/user_guide +COPY --chown=$NB_USER:users ./docs/test $NB_HOME/Underworld/test +COPY --chown=$NB_USER:users ./docs/UWGeodynamics/examples $NB_HOME/Underworld/UWGeodynamics/examples +COPY --chown=$NB_USER:users ./docs/UWGeodynamics/benchmarks $NB_HOME/Underworld/UWGeodynamics/benchmarks +COPY --chown=$NB_USER:users ./docs/UWGeodynamics/tutorials $NB_HOME/Underworld/UWGeodynamics/tutorials EXPOSE 8888 -USER $NB_USER WORKDIR $NB_HOME +USER $NB_USER + +# Declare a volume space +VOLUME $NB_HOME/workspace CMD ["jupyter-lab", "--no-browser", "--ip='0.0.0.0'"] diff --git a/docs/development/release_guidelines.md b/docs/development/release_guidelines.md index bdeae8a03..d25b28b84 100644 --- a/docs/development/release_guidelines.md +++ b/docs/development/release_guidelines.md @@ -26,6 +26,7 @@ Documentation review * Review `development_guidelines.md`. * Review docstrings updates for deprecation warnings. * Check for other DEPRECATE flags in the code. + - `find . -name '*.py' -exec grep -i deprecate {} +` * Check autocomplete to ensure no garbage has slipped in. Non user relevant objects should be made private so they don't appear in autocomplete suggestions. @@ -63,7 +64,7 @@ Testing Creating the release ==================== -* Tag the release in git. +* Tag the release branch in git. * Create the release from within Github. * Check `docker/docker.md` for docker image release information. * Add tagged documentation version at http://underworld2.readthedocs.io/ diff --git a/docs/examples/03_BlankenbachBenchmark.ipynb b/docs/examples/03_BlankenbachBenchmark.ipynb index f3d09a2a0..2e9e17395 100644 --- a/docs/examples/03_BlankenbachBenchmark.ipynb +++ b/docs/examples/03_BlankenbachBenchmark.ipynb @@ -942,27 +942,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rayleigh number = 1.0e+04\n", - "Topography[x=0],[x=max] = 2252.36, -2900.67\n", - "x(topo=0) = 0.539062\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/julian/codes/venv/py310/lib/python3.10/site-packages/numpy/lib/npyio.py:1503: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " X = np.asarray(X)\n" - ] - } - ], + "outputs": [], "source": [ "e1 = topography.evaluate_global( ( 0.,boxHeight) )\n", "e2 = topography.evaluate_global( (boxLength,boxHeight) )\n", @@ -974,8 +956,9 @@ " print('Topography[x=0],[x=max] = {0:.2f}, {1:.2f}'.format(e1, e2))\n", " print('x(topo=0) = {0:.6f}'.format(min_abs_topo_coord))\n", " # output a summary file with benchmark values (useful for parallel runs)\n", - " np.savetxt(outputPath+'summary.txt', [Ra, e1, e2, min_abs_topo_coord, q1, q2, q3, q4])\n", - "\n", + " data = [Ra, e1, e2, min_abs_topo_coord, q1, q2, q3, q4]\n", + " d1 = np.asarray([float(i) for i in data]) # sanitise data into signle array\n", + " np.savetxt(outputPath+'summary.txt', d1)\n", " # Let's add a test to ensure things are working as expected\n", " if case == \"a\":\n", " if not np.isclose(e1,2254.02,rtol=1.e-3):\n", @@ -1003,7 +986,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/docs/install_guides/nci_gadi/sample.pbs b/docs/install_guides/nci_gadi/sample.pbs index 97a7b6b6f..46dc0e895 100644 --- a/docs/install_guides/nci_gadi/sample.pbs +++ b/docs/install_guides/nci_gadi/sample.pbs @@ -9,7 +9,7 @@ #PBS -l wd #PBS -l storage=gdata/m18 -export MODULEPATH=/g/data/m18/modulefiles/:$MODULEPATH​ +export MODULEPATH=/g/data/m18/modulefiles/:$MODULEPATH module load underworld/2.13 MODELNAME="foobar" diff --git a/docs/install_guides/setonix_baremetal.sh b/docs/install_guides/setonix_baremetal.sh new file mode 100644 index 000000000..28c09a8cf --- /dev/null +++ b/docs/install_guides/setonix_baremetal.sh @@ -0,0 +1,30 @@ +#!/bin/bash -l + +## User required input +#SBATCH --account=pawsey0407 +#SBATCH --job-name=bobthejob +#SBATCH --ntasks=3 +#SBATCH --time=00:20:00 + +## Setup job conditions and run environment +#SBATCH --ntasks-per-node=64 # found this is needed ~Apr2023 +#SBATCH --cpus-per-task=1 # OMP_NUM_THREADS equivalent + +# Note we avoid any inadvertent OpenMP threading by setting +export OMP_NUM_THREADS=1 + +# load system packages: py39, mpi, hdf5 +module load python/3.9.15 py-mpi4py/3.1.2-py3.9.15 py-numpy/1.20.3 py-h5py/3.4.0 py-cython/0.29.24 cmake/3.21.4 + +# add custom virtual environment and underworld +export OPT_DIR=/software/projects/pawsey0407/setonix/ +export PYTHONPATH=$OPT_DIR/py39/lib/python3.9/site-packages/:$PYTHONPATH +export PYTHONPATH=$OPT_DIR/underworld/2.14.2/lib/python3.9/site-packages:$PYTHONPATH + +# load custom petsc +export PETSC_DIR=$OPT_DIR/petsc-3.19.0 +export PYTHONPATH=$PETSC_DIR/lib:$PYTHONPATH + +## model name and execution +export model="mymod.py" +srun -n ${SLURM_NTASKS} python3 $model diff --git a/docs/pytests/test_UWGeo_examples.py b/docs/pytests/test_UWGeo_examples.py index 06a9f15a6..0e7f2c01d 100644 --- a/docs/pytests/test_UWGeo_examples.py +++ b/docs/pytests/test_UWGeo_examples.py @@ -2,15 +2,13 @@ import pytest import glob import ntpath -import sys -import underworld as uw from inspect import getsourcefile wdir = ntpath.dirname(getsourcefile(lambda:0))+"/../UWGeodynamics/examples/" +# get ipynb scripts to test scripts = [pytest.param(path, id=ntpath.basename(path)) for path in sorted(glob.glob(wdir+"/*.ipynb"))] @pytest.mark.parametrize('script', scripts) def test_script_execution(script): - subprocess.run(["pytest", "--nbmake", script]) - # subprocess.run([sys.executable, script]) + subprocess.run(["pytest", "--nbmake", script], check=True) diff --git a/docs/pytests/test_UWGeo_user_guide.py b/docs/pytests/test_UWGeo_user_guide.py index e68ad14b9..73fe54960 100644 --- a/docs/pytests/test_UWGeo_user_guide.py +++ b/docs/pytests/test_UWGeo_user_guide.py @@ -2,15 +2,13 @@ import pytest import glob import ntpath -import sys -import underworld as uw from inspect import getsourcefile wdir = ntpath.dirname(getsourcefile(lambda:0))+"/../UWGeodynamics/user_guide/" +# get ipynb scripts to test scripts = [pytest.param(path, id=ntpath.basename(path)) for path in sorted(glob.glob(wdir+"/*.ipynb"))] @pytest.mark.parametrize('script', scripts) def test_script_execution(script): - subprocess.run(["pytest", "--nbmake", script]) - # subprocess.run([sys.executable, script]) + subprocess.run(["pytest", "--nbmake", script], check=True) diff --git a/docs/pytests/test_examples.py b/docs/pytests/test_examples.py index 242afdf96..a1353d271 100644 --- a/docs/pytests/test_examples.py +++ b/docs/pytests/test_examples.py @@ -2,16 +2,13 @@ import pytest import glob import ntpath -import sys -import underworld as uw from inspect import getsourcefile wdir = ntpath.dirname(getsourcefile(lambda:0))+"/../examples/" +# get ipynb scripts to test scripts = [pytest.param(path, id=ntpath.basename(path)) for path in sorted(glob.glob(wdir+"/*.ipynb"))] @pytest.mark.parametrize('script', scripts) def test_script_execution(script): - subprocess.run(["pytest", "--nbmake", script]) - #subprocess.run([sys.executable, "-m", "pytest", "--nbmake", script]) - #subprocess.run([sys.executable, script]) + subprocess.run(["pytest", "--nbmake", script], check=True) diff --git a/docs/pytests/test_user_guide.py b/docs/pytests/test_user_guide.py index a3843df67..faef8536b 100644 --- a/docs/pytests/test_user_guide.py +++ b/docs/pytests/test_user_guide.py @@ -2,15 +2,13 @@ import pytest import glob import ntpath -import sys -import underworld as uw from inspect import getsourcefile wdir = ntpath.dirname(getsourcefile(lambda:0))+"/../user_guide/" +# get ipynb scripts to test scripts = [pytest.param(path, id=ntpath.basename(path)) for path in sorted(glob.glob(wdir+"/*.ipynb"))] @pytest.mark.parametrize('script', scripts) def test_script_execution(script): - subprocess.run(["pytest", "--nbmake", script]) - # subprocess.run([sys.executable, script]) + subprocess.run(["pytest", "--nbmake", script], check=True) diff --git a/docs/pytests/test.py b/docs/pytests/tests.py similarity index 76% rename from docs/pytests/test.py rename to docs/pytests/tests.py index 6d91ab993..ce87e3d4d 100644 --- a/docs/pytests/test.py +++ b/docs/pytests/tests.py @@ -3,22 +3,23 @@ import glob import ntpath import sys -import underworld as uw from inspect import getsourcefile + wdir = ntpath.dirname(getsourcefile(lambda:0))+"/../test/" +# get python scripts to test pyscripts = [pytest.param(path, id=ntpath.basename(path)) for path in sorted(glob.glob(wdir+"/*.py"))] @pytest.mark.parametrize('pyscript', pyscripts) def test_python_execution(pyscript): - cp = subprocess.run([sys.executable, pyscript]) + cp = subprocess.run([sys.executable, pyscript], check=True) assert cp.returncode == 0 +# get ipynb scripts to test ipynbscripts = [pytest.param(path, id=ntpath.basename(path)) for path in sorted(glob.glob(wdir+"/*.ipynb"))] - @pytest.mark.parametrize('ipynbscript', ipynbscripts) def test_ipynb_execution(ipynbscript): - cp = subprocess.run(["pytest", "--nbmake", ipynbscript]) + cp = subprocess.run(["pytest", "--nbmake", ipynbscript], check=True) assert cp.returncode == 0 diff --git a/docs/test/Analytic Soln Convergence Tests.ipynb b/docs/test/Analytic Soln Convergence Tests.ipynb index 88cdfe153..05ba30f6c 100755 --- a/docs/test/Analytic Soln Convergence Tests.ipynb +++ b/docs/test/Analytic Soln Convergence Tests.ipynb @@ -37,11 +37,18 @@ " (\"Kz\", OD(), {\"itol\":1.e-4, \"otol\":1.e-4}, False ),\n", " (\"M\", OD(), {\"itol\":1.e-6, \"otol\":1.e-6}, True ), \n", " ]\n", - " \n", "\n", - "do_analysis = True\n", "graph_all = True\n", "two_d_only = False\n", + "do_analysis = True\n", + "with_matplotlib = True\n", + "\n", + "import underworld as uw\n", + "try:\n", + " import matplotlib\n", + " uw.utils.matplotlib_inline()\n", + "except ModuleNotFoundError:\n", + " with_matplotlib = False\n", "\n", "regress_res = [8,16,32]\n", "orders = [1,2]\n", @@ -54,16 +61,21 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No module named 'lavavu' : module not found! disabling inline visualisation\n" + ] + } + ], "source": [ - "import underworld as uw\n", "import underworld.visualisation as vis \n", "from underworld import function as fn\n", "import math\n", "import numpy as np\n", - "import collections\n", - "\n", - "uw.utils.matplotlib_inline()" + "import collections" ] }, { @@ -238,72 +250,3534 @@ "name": "stdout", "output_type": "stream", "text": [ + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolA 1 8\n", + "Linear solver (DLJ1OG4R__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 8.447e-06 secs / 1 its\n", + " Pressure Solve: = 1.992e-05 secs / 1 its\n", + " Final V Solve: = 3.818e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000398 seconds\n", + "\n", + "Linear solver (DLJ1OG4R__system-execute), solution time 8.237810e-04 (secs)\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", "Performing simulations for solution: SolA 1 16\n", + "Linear solver (9L5WSXDF__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.711e-05 secs / 1 its\n", + " Pressure Solve: = 3.578e-05 secs / 1 its\n", + " Final V Solve: = 1.695e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001128 seconds\n", + "\n", + "Linear solver (9L5WSXDF__system-execute), solution time 1.238623e-03 (secs)\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", "Performing simulations for solution: SolA 1 32\n", + "Linear solver (2C1Q1L4K__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001231 secs / 1 its\n", + " Pressure Solve: = 0.0001583 secs / 1 its\n", + " Final V Solve: = 0.000152 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006424 seconds\n", + "\n", + "Linear solver (2C1Q1L4K__system-execute), solution time 6.632578e-03 (secs)\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolA 2 8\n", + "Linear solver (28J6XK5E__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.393e-05 secs / 1 its\n", + " Pressure Solve: = 9.895e-05 secs / 3 its\n", + " Final V Solve: = 1.703e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001144 seconds\n", + "\n", + "Linear solver (28J6XK5E__system-execute), solution time 1.281114e-03 (secs)\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", "Performing simulations for solution: SolA 2 16\n", + "Linear solver (QV81OBV4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001339 secs / 1 its\n", + " Pressure Solve: = 0.0006194 secs / 3 its\n", + " Final V Solve: = 0.0001502 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008163 seconds\n", + "\n", + "Linear solver (QV81OBV4__system-execute), solution time 8.376631e-03 (secs)\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", "Performing simulations for solution: SolA 2 32\n", + "Linear solver (Z87F0IMN__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001131 secs / 1 its\n", + " Pressure Solve: = 0.004021 secs / 3 its\n", + " Final V Solve: = 0.001119 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.048163 seconds\n", + "\n", + "Linear solver (Z87F0IMN__system-execute), solution time 4.875919e-02 (secs)\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolCx 1 8\n", + "Linear solver (I9Q2KENZ__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 9.549e-06 secs / 1 its\n", + " Pressure Solve: = 6.848e-05 secs / 7 its\n", + " Final V Solve: = 3.317e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000509 seconds\n", + "\n", + "Linear solver (I9Q2KENZ__system-execute), solution time 6.203870e-04 (secs)\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", "Performing simulations for solution: SolCx 1 16\n", + "Linear solver (YCBU6PU7__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.893e-05 secs / 1 its\n", + " Pressure Solve: = 0.0001656 secs / 6 its\n", + " Final V Solve: = 1.676e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001208 seconds\n", + "\n", + "Linear solver (YCBU6PU7__system-execute), solution time 1.315522e-03 (secs)\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", "Performing simulations for solution: SolCx 1 32\n", + "Linear solver (301QC62T__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001542 secs / 1 its\n", + " Pressure Solve: = 0.0009751 secs / 5 its\n", + " Final V Solve: = 0.0001583 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006592 seconds\n", + "\n", + "Linear solver (301QC62T__system-execute), solution time 6.776516e-03 (secs)\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolCx 2 8\n", + "Linear solver (5XZUHZI0__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.114e-05 secs / 1 its\n", + " Pressure Solve: = 0.0005204 secs / 11 its\n", + " Final V Solve: = 3.036e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.002311 seconds\n", + "\n", + "Linear solver (5XZUHZI0__system-execute), solution time 2.551202e-03 (secs)\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", "Performing simulations for solution: SolCx 2 16\n", + "Linear solver (4GZJMA4W__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001294 secs / 1 its\n", + " Pressure Solve: = 0.00196 secs / 11 its\n", + " Final V Solve: = 0.0001404 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008427 seconds\n", + "\n", + "Linear solver (4GZJMA4W__system-execute), solution time 8.761971e-03 (secs)\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", "Performing simulations for solution: SolCx 2 32\n", + "Linear solver (0AI2JUHQ__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001201 secs / 1 its\n", + " Pressure Solve: = 0.01516 secs / 10 its\n", + " Final V Solve: = 0.001454 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.061728 seconds\n", + "\n", + "Linear solver (0AI2JUHQ__system-execute), solution time 6.221665e-02 (secs)\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolKx 1 8\n", + "Linear solver (54TRBM5T__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 9.545e-06 secs / 1 its\n", + " Pressure Solve: = 4.913e-05 secs / 4 its\n", + " Final V Solve: = 3.63e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000492 seconds\n", + "\n", + "Linear solver (54TRBM5T__system-execute), solution time 6.339730e-04 (secs)\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", "Performing simulations for solution: SolKx 1 16\n", + "Linear solver (U3N8G5U0__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 3.02e-05 secs / 1 its\n", + " Pressure Solve: = 0.0001475 secs / 4 its\n", + " Final V Solve: = 2.308e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001300 seconds\n", + "\n", + "Linear solver (U3N8G5U0__system-execute), solution time 1.440279e-03 (secs)\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", "Performing simulations for solution: SolKx 1 32\n", + "Linear solver (M82V0HCU__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001426 secs / 1 its\n", + " Pressure Solve: = 0.0008902 secs / 4 its\n", + " Final V Solve: = 0.0001725 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006629 seconds\n", + "\n", + "Linear solver (M82V0HCU__system-execute), solution time 6.832792e-03 (secs)\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolKx 2 8\n", + "Linear solver (QXRQSRAB__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.54e-05 secs / 1 its\n", + " Pressure Solve: = 0.0001782 secs / 6 its\n", + " Final V Solve: = 1.756e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001352 seconds\n", + "\n", + "Linear solver (QXRQSRAB__system-execute), solution time 1.491348e-03 (secs)\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", "Performing simulations for solution: SolKx 2 16\n", + "Linear solver (EWJJD4BZ__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001259 secs / 1 its\n", + " Pressure Solve: = 0.001121 secs / 6 its\n", + " Final V Solve: = 0.0001467 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006965 seconds\n", + "\n", + "Linear solver (EWJJD4BZ__system-execute), solution time 7.167068e-03 (secs)\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", "Performing simulations for solution: SolKx 2 32\n", + "Linear solver (42CMJVBN__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001111 secs / 1 its\n", + " Pressure Solve: = 0.007059 secs / 5 its\n", + " Final V Solve: = 0.001141 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.051279 seconds\n", + "\n", + "Linear solver (42CMJVBN__system-execute), solution time 5.177867e-02 (secs)\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", "Performing simulations for solution: SolNL 1 8\n", - "Performing simulations for solution: SolNL 1 16\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 8.904e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001079 secs / 14 its\n", + " Final V Solve: = 3.15e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000509 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 6.238430e-04 (secs)\n", + "In SystemLinearEquations_NonLinearExecute\n", + "\n", + "Non linear solver - iteration 0\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 1.106e-05 secs / 1 its\n", + " Pressure Solve: = 0.0001893 secs / 15 its\n", + " Final V Solve: = 5.88e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000739 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 8.761580e-04 (secs)\n", + "Non linear solver - iteration 1\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 1.069e-05 secs / 1 its\n", + " Pressure Solve: = 0.0001386 secs / 15 its\n", + " Final V Solve: = 3.084e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000642 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 7.652760e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.010055 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.00549473e-02; Tolerance 1.0000e-07 - Not converged - 4.899772e-03 (secs)\n", + "\n", + "Non linear solver - iteration 2\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 6.256e-06 secs / 1 its\n", + " Pressure Solve: = 9.985e-05 secs / 15 its\n", + " Final V Solve: = 6.159e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000426 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.958490e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 2 of 500 - Residual 0.0046752 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.67516024e-03; Tolerance 1.0000e-07 - Not converged - 6.381440e-03 (secs)\n", + "\n", + "Non linear solver - iteration 3\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 5.057e-06 secs / 1 its\n", + " Pressure Solve: = 0.000107 secs / 15 its\n", + " Final V Solve: = 2.853e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000427 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 5.028530e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 3 of 500 - Residual 0.0023242 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.32421816e-03; Tolerance 1.0000e-07 - Not converged - 7.945685e-03 (secs)\n", + "\n", + "Non linear solver - iteration 4\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 7.704e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001724 secs / 16 its\n", + " Final V Solve: = 4.822e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000662 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 7.827760e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 4 of 500 - Residual 0.001206 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.20596325e-03; Tolerance 1.0000e-07 - Not converged - 9.915864e-03 (secs)\n", + "\n", + "Non linear solver - iteration 5\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 7.099e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001073 secs / 16 its\n", + " Final V Solve: = 3.129e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000476 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 5.528720e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 5 of 500 - Residual 0.00064429 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.44293466e-04; Tolerance 1.0000e-07 - Not converged - 1.171621e-02 (secs)\n", + "\n", + "Non linear solver - iteration 6\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 6.972e-06 secs / 1 its\n", + " Pressure Solve: = 0.000108 secs / 16 its\n", + " Final V Solve: = 2.778e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000453 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 5.296380e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 6 of 500 - Residual 0.00035138 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.51382320e-04; Tolerance 1.0000e-07 - Not converged - 1.329571e-02 (secs)\n", + "\n", + "Non linear solver - iteration 7\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.376e-06 secs / 1 its\n", + " Pressure Solve: = 0.000102 secs / 16 its\n", + " Final V Solve: = 2.911e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000400 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.638290e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 7 of 500 - Residual 0.00019455 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.94551318e-04; Tolerance 1.0000e-07 - Not converged - 1.475486e-02 (secs)\n", + "\n", + "Non linear solver - iteration 8\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.436e-06 secs / 1 its\n", + " Pressure Solve: = 9.913e-05 secs / 16 its\n", + " Final V Solve: = 2.798e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000404 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.693000e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 8 of 500 - Residual 0.00010897 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.08971791e-04; Tolerance 1.0000e-07 - Not converged - 1.622112e-02 (secs)\n", + "\n", + "Non linear solver - iteration 9\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.151e-06 secs / 1 its\n", + " Pressure Solve: = 9.93e-05 secs / 16 its\n", + " Final V Solve: = 2.78e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000394 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.576710e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 9 of 500 - Residual 6.1603e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.16034685e-05; Tolerance 1.0000e-07 - Not converged - 1.764471e-02 (secs)\n", + "\n", + "Non linear solver - iteration 10\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.666e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001002 secs / 16 its\n", + " Final V Solve: = 2.803e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000411 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.770810e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 10 of 500 - Residual 3.5092e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.50917858e-05; Tolerance 1.0000e-07 - Not converged - 1.910341e-02 (secs)\n", + "\n", + "Non linear solver - iteration 11\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.988e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001006 secs / 16 its\n", + " Final V Solve: = 2.808e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000444 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 5.099590e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 11 of 500 - Residual 2.0119e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.01189080e-05; Tolerance 1.0000e-07 - Not converged - 2.059431e-02 (secs)\n", + "\n", + "Non linear solver - iteration 12\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 5.654e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001045 secs / 16 its\n", + " Final V Solve: = 2.825e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000415 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.799390e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 12 of 500 - Residual 1.1599e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.15987598e-05; Tolerance 1.0000e-07 - Not converged - 2.205542e-02 (secs)\n", + "\n", + "Non linear solver - iteration 13\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.967e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001018 secs / 16 its\n", + " Final V Solve: = 2.871e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000453 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 5.333360e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 13 of 500 - Residual 6.7192e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.71915545e-06; Tolerance 1.0000e-07 - Not converged - 2.356608e-02 (secs)\n", + "\n", + "Non linear solver - iteration 14\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 5.73e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001021 secs / 16 its\n", + " Final V Solve: = 2.776e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000411 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.766130e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 14 of 500 - Residual 3.9089e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.90890148e-06; Tolerance 1.0000e-07 - Not converged - 2.502751e-02 (secs)\n", + "\n", + "Non linear solver - iteration 15\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.601e-06 secs / 1 its\n", + " Pressure Solve: = 9.987e-05 secs / 16 its\n", + " Final V Solve: = 2.802e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000401 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.634940e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 15 of 500 - Residual 2.2825e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.28249955e-06; Tolerance 1.0000e-07 - Not converged - 2.646088e-02 (secs)\n", + "\n", + "Non linear solver - iteration 16\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.059e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001148 secs / 16 its\n", + " Final V Solve: = 3.736e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000427 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.923700e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 16 of 500 - Residual 1.3372e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.33718232e-06; Tolerance 1.0000e-07 - Not converged - 2.794978e-02 (secs)\n", + "\n", + "Non linear solver - iteration 17\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.294e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001003 secs / 16 its\n", + " Final V Solve: = 2.815e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000401 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.661870e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 17 of 500 - Residual 7.8564e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.85643988e-07; Tolerance 1.0000e-07 - Not converged - 2.940494e-02 (secs)\n", + "\n", + "Non linear solver - iteration 18\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 3.976e-06 secs / 1 its\n", + " Pressure Solve: = 9.771e-05 secs / 16 its\n", + " Final V Solve: = 2.778e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000403 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.675940e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 18 of 500 - Residual 4.6277e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.62772005e-07; Tolerance 1.0000e-07 - Not converged - 3.086438e-02 (secs)\n", + "\n", + "Non linear solver - iteration 19\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 6.05e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001026 secs / 16 its\n", + " Final V Solve: = 2.926e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000414 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.788000e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 19 of 500 - Residual 2.732e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.73201077e-07; Tolerance 1.0000e-07 - Not converged - 3.232015e-02 (secs)\n", + "\n", + "Non linear solver - iteration 20\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 4.301e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001022 secs / 16 its\n", + " Final V Solve: = 2.861e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000404 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.652960e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 20 of 500 - Residual 1.6161e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.61605008e-07; Tolerance 1.0000e-07 - Not converged - 3.377202e-02 (secs)\n", + "\n", + "Non linear solver - iteration 21\n", + "Linear solver (GWMTMTW4__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 5.401e-06 secs / 1 its\n", + " Pressure Solve: = 0.0001006 secs / 16 its\n", + " Final V Solve: = 2.794e-06 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.000411 seconds\n", + "\n", + "Linear solver (GWMTMTW4__system-execute), solution time 4.741440e-04 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 21 of 500 - Residual 9.5759e-08 - Tolerance = 1e-07\n", + "Non linear solver - Residual 9.57592830e-08; Tolerance 1.0000e-07 - Converged - 3.521071e-02 (secs)\n", + "\n", + "In func SystemLinearEquations_NonLinearExecute: Converged after 21 iterations.\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", + "Performing simulations for solution: SolNL 1 16Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.392e-05 secs / 1 its\n", + " Pressure Solve: = 0.0003101 secs / 13 its\n", + " Final V Solve: = 1.315e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001228 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.333469e-03 (secs)\n", + "In SystemLinearEquations_NonLinearExecute\n", + "\n", + "Non linear solver - iteration 0\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.37e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002319 secs / 11 its\n", + " Final V Solve: = 1.334e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001160 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.252030e-03 (secs)\n", + "Non linear solver - iteration 1\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.404e-05 secs / 1 its\n", + " Pressure Solve: = 0.0003171 secs / 13 its\n", + " Final V Solve: = 1.357e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001285 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.373283e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.010282 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.02824523e-02; Tolerance 1.0000e-07 - Not converged - 9.701201e-03 (secs)\n", + "\n", + "Non linear solver - iteration 2\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.326e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002837 secs / 13 its\n", + " Final V Solve: = 1.382e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001226 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.321092e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 2 of 500 - Residual 0.0048801 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.88013001e-03; Tolerance 1.0000e-07 - Not converged - 1.457453e-02 (secs)\n", + "\n", + "Non linear solver - iteration 3\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.274e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002734 secs / 12 its\n", + " Final V Solve: = 1.353e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001163 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.246727e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 3 of 500 - Residual 0.0024863 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.48625112e-03; Tolerance 1.0000e-07 - Not converged - 1.942296e-02 (secs)\n", + "\n", + "Non linear solver - iteration 4\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.204e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002528 secs / 12 its\n", + " Final V Solve: = 1.411e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001145 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.232253e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 4 of 500 - Residual 0.0013217 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.32173003e-03; Tolerance 1.0000e-07 - Not converged - 2.430826e-02 (secs)\n", + "\n", + "Non linear solver - iteration 5\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.326e-05 secs / 1 its\n", + " Pressure Solve: = 0.000301 secs / 13 its\n", + " Final V Solve: = 1.412e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001209 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.295005e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 5 of 500 - Residual 0.00072324 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.23235872e-04; Tolerance 1.0000e-07 - Not converged - 2.926595e-02 (secs)\n", + "\n", + "Non linear solver - iteration 6\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.301e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002817 secs / 13 its\n", + " Final V Solve: = 1.34e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001190 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.272967e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 6 of 500 - Residual 0.00040424 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.04236545e-04; Tolerance 1.0000e-07 - Not converged - 3.414706e-02 (secs)\n", + "\n", + "Non linear solver - iteration 7\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.373e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002852 secs / 13 its\n", + " Final V Solve: = 1.329e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001187 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.275915e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 7 of 500 - Residual 0.00022971 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.29710423e-04; Tolerance 1.0000e-07 - Not converged - 3.906948e-02 (secs)\n", + "\n", + "Non linear solver - iteration 8\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.326e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002961 secs / 13 its\n", + " Final V Solve: = 1.354e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001206 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.290132e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 8 of 500 - Residual 0.00013231 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.32311212e-04; Tolerance 1.0000e-07 - Not converged - 4.395658e-02 (secs)\n", + "\n", + "Non linear solver - iteration 9\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.326e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002959 secs / 13 its\n", + " Final V Solve: = 1.398e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001227 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.310501e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 9 of 500 - Residual 7.7084e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.70844388e-05; Tolerance 1.0000e-07 - Not converged - 4.891455e-02 (secs)\n", + "\n", + "Non linear solver - iteration 10\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.475e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002993 secs / 13 its\n", + " Final V Solve: = 1.375e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001260 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.364550e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 10 of 500 - Residual 4.5353e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.53531657e-05; Tolerance 1.0000e-07 - Not converged - 5.403444e-02 (secs)\n", + "\n", + "Non linear solver - iteration 11\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.284e-05 secs / 1 its\n", + " Pressure Solve: = 0.0003341 secs / 13 its\n", + " Final V Solve: = 1.934e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001272 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.357937e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 11 of 500 - Residual 2.6914e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.69137616e-05; Tolerance 1.0000e-07 - Not converged - 5.911685e-02 (secs)\n", + "\n", + "Non linear solver - iteration 12\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.47e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002938 secs / 13 its\n", + " Final V Solve: = 1.366e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001222 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.308952e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 12 of 500 - Residual 1.6092e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.60919292e-05; Tolerance 1.0000e-07 - Not converged - 6.415686e-02 (secs)\n", + "\n", + "Non linear solver - iteration 13\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.327e-05 secs / 1 its\n", + " Pressure Solve: = 0.0003036 secs / 13 its\n", + " Final V Solve: = 1.397e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001277 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.367147e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 13 of 500 - Residual 9.6852e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 9.68518422e-06; Tolerance 1.0000e-07 - Not converged - 6.922107e-02 (secs)\n", + "\n", + "Non linear solver - iteration 14\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.437e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002893 secs / 13 its\n", + " Final V Solve: = 1.392e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001209 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.291574e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 14 of 500 - Residual 5.863e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 5.86296146e-06; Tolerance 1.0000e-07 - Not converged - 7.418465e-02 (secs)\n", + "\n", + "Non linear solver - iteration 15\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.307e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002835 secs / 13 its\n", + " Final V Solve: = 1.35e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001178 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.260472e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 15 of 500 - Residual 3.5671e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.56710930e-06; Tolerance 1.0000e-07 - Not converged - 7.911378e-02 (secs)\n", + "\n", + "Non linear solver - iteration 16\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.297e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002795 secs / 13 its\n", + " Final V Solve: = 1.34e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001170 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.255441e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 16 of 500 - Residual 2.1798e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.17982258e-06; Tolerance 1.0000e-07 - Not converged - 8.395723e-02 (secs)\n", + "\n", + "Non linear solver - iteration 17\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.385e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002917 secs / 13 its\n", + " Final V Solve: = 1.386e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001201 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.288403e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 17 of 500 - Residual 1.3371e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.33714551e-06; Tolerance 1.0000e-07 - Not converged - 8.890044e-02 (secs)\n", + "\n", + "Non linear solver - iteration 18\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.41e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002988 secs / 13 its\n", + " Final V Solve: = 1.395e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001229 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.315662e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 18 of 500 - Residual 8.2294e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 8.22937625e-07; Tolerance 1.0000e-07 - Not converged - 9.393564e-02 (secs)\n", + "\n", + "Non linear solver - iteration 19\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.292e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002922 secs / 13 its\n", + " Final V Solve: = 1.398e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001236 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.326938e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 19 of 500 - Residual 5.0792e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 5.07916453e-07; Tolerance 1.0000e-07 - Not converged - 9.902144e-02 (secs)\n", + "\n", + "Non linear solver - iteration 20\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.413e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002983 secs / 13 its\n", + " Final V Solve: = 1.504e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001224 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.310965e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 20 of 500 - Residual 3.1426e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.14258645e-07; Tolerance 1.0000e-07 - Not converged - 1.042051e-01 (secs)\n", + "\n", + "Non linear solver - iteration 21\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.249e-05 secs / 1 its\n", + " Pressure Solve: = 0.0003159 secs / 13 its\n", + " Final V Solve: = 1.41e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001210 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.290872e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 21 of 500 - Residual 1.9485e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.94853299e-07; Tolerance 1.0000e-07 - Not converged - 1.091938e-01 (secs)\n", + "\n", + "Non linear solver - iteration 22\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.295e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002972 secs / 13 its\n", + " Final V Solve: = 1.391e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001230 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.311799e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 22 of 500 - Residual 1.2104e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.21040497e-07; Tolerance 1.0000e-07 - Not converged - 1.141341e-01 (secs)\n", + "\n", + "Non linear solver - iteration 23\n", + "Linear solver (3XZU4OYK__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.309e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002907 secs / 13 its\n", + " Final V Solve: = 1.386e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001211 seconds\n", + "\n", + "Linear solver (3XZU4OYK__system-execute), solution time 1.294050e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 23 of 500 - Residual 7.531e-08 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.53097304e-08; Tolerance 1.0000e-07 - Converged - 1.191335e-01 (secs)\n", + "\n", + "In func SystemLinearEquations_NonLinearExecute: Converged after 23 iterations.\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001351 secs / 1 its\n", + " Pressure Solve: = 0.001164 secs / 7 its\n", + " Final V Solve: = 0.000139 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006195 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.363222e-03 (secs)\n", "Performing simulations for solution: SolNL 1 32\n", + "In SystemLinearEquations_NonLinearExecute\n", + "\n", + "Non linear solver - iteration 0\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001426 secs / 1 its\n", + " Pressure Solve: = 0.001643 secs / 9 its\n", + " Final V Solve: = 0.0001705 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007286 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.489239e-03 (secs)\n", + "Non linear solver - iteration 1\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001381 secs / 1 its\n", + " Pressure Solve: = 0.001614 secs / 9 its\n", + " Final V Solve: = 0.0001535 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007484 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.698635e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.010329 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.03288678e-02; Tolerance 1.0000e-07 - Not converged - 4.616417e-02 (secs)\n", + "\n", + "Non linear solver - iteration 2\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001323 secs / 1 its\n", + " Pressure Solve: = 0.001556 secs / 9 its\n", + " Final V Solve: = 0.0001465 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006989 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.199617e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 2 of 500 - Residual 0.0049141 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.91412851e-03; Tolerance 1.0000e-07 - Not converged - 6.834611e-02 (secs)\n", + "\n", + "Non linear solver - iteration 3\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001401 secs / 1 its\n", + " Pressure Solve: = 0.001355 secs / 8 its\n", + " Final V Solve: = 0.0001466 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006566 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.774820e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 3 of 500 - Residual 0.002512 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.51196378e-03; Tolerance 1.0000e-07 - Not converged - 9.137215e-02 (secs)\n", + "\n", + "Non linear solver - iteration 4\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001352 secs / 1 its\n", + " Pressure Solve: = 0.001361 secs / 8 its\n", + " Final V Solve: = 0.0001367 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006630 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.807088e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 4 of 500 - Residual 0.0013408 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.34076175e-03; Tolerance 1.0000e-07 - Not converged - 1.129226e-01 (secs)\n", + "\n", + "Non linear solver - iteration 5\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001301 secs / 1 its\n", + " Pressure Solve: = 0.00132 secs / 8 its\n", + " Final V Solve: = 0.0001432 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006649 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.958394e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 5 of 500 - Residual 0.00073708 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.37079494e-04; Tolerance 1.0000e-07 - Not converged - 1.351952e-01 (secs)\n", + "\n", + "Non linear solver - iteration 6\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001346 secs / 1 its\n", + " Pressure Solve: = 0.001231 secs / 8 its\n", + " Final V Solve: = 0.0001348 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006613 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.826756e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 6 of 500 - Residual 0.0004142 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.14197686e-04; Tolerance 1.0000e-07 - Not converged - 1.574607e-01 (secs)\n", + "\n", + "Non linear solver - iteration 7\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0002076 secs / 1 its\n", + " Pressure Solve: = 0.001806 secs / 8 its\n", + " Final V Solve: = 0.0001628 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007918 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 8.108775e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 7 of 500 - Residual 0.00023684 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.36840976e-04; Tolerance 1.0000e-07 - Not converged - 1.809446e-01 (secs)\n", + "\n", + "Non linear solver - iteration 8\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.000132 secs / 1 its\n", + " Pressure Solve: = 0.001378 secs / 8 its\n", + " Final V Solve: = 0.0001978 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006784 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.006772e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 8 of 500 - Residual 0.0001374 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.37402111e-04; Tolerance 1.0000e-07 - Not converged - 2.029995e-01 (secs)\n", + "\n", + "Non linear solver - iteration 9\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001578 secs / 1 its\n", + " Pressure Solve: = 0.001338 secs / 8 its\n", + " Final V Solve: = 0.000138 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006764 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.959978e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 9 of 500 - Residual 8.0712e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 8.07119057e-05; Tolerance 1.0000e-07 - Not converged - 2.282659e-01 (secs)\n", + "\n", + "Non linear solver - iteration 10\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001326 secs / 1 its\n", + " Pressure Solve: = 0.001507 secs / 8 its\n", + " Final V Solve: = 0.0001574 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006934 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.170872e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 10 of 500 - Residual 4.7932e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.79317809e-05; Tolerance 1.0000e-07 - Not converged - 2.509775e-01 (secs)\n", + "\n", + "Non linear solver - iteration 11\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001371 secs / 1 its\n", + " Pressure Solve: = 0.001524 secs / 8 its\n", + " Final V Solve: = 0.0001597 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006983 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.192305e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 11 of 500 - Residual 2.8741e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.87411157e-05; Tolerance 1.0000e-07 - Not converged - 2.732747e-01 (secs)\n", + "\n", + "Non linear solver - iteration 12\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001367 secs / 1 its\n", + " Pressure Solve: = 0.001453 secs / 8 its\n", + " Final V Solve: = 0.0001516 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006891 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.093405e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 12 of 500 - Residual 1.7382e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.73819526e-05; Tolerance 1.0000e-07 - Not converged - 2.958205e-01 (secs)\n", + "\n", + "Non linear solver - iteration 13\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001443 secs / 1 its\n", + " Pressure Solve: = 0.001383 secs / 8 its\n", + " Final V Solve: = 0.0001404 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006786 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.002004e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 13 of 500 - Residual 1.0592e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.05919134e-05; Tolerance 1.0000e-07 - Not converged - 3.195673e-01 (secs)\n", + "\n", + "Non linear solver - iteration 14\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.000139 secs / 1 its\n", + " Pressure Solve: = 0.00129 secs / 8 its\n", + " Final V Solve: = 0.000135 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006569 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.765801e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 14 of 500 - Residual 6.4973e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.49731206e-06; Tolerance 1.0000e-07 - Not converged - 3.416007e-01 (secs)\n", + "\n", + "Non linear solver - iteration 15\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001416 secs / 1 its\n", + " Pressure Solve: = 0.001297 secs / 8 its\n", + " Final V Solve: = 0.0001372 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006529 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.712817e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 15 of 500 - Residual 4.0088e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.00879255e-06; Tolerance 1.0000e-07 - Not converged - 3.634221e-01 (secs)\n", + "\n", + "Non linear solver - iteration 16\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001289 secs / 1 its\n", + " Pressure Solve: = 0.001425 secs / 8 its\n", + " Final V Solve: = 0.0001535 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006792 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.993596e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 16 of 500 - Residual 2.4859e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.48591455e-06; Tolerance 1.0000e-07 - Not converged - 3.853204e-01 (secs)\n", + "\n", + "Non linear solver - iteration 17\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001294 secs / 1 its\n", + " Pressure Solve: = 0.001412 secs / 8 its\n", + " Final V Solve: = 0.0001486 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006689 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.874705e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 17 of 500 - Residual 1.5483e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.54831535e-06; Tolerance 1.0000e-07 - Not converged - 4.072763e-01 (secs)\n", + "\n", + "Non linear solver - iteration 18\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001666 secs / 1 its\n", + " Pressure Solve: = 0.00156 secs / 8 its\n", + " Final V Solve: = 0.0001665 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007207 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.420522e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 18 of 500 - Residual 9.68e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 9.68002216e-07; Tolerance 1.0000e-07 - Not converged - 4.300306e-01 (secs)\n", + "\n", + "Non linear solver - iteration 19\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001351 secs / 1 its\n", + " Pressure Solve: = 0.001345 secs / 8 its\n", + " Final V Solve: = 0.0001428 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006775 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.007758e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 19 of 500 - Residual 6.0717e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.07174914e-07; Tolerance 1.0000e-07 - Not converged - 4.525992e-01 (secs)\n", + "\n", + "Non linear solver - iteration 20\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.000128 secs / 1 its\n", + " Pressure Solve: = 0.001255 secs / 8 its\n", + " Final V Solve: = 0.0001318 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006588 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.790832e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 20 of 500 - Residual 3.8193e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.81926600e-07; Tolerance 1.0000e-07 - Not converged - 4.746087e-01 (secs)\n", + "\n", + "Non linear solver - iteration 21\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001241 secs / 1 its\n", + " Pressure Solve: = 0.001219 secs / 8 its\n", + " Final V Solve: = 0.0001337 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006375 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.533308e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 21 of 500 - Residual 2.4083e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.40830177e-07; Tolerance 1.0000e-07 - Not converged - 4.965489e-01 (secs)\n", + "\n", + "Non linear solver - iteration 22\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001394 secs / 1 its\n", + " Pressure Solve: = 0.001319 secs / 8 its\n", + " Final V Solve: = 0.0001394 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006503 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 6.675319e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 22 of 500 - Residual 1.5218e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.52183646e-07; Tolerance 1.0000e-07 - Not converged - 5.188864e-01 (secs)\n", + "\n", + "Non linear solver - iteration 23\n", + "Linear solver (KSU7ACSG__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001666 secs / 1 its\n", + " Pressure Solve: = 0.001412 secs / 8 its\n", + " Final V Solve: = 0.0001566 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.006800 seconds\n", + "\n", + "Linear solver (KSU7ACSG__system-execute), solution time 7.017872e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 23 of 500 - Residual 9.6346e-08 - Tolerance = 1e-07\n", + "Non linear solver - Residual 9.63460559e-08; Tolerance 1.0000e-07 - Converged - 5.412440e-01 (secs)\n", + "\n", + "In func SystemLinearEquations_NonLinearExecute: Converged after 23 iterations.\n", + "\tGlobal element size: 8x8\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 8x8\n", + "Linear solver (QYQXLYXY__system-execute) \n", "Performing simulations for solution: SolNL 2 8\n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.956e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002534 secs / 10 its\n", + " Final V Solve: = 1.597e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001451 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.577612e-03 (secs)\n", + "In SystemLinearEquations_NonLinearExecute\n", + "\n", + "Non linear solver - iteration 0\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.71e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002604 secs / 11 its\n", + " Final V Solve: = 1.457e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001511 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.696795e-03 (secs)\n", + "Non linear solver - iteration 1\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.399e-05 secs / 1 its\n", + " Pressure Solve: = 0.000254 secs / 11 its\n", + " Final V Solve: = 1.393e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001360 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.473688e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.010422 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.04217289e-02; Tolerance 1.0000e-07 - Not converged - 1.350531e-02 (secs)\n", + "\n", + "Non linear solver - iteration 2\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.426e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002652 secs / 11 its\n", + " Final V Solve: = 1.46e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001390 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.486939e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 2 of 500 - Residual 0.004996 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.99599059e-03; Tolerance 1.0000e-07 - Not converged - 1.989388e-02 (secs)\n", + "\n", + "Non linear solver - iteration 3\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.318e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002665 secs / 11 its\n", + " Final V Solve: = 1.553e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001379 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.473694e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 3 of 500 - Residual 0.0025677 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.56773678e-03; Tolerance 1.0000e-07 - Not converged - 2.635572e-02 (secs)\n", + "\n", + "Non linear solver - iteration 4\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.403e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002611 secs / 11 its\n", + " Final V Solve: = 1.466e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001355 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.450542e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 4 of 500 - Residual 0.0013756 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.37562266e-03; Tolerance 1.0000e-07 - Not converged - 3.284201e-02 (secs)\n", + "\n", + "Non linear solver - iteration 5\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.317e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002696 secs / 11 its\n", + " Final V Solve: = 1.512e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001421 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.526173e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 5 of 500 - Residual 0.00075801 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.58012951e-04; Tolerance 1.0000e-07 - Not converged - 3.936963e-02 (secs)\n", + "\n", + "Non linear solver - iteration 6\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.374e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002699 secs / 11 its\n", + " Final V Solve: = 1.498e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001387 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.496118e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 6 of 500 - Residual 0.00042655 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.26549208e-04; Tolerance 1.0000e-07 - Not converged - 4.589742e-02 (secs)\n", + "\n", + "Non linear solver - iteration 7\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.633e-05 secs / 1 its\n", + " Pressure Solve: = 0.000282 secs / 11 its\n", + " Final V Solve: = 1.644e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001448 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.570084e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 7 of 500 - Residual 0.00024409 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.44087870e-04; Tolerance 1.0000e-07 - Not converged - 5.270087e-02 (secs)\n", + "\n", + "Non linear solver - iteration 8\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.515e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002816 secs / 11 its\n", + " Final V Solve: = 1.557e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001450 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.585842e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 8 of 500 - Residual 0.00014166 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.41657284e-04; Tolerance 1.0000e-07 - Not converged - 5.983016e-02 (secs)\n", + "\n", + "Non linear solver - iteration 9\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.289e-05 secs / 1 its\n", + " Pressure Solve: = 0.000262 secs / 11 its\n", + " Final V Solve: = 1.461e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001377 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.474891e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 9 of 500 - Residual 8.322e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 8.32202160e-05; Tolerance 1.0000e-07 - Not converged - 6.641072e-02 (secs)\n", + "\n", + "Non linear solver - iteration 10\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.737e-05 secs / 1 its\n", + " Pressure Solve: = 0.0003191 secs / 11 its\n", + " Final V Solve: = 1.897e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001565 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.690420e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 10 of 500 - Residual 4.9418e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.94178293e-05; Tolerance 1.0000e-07 - Not converged - 7.321005e-02 (secs)\n", + "\n", + "Non linear solver - iteration 11\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.458e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002589 secs / 11 its\n", + " Final V Solve: = 1.447e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001386 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.500245e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 11 of 500 - Residual 2.9626e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.96258653e-05; Tolerance 1.0000e-07 - Not converged - 8.022936e-02 (secs)\n", + "\n", + "Non linear solver - iteration 12\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.271e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002594 secs / 11 its\n", + " Final V Solve: = 1.461e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001347 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.451686e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 12 of 500 - Residual 1.7911e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.79108515e-05; Tolerance 1.0000e-07 - Not converged - 8.657769e-02 (secs)\n", + "\n", + "Non linear solver - iteration 13\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.267e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002579 secs / 11 its\n", + " Final V Solve: = 1.451e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001350 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.456271e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 13 of 500 - Residual 1.0909e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.09089789e-05; Tolerance 1.0000e-07 - Not converged - 9.297268e-02 (secs)\n", + "\n", + "Non linear solver - iteration 14\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.304e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002565 secs / 11 its\n", + " Final V Solve: = 1.408e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001356 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.453797e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 14 of 500 - Residual 6.6877e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.68765353e-06; Tolerance 1.0000e-07 - Not converged - 9.974708e-02 (secs)\n", + "\n", + "Non linear solver - iteration 15\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 3.362e-05 secs / 1 its\n", + " Pressure Solve: = 0.000421 secs / 11 its\n", + " Final V Solve: = 2.356e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001725 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.837299e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 15 of 500 - Residual 4.1231e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.12305088e-06; Tolerance 1.0000e-07 - Not converged - 1.065069e-01 (secs)\n", + "\n", + "Non linear solver - iteration 16\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.451e-05 secs / 1 its\n", + " Pressure Solve: = 0.000259 secs / 11 its\n", + " Final V Solve: = 1.449e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001340 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.453987e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 16 of 500 - Residual 2.5544e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.55439613e-06; Tolerance 1.0000e-07 - Not converged - 1.131788e-01 (secs)\n", + "\n", + "Non linear solver - iteration 17\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.259e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002462 secs / 11 its\n", + " Final V Solve: = 1.379e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001314 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.411403e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 17 of 500 - Residual 1.5892e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.58923670e-06; Tolerance 1.0000e-07 - Not converged - 1.193479e-01 (secs)\n", + "\n", + "Non linear solver - iteration 18\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.283e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002505 secs / 11 its\n", + " Final V Solve: = 1.413e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001354 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.450062e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 18 of 500 - Residual 9.9234e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 9.92344044e-07; Tolerance 1.0000e-07 - Not converged - 1.256380e-01 (secs)\n", + "\n", + "Non linear solver - iteration 19\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.304e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002698 secs / 11 its\n", + " Final V Solve: = 1.43e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001362 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.462282e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 19 of 500 - Residual 6.2156e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.21564991e-07; Tolerance 1.0000e-07 - Not converged - 1.320009e-01 (secs)\n", + "\n", + "Non linear solver - iteration 20\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.287e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002573 secs / 11 its\n", + " Final V Solve: = 1.461e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001335 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.431364e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 20 of 500 - Residual 3.9036e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 3.90364810e-07; Tolerance 1.0000e-07 - Not converged - 1.383080e-01 (secs)\n", + "\n", + "Non linear solver - iteration 21\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.304e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002582 secs / 11 its\n", + " Final V Solve: = 1.429e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001329 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.430678e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 21 of 500 - Residual 2.4573e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.45726824e-07; Tolerance 1.0000e-07 - Not converged - 1.446322e-01 (secs)\n", + "\n", + "Non linear solver - iteration 22\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.317e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002584 secs / 11 its\n", + " Final V Solve: = 1.467e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001349 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.445162e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 22 of 500 - Residual 1.5499e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.54987060e-07; Tolerance 1.0000e-07 - Not converged - 1.509641e-01 (secs)\n", + "\n", + "Non linear solver - iteration 23\n", + "Linear solver (QYQXLYXY__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 2.232e-05 secs / 1 its\n", + " Pressure Solve: = 0.0002505 secs / 11 its\n", + " Final V Solve: = 1.402e-05 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.001311 seconds\n", + "\n", + "Linear solver (QYQXLYXY__system-execute), solution time 1.411944e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 23 of 500 - Residual 9.7923e-08 - Tolerance = 1e-07\n", + "Non linear solver - Residual 9.79228895e-08; Tolerance 1.0000e-07 - Converged - 1.572587e-01 (secs)\n", + "\n", + "In func SystemLinearEquations_NonLinearExecute: Converged after 23 iterations.\n", + "\tGlobal element size: 16x16\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 16x16\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001296 secs / 1 its\n", + " Pressure Solve: = 0.001549 secs / 9 its\n", + " Final V Solve: = 0.0001301 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007446 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.644703e-03 (secs)\n", "Performing simulations for solution: SolNL 2 16\n", + "In SystemLinearEquations_NonLinearExecute\n", + "\n", + "Non linear solver - iteration 0\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001369 secs / 1 its\n", + " Pressure Solve: = 0.001992 secs / 10 its\n", + " Final V Solve: = 0.0001628 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008256 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.486153e-03 (secs)\n", + "Non linear solver - iteration 1\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0002486 secs / 1 its\n", + " Pressure Solve: = 0.00201 secs / 10 its\n", + " Final V Solve: = 0.0001637 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008320 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.512036e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.010361 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.03609329e-02; Tolerance 1.0000e-07 - Not converged - 5.436998e-02 (secs)\n", + "\n", + "Non linear solver - iteration 2\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001236 secs / 1 its\n", + " Pressure Solve: = 0.001759 secs / 10 its\n", + " Final V Solve: = 0.0001381 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007746 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.059754e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 2 of 500 - Residual 0.0049419 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.94185909e-03; Tolerance 1.0000e-07 - Not converged - 8.096156e-02 (secs)\n", + "\n", + "Non linear solver - iteration 3\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001295 secs / 1 its\n", + " Pressure Solve: = 0.001696 secs / 10 its\n", + " Final V Solve: = 0.0001378 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007587 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.770682e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 3 of 500 - Residual 0.0025316 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.53157747e-03; Tolerance 1.0000e-07 - Not converged - 1.076597e-01 (secs)\n", + "\n", + "Non linear solver - iteration 4\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001229 secs / 1 its\n", + " Pressure Solve: = 0.001626 secs / 10 its\n", + " Final V Solve: = 0.0001337 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007321 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.485977e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 4 of 500 - Residual 0.0013538 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.35383876e-03; Tolerance 1.0000e-07 - Not converged - 1.337834e-01 (secs)\n", + "\n", + "Non linear solver - iteration 5\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001282 secs / 1 its\n", + " Pressure Solve: = 0.001818 secs / 10 its\n", + " Final V Solve: = 0.000144 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007748 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.984477e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 5 of 500 - Residual 0.00074564 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.45638985e-04; Tolerance 1.0000e-07 - Not converged - 1.606199e-01 (secs)\n", + "\n", + "Non linear solver - iteration 6\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001264 secs / 1 its\n", + " Pressure Solve: = 0.001728 secs / 10 its\n", + " Final V Solve: = 0.0001366 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007628 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.799288e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 6 of 500 - Residual 0.0004198 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.19800723e-04; Tolerance 1.0000e-07 - Not converged - 1.869743e-01 (secs)\n", + "\n", + "Non linear solver - iteration 7\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001187 secs / 1 its\n", + " Pressure Solve: = 0.001569 secs / 10 its\n", + " Final V Solve: = 0.0001263 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007219 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.382748e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 7 of 500 - Residual 0.00024054 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.40536865e-04; Tolerance 1.0000e-07 - Not converged - 2.130790e-01 (secs)\n", + "\n", + "Non linear solver - iteration 8\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001202 secs / 1 its\n", + " Pressure Solve: = 0.001568 secs / 10 its\n", + " Final V Solve: = 0.0001181 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007300 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.462838e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 8 of 500 - Residual 0.00013987 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.39865439e-04; Tolerance 1.0000e-07 - Not converged - 2.389677e-01 (secs)\n", + "\n", + "Non linear solver - iteration 9\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001453 secs / 1 its\n", + " Pressure Solve: = 0.001755 secs / 10 its\n", + " Final V Solve: = 0.0001286 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007696 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.907919e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 9 of 500 - Residual 8.2371e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 8.23709856e-05; Tolerance 1.0000e-07 - Not converged - 2.656818e-01 (secs)\n", + "\n", + "Non linear solver - iteration 10\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001263 secs / 1 its\n", + " Pressure Solve: = 0.001747 secs / 10 its\n", + " Final V Solve: = 0.0001346 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007539 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.729322e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 10 of 500 - Residual 4.9059e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.90593990e-05; Tolerance 1.0000e-07 - Not converged - 2.921260e-01 (secs)\n", + "\n", + "Non linear solver - iteration 11\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001248 secs / 1 its\n", + " Pressure Solve: = 0.001856 secs / 10 its\n", + " Final V Solve: = 0.0001448 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007827 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.058473e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 11 of 500 - Residual 2.9513e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.95129742e-05; Tolerance 1.0000e-07 - Not converged - 3.192627e-01 (secs)\n", + "\n", + "Non linear solver - iteration 12\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001244 secs / 1 its\n", + " Pressure Solve: = 0.001869 secs / 10 its\n", + " Final V Solve: = 0.0001426 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007835 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.053359e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 12 of 500 - Residual 1.7913e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.79129342e-05; Tolerance 1.0000e-07 - Not converged - 3.462374e-01 (secs)\n", + "\n", + "Non linear solver - iteration 13\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001343 secs / 1 its\n", + " Pressure Solve: = 0.00182 secs / 10 its\n", + " Final V Solve: = 0.0001437 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007776 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.999392e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 13 of 500 - Residual 1.0958e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.09583175e-05; Tolerance 1.0000e-07 - Not converged - 3.729626e-01 (secs)\n", + "\n", + "Non linear solver - iteration 14\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.000138 secs / 1 its\n", + " Pressure Solve: = 0.001879 secs / 10 its\n", + " Final V Solve: = 0.0001461 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007923 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.143525e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 14 of 500 - Residual 6.7505e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.75053421e-06; Tolerance 1.0000e-07 - Not converged - 4.000267e-01 (secs)\n", + "\n", + "Non linear solver - iteration 15\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001333 secs / 1 its\n", + " Pressure Solve: = 0.001709 secs / 10 its\n", + " Final V Solve: = 0.000131 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007692 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.893968e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 15 of 500 - Residual 4.1839e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.18385218e-06; Tolerance 1.0000e-07 - Not converged - 4.271672e-01 (secs)\n", + "\n", + "Non linear solver - iteration 16\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001676 secs / 1 its\n", + " Pressure Solve: = 0.001795 secs / 10 its\n", + " Final V Solve: = 0.0001488 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007756 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 7.963349e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 16 of 500 - Residual 2.6069e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.60687454e-06; Tolerance 1.0000e-07 - Not converged - 4.540119e-01 (secs)\n", + "\n", + "Non linear solver - iteration 17\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001209 secs / 1 its\n", + " Pressure Solve: = 0.001779 secs / 10 its\n", + " Final V Solve: = 0.0001407 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007798 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.017948e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 17 of 500 - Residual 1.6318e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.63180215e-06; Tolerance 1.0000e-07 - Not converged - 4.812028e-01 (secs)\n", + "\n", + "Non linear solver - iteration 18\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001612 secs / 1 its\n", + " Pressure Solve: = 0.002171 secs / 10 its\n", + " Final V Solve: = 0.0001672 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008545 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.762045e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 18 of 500 - Residual 1.0255e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.02554038e-06; Tolerance 1.0000e-07 - Not converged - 5.091474e-01 (secs)\n", + "\n", + "Non linear solver - iteration 19\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001319 secs / 1 its\n", + " Pressure Solve: = 0.001897 secs / 10 its\n", + " Final V Solve: = 0.000163 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008304 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.536542e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 19 of 500 - Residual 6.4676e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.46763148e-07; Tolerance 1.0000e-07 - Not converged - 5.450582e-01 (secs)\n", + "\n", + "Non linear solver - iteration 20\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001354 secs / 1 its\n", + " Pressure Solve: = 0.00241 secs / 10 its\n", + " Final V Solve: = 0.0001845 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008729 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.980889e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 20 of 500 - Residual 4.0912e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.09116400e-07; Tolerance 1.0000e-07 - Not converged - 5.738744e-01 (secs)\n", + "\n", + "Non linear solver - iteration 21\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001538 secs / 1 its\n", + " Pressure Solve: = 0.00204 secs / 10 its\n", + " Final V Solve: = 0.0001649 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008184 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.406100e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 21 of 500 - Residual 2.5947e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.59471027e-07; Tolerance 1.0000e-07 - Not converged - 6.017344e-01 (secs)\n", + "\n", + "Non linear solver - iteration 22\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001442 secs / 1 its\n", + " Pressure Solve: = 0.001949 secs / 10 its\n", + " Final V Solve: = 0.0001488 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008116 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.341188e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 22 of 500 - Residual 1.6494e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.64940900e-07; Tolerance 1.0000e-07 - Not converged - 6.292785e-01 (secs)\n", + "\n", + "Non linear solver - iteration 23\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001463 secs / 1 its\n", + " Pressure Solve: = 0.002005 secs / 10 its\n", + " Final V Solve: = 0.0001598 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.008105 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.324102e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 23 of 500 - Residual 1.0506e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.05061807e-07; Tolerance 1.0000e-07 - Not converged - 6.567548e-01 (secs)\n", + "\n", + "Non linear solver - iteration 24\n", + "Linear solver (3HJRZ0CR__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0001278 secs / 1 its\n", + " Pressure Solve: = 0.001932 secs / 10 its\n", + " Final V Solve: = 0.0001511 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.007983 seconds\n", + "\n", + "Linear solver (3HJRZ0CR__system-execute), solution time 8.203719e-03 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 24 of 500 - Residual 6.704e-08 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.70404649e-08; Tolerance 1.0000e-07 - Converged - 6.840836e-01 (secs)\n", + "\n", + "In func SystemLinearEquations_NonLinearExecute: Converged after 24 iterations.\n", + "\tGlobal element size: 32x32\n", + "\tLocal offset of rank 0: 0x0\n", + "\tLocal range of rank 0: 32x32\n", + "Linear solver (XDRX5SB9__system-execute) \n", "Performing simulations for solution: SolNL 2 32\n", - "Performing simulations for solution: SolB 1 8\n", - "Performing simulations for solution: SolB 1 16\n", - "Performing simulations for solution: SolB 1 32\n", - "Performing simulations for solution: SolB 2 8\n", - "Performing simulations for solution: SolB 2 16\n", - "Performing simulations for solution: SolB 2 32\n", - "Performing simulations for solution: SolC 1 8\n", - "Performing simulations for solution: SolC 1 16\n", - "Performing simulations for solution: SolC 1 32\n", - "Performing simulations for solution: SolC 2 8\n", - "Performing simulations for solution: SolC 2 16\n", - "Performing simulations for solution: SolC 2 32\n", - "Performing simulations for solution: SolDA 1 8\n", - "Performing simulations for solution: SolDA 1 16\n", - "Performing simulations for solution: SolDA 1 32\n", - "Performing simulations for solution: SolDA 2 8\n", - "Performing simulations for solution: SolDA 2 16\n", - "Performing simulations for solution: SolDA 2 32\n", - "Performing simulations for solution: SolDB2d 1 8\n", - "Performing simulations for solution: SolDB2d 1 16\n", - "Performing simulations for solution: SolDB2d 1 32\n", - "Performing simulations for solution: SolDB2d 2 8\n", - "Performing simulations for solution: SolDB2d 2 16\n", - "Performing simulations for solution: SolDB2d 2 32\n", - "Performing simulations for solution: SolDB3d 1 4\n", - "Performing simulations for solution: SolDB3d 1 8\n", - "Performing simulations for solution: SolDB3d 1 16\n", - "Performing simulations for solution: SolDB3d 2 4\n", - "Performing simulations for solution: SolDB3d 2 8\n", - "Performing simulations for solution: SolDB3d 2 16\n", - "Performing simulations for solution: SolKz 1 8\n", - "Performing simulations for solution: SolKz 1 16\n", - "Performing simulations for solution: SolKz 1 32\n", - "Performing simulations for solution: SolKz 2 8\n", - "Performing simulations for solution: SolKz 2 16\n", - "Performing simulations for solution: SolKz 2 32\n", - "Performing simulations for solution: SolM 1 8\n", - "Performing simulations for solution: SolM 1 16\n", - "Performing simulations for solution: SolM 1 32\n", - "Performing simulations for solution: SolM 2 8\n", - "Performing simulations for solution: SolM 2 16\n", - "Performing simulations for solution: SolM 2 32\n" + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001169 secs / 1 its\n", + " Pressure Solve: = 0.01135 secs / 8 its\n", + " Final V Solve: = 0.001193 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.053408 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.401454e-02 (secs)\n", + "In SystemLinearEquations_NonLinearExecute\n", + "\n", + "Non linear solver - iteration 0\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001039 secs / 1 its\n", + " Pressure Solve: = 0.0125 secs / 10 its\n", + " Final V Solve: = 0.001082 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.053965 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.439430e-02 (secs)\n", + "Non linear solver - iteration 1\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001076 secs / 1 its\n", + " Pressure Solve: = 0.01219 secs / 10 its\n", + " Final V Solve: = 0.001069 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.053621 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.406544e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 1 of 500 - Residual 0.010341 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.03411305e-02; Tolerance 1.0000e-07 - Not converged - 2.547281e-01 (secs)\n", + "\n", + "Non linear solver - iteration 2\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001127 secs / 1 its\n", + " Pressure Solve: = 0.0124 secs / 10 its\n", + " Final V Solve: = 0.001135 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.054638 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.506194e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 2 of 500 - Residual 0.0049188 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.91879350e-03; Tolerance 1.0000e-07 - Not converged - 3.829640e-01 (secs)\n", + "\n", + "Non linear solver - iteration 3\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001045 secs / 1 its\n", + " Pressure Solve: = 0.01308 secs / 10 its\n", + " Final V Solve: = 0.001429 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.058658 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.911398e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 3 of 500 - Residual 0.0025154 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.51535479e-03; Tolerance 1.0000e-07 - Not converged - 5.267837e-01 (secs)\n", + "\n", + "Non linear solver - iteration 4\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0009757 secs / 1 its\n", + " Pressure Solve: = 0.01325 secs / 10 its\n", + " Final V Solve: = 0.00113 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.055502 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.592666e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 4 of 500 - Residual 0.0013437 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.34372453e-03; Tolerance 1.0000e-07 - Not converged - 6.560086e-01 (secs)\n", + "\n", + "Non linear solver - iteration 5\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001151 secs / 1 its\n", + " Pressure Solve: = 0.01717 secs / 10 its\n", + " Final V Solve: = 0.001688 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.060473 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.095144e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 5 of 500 - Residual 0.0007396 - Tolerance = 1e-07\n", + "Non linear solver - Residual 7.39600858e-04; Tolerance 1.0000e-07 - Not converged - 7.915814e-01 (secs)\n", + "\n", + "Non linear solver - iteration 6\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001087 secs / 1 its\n", + " Pressure Solve: = 0.01613 secs / 10 its\n", + " Final V Solve: = 0.001199 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.067534 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.797067e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 6 of 500 - Residual 0.00041626 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.16259304e-04; Tolerance 1.0000e-07 - Not converged - 9.546202e-01 (secs)\n", + "\n", + "Non linear solver - iteration 7\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001195 secs / 1 its\n", + " Pressure Solve: = 0.01303 secs / 10 its\n", + " Final V Solve: = 0.001135 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.055686 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.612911e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 7 of 500 - Residual 0.00023848 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.38475672e-04; Tolerance 1.0000e-07 - Not converged - 1.086808e+00 (secs)\n", + "\n", + "Non linear solver - iteration 8\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001033 secs / 1 its\n", + " Pressure Solve: = 0.01375 secs / 10 its\n", + " Final V Solve: = 0.001227 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.056733 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.719924e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 8 of 500 - Residual 0.00013867 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.38670365e-04; Tolerance 1.0000e-07 - Not converged - 1.217537e+00 (secs)\n", + "\n", + "Non linear solver - iteration 9\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001224 secs / 1 its\n", + " Pressure Solve: = 0.01636 secs / 10 its\n", + " Final V Solve: = 0.001212 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.059192 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.961507e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 9 of 500 - Residual 8.168e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 8.16800586e-05; Tolerance 1.0000e-07 - Not converged - 1.354646e+00 (secs)\n", + "\n", + "Non linear solver - iteration 10\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001064 secs / 1 its\n", + " Pressure Solve: = 0.02312 secs / 10 its\n", + " Final V Solve: = 0.001842 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.074507 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 7.499304e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 10 of 500 - Residual 4.8661e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.86612771e-05; Tolerance 1.0000e-07 - Not converged - 1.517405e+00 (secs)\n", + "\n", + "Non linear solver - iteration 11\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001191 secs / 1 its\n", + " Pressure Solve: = 0.01658 secs / 10 its\n", + " Final V Solve: = 0.001681 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.065115 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.563544e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 11 of 500 - Residual 2.9285e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.92846518e-05; Tolerance 1.0000e-07 - Not converged - 1.668996e+00 (secs)\n", + "\n", + "Non linear solver - iteration 12\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001177 secs / 1 its\n", + " Pressure Solve: = 0.01778 secs / 10 its\n", + " Final V Solve: = 0.001961 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.066381 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.699760e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 12 of 500 - Residual 1.7783e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.77828776e-05; Tolerance 1.0000e-07 - Not converged - 1.828824e+00 (secs)\n", + "\n", + "Non linear solver - iteration 13\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001032 secs / 1 its\n", + " Pressure Solve: = 0.01334 secs / 10 its\n", + " Final V Solve: = 0.001164 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.059504 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.996247e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 13 of 500 - Residual 1.0885e-05 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.08849434e-05; Tolerance 1.0000e-07 - Not converged - 1.969661e+00 (secs)\n", + "\n", + "Non linear solver - iteration 14\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.0009987 secs / 1 its\n", + " Pressure Solve: = 0.01434 secs / 10 its\n", + " Final V Solve: = 0.001217 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.057650 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.810643e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 14 of 500 - Residual 6.7097e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.70969378e-06; Tolerance 1.0000e-07 - Not converged - 2.102113e+00 (secs)\n", + "\n", + "Non linear solver - iteration 15\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001112 secs / 1 its\n", + " Pressure Solve: = 0.01431 secs / 10 its\n", + " Final V Solve: = 0.001209 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.058465 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.890966e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 15 of 500 - Residual 4.1616e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.16155070e-06; Tolerance 1.0000e-07 - Not converged - 2.238984e+00 (secs)\n", + "\n", + "Non linear solver - iteration 16\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001218 secs / 1 its\n", + " Pressure Solve: = 0.01431 secs / 10 its\n", + " Final V Solve: = 0.001247 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.061382 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.186018e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 16 of 500 - Residual 2.595e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.59503068e-06; Tolerance 1.0000e-07 - Not converged - 2.385069e+00 (secs)\n", + "\n", + "Non linear solver - iteration 17\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001081 secs / 1 its\n", + " Pressure Solve: = 0.01416 secs / 10 its\n", + " Final V Solve: = 0.001341 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.059350 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.981037e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 17 of 500 - Residual 1.6258e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.62577430e-06; Tolerance 1.0000e-07 - Not converged - 2.520826e+00 (secs)\n", + "\n", + "Non linear solver - iteration 18\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001101 secs / 1 its\n", + " Pressure Solve: = 0.01353 secs / 10 its\n", + " Final V Solve: = 0.001324 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.058603 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.909867e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 18 of 500 - Residual 1.0227e-06 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.02268294e-06; Tolerance 1.0000e-07 - Not converged - 2.665282e+00 (secs)\n", + "\n", + "Non linear solver - iteration 19\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001068 secs / 1 its\n", + " Pressure Solve: = 0.01707 secs / 10 its\n", + " Final V Solve: = 0.001195 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.062541 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.297828e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 19 of 500 - Residual 6.4558e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.45584132e-07; Tolerance 1.0000e-07 - Not converged - 2.802851e+00 (secs)\n", + "\n", + "Non linear solver - iteration 20\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001556 secs / 1 its\n", + " Pressure Solve: = 0.0205 secs / 10 its\n", + " Final V Solve: = 0.001713 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.071656 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 7.211953e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 20 of 500 - Residual 4.0879e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 4.08786804e-07; Tolerance 1.0000e-07 - Not converged - 2.951471e+00 (secs)\n", + "\n", + "Non linear solver - iteration 21\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.00118 secs / 1 its\n", + " Pressure Solve: = 0.01273 secs / 10 its\n", + " Final V Solve: = 0.001141 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.059208 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 5.990209e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 21 of 500 - Residual 2.5954e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 2.59539767e-07; Tolerance 1.0000e-07 - Not converged - 3.090932e+00 (secs)\n", + "\n", + "Non linear solver - iteration 22\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.00105 secs / 1 its\n", + " Pressure Solve: = 0.01325 secs / 10 its\n", + " Final V Solve: = 0.001125 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.068202 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 6.863695e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 22 of 500 - Residual 1.6517e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.65169947e-07; Tolerance 1.0000e-07 - Not converged - 3.233639e+00 (secs)\n", + "\n", + "Non linear solver - iteration 23\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001581 secs / 1 its\n", + " Pressure Solve: = 0.02019 secs / 10 its\n", + " Final V Solve: = 0.001514 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.078613 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 7.907174e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 23 of 500 - Residual 1.0533e-07 - Tolerance = 1e-07\n", + "Non linear solver - Residual 1.05331397e-07; Tolerance 1.0000e-07 - Not converged - 3.395979e+00 (secs)\n", + "\n", + "Non linear solver - iteration 24\n", + "Linear solver (XDRX5SB9__system-execute) \n", + "\n", + "BSSCR -- Block Stokes Schur Compliment Reduction Solver \n", + "AUGMENTED LAGRANGIAN K2 METHOD - Penalty = 0.000000\n", + "\n", + " Setting schur_pc to \"uw\" \n", + "\n", + "\n", + "SCR Solver Summary:\n", + "\n", + " RHS V Solve: = 0.001723 secs / 1 its\n", + " Pressure Solve: = 0.0182 secs / 10 its\n", + " Final V Solve: = 0.001416 secs / 1 its\n", + "\n", + " Total BSSCR Linear solve time: 0.080650 seconds\n", + "\n", + "Linear solver (XDRX5SB9__system-execute), solution time 8.112384e-02 (secs)\n", + "In func SystemLinearEquations_NonLinearExecute: Iteration 24 of 500 - Residual 6.7295e-08 - Tolerance = 1e-07\n", + "Non linear solver - Residual 6.72950689e-08; Tolerance 1.0000e-07 - Converged - 3.570866e+00 (secs)\n", + "\n", + "In func SystemLinearEquations_NonLinearExecute: Converged after 24 iterations.\n" ] } ], @@ -358,7 +3832,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -369,14 +3845,12 @@ }, { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -406,48 +3880,49 @@ " ypts = np.exp(fitfn(np.log(xpts),*fit))\n", " return xpts, ypts\n", "\n", - " import matplotlib.pyplot as plt\n", - " fig = plt.figure(dpi=200, figsize=(8.27, 11.69/2.))\n", - " plt.subplots_adjust(wspace=.0)\n", - " \n", - " # create some consistent colours & linestyles\n", - " from matplotlib.pyplot import cm\n", - " colours = cm.tab10(np.linspace(0,1,len(solns.keys())))\n", - " scheme = {}\n", - " for it,sol in enumerate(solns.keys()):\n", - " scheme[(sol,pressure_key)] = (colours[it],'--')\n", - " scheme[(sol,velocity_key)] = (colours[it],'-')\n", - "\n", - "\n", - " def create_ax(pos, title=None, other_ax=None):\n", - " ax = plt.subplot(1,2,pos,xscale='log', yscale='log', sharey=other_ax)\n", - " ax.set_title(title,fontsize=8)\n", - " ax.invert_xaxis()\n", - " ax.xaxis.set_ticks(dx)\n", - " ax.xaxis.set_ticklabels([\"$ {{ {} }}^{{-1}}$\".format(x) for x in resolutions])\n", - " ax.grid(axis=\"y\", which=\"both\",linestyle=':',linewidth=0.25)\n", - " ax.tick_params(axis='both', which='major', labelsize=8)\n", - "# ax.set_xlabel(\"dx\", fontsize=8)\n", - " if not other_ax:\n", - " ax.set_ylabel(\"error\", fontsize=8)\n", - "\n", - " # disable minor ticks marks on axis\n", - " for tic in ax.xaxis.get_minor_ticks() + ax.yaxis.get_minor_ticks():\n", - " tic.tick1On = tic.tick2On = False\n", - " tic.label1On = tic.label2On = False\n", - " for tic in ax.xaxis.get_major_ticks() + ax.yaxis.get_major_ticks():\n", - " tic.label.set_fontsize(6)\n", - " # disable tick marks on rhs of other axis\n", - " if other_ax:\n", - " for tic in ax.yaxis.get_major_ticks():\n", + " if with_matplotlib:\n", + " import matplotlib.pyplot as plt\n", + " fig = plt.figure(dpi=200, figsize=(8.27, 11.69/2.))\n", + " plt.subplots_adjust(wspace=.0)\n", + "\n", + " # create some consistent colours & linestyles\n", + " from matplotlib.pyplot import cm\n", + " colours = cm.tab10(np.linspace(0,1,len(solns.keys())))\n", + " scheme = {}\n", + " for it,sol in enumerate(solns.keys()):\n", + " scheme[(sol,pressure_key)] = (colours[it],'--')\n", + " scheme[(sol,velocity_key)] = (colours[it],'-')\n", + "\n", + "\n", + " def create_ax(pos, title=None, other_ax=None):\n", + " ax = plt.subplot(1,2,pos,xscale='log', yscale='log', sharey=other_ax)\n", + " ax.set_title(title,fontsize=8)\n", + " ax.invert_xaxis()\n", + " ax.xaxis.set_ticks(dx)\n", + " ax.xaxis.set_ticklabels([\"$ {{ {} }}^{{-1}}$\".format(x) for x in resolutions])\n", + " ax.grid(axis=\"y\", which=\"both\",linestyle=':',linewidth=0.25)\n", + " ax.tick_params(axis='both', which='major', labelsize=8)\n", + " # ax.set_xlabel(\"dx\", fontsize=8)\n", + " if not other_ax:\n", + " ax.set_ylabel(\"error\", fontsize=8)\n", + "\n", + " # disable minor ticks marks on axis\n", + " for tic in ax.xaxis.get_minor_ticks() + ax.yaxis.get_minor_ticks():\n", " tic.tick1On = tic.tick2On = False\n", " tic.label1On = tic.label2On = False\n", - " return ax\n", - " \n", - " axes = {}\n", - " axes[1] = create_ax(1, title=\"Q1/dQ0\")\n", - " axes[2] = create_ax(2, title=\"Q2/dPc1\", other_ax=axes[1] )\n", - " \n", + " for tic in ax.xaxis.get_major_ticks() + ax.yaxis.get_major_ticks():\n", + " tic.label1.set_fontsize(6)\n", + " # disable tick marks on rhs of other axis\n", + " if other_ax:\n", + " for tic in ax.yaxis.get_major_ticks():\n", + " tic.tick1On = tic.tick2On = False\n", + " tic.label1On = tic.label2On = False\n", + " return ax\n", + "\n", + " axes = {}\n", + " axes[1] = create_ax(1, title=\"Q1/dQ0\")\n", + " axes[2] = create_ax(2, title=\"Q2/dPc1\", other_ax=axes[1] )\n", + "\n", " # get fit results now so we can set plot labels\n", " fits = {}\n", " errs = {}\n", @@ -464,7 +3939,6 @@ " soln_name = key[0]\n", " order = key[1]\n", " velpres = key[2]\n", - " ax = axes[order]\n", " fit = fits[key]\n", " fitdata = get_fit_line(np.reciprocal(list(err.keys()),dtype='double'),fit) \n", "\n", @@ -475,12 +3949,15 @@ " if (soln_name not in solns) or (solns[soln_name].graph==False):\n", " continue\n", "\n", - " col,ls = scheme[(soln_name,velpres)]\n", - " line = ax.plot(*fitdata, linewidth=1., color=col, linestyle=ls)\n", - " if velpres == velocity_key:\n", - " lines[soln_name] = line\n", " \n", - " ax.plot(np.reciprocal(list(err.keys()),dtype='double'), [errval[errtype] for errval in err.values()], 'o', markersize=1., color='black')\n", + " if with_matplotlib:\n", + " ax = axes[order]\n", + " col,ls = scheme[(soln_name,velpres)]\n", + " line = ax.plot(*fitdata, linewidth=1., color=col, linestyle=ls)\n", + " if velpres == velocity_key:\n", + " lines[soln_name] = line\n", + "\n", + " ax.plot(np.reciprocal(list(err.keys()),dtype='double'), [errval[errtype] for errval in err.values()], 'o', markersize=1., color='black')\n", "\n", " lbls = []\n", " lns = []\n", @@ -492,11 +3969,13 @@ " lbls.append(\"{} ({: .2f},{: .2f}), ({: .2f},{: .2f})\".format(soln_name[3:].ljust(4), vel_1, pre_1, vel_2, pre_2))\n", " lns.append(lines[soln_name][0])\n", " \n", - " leg = fig.legend( lns, lbls, loc = (0.15, 0.15), prop={'family': 'monospace', 'size':6})\n", - " leg.set_title(\"Q1 dQ0 Q2 dPc1 \", \n", - " {'family': 'monospace', 'size':6 })\n", - " leg._legend_box.align = \"right\"\n", - " fig.savefig(\"Analytic_Convergence_Graph.png\")" + " if with_matplotlib:\n", + " leg = fig.legend( lns, lbls, loc = (0.15, 0.15), prop={'family': 'monospace', 'size':6})\n", + " leg.set_title(\"Q1 dQ0 Q2 dPc1 \", \n", + " {'family': 'monospace', 'size':6 })\n", + " leg._legend_box.align = \"right\"\n", + " \n", + " fig.savefig(\"Analytic_Convergence_Graph.png\")" ] }, { @@ -514,7 +3993,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -528,9 +4007,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.10.0" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/docs/test/SteadyState.ipynb b/docs/test/SteadyState.ipynb deleted file mode 100644 index 3beebf260..000000000 --- a/docs/test/SteadyState.ipynb +++ /dev/null @@ -1,663 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Steady State Equation\n", - "-----------\n", - "\n", - "\\\\[\n", - "\\nabla \\cdot q = h\n", - "\\\\]\n", - "\n", - "\\\\[\n", - "q = - k \\nabla T\n", - "\\\\]\n", - "\n", - "where:\n", - " * $T$ is a scalar quantity \n", - " * $k$ is diffusion (or conductivity) coefficient\n", - " * $q$ is the heat flux vector \n", - " * $h$ is the source/sink term\n", - "\n", - "-----\n", - "\n", - "Here we consider 2D models in the region, $0 \\leqslant x \\leqslant 1 $ and $ y_{0}\\leqslant y \\leqslant y_{1}$\n", - "\n", - "with no variation in the x direction, i.e. $ \\frac{\\partial T}{\\partial x} = 0 $ and a constant value $ k $ across the domain. \n", - "\n", - "Leading the 1D general solution:\n", - "\n", - "$ T = -\\frac{h}{2 k}y^{2} + c_{0}y + c_{1} $\n", - "\n", - "where $c_{0}, c_{1}$ are arbitrary constants found by applying each model's boundary conditions\n", - "\n", - "Three models are presented below, each with an analytic solution that the numerical results are tested against." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# analytic solution definitions\n", - "def analyticTemperature(y, h, k, c0, c1):\n", - " return -h/(2.*k)*y**2 + c0*y + c1\n", - "\n", - "def exactDeriv(y, h, k, c0):\n", - " return -h/k*y + c0" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import underworld as uw\n", - "import underworld.visualisation as vis\n", - "import numpy as np\n", - "uw.utils.matplotlib_inline()\n", - "import matplotlib.pyplot as pyplot\n", - "import matplotlib.pylab as pylab\n", - "pyplot.ion() # needed to ensure pure python jobs do now hang on show()\n", - "\n", - "rank = uw.mpi.rank\n", - "\n", - "# for machines without matplotlib #\n", - "make_graphs = False\n", - "if uw.utils.is_kernel(): \n", - " make_graphs = True\n", - " try:\n", - " import matplotlib\n", - " except ImportError:\n", - " make_graphs=False\n", - "\n", - "# depth range\n", - "y0 = -.60\n", - "y1 = 1.3\n", - "\n", - "# build mesh and fields\n", - "mesh = uw.mesh.FeMesh_Cartesian( elementType = (\"Q1\"), \n", - " elementRes = (10, 20), \n", - " minCoord = (0., y0), \n", - " maxCoord = (1., y1))\n", - "\n", - "tField = mesh.add_variable( nodeDofCount=1, dataType='double')\n", - "topWall = mesh.specialSets['MaxJ_VertexSet']\n", - "bottomWall = mesh.specialSets['MinJ_VertexSet']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Model 1)\n", - "\n", - " * a fixed temperature condition or `DirichletCondition` - topWall \n", - " \n", - " $ T(x,y_{1}) = T_{1} $\n", - "\n", - " * a heat flow condition or `NeumannCondition` - bottomWall.\n", - "\n", - " $ q \\cdot n_{b} = (\\,0.0\\,,\\, f\\,) \\cdot (\\,0.0\\,,\\,-1.0\\,) = - f$\n", - " \n", - " **Note** The heat flow is calculated using the heat flux vector $q$ multiplied by the outward surface normal, $n$. \n", - " The bottom surface outward normal $n_{b}$ point along the negative j-axis \n", - "\n", - "\n", - "When the `NeumannCondition` object is associated with the `SteadyStateHeat` system it defines a flux along a boundary such that:\n", - " \n", - "$ q \\cdot n = \\phi $ at $ \\Gamma_{\\phi} $\n", - "\n", - "where:\n", - "* $ \\Gamma_{\\phi} $ is the set of vertices along the surface of the domain, \n", - "* $\\phi $ is the scalar flow associated with the vector flux $q$ along $\\Gamma_{\\phi}$\n", - "\n", - "\n", - "---------------\n", - "\n", - "An example: Defining a scalar field's flux at the bottom wall in a 2D rectangular domain.\n", - "\n", - "The outward facing normal vector at the bottom wall $\\mathbf{n}\\mid_{(x,y_{0})}=\\left[0,-1\\right] $) and the imposed flux vector $k \\nabla T = \\left[0, \\phi\\right]$\n", - "\n", - "The `NeumannCondition` object definition of this condition would be: \n", - "\n", - "```\n", - "nbc = uw.conditions.NeumannCondition( fn_flux= -1.0 * phi, variable=tField,\n", - " indexSetsPerDof=mesh.specialSets[\"MinJ_VertexSet\"] )\n", - "```\n", - "\n", - "Applies a 'fn_flux' to the scalar 'variable' `MeshVariable` over the boundary vertices in the set 'indexSetsPerDof'. The factor -1 is from the vector multiplication with the outward facing normal vector.\n", - "\n", - "Here `phi` can be a `underworld.Function` or `underworld.MeshVariable` type.\n", - "\n", - "------\n", - "\n", - "Arbitrary constants are:\n", - "\n", - "$c_{0} = \\frac{1}{k} (\\,f + hy_{0}\\,) $\n", - "\n", - "$c_{1} = T_{1} + \\frac{h}{2 k}y_{1}^2 - c_{0}y_{1} $\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "T1 = 8.0 # surface temperature\n", - "k = 6.7 # diffusivity\n", - "h = 8.0 # heat production, source term\n", - "f = 2. \n", - "\n", - "# analytic solution definitions\n", - "# 1 dirichlet conditions (top) + 1 neumann (bottom)\n", - "c0 = 1./k*(f+h*y0)\n", - "c1 = T1 + h/(2.0*k)*y1**2 - c0*y1" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "for ii in topWall:\n", - " tField.data[ii] = T1\n", - "\n", - "# flag the dirichlet conditions on the topWall only\n", - "bc = uw.conditions.DirichletCondition(tField, indexSetsPerDof=(topWall) )\n", - "\n", - "# define neumann condition\n", - "nbc = uw.conditions.NeumannCondition( variable=tField,\n", - " fn_flux=-f,\n", - " indexSetsPerDof=(bottomWall))\n", - "\n", - "# flag the dirichlet conditions on the topWall only\n", - "bc = uw.conditions.DirichletCondition(tField, indexSetsPerDof=(topWall) )\n", - "\n", - "# define heat eq. system\n", - "ss = uw.systems.SteadyStateHeat( temperatureField = tField,\n", - " fn_diffusivity = k,\n", - " fn_heating = h,\n", - " conditions = [bc, nbc] ) " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "solver = uw.systems.Solver(ss)\n", - "solver.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numerical flux at y = -0.6 is 0.241786540078\n", - "Exact flux at y = -0.6 is 0.2985074626865672\n", - "\n", - "Abs. error = 2.581e-04\n", - "Rel. error = 6.156e-06\n" - ] - } - ], - "source": [ - "# create numpy arrays for analytics, parallel check first\n", - "\n", - "yvals = np.zeros(len(mesh.specialSets['MinI_VertexSet']))\n", - "ycoord = np.zeros_like(yvals)\n", - "analytic = np.zeros_like(yvals)\n", - "\n", - "ids = mesh.specialSets['MinI_VertexSet']\n", - "yvals[:] = tField.evaluate(ids).T\n", - "\n", - "ycoord = tField.mesh.data[ids.data,[1]]\n", - "analytic = analyticTemperature(ycoord, h, k, c0, c1)\n", - "\n", - "# measure border flux, analytic is easy, parallel check needed for numeric result\n", - "yspot = y0\n", - "ana_flux = exactDeriv(yspot,h,k,c0)\n", - "\n", - "tmp = tField.fn_gradient.evaluate_global([0.2,yspot])\n", - "if tmp is not None: num_flux = tmp[0][1]\n", - "else: num_flux = 0.\n", - "\n", - "\n", - "from mpi4py import MPI\n", - "comm = MPI.COMM_WORLD\n", - "# assuming order in the allgather is the same\n", - "coords = comm.allgather(ycoord)\n", - "numerical = comm.allgather(yvals)\n", - "\n", - "if make_graphs:\n", - "\n", - " # 1st build exact solution hiRes\n", - " big = np.linspace(y0,y1)\n", - " cool = analyticTemperature(big, h, k, c0, c1)\n", - "\n", - " pylab.rcParams[ 'figure.figsize'] = 12, 6\n", - " pyplot.plot(coords, numerical, 'o', color = 'black', label='numerical') \n", - " pyplot.plot(big, cool, color = 'red', label=\"exact\") \n", - " pyplot.xlabel('Y coords')\n", - " pyplot.ylabel('Temperature')\n", - " pyplot.show()\n", - "\n", - "\n", - "if rank == 0:\n", - " threshold = 1.0e-4\n", - " yspot = y0\n", - " abserr = np.linalg.norm(analytic - yvals)\n", - " mag = np.linalg.norm(analytic)\n", - " relerr = abserr / mag\n", - " print(\"Numerical flux at y = \" ,yspot,\"is\", num_flux)\n", - " print(\"Exact flux at y = \" ,yspot,\"is\", ana_flux)\n", - " print(\"\\nAbs. error = {0:.3e}\".format(abserr))\n", - " print(\"Rel. error = {0:.3e}\".format(relerr))\n", - " if relerr > threshold:\n", - " raise RuntimeError(\"The numerical solution is outside the error threshold of the analytic solution.\" \\\n", - " \"The Relative error was \", relerr,\" the threshold is \", threshold)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model 2)\n", - "\n", - " * a fixed temperature condition or Dirichlet BC - bottomWall \n", - " \n", - " $ T(x,y_{0}) = T_{0} $\n", - "\n", - " * a heat flow condition or Neumann BC - topWall.\n", - "\n", - " $ q \\cdot n_{u} = (\\,0.0\\,,\\, f\\,) \\cdot (\\,0.0\\,,\\,1.0\\,) = f$\n", - " \n", - " **Note** The top surface outward normal $n_{u}$ point along the j-axis \n", - "\n", - "------\n", - "\n", - "Arbitrary constants are:\n", - "\n", - "$c_{0} = \\frac{1}{k} (\\,f + hy_{1}\\,) $\n", - "\n", - "$c_{1} = T_{0} + \\frac{h}{2 k}y_{0}^2 - c_{0}y_{0} $" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "T0 = 8.0 # surface temperature\n", - "k = 2.2 # diffusivity\n", - "h = -7.4 # heat production, source term\n", - "f = 4.0 # temperature flow, implies negative gradient\n", - "\n", - "# analytic solution definitions\n", - "# 1 dirichlet conditions (top) + 1 neumann (bottom)\n", - "c0 = 1.0*(f+h*y1)/k\n", - "c1 = T0 + h/(2.0*k)*y0**2 - c0*y0" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "for ii in bottomWall:\n", - " tField.data[ii] = T0\n", - "\n", - "# define neumann condition\n", - "nbc = uw.conditions.NeumannCondition( fn_flux=f, \n", - " variable=tField, \n", - " indexSetsPerDof=(topWall) )\n", - "\n", - "# flag the dirichlet conditions on the topWall only\n", - "bc = uw.conditions.DirichletCondition(tField, indexSetsPerDof=(bottomWall) )\n", - "\n", - "\n", - "# define heat eq. system\n", - "ss = uw.systems.SteadyStateHeat( temperatureField = tField,\n", - " fn_diffusivity = k,\n", - " fn_heating = h,\n", - " conditions = [bc, nbc] ) " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "solver = uw.systems.Solver(ss)\n", - "solver.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numerical flux at y = 1.3 is -4.41274249682\n", - "Exact flux at y = 1.3 is -4.57272727273\n", - "\n", - "Abs. error = 1.252e-03\n", - "Rel. error = 4.705e-05\n" - ] - } - ], - "source": [ - "# create numpy arrays for analytics\n", - "yvals = np.zeros(len(mesh.specialSets['MinI_VertexSet']))\n", - "ycoord = np.zeros_like(yvals)\n", - "analytic = np.zeros_like(yvals)\n", - "\n", - "ids = mesh.specialSets['MinI_VertexSet']\n", - "yvals[:] = tField.evaluate(ids).T\n", - "\n", - "ycoord = tField.mesh.data[ids.data,[1]]\n", - "analytic = analyticTemperature(ycoord, h, k, c0, c1)\n", - "\n", - "'''\n", - "abserr = uw.utils._nps_2norm(analytic - yvals)\n", - "mag = uw.utils._nps_2norm(analytic)\n", - "relerr = abserr / mag\n", - "'''\n", - "\n", - "# measure border flux, analytic is easy, parallel check needed for numeric result\n", - "yspot = y0\n", - "ana_flux = exactDeriv(yspot,h,k,c0)\n", - "\n", - "tmp = tField.fn_gradient.evaluate_global([0.2,yspot])\n", - "if tmp is not None: num_flux = tmp[0][1]\n", - "else: num_flux = 0.\n", - "\n", - "from mpi4py import MPI\n", - "comm = MPI.COMM_WORLD\n", - "# assuming order in the allgather is the same\n", - "coords = comm.allgather(ycoord)\n", - "numerical = comm.allgather(yvals)\n", - "\n", - "if make_graphs:\n", - "\n", - " # 1st build exact solution hiRes\n", - " big = np.linspace(y0,y1)\n", - " cool = analyticTemperature(big, h, k, c0, c1)\n", - "\n", - " pylab.rcParams[ 'figure.figsize'] = 12, 6\n", - " pyplot.plot(coords, numerical, 'o', color = 'black', label='numerical') \n", - " pyplot.plot(big, cool, color = 'red', label=\"exact\") \n", - " pyplot.xlabel('Y coords')\n", - " pyplot.ylabel('Temperature')\n", - " pyplot.show()\n", - "\n", - "\n", - "if rank == 0:\n", - " threshold = 1.0e-4\n", - " yspot = y1\n", - " abserr = np.linalg.norm(analytic - yvals)\n", - " mag = np.linalg.norm(analytic)\n", - " relerr = abserr / mag\n", - " print(\"Numerical flux at y = \" ,yspot,\"is\", num_flux)\n", - " print(\"Exact flux at y = \" ,yspot,\"is\", ana_flux)\n", - " print(\"\\nAbs. error = {0:.3e}\".format(abserr))\n", - " print(\"Rel. error = {0:.3e}\".format(relerr))\n", - " if relerr > threshold:\n", - " raise RuntimeError(\"The numerical solution is outside the error threshold of the analytic solution.\" \\\n", - " \"The Relative error was \", relerr,\" the threshold is \", threshold)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Model 3) \n", - "\n", - "2D, Steady State Heat Equation with Dirichlet BC at the top and bottom surfaces.\n", - "\n", - "$T(x,y_{1}) = T_{1}$\n", - "\n", - "$ T(x,y_{0}) = T_{0} $\n", - "\n", - "------\n", - "\n", - "arbitrary constants are:\n", - "\n", - "$ c_{0} = \\frac{1}{y_{1}-y_{0}} \\left[ T_{1}-T_{0}+\\frac{h} {2k}(y_{1}^2-y_{0}^2) \\right] $\n", - "\n", - "$c_{1} = T_{1} + \\frac{h}{2k}y_{1}^2 - c_{0}y_{1}$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Model parameters\n", - "T1 = 8.0 # top surface temperature\n", - "T0 = 4.0 # bottom surface temperature\n", - "k = 0.50 # diffusivity\n", - "h = 10 # heat production, source term\n", - "\n", - "# arbitrary constant given the 2 dirichlet conditions\n", - "c0 = (T1-T0+h/(2*k)*(y1**2-y0**2)) / (y1-y0)\n", - "c1 = T1 + h/(2*k)*y1**2 - c0*y1" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# set boundary conditions\n", - "for ii in topWall:\n", - " tField.data[ii] = T1\n", - "for ii in bottomWall:\n", - " tField.data[ii] = T0\n", - "\n", - "# flag boundary conditions\n", - "bc = uw.conditions.DirichletCondition(tField, indexSetsPerDof=(topWall+bottomWall) )\n", - "\n", - "# define heat eq. system\n", - "ss = uw.systems.SteadyStateHeat( temperatureField = tField,\n", - " fn_diffusivity = k,\n", - " fn_heating = h,\n", - " conditions = [bc] )" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "solver = uw.systems.Solver(ss)\n", - "solver.solve()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# create numpy arrays for analytics\n", - "yvals = np.zeros(len(mesh.specialSets['MinI_VertexSet']))\n", - "ycoord = np.zeros_like(yvals)\n", - "analytic = np.zeros_like(yvals)\n", - "\n", - "ids = mesh.specialSets['MinI_VertexSet']\n", - "yvals[:] = tField.evaluate(ids).T\n", - "\n", - "ycoord = tField.mesh.data[ids.data,[1]]\n", - "analytic = analyticTemperature(ycoord, h, k, c0, c1)\n", - "\n", - "'''\n", - "abserr = uw.utils._nps_2norm(analytic - yvals)\n", - "mag = uw.utils._nps_2norm(analytic)\n", - "relerr = abserr / mag\n", - "'''\n", - "# measure border flux, analytic is easy, parallel check needed for numeric result\n", - "yspot = y0\n", - "ana_flux = exactDeriv(yspot,h,k,c0)\n", - "\n", - "tmp = tField.fn_gradient.evaluate_global([0.2,yspot])\n", - "if tmp is not None: num_flux = tmp[0][1]\n", - "else: num_flux = 0.\n", - "\n", - "from mpi4py import MPI\n", - "comm = MPI.COMM_WORLD\n", - "# assuming order in the allgather is the same\n", - "coords = comm.allgather(ycoord)\n", - "numerical = comm.allgather(yvals)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Numerical flux at y = -0.6 is 20.1552435024\n", - "Exact flux at y= -0.6 is 21.10526315789474\n", - "\n", - "Abs. error = 1.988e-04\n", - "Rel. error = 3.573e-06\n" - ] - } - ], - "source": [ - "if rank == 0:\n", - " threshold = 1.0e-4\n", - " yspot = y0\n", - " abserr = np.linalg.norm(analytic - yvals)\n", - " mag = np.linalg.norm(analytic)\n", - " relerr = abserr / mag\n", - " print(\"Numerical flux at y = \" ,yspot,\"is\", num_flux)\n", - " print(\"Exact flux at y=\" ,yspot,\"is\", ana_flux)\n", - " print(\"\\nAbs. error = {0:.3e}\".format(abserr))\n", - " print(\"Rel. error = {0:.3e}\".format(relerr))\n", - " if relerr > threshold:\n", - " raise RuntimeError(\"The numerical solution is outside the error threshold of the analytic solution.\" \\\n", - " \"The Relative error was \", relerr,\" the threshold is \", threshold) \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "if make_graphs:\n", - "\n", - " # 1st build exact solution hiRes\n", - " big = np.linspace(y0,y1)\n", - " cool = analyticTemperature(big, h, k, c0, c1)\n", - "\n", - " pylab.rcParams[ 'figure.figsize'] = 12, 6\n", - " pyplot.plot(coords, numerical, 'o', color = 'black', label='numerical') \n", - " pyplot.plot(big, cool, color = 'red', label=\"exact\") \n", - " pyplot.xlabel('Y coords')\n", - " pyplot.ylabel('Temperature')\n", - " pyplot.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/docs/test/SteadyState.py b/docs/test/SteadyState.py index 408e01512..0738b3d82 100644 --- a/docs/test/SteadyState.py +++ b/docs/test/SteadyState.py @@ -5,9 +5,9 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.13.1 +# jupytext_version: 1.14.5 # kernelspec: -# display_name: Python 3 +# display_name: Python 3 (ipykernel) # language: python # name: python3 # --- @@ -56,10 +56,6 @@ def exactDeriv(y, h, k, c0): import underworld as uw import underworld.visualisation as vis import numpy as np -uw.utils.matplotlib_inline() -import matplotlib.pyplot as pyplot -import matplotlib.pylab as pylab -pyplot.ion() # needed to ensure pure python jobs do now hang on show() rank = uw.mpi.rank @@ -69,6 +65,12 @@ def exactDeriv(y, h, k, c0): make_graphs = True try: import matplotlib + import matplotlib.pyplot as pyplot + uw.utils.matplotlib_inline() + + import matplotlib.pylab as pylab + pyplot.ion() # needed to ensure pure python jobs do now hang on show() + except ImportError: make_graphs=False @@ -437,7 +439,7 @@ def exactDeriv(y, h, k, c0): if relerr > threshold: raise RuntimeError("The numerical solution is outside the error threshold of the analytic solution." \ "The Relative error was ", relerr," the threshold is ", threshold) - + # + diff --git a/docs/test/UWGeodynamics/image_tests.py b/docs/test/UWGeodynamics/image_tests.py index d6f5bef4d..2ebdb8bf3 100644 --- a/docs/test/UWGeodynamics/image_tests.py +++ b/docs/test/UWGeodynamics/image_tests.py @@ -4,8 +4,8 @@ path = os.path.abspath('./image_tests/') #Check if the viewer is working -import glucifer -if not glucifer.lavavu: +import underworld.visualisation as vis +if not vis.lavavu: print("Image tests skipped, Viewer disabled") exit() diff --git a/docs/test/func_debug_messages.py b/docs/test/func_debug_messages.py index 8014be541..6954b346a 100644 --- a/docs/test/func_debug_messages.py +++ b/docs/test/func_debug_messages.py @@ -29,6 +29,7 @@ def do_test_py(test,expected_message): raise RuntimeError("Incorrect error message encountered for {}. \n" "Expected:\n{}\n\n" "Encountered:\n{}\n\n".format(test,strmessage,strenderr)) + def do_test_jupyter(test,expected_message): command = "jupyter nbconvert --to python --execute {}".format(test) result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) @@ -42,7 +43,14 @@ def do_test_jupyter(test,expected_message): # default messages do_test_py("outside_domain",outside_domain_message) - do_test_jupyter("func_debug_messages_notebook.ipynb_do_not_run_direct", outside_domain_message_jupyter) + can_nbconvert = True + try: + import jupyter + import nbconvert + except ModuleNotFoundError: + can_nbconvert = False + if can_nbconvert: + do_test_jupyter("func_debug_messages_notebook.ipynb_do_not_run_direct", outside_domain_message_jupyter) # no func messages import os @@ -54,3 +62,5 @@ def do_test_jupyter(test,expected_message): # is difficult due to different MPI implementations giving # different error messages, and also due to arbitrary # ordering of messages. + + diff --git a/docs/test/image_tests.py b/docs/test/image_tests.py index 54aa53e76..b091ef402 100644 --- a/docs/test/image_tests.py +++ b/docs/test/image_tests.py @@ -1,6 +1,5 @@ #!/usr/bin/env python3 import os -import importlib import imp import ntpath from inspect import getsourcefile @@ -14,6 +13,11 @@ if not vis.lavavu: print("Image tests skipped, Viewer disabled") exit() +try: + import nbconvert +except ModuleNotFoundError: + print("Skipping image_test.py as nbconvert can't be found") + exit() for d in os.listdir(path): testd = os.path.join(path,d) diff --git a/pyproject.toml b/pyproject.toml index bf8e1fbaa..cf8bd0e62 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,3 @@ [build-system] build-backend = "setuptools.build_meta" -requires = ["setuptools", "numpy"] +requires = ["setuptools","numpy>=1.20.3","swig>=4.0.0","cmake>=0.29.24"] diff --git a/setup.py b/setup.py index 93771a875..9ceb084e0 100755 --- a/setup.py +++ b/setup.py @@ -32,9 +32,9 @@ To set the required PETSc, set the ``PETSC_DIR`` environment variable (or install the ``petsc`` Python package). Provide any ``Underworld`` ./configure options using the environmental variable ``UW_CONFIGURE_OPTIONS``. - """ -import sys, os +import sys +import os import shutil import platform @@ -48,7 +48,6 @@ from setuptools.command.build_ext import build_ext - class CMakeExtension(Extension): """ Custom setuptools extension that configures a CMake project. @@ -185,9 +184,6 @@ def build_extension(self, ext: CMakeExtension) -> None: '--config', ext.cmake_build_type ] - # CMake install target - install_target = "install" - if platform.system() == "Windows": configure_args += [ @@ -260,12 +256,15 @@ def extend_cmake_prefix_path(path: str) -> None: os.environ["CMAKE_PREFIX_PATH"] = str(path) - metadata = { - 'provides' : ['underworld'], - 'zip_safe' : False, - 'install_requires' : ['numpy>=1.22.1','mpi4py>=1.2.2', 'h5py', 'pint', 'scipy'] + 'provides': ['underworld'], + 'zip_safe': False, + 'install_requires': ['numpy>=1.20.3', 'mpi4py>=1.2.2', 'h5py', 'pint', 'scipy'], + 'extras_require': { + 'full': ["badlands","lavavu","matplotlib","nbmake"], # for all 3rd party packages + }, } + classifiers = """ Development Status :: 5 - Production/Stable Intended Audience :: Developers @@ -290,7 +289,7 @@ def extend_cmake_prefix_path(path: str) -> None: if not os.path.isfile(idfile): import uuid with open(idfile, "w+") as f: - f.write("uwid = \'" + str(uuid.uuid4()) + "\'" ) + f.write("uwid = \'" + str(uuid.uuid4()) + "\'") setup(name='underworld', version=version['__version__'], @@ -299,8 +298,8 @@ def extend_cmake_prefix_path(path: str) -> None: functionality of the code running in a parallel HPC environment.", long_description=long_description, long_description_content_type="text/markdown", - classifiers= classifiers.split('\n')[1:-1], - keywords = ['Underworld', 'MPI', 'Geodynamics'], + classifiers=classifiers.split('\n')[1:-1], + keywords=['Underworld', 'MPI', 'Geodynamics'], platforms=['POSIX'], license='LGPL-3', diff --git a/underworld/UWGeodynamics/_melt.py b/underworld/UWGeodynamics/_melt.py index b431c8cb7..a9920aebd 100644 --- a/underworld/UWGeodynamics/_melt.py +++ b/underworld/UWGeodynamics/_melt.py @@ -24,8 +24,8 @@ def temperature(self, pressure): def plot(self, pressure): import pylab as plt - temperature = dimensionalise(self.temperature(pressure), u.kelvin) - pressure = dimensionalise(pressure, u.pascal) + temperature = dimensionalise(self.temperature(pressure), u.kelvin).m + pressure = dimensionalise(pressure, u.pascal).m plt.plot(temperature, pressure) plt.gca().invert_yaxis() plt.show() diff --git a/underworld/UWGeodynamics/_model.py b/underworld/UWGeodynamics/_model.py index a9f3b715f..0d1574cf4 100644 --- a/underworld/UWGeodynamics/_model.py +++ b/underworld/UWGeodynamics/_model.py @@ -839,6 +839,12 @@ def _advdiffSystem(self): else: HeatProdMap[material.index] = 0. + # Melt heating effects if enabled + if material.latentHeatFusion and self.dt.value: + dynamicHeating = self._get_dynamic_heating(material) + HeatProdMap[material.index] += dynamicHeating + + self.HeatProdFn = fn.branching.map(fn_key=self.materialField, mapping=HeatProdMap) @@ -1523,17 +1529,24 @@ def solve(self): self._solver = False def _init_temperature_variables(self): + """ + Mesh variables definitions for temperature, tempertaureDot (time + derivative of temperature ) and heatFlux along boundaries walls. + """ + # called '_temperature' because of getter/setter usage in Model class self._temperature = MeshVariable(mesh=self.mesh, nodeDofCount=1) - self._temperatureDot = MeshVariable(mesh=self.mesh, - nodeDofCount=1) - self._heatFlux = MeshVariable(mesh=self.mesh, - nodeDofCount=1) - self._temperatureDot.data[...] = 0. - self._heatFlux.data[...] = 0. self.mesh_variables["temperature"] = self._temperature self.restart_variables["temperature"] = self._temperature + self._temperatureDot = self.add_mesh_variable(name="temperatureDot", + nodeDofCount=1, + restart_variable=True) + + self._heatFlux = self.add_mesh_variable(name="_heatFlux", + nodeDofCount=1, + restart_variable=False) + def init_model(self, temperature="steady-state", pressure="lithostatic", defaultStrainRate=1e-15 / u.second): """ Initialise the Temperature Field as steady state, @@ -2492,7 +2505,7 @@ def checkpoint_fields(self, fields=None, checkpointID=None, if isinstance(time, u.Quantity) and self.output_units: time = time.to(self.output_units) - if Model._advector or Model._freeSurface: + if Model._advector or Model.freeSurface: mesh_name = 'mesh-%s' % checkpointID mesh_prefix = os.path.join(outputDir, mesh_name) mH = Model.mesh.save('%s.h5' % mesh_prefix, @@ -2522,7 +2535,7 @@ def checkpoint_fields(self, fields=None, checkpointID=None, comm.Barrier() for field in fields: - if field == "temperature" and not Model.temperature: + if (field in ["temperature", "temperatureDot"]) and not Model.temperature: continue if field in Model.mesh_variables.keys(): field = str(field) @@ -2809,7 +2822,7 @@ def reload_mesh(self, step): Model = self.Model - if Model._advector: + if Model._advector or Model._freeSurface: Model.mesh.load(os.path.join(self.restartDir, 'mesh-%s.h5' % step)) else: Model.mesh.load(os.path.join(self.restartDir, "mesh.h5")) @@ -2836,10 +2849,32 @@ def reload_restart_variables(self, step): Model = self.Model + import os + for field in Model.restart_variables: obj = getattr(Model, field) path = os.path.join(self.restartDir, field + "-%s.h5" % step) + if field == "temperatureDot": + """ + Because version 2.13 and lower didn't save the temperatureDot field + by default it's treated optionally here" + """ + if not os.path.exists(path): + if rank == 0: + os = \ + """******************************************************* + Warning, couldn't find temperatureDot field to reload for + the SUPG solver. This may cause temperature instabilities + after the restart solve. Please check the restart + temperature field closely. + To avoid this message please ensure a 'temperatureDot' + .h5 file is available at restart." + *******************************************************""" + print(os) + continue # don't try load, continue to the next restart_variable + obj.load(str(path)) + if rank == 0: now = datetime.now().strftime('%Y-%m-%d %H:%M:%S') print("{0} loaded".format(field) + '(' + now + ')') diff --git a/underworld/UWGeodynamics/_rcParams.py b/underworld/UWGeodynamics/_rcParams.py index e2e0c15db..5098aa355 100644 --- a/underworld/UWGeodynamics/_rcParams.py +++ b/underworld/UWGeodynamics/_rcParams.py @@ -17,6 +17,7 @@ "nonlinear.max.iterations": [500, validate_int], "default.outputs" : [["temperature", + "temperatureDot", "pressureField", "strainRateField", "velocityField", diff --git a/underworld/UWGeodynamics/_utils.py b/underworld/UWGeodynamics/_utils.py index b17c8272a..cbe82b483 100644 --- a/underworld/UWGeodynamics/_utils.py +++ b/underworld/UWGeodynamics/_utils.py @@ -6,7 +6,6 @@ import os import operator as op from underworld.scaling import non_dimensionalise as nd -from underworld.scaling import dimensionalise from underworld.scaling import units as u from underworld.utils import _swarmvarschema from underworld.swarm import Swarm, SwarmVariable @@ -597,7 +596,7 @@ def get_wall_indices(self): # Return new indexSet for the wall mesh = self.Model.mesh - nodes = np.arange(mesh.nodesLocal).astype(np.int) + nodes = np.arange(mesh.nodesLocal).astype(int) mask = self.wallFn.evaluate(mesh) mask = mask[:mesh.nodesLocal] nodes = nodes[mask.flatten()] diff --git a/underworld/UWGeodynamics/surfaceProcesses.py b/underworld/UWGeodynamics/surfaceProcesses.py index c3dc8f87a..507122e79 100644 --- a/underworld/UWGeodynamics/surfaceProcesses.py +++ b/underworld/UWGeodynamics/surfaceProcesses.py @@ -161,8 +161,8 @@ def _generate_dem(self): """ # Calculate number of nodes from required resolution. - nx = np.int((self.maxCoord[0] - self.minCoord[0]) / self.resolution) - ny = np.int((self.maxCoord[1] - self.minCoord[1]) / self.resolution) + nx = np.int32((self.maxCoord[0] - self.minCoord[0]) / self.resolution) + ny = np.int32((self.maxCoord[1] - self.minCoord[1]) / self.resolution) nx += 1 ny += 1 @@ -523,6 +523,7 @@ def __init__(self, airIndex, sedimentIndex, D, surfaceArray, updateSurfaceLB=0.* updateSurfaceRB : Distance to update surface from right boundary, default is 0 km which results in a free slip boundary + '''updated''' ***All units are converted under the hood*** @@ -536,200 +537,256 @@ def __init__(self, airIndex, sedimentIndex, D, surfaceArray, updateSurfaceLB=0.* surfaceArray = coords ) *** - """ + """ self.airIndex = airIndex self.sedimentIndex = sedimentIndex self.timeField = timeField - self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer).magnitude - self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer).magnitude - # self.surfaceElevation = surfaceElevation.to(u.kilometer).magnitude - self.D = D.to(u.kilometer**2 / u.year).magnitude + + self.D = D.to(u.kilometer**2 / u.year) + + + ### a conversion, will throw an error if units are neglected + self.surfaceArray = surfaceArray + self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer) + self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer) + + self.Model = Model - self.surfaceArray = surfaceArray - self.surface_dt_diffusion = None + + self.originalZ = None + self.min_dist = None + self.nd_coords = None self.dx = None - self.surface_data_local = None - self.original_surface = None + + ''' function to create grid for surface ''' + def _init_model(self): + ''' creates a PT output ''' + ### automatically non-dimensionalises the imput coords if they have a dim + self.Model.add_passive_tracers(name="surface", vertices=nd(self.surfaceArray), advect=False) - self.Model.add_passive_tracers(name="surface", vertices=self.surfaceArray, advect=False) + self.Model.surface_tracers.allow_parallel_nn = True - self.dx = dimensionalise((self.surfaceArray[:,0][1] - self.surfaceArray[:,0][0]), u.kilometer).magnitude + self.nd_coords = nd(self.surfaceArray) - '''set up custom tracers for surface''' - x_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].min() - x_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].max() + ### get distance between 1st and 2nd x coord and y coords to determine min distance between grid points + x = np.sort(np.unique(self.nd_coords[:,0])) - ''' create surface tracers to advect on each node''' - self.surface_data_local = np.zeros_like(self.surfaceArray[(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)]) + self.min_dist = np.diff(x).min() - self.surface_data_local[:,0] = self.surfaceArray[:,0][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] - self.surface_data_local[:,1] = self.surfaceArray[:,1][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] + # self.dx = np.diff(x).min() - # # Spline original surface for no slip condition near boundary - self.original_surface = interp1d(self.surfaceArray[:,0], self.surfaceArray[:,1], kind='cubic', fill_value='extrapolate') + ### create copy of original surface + self.originalZ = self.nd_coords[:,1] + #### add variables for tracking that aren't included in UWGeo + self.Model.surface_tracers.z_coord = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) + + self.Model.surface_tracers.D = self.Model.surface_tracers.add_variable( dataType="double", count=1 ) - self.surface_dt_diffusion = (0.2 * (self.dx * self.dx / self.D)) + if self.Model.surface_tracers.data.size != 0: + ### erosion is downward (negative) + self.Model.surface_tracers.D.data[:,0] = abs( np.repeat( nd(self.D), self.Model.surface_tracers.data.shape[0] ) ) + self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,1] - def solve(self, dt): + comm.barrier() - root_proc = 0 - z_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].max() - z_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].min() + ### add fields to track - ### collect surface data on each node - if z_max_local >= self.surface_data_local[:,1].min(): - ### gets the x and z data of the surface tracers - x_data = np.ascontiguousarray(self.surface_data_local[:,0].copy()) - z_data = np.ascontiguousarray(self.surface_data_local[:,1].copy()) + ### track velocity field on tracers + self.Model.surface_tracers.add_tracked_field(self.Model.velocityField, + name="surface_vel", + units=u.centimeter/u.year, + dataType="float", count=self.Model.mesh.dim) - ### Get the velocity of the surface tracers - tracer_velocity = self.Model.velocityField.evaluate(self.surface_data_local) - vx = np.ascontiguousarray(tracer_velocity[:,0]) - vz = np.ascontiguousarray(tracer_velocity[:,1]) + self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.particleCoordinates, + name="coords", + units=u.centimeter/u.year, + dataType="float", count=self.Model.mesh.dim) + + ## track the surface coordinates (could change to only show the height) + self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.z_coord, + name="topo_height", + units=u.kilometer, + dataType="float", count=1) + + ### track the diffusive surface rate + self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.D, + name="Diffusive rate", + units=u.meter**2/u.year, + dataType="float", count=1) + + + comm.barrier() + + def solve(self, dt): + + + if self.Model.surface_tracers.data.shape[0] > 0: + ### evaluate on all nodes and get the tracer velocity on root proc + tracer_velocity_local = self.Model.velocityField.evaluate(self.Model.surface_tracers.data) + x_local = nd(self.Model.x.evaluate(self.Model.surface_tracers.data)) + y_local = nd(self.Model.y.evaluate(self.Model.surface_tracers.data)) + + x = np.ascontiguousarray(x_local) + y = np.ascontiguousarray(y_local) + vx = np.ascontiguousarray(tracer_velocity_local[:,0]) + vy = np.ascontiguousarray(tracer_velocity_local[:,1]) else: - ### creates dummy data on nodes without the surface - x_data = np.array([None], dtype='float64') - z_data = np.array([None], dtype='float64') + x = np.array([None], dtype='float64') + y = np.array([None], dtype='float64') vx = np.array([None], dtype='float64') - vz = np.array([None], dtype='float64') + vy = np.array([None], dtype='float64') + comm.barrier() ### Collect local array sizes using the high-level mpi4py gather - sendcounts = np.array(comm.gather(len(x_data), root=root_proc)) + sendcounts = np.array(comm.gather(len(x), root=0)) comm.barrier() - if rank == root_proc: + if rank == 0: ### creates dummy data on all nodes to store the surface # surface_data = np.zeros((npoints,2)) - x_surface_data = np.zeros((sum(sendcounts)), dtype='float64') - z_surface_data = np.zeros((sum(sendcounts)), dtype='float64') + x_data = np.zeros((sum(sendcounts)), dtype='float64') + y_data = np.zeros((sum(sendcounts)), dtype='float64') vx_data = np.zeros((sum(sendcounts)), dtype='float64') - vz_data = np.zeros((sum(sendcounts)), dtype='float64') - surface_data = np.zeros((sum(sendcounts), 4), dtype='float64') + vy_data = np.zeros((sum(sendcounts)), dtype='float64') else: - x_surface_data = None - z_surface_data = None + x_data = None + y_data = None vx_data = None - vz_data = None - surface_data = None + vy_data = None ### store the surface spline on each node f1 = None - comm.barrier() + comm.Gatherv(sendbuf=x, recvbuf=(x_data, sendcounts), root=0) - ## gather x values, can't do them together - comm.Gatherv(sendbuf=x_data, recvbuf=(x_surface_data, sendcounts), root=root_proc) - ## gather z values - comm.Gatherv(sendbuf=z_data, recvbuf=(z_surface_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=y, recvbuf=(y_data, sendcounts), root=0) ### gather velocity values - comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=0) + + comm.Gatherv(sendbuf=vy, recvbuf=(vy_data, sendcounts), root=0) - comm.Gatherv(sendbuf=vz, recvbuf=(vz_data, sendcounts), root=root_proc) + if rank == 0: + nd_D = nd( self.D ) - if rank == root_proc: - ### Put back into combined array - surface_data[:,0] = x_surface_data - surface_data[:,1] = z_surface_data + surface_data = np.zeros((len(x_data), 4), dtype='float64') + surface_data[:,0] = x_data + surface_data[:,1] = y_data surface_data[:,2] = vx_data - surface_data[:,3] = vz_data + surface_data[:,3] = vy_data - ### remove dummy data surface_data = surface_data[~np.isnan(surface_data[:,0])] - - ### sort by x values - surface_data = surface_data[np.argsort(surface_data[:, 0])] - + surface_data = surface_data[np.argsort(surface_data[:,0])] # # Advect top surface - x2 = surface_data[:,0] + (surface_data[:,2] * dt) - z2 = surface_data[:,1] + (surface_data[:,3] * dt) + x_new = (surface_data[:,0] + (surface_data[:,2]*dt)) + y_new = (surface_data[:,1] + (surface_data[:,3]*dt)) + ## Spline top surface + f = interp1d(x_new, y_new, kind='cubic', fill_value='extrapolate') + + ''' interpolate new surface back onto original grid ''' + x_nd = self.nd_coords[:,0] + z_nd = f(x_nd) - # # Spline top surface - f = interp1d(x2, z2, kind='cubic', fill_value='extrapolate') + ### time to diffuse surface based on Model dt + total_time = dt - ### update surface tracer position - # surface_data[:,0] = (surface_data[:,0]) - surface_data[:,1] = f(surface_data[:,0]) + '''Velocity surface process''' + '''erosion dt for vel model''' + surface_dt_diffusion = ( 0.2 * ( (self.min_dist**2) / nd_D ) ) - ### gets the x and y coordinates from the tracers - x = dimensionalise(surface_data[:,0], u.kilometer).magnitude - z = dimensionalise(surface_data[:,1], u.kilometer).magnitude + vel_for_surface = max(abs(vx_data.max()), abs(vy_data.max())) + surface_dt_vel = (0.2 * ( self.min_dist / vel_for_surface) ) - ### time to diffuse surface based on Model dt - total_time = (dimensionalise(dt, u.year)).magnitude + surface_dt = min(surface_dt_diffusion, surface_dt_vel) - '''Diffusion surface process''' - '''erosion dt for diffusion surface''' - surface_time = min(self.surface_dt_diffusion, total_time) + surf_time = min(surface_dt, total_time) - nts = math.ceil(total_time/surface_time) + nts = math.ceil(total_time/surf_time) + + surf_dt = (total_time / nts) + + print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) - surface_dt = total_time / nts - print('SP total time:', round(total_time,2), 'years, timestep:', round(surface_dt,2), 'years, No. of its:', nts, flush=True) ### Basic Hillslope diffusion for i in range(nts): - qs = -self.D * np.diff(z)/self.dx - dzdt = -np.diff(qs)/self.dx - + qs = -nd_D * np.diff(z_nd)/np.diff(x_nd) + dzdt = -np.diff(qs)/np.diff(x_nd[:-1]) - z[1:-1] += dzdt*surface_dt + z_nd[1:-1] += dzdt*surface_dt - x_nd = nd(x*u.kilometer) - z_nd = nd(z*u.kilometer) - ''' updates material near to boundary back to original coordinates ''' - z_original_surface = self.original_surface(x_nd) - z_nd[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] = z_original_surface[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] + ''' creates no movement condition near boundary ''' + ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + ''' This changes the height to the points original height ''' + resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) - ### creates function for the new surface that has eroded, to be broadcast back to nodes - f1 = interp1d(x_nd, z_nd, fill_value='extrapolate', kind='cubic') + z_nd[resetArea_x] = self.originalZ[resetArea_x] + - # print('finished surface process on global rank:', rank, flush= True) + ### creates function for the new surface that has eroded, to be broadcast back to nodes + f1 = interp1d(self.nd_coords[:,0], z_nd, fill_value='extrapolate', kind='cubic') comm.barrier() '''broadcast the new surface''' ### broadcast function for the surface - f1 = comm.bcast(f1, root=root_proc) + f1 = comm.bcast(f1, root=0) + + + + ### update the z coord of the surface array + self.nd_coords[:,1] = f1(self.nd_coords[:,0]) comm.barrier() - ''' replaces the new diffused surface data, only changes z as x values don't change ''' - ### update the surface on individual nodes - self.surface_data_local[:,1] = f1(self.surface_data_local[:,0]) - ### update the global surface tracers + + ### has to be done on all procs due to an internal comm barrier in deform swarm (?) with self.Model.surface_tracers.deform_swarm(): self.Model.surface_tracers.data[:,1] = f1(self.Model.surface_tracers.data[:,0]) + comm.barrier() + + if self.Model.surface_tracers.data.size != 0: + ### update the surface only on procs that have the tracers + self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,1] + + comm.barrier() + + ### update the time of the sediment and air material as sed & erosion occurs + if self.timeField: + ### Set newly deposited sediment time to 0 (to record deposition time) + self.Model.timeField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = 0. + ### reset air material time back to the model time + self.Model.timeField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.Model.timeField.data.max() + '''Erode surface/deposit sed based on the surface''' ### update the material on each node according to the spline function for the surface self.Model.materialField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.airIndex self.Model.materialField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = self.sedimentIndex - comm.barrier() return @@ -761,8 +818,6 @@ def __init__(self, airIndex, sedimentIndex, sedimentationRate, erosionRate, surf Distance to update surface from right boundary, default is 0 km which results in a free slip boundary - - ***All units are converted under the hood*** *** @@ -782,196 +837,723 @@ def __init__(self, airIndex, sedimentIndex, sedimentationRate, erosionRate, surf self.airIndex = airIndex self.sedimentIndex = sedimentIndex self.timeField = timeField - self.dx = None - self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer).magnitude - self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer).magnitude - self.surfaceElevation = surfaceElevation.to(u.kilometer).magnitude - self.sedimentationRate = abs(sedimentationRate.to(u.kilometer / u.year).magnitude) - self.erosionRate = -1. * abs(erosionRate.to(u.kilometer / u.year).magnitude) + + self.ve = sedimentationRate.to(u.kilometer / u.year) + self.vs = erosionRate.to(u.kilometer / u.year) + + ### a conversion, will throw an error if units are neglected + self.surfaceArray = surfaceArray + self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer) + self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer) + + + self.surfaceElevation = surfaceElevation.to(u.kilometer) self.Model = Model - self.surfaceArray = surfaceArray - self.surface_data_local = None - self.original_surface = None + self.originalZ = None + self.min_dist = None + self.nd_coords = None + + self.tkey = self.__class__.__name__+"_surface" + def _init_model(self): - self.Model.add_passive_tracers(name="surface", vertices=self.surfaceArray, advect=False) + ''' creates a PT output ''' + ### automatically non-dimensionalises the imput coords if they have a dim + ## TODO: Fix naming for internal passive tracer swarm + self.Model.add_passive_tracers(name=self.tkey, vertices=nd(self.surfaceArray), advect=False) - self.dx = dimensionalise((self.surfaceArray[:,0][1] - self.surfaceArray[:,0][0]), u.kilometer).magnitude + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + st.allow_parallel_nn = True - '''set up custom tracers for surface''' - x_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].min() - x_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,0].max() + self.nd_coords = nd(self.surfaceArray) - ''' create surface tracers to advect on each node''' - self.surface_data_local = np.zeros_like(self.surfaceArray[(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)]) + ### get distance between 1st and 2nd x coord and y coords to determine min distance between grid points + x = np.sort(np.unique(self.nd_coords[:,0])) - self.surface_data_local[:,0] = self.surfaceArray[:,0][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] - self.surface_data_local[:,1] = self.surfaceArray[:,1][(self.surfaceArray[:,0] >= x_min_local) & (self.surfaceArray[:,0] <= x_max_local)] + self.min_dist = np.diff(x).min() - # # Spline original surface for no slip condition near boundary - self.original_surface = interp1d(self.surfaceArray[:,0], self.surfaceArray[:,1], kind='cubic', fill_value='extrapolate') + ### create copy of original surface + self.originalZ = self.nd_coords[:,1] + comm.barrier() - def solve(self, dt): - root_proc = 0 - z_max_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].max() - z_min_local = self.Model.mesh.data[:self.Model.mesh.nodesLocal,1].min() + ### add fields to track - ### collect surface data on each node - if z_max_local >= self.surface_data_local[:,1].min(): - ### gets the x and z data of the surface tracers - x_data = np.ascontiguousarray(self.surface_data_local[:,0].copy()) - z_data = np.ascontiguousarray(self.surface_data_local[:,1].copy()) + ### track velocity field on tracers +# st.add_tracked_field(self.Model.velocityField, +# name="surface_vel", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) +# +# st.add_tracked_field(st.particleCoordinates, +# name="coords", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) - ### Get the velocity of the surface tracers - tracer_velocity = self.Model.velocityField.evaluate(self.surface_data_local) - vx = np.ascontiguousarray(tracer_velocity[:,0]) - vz = np.ascontiguousarray(tracer_velocity[:,1]) + comm.barrier() + + def solve(self, dt): + + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + if st.data.shape[0] > 0: + ### evaluate on all nodes and get the tracer velocity on root proc + tracer_velocity_local = self.Model.velocityField.evaluate(st.data) + x_local = nd(self.Model.x.evaluate(st.data)) + y_local = nd(self.Model.y.evaluate(st.data)) + + x = np.ascontiguousarray(x_local) + y = np.ascontiguousarray(y_local) + vx = np.ascontiguousarray(tracer_velocity_local[:,0]) + vy = np.ascontiguousarray(tracer_velocity_local[:,1]) else: - ### creates dummy data on nodes without the surface - x_data = np.array([None], dtype='float64') - z_data = np.array([None], dtype='float64') + x = np.array([None], dtype='float64') + y = np.array([None], dtype='float64') vx = np.array([None], dtype='float64') - vz = np.array([None], dtype='float64') + vy = np.array([None], dtype='float64') + comm.barrier() ### Collect local array sizes using the high-level mpi4py gather - sendcounts = np.array(comm.gather(len(x_data), root=root_proc)) + sendcounts = np.array(comm.gather(len(x), root=0)) comm.barrier() - if rank == root_proc: + if rank == 0: ### creates dummy data on all nodes to store the surface # surface_data = np.zeros((npoints,2)) - x_surface_data = np.zeros((sum(sendcounts)), dtype='float64') - z_surface_data = np.zeros((sum(sendcounts)), dtype='float64') + x_data = np.zeros((sum(sendcounts)), dtype='float64') + y_data = np.zeros((sum(sendcounts)), dtype='float64') vx_data = np.zeros((sum(sendcounts)), dtype='float64') - vz_data = np.zeros((sum(sendcounts)), dtype='float64') - surface_data = np.zeros((sum(sendcounts), 4), dtype='float64') + vy_data = np.zeros((sum(sendcounts)), dtype='float64') else: - x_surface_data = None - z_surface_data = None + x_data = None + y_data = None vx_data = None - vz_data = None - surface_data = None + vy_data = None ### store the surface spline on each node f1 = None - comm.barrier() + comm.Gatherv(sendbuf=x, recvbuf=(x_data, sendcounts), root=0) - ## gather x values, can't do them together - comm.Gatherv(sendbuf=x_data, recvbuf=(x_surface_data, sendcounts), root=root_proc) - ## gather z values - comm.Gatherv(sendbuf=z_data, recvbuf=(z_surface_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=y, recvbuf=(y_data, sendcounts), root=0) ### gather velocity values - comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=0) - comm.Gatherv(sendbuf=vz, recvbuf=(vz_data, sendcounts), root=root_proc) + comm.Gatherv(sendbuf=vy, recvbuf=(vy_data, sendcounts), root=0) + if rank == 0: - if rank == root_proc: - ### Put back into combined array - surface_data[:,0] = x_surface_data - surface_data[:,1] = z_surface_data - surface_data[:,2] = vx_data - surface_data[:,3] = vz_data + nd_ve = -1. * abs( nd(self.ve) ) ### erode down(negative) + nd_vs = 1. * abs( nd(self.vs) ) ### sed up (positive) + surface_data = np.zeros((len(x_data), 4), dtype='float64') + surface_data[:,0] = x_data + surface_data[:,1] = y_data + surface_data[:,2] = vx_data + surface_data[:,3] = vy_data - ### remove dummy data surface_data = surface_data[~np.isnan(surface_data[:,0])] + surface_data = surface_data[np.argsort(surface_data[:,0])] + + # # Advect top surface + x_new = (surface_data[:,0] + (surface_data[:,2]*dt)) + y_new = (surface_data[:,1] + (surface_data[:,3]*dt)) - ### sort by x values - surface_data = surface_data[np.argsort(surface_data[:, 0])] + ## Spline top surface + f = interp1d(x_new, y_new, kind='cubic', fill_value='extrapolate') + + ''' interpolate new surface back onto original grid ''' + z_nd = f(self.nd_coords[:,0]) + ### Ve and Vs for loop to preserve original values + Ve_loop = np.zeros_like(z_nd, dtype='float64') + Vs_loop = np.zeros_like(z_nd, dtype='float64') - # # Advect top surface - x2 = surface_data[:,0] + (surface_data[:,2] * dt) - z2 = surface_data[:,1] + (surface_data[:,3] * dt) + ### time to diffuse surface based on Model dt + total_time = dt + + '''Velocity surface process''' + + '''erosion dt for vel model''' + vel_for_surface = max(vx_data.max(), vy_data.max()) + Vel_for_surface = max(abs(nd_ve), abs(nd_ve), abs(vx_data.max()), abs(vy_data.max())) + + surface_dt_vel = (0.2 * (self.min_dist / Vel_for_surface) ) + + surf_time = min(surface_dt_vel, total_time) + + nts = math.ceil(total_time/surf_time) + + surf_dt = (total_time / nts) + + print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) + + + ### Velocity erosion/sedimentation rates for the surface + for i in range(nts): + ''' determine if particle is above or below the original surface elevation ''' + ''' erosion function ''' + Ve_loop[:] = nd(0. * u.kilometer/u.year) + Ve_loop[(z_nd > nd(self.surfaceElevation))] = nd_ve + + ''' sedimentation function ''' + Vs_loop[:] = nd(0. * u.kilometer/u.year) + Vs_loop[(z_nd <= nd(self.surfaceElevation))] = nd_vs + + + dzdt = Vs_loop + Ve_loop + + z_nd += (dzdt[:]*surf_dt) + + + ''' creates no movement condition near boundary ''' + ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + ''' This changes the height to the points original height ''' + resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) + + + z_nd[resetArea_x] = self.originalZ[resetArea_x] + + + ### creates function for the new surface that has eroded, to be broadcast back to nodes + f1 = interp1d(self.nd_coords[:,0], z_nd, fill_value='extrapolate', kind='cubic') + + comm.barrier() + + '''broadcast the new surface''' + ### broadcast function for the surface + f1 = comm.bcast(f1, root=0) + + + + ### update the z coord of the surface array + self.nd_coords[:,1] = f1(self.nd_coords[:,0]) + + comm.barrier() + + + ### has to be done on all procs due to an internal comm barrier in deform swarm (?) + with st.deform_swarm(): + st.data[:,1] = f1(st.data[:,0]) + + comm.barrier() + + ### update the time of the sediment and air material as sed & erosion occurs + if self.timeField: + ### Set newly deposited sediment time to 0 (to record deposition time) + self.Model.timeField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = 0. + ### reset air material time back to the model time + self.Model.timeField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.Model.timeField.data.max() + + '''Erode surface/deposit sed based on the surface''' + ### update the material on each node according to the spline function for the surface + self.Model.materialField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.airIndex + self.Model.materialField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = self.sedimentIndex + + + + return + + +class velocitySurface_3D(SurfaceProcesses): + """velocity surface erosion + """ + + def __init__(self, airIndex, sedimentIndex, + vs_condition, ve_condition, + surfaceArray, + method='nearest', + surfaceElevation=0.*u.kilometer, + updateSurfaceLB=0.*u.kilometer, updateSurfaceRB=0.*u.kilometer, + updateSurfaceTB=0.*u.kilometer, updateSurfaceBB=0.*u.kilometer, + Model=None, timeField=None): + """ + Parameters + ---------- + + airIndex : + air index + sedimentIndex : + sediment Index + + ve_condition : + Condition that contains the erosion rate based on the x and y coord + vs_condition : + Condition that contains the sedimentation rate based on the x and y coord + + surfaceArray : + coords at which the surface will be construced (x, y, z) + surfaceElevation : + boundary between air and crust/material, unit length of y coord to be given + + method : + Interpolation method for scipy griddata function to use for the surface, default is 'nearest', which is quicker at high res + + updateSurfaceLB : + Distance to update surface from left boundary, default is 0 for free slip, unit length required + updateSurfaceRB : + Distance to update surface from right boundary, default is 0 for free slip, unit length required + + updateSurfaceTB : + Distance to update surface from top boundary, default is 0 for free slip, unit length required + updateSurfaceBB : + Distance to update surface from bottom boundary, default is 0 for free slip, unit length required + + + ***All units are converted under the hood*** + + *** + usage: + x = np.linspace(Model.minCoord[0], Model.maxCoord[0], 100) + y = np.linspace(Model.minCoord[1], Model.maxCoord[1], 100) + + xi, yi = np.meshgrid(x, y) + + coords = np.zeros(shape=(xi.flatten().shape[0], 3)) + coords[:,0] = xi.flatten() + coords[:,1] = yi.flatten() + coords[:,2] = np.zeros_like(coords[:,0]) ### or any array with same shape as x and y coords with the initial height - # # Spline top surface - f = interp1d(x2, z2, kind='cubic', fill_value='extrapolate') + coords = coords * u.kilometer - ### update surface tracer position - # surface_data[:,0] = (surface_data[:,0]) - surface_data[:,1] = f(surface_data[:,0]) + ve_conditions = fn.branching.conditional([((Model.x >= 0.5) & (Model.y >=0.5), GEO.nd(1 * u.millimeter/u.year)), + (True, GEO.nd(0.5 * u.millimeter/u.year))]) + vs_conditions = fn.branching.conditional([(True, GEO.nd(0.05 * u.millimeter/u.year))]) - ### gets the x and y coordinates from the tracers - x = dimensionalise(surface_data[:,0], u.kilometer).magnitude - z = dimensionalise(surface_data[:,1], u.kilometer).magnitude + Model.surfaceProcesses = velocitySurface3D(airIndex=air.index, + sedimentIndex=Sediment.index, + surfaceArray = coords, ### grid with surface points (x, y, z) + vs_condition = vs_conditions, ### sedimentation rate at each grid point + ve_condition = ve_conditions, ### erosion rate at each grid point + surfaceElevation=air.bottom) + *** + + """ + + self.airIndex = airIndex + self.sedimentIndex = sedimentIndex + self.timeField = timeField + + self.ve_condition = ve_condition + self.vs_condition = vs_condition + + self.method = method + + ### a conversion, will throw an error if units are neglected + self.surfaceArray = surfaceArray.to(u.kilometer) + self.updateSurfaceLB = updateSurfaceLB.to(u.kilometer) + self.updateSurfaceRB = updateSurfaceRB.to(u.kilometer) + + self.updateSurfaceTB = updateSurfaceTB.to(u.kilometer) + self.updateSurfaceBB = updateSurfaceBB.to(u.kilometer) + + self.surfaceElevation = surfaceElevation.to(u.kilometer) + self.Model = Model + + self.originalZ = None + self.z_surf = None + self.min_dist = None + self.nd_coords = None + + # we save the key of the passive tracer swarm, rather than the instance, because + # upon restarts the instance can be replaced + self.tkey = self.__class__.__name__+"_surface" + + + def _init_model(self): + ### automatically non-dimensionalises the imput coords if they have a dim + self.Model.add_passive_tracers(name=self.tkey, vertices=self.surfaceArray, advect=False) + + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + st.allow_parallel_nn = True + + self.nd_coords = nd(self.surfaceArray) + + ### get distance between 1st and 2nd x coord and y coords to determine min distance between grid points + x = np.sort(np.unique(self.nd_coords[:,0])) + y = np.sort(np.unique(self.nd_coords[:,1])) + dx = np.diff(x).min() + dy = np.diff(y).min() + + self.min_dist = min(dx, dy) + + ### create copy of original surface + self.originalZ = self.nd_coords[:,2] + + comm.barrier() + + + + + ### add fields to track + + ### track velocity field on tracers +# self.Model.surface_tracers.add_tracked_field(self.Model.velocityField, +# name="surface_vel", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) +# +# self.Model.surface_tracers.add_tracked_field(self.Model.surface_tracers.particleCoordinates, +# name="coords", +# units=u.centimeter/u.year, +# dataType="float", count=self.Model.mesh.dim) +# + + # def solve(self, dt): + + # ### evaluate on all nodes and get the tracer velocity on root proc + # tracer_velocity = self.Model.velocityField.evaluate_global(self.nd_coords) + + # ### utilises the evaluate_global to get values that are across multiple CPUs on root CPU + # ve = (self.ve_condition.evaluate_global(self.nd_coords)) + # vs = (self.vs_condition.evaluate_global(self.nd_coords)) + + + # comm.barrier() + + + # if rank == 0: + + # ve = -1. * abs(ve) ### erode down(negative) + # vs = 1. * abs(vs) ### sed up (positive) + + # # # Advect top surface + # x_new = (self.nd_coords[:,0] + (tracer_velocity[:,0]*dt)) + # y_new = (self.nd_coords[:,1] + (tracer_velocity[:,1]*dt)) + # z_new = (self.nd_coords[:,2] + (tracer_velocity[:,2]*dt)) + + + # ''' interpolate new surface back onto original grid ''' + # #### griddata seems to be okay, rbf was causing issues with memory usage in parallel + # z_nd = griddata((x_new, y_new), z_new, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() + + + # ### Ve and Vs for loop to preserve original values + # Ve_loop = np.zeros_like(z_nd, dtype='float64') + # Vs_loop = np.zeros_like(z_nd, dtype='float64') + + + # ### time to diffuse surface based on Model dt + # total_time = dt + + # '''Velocity surface process''' + + # '''erosion dt for vel model''' + # Vel_for_surface = max(abs(vs).max(), abs(ve).max(), abs(tracer_velocity).max()) + + # surface_dt_vel = (0.2 * (self.min_dist / Vel_for_surface) ) + + # surf_time = min(surface_dt_vel, total_time) + + # nts = math.ceil(total_time/surf_time) + + # surf_dt = (total_time / nts) + + # print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) + + + # ### Velocity erosion/sedimentation rates for the surface + # for i in range(nts): + # ''' determine if particle is above or below the original surface elevation ''' + # ''' erosion function ''' + # Ve_loop[:] = nd(0. * u.kilometer/u.year) + # Ve_loop[(z_nd > nd(self.surfaceElevation))] = ve[:,0][(z_nd > nd(self.surfaceElevation))] + + # ''' sedimentation function ''' + # Vs_loop[:] = nd(0. * u.kilometer/u.year) + # Vs_loop[(z_nd <= nd(self.surfaceElevation))] = vs[:,0][(z_nd <= nd(self.surfaceElevation))] + + + # dzdt = Vs_loop + Ve_loop + + # z_nd += (dzdt[:]*surf_dt) + + + # ''' creates no movement condition near boundary ''' + # ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + # ''' This changes the height to the points original height ''' + # resetArea_x = (self.nd_coords[:,0] < nd(self.updateSurfaceLB)) | (self.nd_coords[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) + + # resetArea_y = (self.nd_coords[:,1] < nd(self.updateSurfaceBB)) | (self.nd_coords[:,1] > (nd(self.Model.maxCoord[1]) - (nd(self.updateSurfaceTB)))) + + + # z_nd[resetArea_x | resetArea_y] = self.originalZ[resetArea_x | resetArea_y] + + + # self.z_new = z_nd + + + # comm.barrier() + + # '''broadcast the new surface''' + # ### broadcast function for the surface + # self.z_new = comm.bcast(self.z_new, root=0) + + + # comm.barrier() + + # ### update the z coord of the surface array + # self.nd_coords[:,2] = self.z_new + + # comm.barrier() + + + # ### has to be done on all procs due to an internal comm barrier in deform swarm (?) + # with self.Model.surface_tracers.deform_swarm(): + # self.Model.surface_tracers.data[:,2] = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.Model.surface_tracers.data[:,0], self.Model.surface_tracers.data[:,1]), method=self.method).ravel() + + # comm.barrier() + + # if self.Model.surface_tracers.data.size != 0: + # ### update the surface only on procs that have the tracers + # self.Model.surface_tracers.z_coord.data[:,0] = self.Model.surface_tracers.data[:,2] + + # comm.barrier() + + + # ### cacluate surface for swarm particles + # z_new_surface = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_new, (self.Model.swarm.data[:,0], self.Model.swarm.data[:,1]), method=self.method).ravel() + + # comm.barrier() + + # ### update the time of the sediment and air material as sed & erosion occurs + # if self.timeField: + # ### Set newly deposited sediment time to 0 (to record deposition time) + # self.Model.timeField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = 0. + # ### reset air material time back to the model time + # self.Model.timeField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.Model.timeField.data.max() + + # '''Erode surface/deposit sed based on the surface''' + # ### update the material on each node according to the spline function for the surface + # self.Model.materialField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.airIndex + # self.Model.materialField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = self.sedimentIndex + + # comm.barrier() + + + + # return + + def solve(self, dt): + st = self.Model.passive_tracers[self.tkey] + assert( st != None, f"Error getting passive tracer {self.tkey}") + if st.data.shape[0] > 0: + x = np.ascontiguousarray(st.data[:,0]) + y = np.ascontiguousarray(st.data[:,1]) + z = np.ascontiguousarray(st.data[:,2]) + + ### evaluate to get the tracer velocity + tracer_velocity = self.Model.velocityField.evaluate(st.data) + vx = np.ascontiguousarray(tracer_velocity[:,0]) + vy = np.ascontiguousarray(tracer_velocity[:,1]) + vz = np.ascontiguousarray(tracer_velocity[:,2]) + + ### evaluate to get the ve and vs values + ve = np.ascontiguousarray(self.ve_condition.evaluate(st.data)) + vs = np.ascontiguousarray(self.vs_condition.evaluate(st.data)) + else: + x = np.array([None], dtype='float64') + y = np.array([None], dtype='float64') + z = np.array([None], dtype='float64') + tracer_velocity = np.array([None], dtype='float64') + vx = np.array([None], dtype='float64') + vy = np.array([None], dtype='float64') + vz = np.array([None], dtype='float64') + ve = np.array([None], dtype='float64') + vs = np.array([None], dtype='float64') + + + + comm.barrier() + + sendcounts = np.array(comm.gather(len(x), root=0)) + + comm.barrier() + + if rank == 0: + ### creates dummy data on all nodes to store the surface + # surface_data = np.zeros((npoints,2)) + x_data = np.zeros((sum(sendcounts)), dtype='float64') + y_data = np.zeros((sum(sendcounts)), dtype='float64') + z_data = np.zeros((sum(sendcounts)), dtype='float64') + + vx_data = np.zeros((sum(sendcounts)), dtype='float64') + vy_data = np.zeros((sum(sendcounts)), dtype='float64') + vz_data = np.zeros((sum(sendcounts)), dtype='float64') + + ve_data = np.zeros((sum(sendcounts)), dtype='float64') + vs_data = np.zeros((sum(sendcounts)), dtype='float64') + + else: + x_data = None + y_data = None + z_data = None + vx_data = None + vy_data = None + vz_data = None + ve_data = None + vs_data = None + + comm.Gatherv(sendbuf=x, recvbuf=(x_data, sendcounts), root=0) + comm.Gatherv(sendbuf=y, recvbuf=(y_data, sendcounts), root=0) + comm.Gatherv(sendbuf=z, recvbuf=(z_data, sendcounts), root=0) + + ### gather velocity values + comm.Gatherv(sendbuf=vx, recvbuf=(vx_data, sendcounts), root=0) + comm.Gatherv(sendbuf=vy, recvbuf=(vy_data, sendcounts), root=0) + comm.Gatherv(sendbuf=vz, recvbuf=(vz_data, sendcounts), root=0) + + ### Gather SP values + comm.Gatherv(sendbuf=ve, recvbuf=(ve_data, sendcounts), root=0) + comm.Gatherv(sendbuf=vs, recvbuf=(vs_data, sendcounts), root=0) + + + + if rank == 0: + + surface_data = np.zeros((len(x_data), 8), dtype='float64') + surface_data[:,0] = x_data + surface_data[:,1] = y_data + surface_data[:,2] = z_data + + surface_data[:,3] = vx_data + surface_data[:,4] = vy_data + surface_data[:,5] = vz_data + + surface_data[:,6] = ve_data + surface_data[:,7] = vs_data + + surface_data = surface_data[~np.isnan(surface_data[:,0])] + # surface_data = surface_data[np.argsort(surface_data[:,0])] + + ve = -1. * abs(surface_data[:,6]) ### erode down(negative) + vs = 1. * abs(surface_data[:,7]) ### sed up (positive) + + # # Advect top surface + x_new = (surface_data[:,0] + (surface_data[:,3]*dt)) + y_new = (surface_data[:,1] + (surface_data[:,4]*dt)) + z_new = (surface_data[:,2] + (surface_data[:,5]*dt)) + + + ''' interpolate new surface back onto original grid ''' + #### griddata seems to be okay, rbf was causing issues with memory usage in parallel + # z_nd = griddata((x_new, y_new), z_new, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() + z_nd = griddata((x_new, y_new), z_new, (surface_data[:,0], surface_data[:,1]), method=self.method).ravel() + + + ### Ve and Vs for loop to preserve original values + Ve_loop = np.zeros_like(z_nd, dtype='float64') + Vs_loop = np.zeros_like(z_nd, dtype='float64') ### time to diffuse surface based on Model dt - total_time = (dimensionalise(dt, u.year)).magnitude + total_time = dt '''Velocity surface process''' '''erosion dt for vel model''' + Vel_for_surface = surface_data[:,3:].max() - Vel_for_surface = max(abs(self.erosionRate * u.kilometer / u.year),abs(self.sedimentationRate*u.kilometer / u.year), abs(dimensionalise(self.Model.velocityField.data.max(), u.kilometer/u.year))) - + surface_dt_vel = (0.2 * (self.min_dist / Vel_for_surface) ) - surface_dt_vel = (0.2 * (self.dx / Vel_for_surface.magnitude)) + surf_time = min(surface_dt_vel, total_time) - surface_time = min(surface_dt_vel, total_time) + nts = math.ceil(total_time/surf_time) + + surf_dt = (total_time / nts) - nts = math.ceil(total_time/surface_time) - surface_dt = total_time / nts - - print('SP total time:', round(total_time,2), 'years, timestep:', round(surface_dt,2), 'years, No. of its:', nts, flush=True) + print('SP total time:', dimensionalise(total_time, u.year), 'timestep:', dimensionalise(surf_dt, u.year), 'No. of its:', nts, flush=True) ### Velocity erosion/sedimentation rates for the surface for i in range(nts): - Ve_loop = np.where(z <= 0., 0., self.erosionRate) - Vs_loop = np.where(z >= 0., 0., self.sedimentationRate) + ''' determine if particle is above or below the original surface elevation ''' + ''' erosion function ''' + Ve_loop[:] = nd(0. * u.kilometer/u.year) + Ve_loop[(z_nd > nd(self.surfaceElevation))] = ve[(z_nd > nd(self.surfaceElevation))] + + ''' sedimentation function ''' + Vs_loop[:] = nd(0. * u.kilometer/u.year) + Vs_loop[(z_nd <= nd(self.surfaceElevation))] = vs[(z_nd <= nd(self.surfaceElevation))] + dzdt = Vs_loop + Ve_loop - z[:] += dzdt*surface_dt + z_nd += (dzdt[:]*surf_dt) - x_nd = nd(x*u.kilometer) + ''' creates no movement condition near boundary ''' + ''' important when imposing a velocity as particles are easily deformed near the imposed condition''' + ''' This changes the height to the points original height ''' + resetArea_x = (surface_data[:,0] < nd(self.updateSurfaceLB)) | (surface_data[:,0] > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB)))) + + resetArea_y = (surface_data[:,1] < nd(self.updateSurfaceBB)) | (surface_data[:,1] > (nd(self.Model.maxCoord[1]) - (nd(self.updateSurfaceTB)))) - z_nd = nd(z*u.kilometer) - ''' updates material near to boundary back to original coordinates ''' - z_original_surface = self.original_surface(x_nd) - z_nd[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] = z_original_surface[(x_nd < nd(self.updateSurfaceLB * u.kilometer)) | (x_nd > (nd(self.Model.maxCoord[0]) - (nd(self.updateSurfaceRB * u.kilometer))))] + z_nd[resetArea_x | resetArea_y] = self.originalZ[resetArea_x | resetArea_y] + - ### creates function for the new surface that has eroded, to be broadcast back to nodes - f1 = interp1d(x_nd, z_nd, fill_value='extrapolate', kind='cubic') + self.z_surf = griddata((surface_data[:,0], surface_data[:,1]), z_nd, (self.nd_coords[:,0], self.nd_coords[:,1]), method=self.method).ravel() comm.barrier() '''broadcast the new surface''' ### broadcast function for the surface - f1 = comm.bcast(f1, root=root_proc) + self.z_surf = comm.bcast(self.z_surf, root=0) + comm.barrier() - ''' replaces the new diffused surface data, only changes z as x values don't change ''' - ### update the surface on individual nodes - self.surface_data_local[:,1] = f1(self.surface_data_local[:,0]) - ### update the global surface tracers - with self.Model.surface_tracers.deform_swarm(): - self.Model.surface_tracers.data[:,1] = f1(self.Model.surface_tracers.data[:,0]) + ### update the z coord of the surface array + self.nd_coords[:,2] = self.z_surf + + comm.barrier() + + + ### has to be done on all procs due to an internal comm barrier in deform swarm (?) + with st.deform_swarm(): + st.data[:,2] = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_surf, (st.data[:,0], st.data[:,1]), method=self.method).ravel() + + comm.barrier() + + if st.data.size != 0: + ### update the surface only on procs that have the tracers + st.z_coord.data[:,0] = st.data[:,2] + + comm.barrier() + + + ### cacluate surface for swarm particles + z_new_surface = griddata((self.nd_coords[:,0], self.nd_coords[:,1]), self.z_surf, (self.Model.swarm.data[:,0], self.Model.swarm.data[:,1]), method=self.method).ravel() + + comm.barrier() + + ### update the time of the sediment and air material as sed & erosion occurs + if self.timeField: + ### Set newly deposited sediment time to 0 (to record deposition time) + self.Model.timeField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = 0. + ### reset air material time back to the model time + self.Model.timeField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.Model.timeField.data.max() '''Erode surface/deposit sed based on the surface''' ### update the material on each node according to the spline function for the surface - self.Model.materialField.data[(self.Model.swarm.data[:,1] > f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] != self.airIndex)] = self.airIndex - self.Model.materialField.data[(self.Model.swarm.data[:,1] < f1(self.Model.swarm.data[:,0])) & (self.Model.materialField.data[:,0] == self.airIndex)] = self.sedimentIndex - + self.Model.materialField.data[(self.Model.swarm.data[:,2] >= z_new_surface) & (self.Model.materialField.data[:,0] != self.airIndex) ] = self.airIndex + self.Model.materialField.data[(self.Model.swarm.data[:,2] < z_new_surface) & (self.Model.materialField.data[:,0] == self.airIndex) ] = self.sedimentIndex comm.barrier() return + diff --git a/underworld/_version.py b/underworld/_version.py index 6adcc1a34..41cf90435 100644 --- a/underworld/_version.py +++ b/underworld/_version.py @@ -1 +1 @@ -__version__ = "2.14.0b" +__version__ = "2.15.0b" diff --git a/underworld/container/_indexset.py b/underworld/container/_indexset.py index 782e9c9dc..0cdcfccb9 100755 --- a/underworld/container/_indexset.py +++ b/underworld/container/_indexset.py @@ -198,7 +198,7 @@ def _addremove(self, indices, isadding): except: raise RuntimeError("An unknown error occurred relating to the object passed in.") - self._AddOrRemoveWithNumpyArray( np.fromiter(indices, np.int), isadding ) + self._AddOrRemoveWithNumpyArray( np.fromiter(indices, int), isadding ) def AND(self, indices): """ diff --git a/underworld/swarm/_swarm.py b/underworld/swarm/_swarm.py index 9fb434e9c..736b95295 100644 --- a/underworld/swarm/_swarm.py +++ b/underworld/swarm/_swarm.py @@ -1,11 +1,11 @@ -##~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~## +# ~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~d~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~## ## ## ## This file forms part of the Underworld geophysics modelling application. ## ## ## ## For full license and copyright information, please refer to the LICENSE.md file ## ## located at the project root, or contact the authors. ## ## ## -##~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~## +# ~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~## import underworld._stgermain as _stgermain import underworld.libUnderworld.libUnderworldPy.Function as _cfn import numpy as np @@ -15,10 +15,9 @@ import underworld.libUnderworld as libUnderworld import underworld as uw from mpi4py import MPI -import h5py import contextlib from underworld.scaling import non_dimensionalise -from underworld.scaling import units as u +from underworld.scaling import units as u from pint.errors import UndefinedUnitError @@ -42,7 +41,7 @@ class Swarm(_swarmabstract.SwarmAbstract, function.FunctionInput, _stgermain.Sav Example ------- Create a swarm with some variables: - + >>> # First we need a mesh: >>> mesh = uw.mesh.FeMesh_Cartesian( elementType='Q1/dQ0', elementRes=(16,16), minCoord=(0.,0.), maxCoord=(1.,1.) ) >>> # Create empty swarm: @@ -96,12 +95,13 @@ class Swarm(_swarmabstract.SwarmAbstract, function.FunctionInput, _stgermain.Sav """ - _objectsDict = { "_swarm": "GeneralSwarm", - "_cellLayout" : "ElementCellLayout", - "_pMovementHandler" : "ParticleMovementHandler", - "_escapedRoutine" : "EscapedRoutine", - "_particleShadowSync" : "ParticleShadowSync" - } + _objectsDict = { + "_swarm": "GeneralSwarm", + "_cellLayout": "ElementCellLayout", + "_pMovementHandler": "ParticleMovementHandler", + "_escapedRoutine": "EscapedRoutine", + "_particleShadowSync": "ParticleShadowSync" + } def __init__(self, mesh, particleEscape=False, **kwargs): @@ -111,6 +111,7 @@ def __init__(self, mesh, particleEscape=False, **kwargs): # any particles that are found wanting are culled accordingly. import weakref wkref = weakref.ref(self) # use weakref to avoid circular dependency here + def _update_owners(): selfguy = wkref() if selfguy: # if for some reason the swarm is gone, this will be none, in which case we're done here. @@ -119,19 +120,18 @@ def _update_owners(): # init this to -1 to signify no mapping has occurred self._checkpointMapsToState = -1 - + # build parent - super(Swarm,self).__init__(mesh, **kwargs) + super(Swarm, self).__init__(mesh, **kwargs) def _setup(self): if self._cself.particleCoordVariable: self._particleCoordinates = svar.SwarmVariable(self, "double", self.mesh.dim, _cself=self._cself.particleCoordVariable, writeable=False) - - def _add_to_stg_dict(self,componentDictionary): + def _add_to_stg_dict(self, componentDictionary): # call parents method - super(Swarm,self)._add_to_stg_dict(componentDictionary) + super(Swarm, self)._add_to_stg_dict(componentDictionary) componentDictionary[ self._swarm.name ][ "dim"] = self._mesh.dim componentDictionary[ self._swarm.name ][ "CellLayout"] = self._cellLayout.name diff --git a/underworld/swarm/_swarmvariable.py b/underworld/swarm/_swarmvariable.py index 1cc369863..24282c611 100644 --- a/underworld/swarm/_swarmvariable.py +++ b/underworld/swarm/_swarmvariable.py @@ -8,11 +8,9 @@ ##~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~#~## import underworld as uw import underworld._stgermain as _stgermain -import underworld.mesh as mesh import numpy as np import underworld.libUnderworld as libUnderworld from . import _swarmabstract as sab -from . import _swarm import underworld.function as function import underworld.libUnderworld.libUnderworldPy.Function as _cfn from mpi4py import MPI @@ -24,6 +22,7 @@ from underworld.scaling import dimensionalise, pint_degc_labels from pint.errors import UndefinedUnitError + class SwarmVariable(_stgermain.StgClass, function.Function): """ The SwarmVariable class allows users to add data to swarm particles. The data @@ -341,7 +340,7 @@ def load( self, filename, collective=False ): try: iunits = u.Quantity(h5f.attrs['units']) - except (UndefinedUnitError) as e: + except (UndefinedUnitError, RuntimeError, KeyError): # if no units - don't scale amd finish return diff --git a/underworld/systems/_advectiondiffusion.py b/underworld/systems/_advectiondiffusion.py index e319530db..bfc460fe2 100644 --- a/underworld/systems/_advectiondiffusion.py +++ b/underworld/systems/_advectiondiffusion.py @@ -338,7 +338,7 @@ def _phiStar_stripy_old(self, dt): # layout = uw.swarm.layouts.PerCellGaussLayout(mswarm, gaussPointCount=5) # mswarm.populate_using_layout(layout) - local_nId = -1 * np.ones(mesh.nodesGlobal, dtype=np.int) + local_nId = -1 * np.ones(mesh.nodesGlobal, dtype=int) for i, gId in enumerate(mesh.data_nodegId): local_nId[gId] = i @@ -414,7 +414,7 @@ def _build_phiStar_swarm(self, ratio=0.9): mswarm_home_pts = mswarm.add_variable(dataType="double", count=mesh.dim) mswarm_phiStar = mswarm.add_variable(dataType="float", count=1) - local_nId = -1 * np.ones(mesh.nodesGlobal, dtype=np.int) + local_nId = -1 * np.ones(mesh.nodesGlobal, dtype=int) for i, gId in enumerate(mesh.data_nodegId): local_nId[gId] = i @@ -600,7 +600,7 @@ def _phiStar_rbf(self, dt, smooth=0.9): stencil_size = 7**mesh.dim # I think this can be eliminated at some stage ... - local_nId = -1 * np.ones(mesh.nodesGlobal, dtype=np.int) + local_nId = -1 * np.ones(mesh.nodesGlobal, dtype=int) for i, gId in enumerate(mesh.data_nodegId): local_nId[gId] = i diff --git a/underworld/utils/_io.py b/underworld/utils/_io.py index 8fb8ae56b..aabdd9fec 100644 --- a/underworld/utils/_io.py +++ b/underworld/utils/_io.py @@ -95,6 +95,11 @@ def __enter__(self): if uw.mpi.rank != 0: self.kwargs.update( {"mode": 'a'} ) + ## check if File exists if we aren't writing to disk + if self.kwargs["mode"] != 'w' and not os.path.exists(self.kwargs["name"]): + fname = self.kwargs["name"] + raise RuntimeError(f"Can't open file \' {fname} \' ") + self.h5f = h5py.File(*self.args, **self.kwargs) return self.h5f