diff --git a/jax_sph/defaults.py b/jax_sph/defaults.py index 98292b7..4e2223d 100644 --- a/jax_sph/defaults.py +++ b/jax_sph/defaults.py @@ -94,7 +94,14 @@ def set_defaults(cfg: DictConfig = OmegaConf.create({})) -> DictConfig: ### kernel cfg.kernel = OmegaConf.create({}) - # Kernel name. One of "QSK" (quintic spline kernel) or "WC2K" (Wendland C2 kernel) + # Kernel name, choose one of: + # "CSK" (cubic spline kernel) + # "QSK" (quintic spline kernel) + # "WC2K" (Wendland C2 kernel) + # "WC4K" (Wendland C4 kernel) + # "WC6K" (Wendland C4 kernel) + # "GK" (gaussian kernel) + # "SGK" (super gaussian kernel) cfg.kernel.name = "QSK" # previously: kernel # Smoothing length factor cfg.kernel.h_factor = 1.0 # new. Should default to 1.3 WC2K and 1.0 QSK diff --git a/jax_sph/kernel.py b/jax_sph/kernel.py index 580509d..40b4ca5 100644 --- a/jax_sph/kernel.py +++ b/jax_sph/kernel.py @@ -23,6 +23,31 @@ def grad_w(self, r): return grad(self.w)(r) +class CubicKernel(BaseKernel): + """The cubic kernel function of Monaghan.""" + + def __init__(self, h, dim=3): + self._one_over_h = 1.0 / h + + self._normalized_cutoff = 2.0 + self.cutoff = self._normalized_cutoff * h + if dim == 1: + self._sigma = 2.0 / 3.0 * self._one_over_h + elif dim == 2: + self._sigma = 10.0 / 7.0 / jnp.pi * self._one_over_h**2 + elif dim == 3: + self._sigma = 1.0 / jnp.pi * self._one_over_h**3 + + def w(self, r): + q = r * self._one_over_h + c1 = jnp.where(1 - q >= 0, 1, 0) + c2 = jnp.where(jnp.logical_and(2 - q < 1, 2 - q >= 0), 1, 0) + q1 = 1 - 1.5 * q**2 * (1 - q / 2) + q2 = 0.25 * (2 - q) ** 3 + + return self._sigma * (q1 * c1 + q2 * c2) + + class QuinticKernel(BaseKernel): """The quintic kernel function of Morris.""" @@ -76,3 +101,101 @@ def w(self, r): q2 = 2.0 * q + 1.0 return self._sigma * (q1**4 * q2) + + +class WendlandC4Kernel(BaseKernel): + """The 5th-order C4 kernel function of Wendland.""" + + def __init__(self, h, dim=3): + self._one_over_h = 1.0 / h + self.dim = dim + + self._normalized_cutoff = 2.0 + self.cutoff = self._normalized_cutoff * h + if dim == 1: + self._sigma = 3.0 / 4.0 * self._one_over_h + elif dim == 2: + self._sigma = 9.0 / 4.0 / jnp.pi * self._one_over_h**2 + elif dim == 3: + self._sigma = 495.0 / 256.0 / jnp.pi * self._one_over_h**3 + + def w(self, r): + if self.dim == 1: + q = r * self._one_over_h + q1 = jnp.maximum(0.0, 1.0 - 0.5 * q) + q2 = 2.0 * q**2 + 2.5 * q + 1.0 + + return self._sigma * (q1**5 * q2) + else: + q = r * self._one_over_h + q1 = jnp.maximum(0.0, 1.0 - 0.5 * q) + q2 = 35.0 / 12.0 * q**2 + 3 * q + 1.0 + + return self._sigma * (q1**6 * q2) + + +class WendlandC6Kernel(BaseKernel): + """The 5th-order C6 kernel function of Wendland.""" + + def __init__(self, h, dim=3): + self._one_over_h = 1.0 / h + self.dim = dim + + self._normalized_cutoff = 2.0 + self.cutoff = self._normalized_cutoff * h + if dim == 1: + self._sigma = 55.0 / 64.0 * self._one_over_h + elif dim == 2: + self._sigma = 78.0 / 28.0 / jnp.pi * self._one_over_h**2 + elif dim == 3: + self._sigma = 1365.0 / 512.0 / jnp.pi * self._one_over_h**3 + + def w(self, r): + if self.dim == 1: + q = r * self._one_over_h + q1 = jnp.maximum(0.0, 1.0 - 0.5 * q) + q2 = 21.0 / 8.0 * q**3 + 19.0 / 4.0 * q**2 + 3.5 * q + 1.0 + + return self._sigma * (q1**7 * q2) + else: + q = r * self._one_over_h + q1 = jnp.maximum(0.0, 1.0 - 0.5 * q) + q2 = 4.0 * q**3 + 6.25 * q**2 + 4 * q + 1.0 + + return self._sigma * (q1**8 * q2) + + +class GaussianKernel(BaseKernel): + """The gaussian kernel function of Monaghan.""" + + def __init__(self, h, dim=3): + self._one_over_h = 1.0 / h + + self._normalized_cutoff = 3.0 + self.cutoff = self._normalized_cutoff * h + self._sigma = 1.0 / jnp.pi ** (dim / 2) * self._one_over_h ** (dim) + + def w(self, r): + q = r * self._one_over_h + q1 = jnp.where(3 - q >= 0, 1, 0) + + return self._sigma * q1 * jnp.exp(-(q**2)) + + +class SuperGaussianKernel(BaseKernel): + # TODO: We want this? Intendent but negativ in some regions + """The supergaussian kernel function of Monaghan.""" + + def __init__(self, h, dim=3): + self._one_over_h = 1.0 / h + self.dim = dim + + self._normalized_cutoff = 3.0 + self.cutoff = self._normalized_cutoff * h + self._sigma = 1.0 / jnp.pi ** (dim / 2) * self._one_over_h ** (dim) + + def w(self, r): + q = r * self._one_over_h + q1 = jnp.where(3 - q >= 0, 1, 0) + + return self._sigma * q1 * jnp.exp(-(q**2)) * (self.dim / 2 + 1 - q**2) diff --git a/jax_sph/solver.py b/jax_sph/solver.py index 784084a..d5fb9d3 100644 --- a/jax_sph/solver.py +++ b/jax_sph/solver.py @@ -7,7 +7,15 @@ from jax_md import space from jax_sph.eos import RIEMANNEoS, TaitEoS -from jax_sph.kernel import QuinticKernel, WendlandC2Kernel +from jax_sph.kernel import ( + CubicKernel, + GaussianKernel, + QuinticKernel, + SuperGaussianKernel, + WendlandC2Kernel, + WendlandC4Kernel, + WendlandC6Kernel, +) from jax_sph.utils import Tag, wall_tags EPS = jnp.finfo(float).eps @@ -478,10 +486,21 @@ def __init__( self.is_heat_conduction = is_heat_conduction _beta_fn = limiter_fn_wrapper(eta_limiter, c_ref) - if kernel == "QSK": - self._kernel_fn = QuinticKernel(h=dx, dim=dim) - elif kernel == "WC2K": - self._kernel_fn = WendlandC2Kernel(h=1.3 * dx, dim=dim) + match kernel: + case "CSK": + self._kernel_fn = CubicKernel(h=dx, dim=dim) + case "QSK": + self._kernel_fn = QuinticKernel(h=dx, dim=dim) + case "WC2K": + self._kernel_fn = WendlandC2Kernel(h=1.3 * dx, dim=dim) + case "WC4K": + self._kernel_fn = WendlandC4Kernel(h=1.3 * dx, dim=dim) + case "WC6K": + self._kernel_fn = WendlandC6Kernel(h=1.3 * dx, dim=dim) + case "GK": + self._kernel_fn = GaussianKernel(h=dx, dim=dim) + case "SGK": + self._kernel_fn = SuperGaussianKernel(h=dx, dim=dim) self._gwbc_fn = gwbc_fn_wrapper(is_free_slip, is_heat_conduction, eos) self._free_weight, self._heat_bc = gwbc_fn_riemann_wrapper( diff --git a/notebooks/kernel_plots.ipynb b/notebooks/kernel_plots.ipynb new file mode 100644 index 0000000..d18992b --- /dev/null +++ b/notebooks/kernel_plots.ipynb @@ -0,0 +1,121 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from jax import vmap\n", + "from jax_sph.kernel import (\n", + " CubicKernel,\n", + " GaussianKernel,\n", + " QuinticKernel,\n", + " SuperGaussianKernel,\n", + " WendlandC2Kernel,\n", + " WendlandC4Kernel,\n", + " WendlandC6Kernel,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plots of kernels and their gradients evaluated in 1D\n", + "calculate the kernel values itself and the values of the gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "t = jnp.linspace(0,3, 100)\n", + "kernels = [\n", + " (CubicKernel, 1),\n", + " (QuinticKernel, 1),\n", + " (WendlandC2Kernel, 1.3),\n", + " (WendlandC4Kernel, 1.3),\n", + " (WendlandC6Kernel, 1.3),\n", + " (GaussianKernel, 1),\n", + " (SuperGaussianKernel, 1),\n", + " ]\n", + "w = []\n", + "w_grad = []\n", + "for k in kernels:\n", + " kernel_fn = k[0](k[1], 1)\n", + " w.append(vmap(kernel_fn.w)(t))\n", + " w_grad.append(vmap(kernel_fn.grad_w)(t))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "plot values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n", + "for i in range(jnp.shape(w)[0]):\n", + " axs[0].plot(t, w[i], label=str(kernels[i][0].__name__))\n", + " axs[1].plot(t, w_grad[i], label=str(kernels[i][0].__name__))\n", + "\n", + "axs[0].legend()\n", + "axs[1].legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/test_kernel.py b/tests/test_kernel.py index cbddfe4..f79ca29 100644 --- a/tests/test_kernel.py +++ b/tests/test_kernel.py @@ -4,11 +4,26 @@ import pytest from jax import vmap -from jax_sph.kernel import QuinticKernel, WendlandC2Kernel +from jax_sph.kernel import ( + CubicKernel, + GaussianKernel, + QuinticKernel, + WendlandC2Kernel, + WendlandC4Kernel, + WendlandC6Kernel, +) @pytest.mark.parametrize( - "Kernel, dx_factor", [(QuinticKernel, 1), (WendlandC2Kernel, 1.3)] + "Kernel, dx_factor", + [ + (CubicKernel, 1), + (QuinticKernel, 1), + (WendlandC2Kernel, 1.3), + (WendlandC4Kernel, 1.3), + (WendlandC6Kernel, 1.3), + (GaussianKernel, 1), + ], ) def test_kernel_1d(Kernel, dx_factor): """Test the interpolation kernels in 1 dimension.""" diff --git a/tests/test_pf2d.py b/tests/test_pf2d.py new file mode 100644 index 0000000..36f4105 --- /dev/null +++ b/tests/test_pf2d.py @@ -0,0 +1,115 @@ +"""Test a full run of the solver on the Poiseuille flow case from the validations.""" + +import os + +import jax.numpy as jnp +import numpy as np +import pytest +from jax import config +from omegaconf import OmegaConf + +from main import load_embedded_configs + + +def u_series_exp(y, t, n_max=10): + """Analytical solution to unsteady Poiseuille flow (low Re) + + Based on Series expansion as shown in: + "Modeling Low Reynolds Number Incompressible Flows Using SPH" + ba Morris et al. 1997 + """ + + eta = 100.0 # dynamic viscosity + rho = 1.0 # denstiy + nu = eta / rho # kinematic viscosity + u_max = 1.25 # max velocity in middle of channel + d = 1.0 # channel width + fx = -8 * nu * u_max / d**2 + offset = fx / (2 * nu) * y * (y - d) + + def term(n): + base = np.pi * (2 * n + 1) / d + prefactor = 4 * fx / (nu * base**3 * d) + sin_term = np.sin(base * y) + exp_term = np.exp(-(base**2) * nu * t) + return prefactor * sin_term * exp_term + + res = offset + for i in range(n_max): + res += term(i) + + return res + + +@pytest.fixture +def setup_simulation(): + y_axis = np.linspace(0, 1, 21) + t_dimless = [0.0005, 0.001, 0.005] + # get analytical solution + ref_solutions = [] + for t_val in t_dimless: + ref_solutions.append(u_series_exp(y_axis, t_val)) + return y_axis, t_dimless, ref_solutions + + +def run_simulation(tmp_path, tvf, solver): + """Emulate `main.py`.""" + data_path = tmp_path / f"pf_test_{tvf}" + + cli_args = OmegaConf.create( + { + "config": "cases/pf.yaml", + "case": {"dx": 0.0333333}, + "solver": {"name": solver, "tvf": tvf, "dt": 0.000002, "t_end": 0.005}, + "io": {"write_every": 250, "data_path": str(data_path)}, + } + ) + cfg = load_embedded_configs(cli_args) + + # Specify cuda device. These setting must be done before importing jax-md. + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 from TensorFlow + os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.gpu) + os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = str(cfg.xla_mem_fraction) + + if cfg.dtype == "float64": + config.update("jax_enable_x64", True) + + from jax_sph.simulate import simulate + + simulate(cfg) + + return data_path + + +def get_solution(data_path, t_dimless, y_axis): + from jax_sph.utils import sph_interpolator + + dir = os.listdir(data_path)[0] + cfg = OmegaConf.load(data_path / dir / "config.yaml") + step_max = np.array(np.rint(cfg.solver.t_end / cfg.solver.dt), dtype=int) + digits = len(str(step_max)) + + y_axis += 3 * cfg.case.dx + rs = 0.2 * jnp.ones([y_axis.shape[0], 2]) + rs = rs.at[:, 1].set(y_axis) + solutions = [] + for i in range(len(t_dimless)): + file_name = ( + "traj_" + str(int(t_dimless[i] / cfg.solver.dt)).zfill(digits) + ".h5" + ) + src_path = data_path / dir / file_name + interp_vel_fn = sph_interpolator(cfg, src_path) + solutions.append(interp_vel_fn(src_path, rs, prop="u", dim_ind=0)) + return solutions + + +@pytest.mark.parametrize("tvf, solver", [(0.0, "SPH"), (1.0, "SPH")]) # (0.0, "RIE") +def test_pf2d(tvf, solver, tmp_path, setup_simulation): + """Test whether the poiseuille flow simulation matches the analytical solution""" + y_axis, t_dimless, ref_solutions = setup_simulation + data_path = run_simulation(tmp_path, tvf, solver) + # print(f"tmp_path = {tmp_path}, subdirs = {subdirs}") + solutions = get_solution(data_path, t_dimless, y_axis) + # print(f"solution: {solutions[-1]} \nref_solution: {ref_solutions[-1]}") + for sol, ref_sol in zip(solutions, ref_solutions): + assert np.allclose(sol, ref_sol, atol=1e-2), "Velocity profile does not match." diff --git a/validation/tgv2d.sh b/validation/tgv2d.sh index 751540c..6893510 100644 --- a/validation/tgv2d.sh +++ b/validation/tgv2d.sh @@ -1,6 +1,6 @@ #!/bin/bash # Generate validation data and validate 2D TGV -# with number of particles per direction nx = [20, 50, 100] +# with number of particles per direction nx = [50, 100] # Reference result from: # "A Transport Velocty [...]", Adami 2012