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