diff --git a/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb b/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
index ded1b3321281441b8573c78e911c536b9ceaeef4..b1b4e623dddbbabdd4f96f503a84ed52e2db7423 100644
--- a/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
+++ b/doc/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
@@ -16,7 +16,6 @@
"from pystencils.session import *\n",
"from lbmpy.session import *\n",
"\n",
- "from pystencils.simp import sympy_cse\n",
"from pystencils.boundaries import BoundaryHandling\n",
"\n",
"from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle\n",
@@ -208,7 +207,7 @@
" "
],
"text/plain": [
- "<lbmpy.phasefield_allen_cahn.parameter_calculation.AllenCahnParameters at 0x126d30cd0>"
+ "<lbmpy.phasefield_allen_cahn.parameter_calculation.AllenCahnParameters at 0x1329b88b0>"
]
},
"execution_count": 6,
@@ -387,7 +386,7 @@
"</table>"
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x126d44ee0>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x1346b22e0>"
]
},
"execution_count": 9,
@@ -402,7 +401,7 @@
" delta_equilibrium=False,\n",
" force=sp.symbols(\"F_:2\"), velocity_input=u,\n",
" weighted=True, relaxation_rates=[0, w_c, w_c, 1, 1, 1, 1, 1, 1],\n",
- " output={'density': C_tmp}, kernel_type='stream_pull_collide')\n",
+ " output={'density': C_tmp})\n",
"\n",
"method_phase = create_lb_method(lbm_config=config_phase)\n",
"\n",
@@ -501,7 +500,7 @@
"</table>"
],
"text/plain": [
- "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x126d22eb0>"
+ "<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x137772a30>"
]
},
"execution_count": 10,
@@ -513,8 +512,7 @@
"omega = parameters.omega(C)\n",
"config_hydro = LBMConfig(stencil=stencil_hydro, method=Method.MRT, compressible=False,\n",
" weighted=True, relaxation_rates=[omega, 1, 1, 1],\n",
- " force=sp.symbols(\"F_:2\"),\n",
- " output={'velocity': u}, kernel_type='collide_stream_push')\n",
+ " force=sp.symbols(\"F_:2\"), output={'velocity': u})\n",
"\n",
"method_hydro = create_lb_method(lbm_config=config_hydro)\n",
"\n",
@@ -594,7 +592,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x1417f86d0>"
+ "<matplotlib.image.AxesImage at 0x137ad6bb0>"
]
},
"execution_count": 13,
@@ -603,7 +601,7 @@
},
{
"data": {
- "image/png": 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PfVn70MN58d+dlf93yaX54BsvyTsu2JwfO/mOnL/iQF40tiorMv68j8YO2jCTGeSJ4cHcO7kqf/b4a/OHW16Xkz57QjZ8cV/G7r0/wyeeSHNBJgCWKPEKAPOoTU5msHdv6qtPZd23XpTh7Wfloz/wpvze616fH7pgW3789Dvyfat3ZO34MCfUeFbWeMYylrHUd3yfYVqGGeZQG+TpNsiewVjunDgrtz58UT5/18tz2pdW5GVfeSJj92/NcN8TGUwecoowAEuaeAWA+dba1FWJH3k0te+JnHnvSTnzb9bmgVd+b/7LBa/KwVfuz/efsz0bT30gLz/uobxkxb6cXBNZWcMkyYE2nqfaqjw0eVq2HXhJNu99ab764Pqsuvv4vPiuQV61dW/y0J4MH39StAKwbIhXADhWnonYRx9L7Xs8L7p/dU75wikZnLEmj6w/Lx86++XZ/5LKxGmDtJMGGVs19TnV4cR46qnxrH5kPMfvannRtwd5+fanMr57V4Z792U4MZE2GIhWAJYV8QoAx1praZOTaZOTGT79dGrX7pywdVVOPP641HHHpR2/Olm9Km3F1Odga3KYTBxM7Z9IO3Agbf+BtIMHMylYAVjGxCsALKRpIZv9+6fGaiw19g+feW1J2rAlbfj3+wDAcideAWCxPBOlbfD3nQoAzOzY/NV0AAAAmEfiFQAAgO6JVwAAALonXgEAAOieeAUAAKB74hUAAIDuiVcAAAC6J14BAADonngFAACge+IVAACA7olXAAAAuideAQAA6J54BQAAoHviFQAAgO6JVwAAALonXgEAAOieeAUAAKB74hUAAIDuiVcAAAC6J14BAADonngFAACge3OO16oar6q/raqPjh6fVlW3VdU9o9s107Z9d1Vtq6qtVXXZXF8bAACA5WE+jrz+fJK7pz2+Psmm1tqGJJtGj1NVFyS5KsmFSS5P8t6qGp+H1wcAAGCJm1O8VtX6JD+W5HemDV+R5KbR/ZuSXDlt/ObW2kRr7b4k25JcPJfXBwAAYHmY65HX/5Hk3ycZThs7s7W2M0lGt2eMxs9O8uC07baPxr5LVV1bVZuravOePXvmOEUAAABe6GYdr1X1T5Psbq19+bnuMsNYm2nD1tqNrbWNrbWNa9eune0UAQAAWCJWzGHfNyT58ar60STHJTm5qv5Pkl1Vta61trOq1iXZPdp+e5Jzpu2/PsmOObw+AAAAy8Ssj7y21t7dWlvfWjs3Uxdi+svW2k8luTXJ1aPNrk7ykdH9W5NcVVWrq+q8JBuS3D7rmQMAALBszOXI6+G8J8ktVXVNkgeSvD1JWmtbquqWJHclmUxyXWttcAxeHwAAgCVmXuK1tfbpJJ8e3X8kyaWH2e6GJDfMx2sCAACwfMzH33kFAACAY0q8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANC9WcdrVZ1TVZ+qqruraktV/fxo/LSquq2q7hndrpm2z7uraltVba2qy+bjHwAAAMDSN5cjr5NJfrG19qoklyS5rqouSHJ9kk2ttQ1JNo0eZ/TcVUkuTHJ5kvdW1fhcJg8AAMDyMOt4ba3tbK19ZXT/iSR3Jzk7yRVJbhptdlOSK0f3r0hyc2ttorV2X5JtSS6e7esDAACwfMzLZ16r6twkP5Dki0nObK3tTKYCN8kZo83OTvLgtN22j8YAAADgiOYcr1V1UpI/TvJvW2uPH2nTGcbaYb7ntVW1uao279mzZ65TBAAA4AVuTvFaVSszFa5/0Fr7k9HwrqpaN3p+XZLdo/HtSc6Ztvv6JDtm+r6ttRtbaxtbaxvXrl07lykCAACwBMzlasOV5P1J7m6t/dq0p25NcvXo/tVJPjJt/KqqWl1V5yXZkOT22b4+AAAAy8eKOez7hiTvTHJnVd0xGvsPSd6T5JaquibJA0neniSttS1VdUuSuzJ1peLrWmuDObw+AAAAy8Ss47W19tnM/DnWJLn0MPvckOSG2b4mAAAAy9O8XG0YAAAAjiXxCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdE68AAAB0T7wCAADQPfEKAABA98QrAAAA3ROvAAAAdE+8AgAA0D3xCgAAQPfEKwAAAN0TrwAAAHRPvAIAANA98QoAAED3xCsAAADdW/B4rarLq2prVW2rqusX+vUBAAB44VnQeK2q8SS/neStSS5I8o6qumAh5wAAAMALz0Ifeb04ybbW2r2ttYNJbk5yxQLPAQAAgBeYhY7Xs5M8OO3x9tEYAAAAHNaKBX69mmGsfddGVdcmuXb08Mmq2npMZ7V8nZ7k4cWeBMuStcdisfbmQdVMb+c8B9Yfi8XaY7E817X3Pc/lmy10vG5Pcs60x+uT7Hj2Rq21G5PcuFCTWq6qanNrbeNiz4Plx9pjsVh7LCbrj8Vi7bFY5nvtLfRpw19KsqGqzquqVUmuSnLrAs8BAACAF5gFPfLaWpusqncl+USS8SQfaK1tWcg5AAAA8MKz0KcNp7X250n+fKFflxk5NZvFYu2xWKw9FpP1x2Kx9lgs87r2qrXvul4SAAAAdGWhP/MKAAAAz5t4XaKq6u1VtaWqhlW18VnPvbuqtlXV1qq6bNr4a6vqztFzv1mjv4VQVaur6v+Oxr9YVecu8D+HF7Cq+s9V9e2qumP09aPTnnteaxHmoqouH621bVV1/WLPh6Wnqu4f/ey6o6o2j8ZOq6rbquqe0e2aadvP+DMQjqaqPlBVu6vqa9PGnvda837LbBxm/S3I73viden6WpKfTPKZ6YNVdUGmrvJ8YZLLk7y3qsZHT78vU39fd8Po6/LR+DVJHmutvTzJryf5r8d89iw1v95au2j09efJrNcizMpobf12krcmuSDJO0ZrEObbW0Y/6575H8fXJ9nUWtuQZNPo8dF+BsLRfDDf/d44m7Xm/ZbZ+GBmXivH/Pc98bpEtdbubq1tneGpK5Lc3FqbaK3dl2Rbkoural2Sk1trn29TH4T+vSRXTtvnptH9DyW51P+ZYx7MZi3CbF2cZFtr7d7W2sEkN2dqDcKxNv099KZ853vrd/0MXPjp8ULUWvtMkkefNfy81pr3W2brMOvvcOZ1/YnX5efsJA9Oe7x9NHb26P6zx79jn9baZJJ9SV58zGfKUvKuqvrq6DSTZ05jms1ahNk63HqD+dSSfLKqvlxV147Gzmyt7UyS0e0Zo3Frkvn2fNea91vm2zH/fU+8voBV1V9U1ddm+DrS0YSZjpi2I4wfaR9IctS1+L4kL0tyUZKdSf77M7vN8K2OthZhtqwrFsIbWms/mKnT06+rqjcdYVtrkoXi/ZaFsCC/7y3433ll/rTWfngWu21Pcs60x+uT7BiNr59hfPo+26tqRZJT8txPFWAZeK5rsar+d5KPjh7OZi3CbB1uvcG8aa3tGN3urqoPZ+o04F1Vta61tnN0mtzu0ebWJPPt+a4177fMm9barmfuH8vf9xx5XX5uTXLV6ArC52Xqw9G3j04veaKqLhl9nvWnk3xk2j5Xj+6/LclfNn8gmOdo9Ab6jJ/I1MXEktmtRZitLyXZUFXnVdWqTF084tZFnhNLSFWdWFUveuZ+kn+SqZ93099Dr853vrd+18/AhZ01S8zzWmveb5lPC/X7niOvS1RV/USS30qyNsmfVdUdrbXLWmtbquqWJHclmUxyXWttMNrtZzN19bDjk3xs9JUk70/y+1W1LVNHXK9auH8JS8B/q6qLMnUqyP1J/nWSzHItwqy01iar6l1JPpFkPMkHWmtbFnlaLC1nJvnw6HqGK5L8YWvt41X1pSS3VNU1SR5I8vbkqD8D4Yiq6o+SvDnJ6VW1PckvJXlPnv9a837L83aY9ffmhfh9rxxAAwAAoHdOGwYAAKB74hUAAIDuiVcAAAC6J14BAADonngFAACge+IVAACA7olXAAAAuideAQAA6N7/B14Dy2PIVC98AAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
@@ -670,7 +668,7 @@
"outputs": [],
"source": [
"force_h = interface_tracking_force(C, stencil_phase, parameters)\n",
- "hydro_force = hydrodynamic_force(g, C, method_hydro, parameters, body_force)"
+ "hydro_force = hydrodynamic_force(C, method_hydro, parameters, body_force)"
]
},
{
@@ -729,13 +727,14 @@
},
"outputs": [],
"source": [
- "force_Assignments = hydrodynamic_force_assignments(g, u, C, method_hydro, parameters, body_force)\n",
+ "force_Assignments = hydrodynamic_force_assignments(u, C, method_hydro, parameters, body_force)\n",
"\n",
"lbm_optimisation = LBMOptimisation(symbolic_field=g, symbolic_temporary_field=g_tmp)\n",
"hydro_lb_update_rule = create_lb_update_rule(lbm_config=config_hydro,\n",
" lbm_optimisation=lbm_optimisation)\n",
"\n",
- "hydro_lb_update_rule = add_hydrodynamic_force(hydro_lb_update_rule, force_Assignments, C, g, parameters)\n",
+ "hydro_lb_update_rule = add_hydrodynamic_force(hydro_lb_update_rule, force_Assignments, C, g, parameters,\n",
+ " config_hydro)\n",
"\n",
"ast_kernel = ps.create_kernel(hydro_lb_update_rule, target=dh.default_target, cpu_openmp=True)\n",
"kernel_hydro_lb = ast_kernel.compile()"
@@ -765,19 +764,19 @@
"periodic_BC_C = dh.synchronization_function(C.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
"\n",
"periodic_BC_g = LBMPeriodicityHandling(stencil=stencil_hydro, data_handling=dh, pdf_field_name=g.name,\n",
- " streaming_pattern='push')\n",
+ " streaming_pattern=config_hydro.streaming_pattern)\n",
"periodic_BC_h = LBMPeriodicityHandling(stencil=stencil_phase, data_handling=dh, pdf_field_name=h.name,\n",
- " streaming_pattern='pull')\n",
+ " streaming_pattern=config_phase.streaming_pattern)\n",
"\n",
"# No slip boundary for the phasefield lbm\n",
"bh_allen_cahn = LatticeBoltzmannBoundaryHandling(method_phase, dh, 'h',\n",
" target=dh.default_target, name='boundary_handling_h',\n",
- " streaming_pattern='pull')\n",
+ " streaming_pattern=config_phase.streaming_pattern)\n",
"\n",
"# No slip boundary for the velocityfield lbm\n",
"bh_hydro = LatticeBoltzmannBoundaryHandling(method_hydro, dh, 'g' ,\n",
" target=dh.default_target, name='boundary_handling_g',\n",
- " streaming_pattern='push')\n",
+ " streaming_pattern=config_hydro.streaming_pattern)\n",
"\n",
"contact_angle = BoundaryHandling(dh, C.name, stencil_hydro, target=dh.default_target)\n",
"contact = ContactAngle(90, parameters.interface_thickness)\n",
@@ -827,19 +826,17 @@
" periodic_BC_h()\n",
" bh_allen_cahn() \n",
" dh.run_kernel(kernel_allen_cahn_lb, **parameters.symbolic_to_numeric_map)\n",
- " dh.swap(\"C\", \"C_tmp\")\n",
+ " # Solve the hydro LB step with boundary conditions\n",
+ " periodic_BC_g()\n",
+ " bh_hydro()\n",
+ " dh.run_kernel(kernel_hydro_lb, **parameters.symbolic_to_numeric_map)\n",
" \n",
+ " dh.swap(\"C\", \"C_tmp\")\n",
" # apply the three phase-phase contact angle\n",
" contact_angle()\n",
" # periodic BC of the phase-field\n",
" periodic_BC_C()\n",
" \n",
- " # solve the hydro LB step with boundary conditions\n",
- " dh.run_kernel(kernel_hydro_lb, **parameters.symbolic_to_numeric_map)\n",
- " periodic_BC_g()\n",
- " bh_hydro()\n",
- "\n",
- " \n",
" # field swaps\n",
" dh.swap(\"h\", \"h_tmp\")\n",
" dh.swap(\"g\", \"g_tmp\")"
@@ -856,7 +853,7 @@
"data": {
"text/html": [
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],
@@ -917,7 +914,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.0rc1"
+ "version": "3.11.4"
}
},
"nbformat": 4,
diff --git a/doc/notebooks/12_Thermocapillary_flows_heated_channel.ipynb b/doc/notebooks/12_Thermocapillary_flows_heated_channel.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6637d580007cd82491163bf3ce85058e2ff1f950
--- /dev/null
+++ b/doc/notebooks/12_Thermocapillary_flows_heated_channel.ipynb
@@ -0,0 +1,977 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Thermocapillary flows: 2D Planar heated channel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pystencils.session import *\n",
+ "from lbmpy.session import *\n",
+ "\n",
+ "from pystencils.boundaries import BoundaryHandling\n",
+ "\n",
+ "from lbmpy.phasefield_allen_cahn.analytical import analytical_solution_microchannel\n",
+ "from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle\n",
+ "from lbmpy.phasefield_allen_cahn.kernel_equations import *\n",
+ "from lbmpy.phasefield_allen_cahn.numerical_solver import get_runge_kutta_update_assignments\n",
+ "from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_parameters_rti, AllenCahnParameters\n",
+ "from lbmpy.advanced_streaming import LBMPeriodicityHandling\n",
+ "from lbmpy.boundaries import NoSlip, LatticeBoltzmannBoundaryHandling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If `cupy` is installed the simulation automatically runs on GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import cupy\n",
+ "except ImportError:\n",
+ " cupy = None\n",
+ " gpu = False\n",
+ " target = ps.Target.CPU\n",
+ " print('No cupy installed')\n",
+ "\n",
+ "if cupy:\n",
+ " gpu = True\n",
+ " target = ps.Target.GPU"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "\n",
+ "In this tutorial, we will provide an introduction to thermocapillary flows and solve an example setup using `lbmpy` and `pystencils`. This tutorial builds upon the conservative Allen-Cahn tutorial. Thus it is highly recommended to read mentioned tutorial first.\n",
+ "\n",
+ "Thermocapillary flows refer to the motion of fluids induced by temperature gradients at liquid interfaces. They play a crucial role in various natural and industrial processes, such as microfluidics, materials processing, and the behavior of liquid droplets in microgravity environments.\n",
+ "\n",
+ "In this tutorial the motion of fluid in a heated microchannel is investigated. This problem has been addressed by numerous authors for example [here](https://doi.org/10.1016/j.ijthermalsci.2010.02.003) to verify thermocapillary flow models. The advantage of this problem is the existance of an analytical solution which is commonly very hard to find in these complex flow problems. Here, we will apply a second order accurate Runge Kutta scheme to solve the heat equation."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Geometry Setup\n",
+ "\n",
+ "First of all the stencils for the phase-field LB step as well as the stencil for the hydrodynamic LB step are defined. According to the stencils the simulation runs either in 2D or 3D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stencil_phase = LBStencil(Stencil.D2Q9)\n",
+ "stencil_hydro = LBStencil(Stencil.D2Q9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Definition of the parameters used in this tutorial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# domain \n",
+ "L0 = 256\n",
+ "domain_size = (2 * L0, L0)\n",
+ "\n",
+ "# initial timesteps for the temperature solver\n",
+ "timesteps_temperature = 10000\n",
+ "# timesteps for the whole simulation\n",
+ "timesteps = 400000\n",
+ "\n",
+ "# Parameters of the simulation\n",
+ "parameters = ThermocapillaryParameters(density_heavy=1.0, density_light=1.0,\n",
+ " dynamic_viscosity_heavy=0.2, dynamic_viscosity_light=0.2,\n",
+ " surface_tension=0.0,\n",
+ " heat_conductivity_heavy=0.2, heat_conductivity_light=0.2,\n",
+ " mobility=0.05, interface_thickness=4,\n",
+ " sigma_ref=0.025, sigma_t=-5e-4)\n",
+ "\n",
+ "T_h = 20\n",
+ "T_c = 10\n",
+ "T_0 = 4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that the Parametes are defined in the `ThermocapillaryParameters` class. This has the advantage that the class defines for every parameter a symbolic representation. Thus, in the later derivation of the update rules only symbolic parameters occure and the numerical values will come as input parameters via `parameters.symbolic_to_numeric_map`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr style=\"border:none\">\n",
+ " <th style=\"border:none\" >Name</th>\n",
+ " <th style=\"border:none\" >SymPy Symbol </th>\n",
+ " <th style=\"border:none\" >Value</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Density heavy phase</td>\n",
+ " <td style=\"border:none\">$\\rho_{H}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Density light phase</td>\n",
+ " <td style=\"border:none\">$\\rho_{L}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Relaxation time heavy phase</td>\n",
+ " <td style=\"border:none\">$\\tau_{H}$</td>\n",
+ " <td style=\"border:none\">$0.6$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Relaxation time light phase</td>\n",
+ " <td style=\"border:none\">$\\tau_{L}$</td>\n",
+ " <td style=\"border:none\">$0.6$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Relaxation rate Allen Cahn LB</td>\n",
+ " <td style=\"border:none\">$\\omega_{\\phi}$</td>\n",
+ " <td style=\"border:none\">$1.53846153846154$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Gravitational acceleration</td>\n",
+ " <td style=\"border:none\">$F_{g}$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Interface thickness</td>\n",
+ " <td style=\"border:none\">$W$</td>\n",
+ " <td style=\"border:none\">$4$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Mobility</td>\n",
+ " <td style=\"border:none\">$M_{m}$</td>\n",
+ " <td style=\"border:none\">$0.05$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Surface tension</td>\n",
+ " <td style=\"border:none\">$\\sigma$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Heat Conductivity Heavy</td>\n",
+ " <td style=\"border:none\">$\\kappa_{H}$</td>\n",
+ " <td style=\"border:none\">$0.2$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Heat Conductivity Light</td>\n",
+ " <td style=\"border:none\">$\\kappa_{L}$</td>\n",
+ " <td style=\"border:none\">$0.2$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Sigma Ref</td>\n",
+ " <td style=\"border:none\">$\\sigma_{ref}$</td>\n",
+ " <td style=\"border:none\">$0.025$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Sigma T</td>\n",
+ " <td style=\"border:none\">$\\sigma_{T}$</td>\n",
+ " <td style=\"border:none\">$-0.0005$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Temperature Ref</td>\n",
+ " <td style=\"border:none\">$T_{ref}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " </tr>\n",
+ "\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<lbmpy.phasefield_allen_cahn.parameter_calculation.ThermocapillaryParameters at 0x7f19a53a4b90>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fields\n",
+ "\n",
+ "As a next step all fields which are needed get defined. To do so we create a `datahandling` object. More details about it can be found in the third tutorial of the [pystencils framework]( http://pycodegen.pages.walberla.net/pystencils/). Basically it holds all fields and manages the kernel runs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a datahandling object\n",
+ "dh = ps.create_data_handling((domain_size), periodicity=(True, False), default_target=target)\n",
+ "\n",
+ "# LBM PDF arrays \n",
+ "g = dh.add_array(\"g\", values_per_cell=len(stencil_hydro))\n",
+ "dh.fill(\"g\", 0.0, ghost_layers=True)\n",
+ "h = dh.add_array(\"h\",values_per_cell=len(stencil_phase))\n",
+ "dh.fill(\"h\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "g_tmp = dh.add_array(\"g_tmp\", values_per_cell=len(stencil_hydro))\n",
+ "dh.fill(\"g_tmp\", 0.0, ghost_layers=True)\n",
+ "h_tmp = dh.add_array(\"h_tmp\",values_per_cell=len(stencil_phase))\n",
+ "dh.fill(\"h_tmp\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "# velocity, phase-field and temperature array\n",
+ "u = dh.add_array(\"u\", values_per_cell=dh.dim)\n",
+ "dh.fill(\"u\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "C = dh.add_array(\"C\")\n",
+ "dh.fill(\"C\", 0.0, ghost_layers=True)\n",
+ "C_tmp = dh.add_array(\"C_tmp\")\n",
+ "dh.fill(\"C_tmp\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "temperature = dh.add_array(\"temperature\")\n",
+ "dh.fill(\"temperature\", T_c, ghost_layers=True)\n",
+ "\n",
+ "# temporary array for the RK scheme\n",
+ "RK1 = dh.add_array(\"RK1\")\n",
+ "dh.fill(\"RK1\", 0.0, ghost_layers=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parameter definition\n",
+ "\n",
+ "The next step is to calculate all parameters which are needed for the simulation. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# relaxation rate for the phase-field LBM step\n",
+ "w_c = 1.0/(0.5 + (3.0 * parameters.symbolic_mobility))\n",
+ "# relaxation rate for the hydrodynamic LBM step\n",
+ "omega = parameters.omega(C)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# density for the whole domain\n",
+ "rho_L = parameters.symbolic_density_light\n",
+ "rho_H = parameters.symbolic_density_heavy\n",
+ "density = rho_L + C.center * (rho_H - rho_L)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of the lattice Boltzmann methods"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Central-Moment-Based Method\n",
+ " </th>\n",
+ " <td>Stencil: D2Q9</td>\n",
+ " <td>Zero-Centered Storage: ✗</td>\n",
+ " <td>Force Model: Guo</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Continuous Hydrodynamic Maxwellian Equilibrium\n",
+ " </th>\n",
+ " <td rowspan=\"2\" style=\"width: 50%; text-align: center\">\n",
+ " $f (\\rho, \\left( u_{0}, \\ u_{1}\\right), \\left( v_{0}, \\ v_{1}\\right)) \n",
+ " = \\frac{3 \\rho e^{- \\frac{3 \\left(- u_{0} + v_{0}\\right)^{2}}{2} - \\frac{3 \\left(- u_{1} + v_{1}\\right)^{2}}{2}}}{2 \\pi}$\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>Compressible: ✓</td>\n",
+ " <td>Deviation Only: ✗</td>\n",
+ " <td>Order: 2</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr> <th colspan=\"3\" style=\"text-align: left\"> Relaxation Info </th> </tr>\n",
+ " <tr>\n",
+ " <th>Central Moment</th>\n",
+ " <th>Eq. Value </th>\n",
+ " <th>Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2 \\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\rho}{9}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod at 0x7f19a2500d50>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "config_phase = LBMConfig(stencil=stencil_phase, method=Method.CENTRAL_MOMENT,\n",
+ " compressible=True, zero_centered=False,\n",
+ " relaxation_rate=w_c,\n",
+ " force=sp.symbols(f\"F_:{stencil_phase.D}\"),\n",
+ " output={'density': C_tmp}, \n",
+ " velocity_input=u)\n",
+ "\n",
+ "method_phase = create_lb_method(config_phase)\n",
+ "method_phase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Central-Moment-Based Method\n",
+ " </th>\n",
+ " <td>Stencil: D2Q9</td>\n",
+ " <td>Zero-Centered Storage: ✓</td>\n",
+ " <td>Force Model: Guo</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Continuous Hydrodynamic Maxwellian Equilibrium\n",
+ " </th>\n",
+ " <td rowspan=\"2\" style=\"width: 50%; text-align: center\">\n",
+ " $f (\\rho, \\left( u_{0}, \\ u_{1}\\right), \\left( v_{0}, \\ v_{1}\\right)) \n",
+ " = \\frac{3 \\delta_{\\rho} e^{- \\frac{3 v_{0}^{2}}{2} - \\frac{3 v_{1}^{2}}{2}}}{2 \\pi} + \\frac{3 e^{- \\frac{3 \\left(- u_{0} + v_{0}\\right)^{2}}{2} - \\frac{3 \\left(- u_{1} + v_{1}\\right)^{2}}{2}}}{2 \\pi}$\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>Compressible: ✗</td>\n",
+ " <td>Deviation Only: ✗</td>\n",
+ " <td>Order: 2</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr> <th colspan=\"3\" style=\"text-align: left\"> Relaxation Info </th> </tr>\n",
+ " <tr>\n",
+ " <th>Central Moment</th>\n",
+ " <th>Eq. Value </th>\n",
+ " <th>Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$- \\delta_{\\rho} u_{0}$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$- \\delta_{\\rho} u_{1}$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$\\delta_{\\rho} u_{0} u_{1}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2}{2 {C}_{(0,0)} \\left(\\tau_{H} - \\tau_{L}\\right) + 2 \\tau_{L} + 1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\delta_{\\rho} u_{0}^{2} - \\delta_{\\rho} u_{1}^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2}{2 {C}_{(0,0)} \\left(\\tau_{H} - \\tau_{L}\\right) + 2 \\tau_{L} + 1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\delta_{\\rho} u_{0}^{2} + \\delta_{\\rho} u_{1}^{2} + \\frac{2 \\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2}{2 {C}_{(0,0)} \\left(\\tau_{H} - \\tau_{L}\\right) + 2 \\tau_{L} + 1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y$</td>\n",
+ " <td style=\"border:none\">$- \\frac{\\delta_{\\rho} u_{1}}{3}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y^{2}$</td>\n",
+ " <td style=\"border:none\">$- \\frac{\\delta_{\\rho} u_{0}}{3}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\delta_{\\rho} u_{0}^{2}}{3} + \\frac{\\delta_{\\rho} u_{1}^{2}}{3} + \\frac{\\rho}{9}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod at 0x7f199f3efe50>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "config_hydro = LBMConfig(stencil=stencil_phase, method=Method.CENTRAL_MOMENT,\n",
+ " compressible=False,\n",
+ " force=sp.symbols(f\"F_:{stencil_hydro.D}\"),\n",
+ " output={'velocity': u},\n",
+ " relaxation_rates=[omega, omega, 1, 1])\n",
+ "\n",
+ "\n",
+ "method_hydro = create_lb_method(config_hydro)\n",
+ "method_hydro"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h_updates = initializer_kernel_phase_field_lb(method_phase, C, u, h, parameters)\n",
+ "g_updates = initializer_kernel_hydro_lb(method_hydro, 1.0, u, g)\n",
+ "\n",
+ "h_init = ps.create_kernel(h_updates, target=dh.default_target, cpu_openmp=True).compile()\n",
+ "g_init = ps.create_kernel(g_updates, target=dh.default_target, cpu_openmp=True).compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Initialisation of the phase-field, as well as the temperature array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# initialize the domain\n",
+ "def Initialize_distributions():\n",
+ " Nx = domain_size[0]\n",
+ " Ny = domain_size[1]\n",
+ " W = parameters.interface_thickness\n",
+ " \n",
+ " for block in dh.iterate(ghost_layers=True, inner_ghost_layers=False):\n",
+ " x = np.zeros_like(block.midpoint_arrays[0])\n",
+ " x[:, :] = block.midpoint_arrays[0]\n",
+ " \n",
+ " normalised_x = np.zeros_like(x[:, 0])\n",
+ " normalised_x[:] = x[:, 0] - L0\n",
+ " omega = np.pi / L0\n",
+ " # bottom wall\n",
+ " block[\"temperature\"][:, 0] = T_h + T_0 * np.cos(omega * normalised_x)\n",
+ " # top wall\n",
+ " block[\"temperature\"][:, -1] = T_c\n",
+ " \n",
+ " y = np.zeros_like(block.midpoint_arrays[1])\n",
+ " y[:, :] = block.midpoint_arrays[1]\n",
+ " \n",
+ " y += Ny // 2\n",
+ " init_values = 0.5 + 0.5 * np.tanh((y - Ny) / (W / 2))\n",
+ " block[\"C\"][:, :] = init_values\n",
+ " block[\"C_tmp\"][:, :] = init_values\n",
+ " \n",
+ " if gpu:\n",
+ " dh.all_to_gpu() \n",
+ " \n",
+ " dh.run_kernel(h_init, **parameters.symbolic_to_numeric_map)\n",
+ " dh.run_kernel(g_init)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "force_h = force_h = interface_tracking_force(C, stencil_phase, parameters)\n",
+ "hydro_force = hydrodynamic_force(C, method_hydro, parameters, body_force=[0, 0, 0],\n",
+ " temperature_field=temperature)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of the LB update rules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lbm_optimisation = LBMOptimisation(symbolic_field=h, symbolic_temporary_field=h_tmp)\n",
+ "allen_cahn_lb = create_lb_update_rule(lbm_config=config_phase,\n",
+ " lbm_optimisation=lbm_optimisation)\n",
+ "\n",
+ "allen_cahn_lb = add_interface_tracking_force(allen_cahn_lb, force_h)\n",
+ "\n",
+ "ast_allen_cahn_lb = ps.create_kernel(allen_cahn_lb, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_allen_cahn_lb = ast_allen_cahn_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "force_Assignments = hydrodynamic_force_assignments(u, C, method_hydro, parameters,\n",
+ " body_force=[0, 0, 0], temperature_field=temperature)\n",
+ "\n",
+ "lbm_optimisation = LBMOptimisation(symbolic_field=g, symbolic_temporary_field=g_tmp)\n",
+ "hydro_lb_update_rule = create_lb_update_rule(lbm_config=config_hydro,\n",
+ " lbm_optimisation=lbm_optimisation)\n",
+ "hydro_lb_update_rule = add_hydrodynamic_force(hydro_lb_update_rule, force_Assignments, C, g,\n",
+ " parameters, config_hydro) \n",
+ "\n",
+ "ast_hydro_lb = ps.create_kernel(hydro_lb_update_rule, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_hydro_lb = ast_hydro_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup of the RK scheme to solve the temperature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a = get_runge_kutta_update_assignments(stencil_hydro, C, temperature, u, [RK1, ],\n",
+ " parameters.heat_conductivity_heavy,\n",
+ " parameters.heat_conductivity_light,\n",
+ " 1.0, 1.0, density)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "init_RK = [ps.Assignment(RK1.center, temperature.center)]\n",
+ "init_RK_kernel = ps.create_kernel(init_RK, target=dh.default_target, cpu_openmp=True).compile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TempUpdate1_kernel = ps.create_kernel(a[0], target=dh.default_target, cpu_openmp=True).compile()\n",
+ "TempUpdate2_kernel = ps.create_kernel(a[1], target=dh.default_target, cpu_openmp=True).compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Boundary Conditions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# periodic Boundarys for g, h and C\n",
+ "periodic_BC_C = dh.synchronization_function(C.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
+ "periodic_BC_T = dh.synchronization_function(temperature.name, target=dh.default_target, optimization = {\"openmp\": True})\n",
+ "\n",
+ "\n",
+ "periodic_BC_g = LBMPeriodicityHandling(stencil=stencil_hydro, data_handling=dh, pdf_field_name=g.name,\n",
+ " streaming_pattern='pull')\n",
+ "periodic_BC_h = LBMPeriodicityHandling(stencil=stencil_phase, data_handling=dh, pdf_field_name=h.name,\n",
+ " streaming_pattern='pull')\n",
+ "\n",
+ "# No slip boundary for the phasefield lbm\n",
+ "bh_allen_cahn = LatticeBoltzmannBoundaryHandling(method_phase, dh, 'h',\n",
+ " target=dh.default_target, name='boundary_handling_h',\n",
+ " streaming_pattern='pull')\n",
+ "\n",
+ "# No slip boundary for the velocityfield lbm\n",
+ "bh_hydro = LatticeBoltzmannBoundaryHandling(method_hydro, dh, g.name,\n",
+ " target=dh.default_target, name='boundary_handling_g',\n",
+ " streaming_pattern='pull')\n",
+ "\n",
+ "wall = NoSlip()\n",
+ "bh_allen_cahn.set_boundary(wall, make_slice[:, 0])\n",
+ "bh_allen_cahn.set_boundary(wall, make_slice[:, -1])\n",
+ "\n",
+ "bh_hydro.set_boundary(wall, make_slice[:, 0])\n",
+ "bh_hydro.set_boundary(wall, make_slice[:, -1])\n",
+ "\n",
+ "\n",
+ "bh_allen_cahn.prepare()\n",
+ "bh_hydro.prepare()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Full timestep"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def temp_update():\n",
+ " dh.run_kernel(init_RK_kernel, **parameters.symbolic_to_numeric_map)\n",
+ " dh.run_kernel(TempUpdate1_kernel, **parameters.symbolic_to_numeric_map)\n",
+ " dh.run_kernel(TempUpdate2_kernel, **parameters.symbolic_to_numeric_map)\n",
+ " periodic_BC_T()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# definition of the timestep for the immiscible fluids model\n",
+ "def timeloop():\n",
+ " # Solve the interface tracking LB step with boundary conditions\n",
+ " periodic_BC_h()\n",
+ " bh_allen_cahn() \n",
+ " dh.run_kernel(kernel_allen_cahn_lb, **parameters.symbolic_to_numeric_map)\n",
+ " # Solve the hydro LB step with boundary conditions\n",
+ " periodic_BC_g()\n",
+ " bh_hydro()\n",
+ " dh.run_kernel(kernel_hydro_lb, **parameters.symbolic_to_numeric_map)\n",
+ " \n",
+ " dh.swap(\"C\", \"C_tmp\")\n",
+ " # periodic BC of the phase-field\n",
+ " periodic_BC_C()\n",
+ " \n",
+ " # Update the temperature field\n",
+ " temp_update()\n",
+ " \n",
+ " # field swaps\n",
+ " dh.swap(\"h\", \"h_tmp\")\n",
+ " dh.swap(\"g\", \"g_tmp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "Initialize_distributions()\n",
+ "\n",
+ "if 'is_test_run' not in globals():\n",
+ " \n",
+ " for i in range(0, timesteps_temperature):\n",
+ " temp_update()\n",
+ "\n",
+ " for i in range(0, timesteps + 1): \n",
+ " timeloop()\n",
+ "else:\n",
+ " timeloop()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " if gpu:\n",
+ " dh.all_to_cpu()\n",
+ "\n",
+ " plt.scalar_field(dh.cpu_arrays[\"C\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " plt.scalar_field(dh.cpu_arrays[\"temperature\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " nx = domain_size[0]\n",
+ " ny = domain_size[1]\n",
+ "\n",
+ " myDatX = np.arange(nx)-L0 \n",
+ " myDatY = np.arange(ny)-L0//2\n",
+ " XX, YY = np.meshgrid(myDatX, myDatY)\n",
+ "\n",
+ " u_calc = dh.gather_array(u.name, ghost_layers=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " k_h = parameters.heat_conductivity_heavy\n",
+ " k_l = parameters.heat_conductivity_light\n",
+ " sigma_T = parameters.sigma_t\n",
+ " mu_L = parameters.dynamic_viscosity_light\n",
+ " x, y, u_x, u_y, t_a = analytical_solution_microchannel(L0, nx, ny, k_h, k_l, T_h, T_c, T_0, sigma_T, mu_L)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " fig, ax = plt.subplots()\n",
+ " fig.set_figheight(5)\n",
+ " fig.set_figwidth(10)\n",
+ " levels = range(11,24)\n",
+ " CS1 = ax.contour(x, y, t_a,linestyles='dashed', levels =levels, colors =['k'])\n",
+ " plt.grid()\n",
+ " CS2 = plt.contour(XX, YY, dh.gather_array(temperature.name, ghost_layers=False).T, levels =levels, colors =['k'])\n",
+ " clabels = ax.clabel(CS2, inline=1, fontsize=10,fmt='%2.0lf')\n",
+ " [txt.set_bbox(dict(facecolor='white', edgecolor='none', pad=0)) for txt in clabels]\n",
+ " plt.ylim((-128,128))\n",
+ " plt.xlim((-256,256))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2000x1000 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " fig1, ax = plt.subplots()\n",
+ " fig1.set_figheight(10)\n",
+ " fig1.set_figwidth(20)\n",
+ " excludeN = 15\n",
+ "\n",
+ " CS1 = ax.quiver(x[::excludeN,::excludeN]+1.1, y[::excludeN,::excludeN]-2.5, u_x.T[::excludeN,::excludeN].T, u_y.T[::excludeN,::excludeN].T,\n",
+ " angles='xy', scale_units='xy', scale=0.00001, color='r')\n",
+ " CS1 = ax.quiver(XX[::excludeN,::excludeN]+1.1, YY[::excludeN,::excludeN]-2, u_calc[::excludeN,::excludeN, 0].T, u_calc[::excludeN,::excludeN, 1].T,\n",
+ " angles='xy', scale_units='xy', scale=0.00001)\n",
+ "\n",
+ " plt.grid()\n",
+ " plt.ylim((-128,128))\n",
+ " plt.xlim((-256,256))"
+ ]
+ }
+ ],
+ "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": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/notebooks/13_Thermocapillary_flows_droplet_motion.ipynb b/doc/notebooks/13_Thermocapillary_flows_droplet_motion.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fb030238d89a359abc1a52b82b0951961116643d
--- /dev/null
+++ b/doc/notebooks/13_Thermocapillary_flows_droplet_motion.ipynb
@@ -0,0 +1,1093 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Thermocapillary flows: 2D Droplet motion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pystencils.session import *\n",
+ "from lbmpy.session import *\n",
+ "\n",
+ "from pystencils.astnodes import LoopOverCoordinate\n",
+ "from pystencils.boundaries import BoundaryHandling\n",
+ "\n",
+ "from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle\n",
+ "from lbmpy.phasefield_allen_cahn.kernel_equations import *\n",
+ "from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_parameters_droplet_migration, AllenCahnParameters\n",
+ "from lbmpy.advanced_streaming import LBMPeriodicityHandling\n",
+ "from lbmpy.boundaries import NoSlip, LatticeBoltzmannBoundaryHandling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If `cupy` is installed the simulation automatically runs on GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import cupy\n",
+ "except ImportError:\n",
+ " cupy = None\n",
+ " gpu = False\n",
+ " target = ps.Target.CPU\n",
+ " print('No cupy installed')\n",
+ "\n",
+ "if cupy:\n",
+ " gpu = True\n",
+ " target = ps.Target.GPU"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "\n",
+ "In this tutorial, we will provide an introduction to thermocapillary flows and solve an example setup using `lbmpy` and `pystencils`. This tutorial builds upon the conservative Allen-Cahn tutorial. Thus it is highly recommended to read mentioned tutorial first.\n",
+ "\n",
+ "Thermocapillary flows refer to the motion of fluids induced by temperature gradients at liquid interfaces. They play a crucial role in various natural and industrial processes, such as microfluidics, materials processing, and the behavior of liquid droplets in microgravity environments.\n",
+ "\n",
+ "The experimental setup shown in this tutorial encompasses the application of thermocapillary `LBM` to investigate the dynamic behavior of a droplet within a microchannel. To replicate the thermal effects induced by a laser, a heat source is introduced within the channel as,\n",
+ "\n",
+ "$$\n",
+ "\\begin{align}\n",
+ "\t\\label{eq:HeatSource}\n",
+ "\tq_T =\n",
+ "\t\\begin{cases}\n",
+ "\t\tQ_s \\exp{\\left(-2 \\frac{\\left(x - x_s\\right)^2 + \\left(y - y_s\\right)^2 + \\left(z - z_s\\right)^2}{w_s^2}\\right)}, & \\text{if } \\left[\\left(x - x_s\\right)^2 + \\left(y - y_s\\right)^2 + \\left(z - z_s\\right)^2\\right] \\geq d_s^2 \\\\\n",
+ "\t\t0, & \\text{otherwise}\n",
+ "\t\\end{cases};\n",
+ "\\end{align}\n",
+ "$$\n",
+ "\n",
+ "\\noindent Here, $Q_s$ signifies the maximum heat flux generated by the laser, while $x_s$, $y_s$, and $z_s$ denote the precise laser position. For the two-dimensional cases, the $z$-component is disregarded. The extent of heat dispersion is defined by $d_s$, while $w_s$ serves as a key parameter governing the heat flux profile."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Geometry Setup\n",
+ "\n",
+ "First of all the stencils for the phase-field LB step as well as the stencil for the hydrodynamic LB step are defined. According to the stencils the simulation runs either in 2D or 3D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stencil_phase = LBStencil(Stencil.D2Q9)\n",
+ "stencil_hydro = LBStencil(Stencil.D2Q9)\n",
+ "stencil_thermal = LBStencil(Stencil.D2Q9)\n",
+ "assert(stencil_phase.D == stencil_hydro.D == stencil_thermal.D)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Definition of the parameters used in this tutorial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pysical simulation time (timesteps will be calculated with reference time)\n",
+ "simulation_time = 20\n",
+ "# domain \n",
+ "Radius = 32\n",
+ "domain_size = (8 * Radius, 2 * Radius)\n",
+ "midpoint = (65, 0)\n",
+ "\n",
+ "T_c = 0\n",
+ "\n",
+ "# time step\n",
+ "timesteps_temperature = int(10000)\n",
+ "\n",
+ "Ca = 0.01\n",
+ "Re = 0.16\n",
+ "Ma = 0.08\n",
+ "Pe = 1.0\n",
+ "\n",
+ "sigma_ref = 5e-3\n",
+ "heat_ratio = 1\n",
+ "\n",
+ "parameters = calculate_parameters_droplet_migration(radius=Radius, reynolds_number=Re,\n",
+ " capillary_number=Ca, marangoni_number=Ma,\n",
+ " peclet_number=Pe, viscosity_ratio=1,\n",
+ " heat_ratio=heat_ratio, interface_width=4,\n",
+ " reference_surface_tension=sigma_ref)\n",
+ "parameters.interface_thickness = 4\n",
+ "u_max = parameters.velocity_wall\n",
+ "timesteps = simulation_time * parameters.reference_time\n",
+ "\n",
+ "contact_angle_degree = 90"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr style=\"border:none\">\n",
+ " <th style=\"border:none\" >Name</th>\n",
+ " <th style=\"border:none\" >SymPy Symbol </th>\n",
+ " <th style=\"border:none\" >Value</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Density heavy phase</td>\n",
+ " <td style=\"border:none\">$\\rho_{H}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Density light phase</td>\n",
+ " <td style=\"border:none\">$\\rho_{L}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Relaxation time heavy phase</td>\n",
+ " <td style=\"border:none\">$\\tau_{H}$</td>\n",
+ " <td style=\"border:none\">$0.3$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Relaxation time light phase</td>\n",
+ " <td style=\"border:none\">$\\tau_{L}$</td>\n",
+ " <td style=\"border:none\">$0.3$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Relaxation rate Allen Cahn LB</td>\n",
+ " <td style=\"border:none\">$\\omega_{\\phi}$</td>\n",
+ " <td style=\"border:none\">$1.97628458498024$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Gravitational acceleration</td>\n",
+ " <td style=\"border:none\">$F_{g}$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Interface thickness</td>\n",
+ " <td style=\"border:none\">$W$</td>\n",
+ " <td style=\"border:none\">$4$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Mobility</td>\n",
+ " <td style=\"border:none\">$M_{m}$</td>\n",
+ " <td style=\"border:none\">$0.002$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Surface tension</td>\n",
+ " <td style=\"border:none\">$\\sigma$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Heat Conductivity Heavy</td>\n",
+ " <td style=\"border:none\">$\\kappa_{H}$</td>\n",
+ " <td style=\"border:none\">$0.2$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Heat Conductivity Light</td>\n",
+ " <td style=\"border:none\">$\\kappa_{L}$</td>\n",
+ " <td style=\"border:none\">$0.2$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Sigma Ref</td>\n",
+ " <td style=\"border:none\">$\\sigma_{ref}$</td>\n",
+ " <td style=\"border:none\">$0.005$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Sigma T</td>\n",
+ " <td style=\"border:none\">$\\sigma_{T}$</td>\n",
+ " <td style=\"border:none\">$0.0002$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">Temperature Ref</td>\n",
+ " <td style=\"border:none\">$T_{ref}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " </tr>\n",
+ "\n",
+ " </table>\n",
+ " "
+ ],
+ "text/plain": [
+ "<lbmpy.phasefield_allen_cahn.parameter_calculation.ThermocapillaryParameters at 0x7faa3b792690>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "parameters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fields\n",
+ "\n",
+ "As a next step all fields which are needed get defined. To do so we create a `datahandling` object. More details about it can be found in the third tutorial of the [pystencils framework]( http://pycodegen.pages.walberla.net/pystencils/). Basically it holds all fields and manages the kernel runs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a datahandling object\n",
+ "dh = ps.create_data_handling((domain_size), periodicity=(True, False), default_target=target)\n",
+ "\n",
+ "# fields \n",
+ "g = dh.add_array(\"g\", values_per_cell=len(stencil_hydro))\n",
+ "dh.fill(\"g\", 0.0, ghost_layers=True)\n",
+ "h = dh.add_array(\"h\",values_per_cell=len(stencil_phase))\n",
+ "dh.fill(\"h\", 0.0, ghost_layers=True)\n",
+ "f = dh.add_array(\"f\",values_per_cell=len(stencil_thermal))\n",
+ "dh.fill(\"f\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "g_tmp = dh.add_array(\"g_tmp\", values_per_cell=len(stencil_hydro))\n",
+ "dh.fill(\"g_tmp\", 0.0, ghost_layers=True)\n",
+ "h_tmp = dh.add_array(\"h_tmp\",values_per_cell=len(stencil_phase))\n",
+ "dh.fill(\"h_tmp\", 0.0, ghost_layers=True)\n",
+ "f_tmp = dh.add_array(\"f_tmp\",values_per_cell=len(stencil_thermal))\n",
+ "dh.fill(\"f_tmp\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "u = dh.add_array(\"u\", values_per_cell=dh.dim)\n",
+ "dh.fill(\"u\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "C = dh.add_array(\"C\")\n",
+ "dh.fill(\"C\", 0.0, ghost_layers=True)\n",
+ "C_tmp = dh.add_array(\"C_tmp\")\n",
+ "dh.fill(\"C_tmp\", 0.0, ghost_layers=True)\n",
+ "\n",
+ "temperature = dh.add_array(\"temperature\")\n",
+ "dh.fill(\"temperature\", parameters.tmp_ref, ghost_layers=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parameter definition\n",
+ "\n",
+ "The next step is to calculate all parameters which are needed for the simulation. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "one = sp.Number(1)\n",
+ "two = sp.Number(2)\n",
+ "k_l = parameters.symbolic_heat_conductivity_light\n",
+ "k_h = parameters.symbolic_heat_conductivity_heavy\n",
+ "\n",
+ "# relaxation rate for the phase-field LBM step\n",
+ "w_c = 1.0/(0.5 + (3.0 * parameters.symbolic_mobility))\n",
+ "# relaxation rate for the hydrodynamic LBM step\n",
+ "omega = parameters.omega(C)\n",
+ "# relaxation rate for the thermal LBM solver\n",
+ "conductivity = ((one - C.center) / two) * k_l + ((one + C.center) / two) * k_h\n",
+ "w_t = one/(sp.Rational(1, 2) + (sp.Number(3) * conductivity))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# density for the whole domain\n",
+ "rho_L = parameters.symbolic_density_light\n",
+ "rho_H = parameters.symbolic_density_heavy\n",
+ "density = rho_L + C.center * (rho_H - rho_L)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of the lattice Boltzmann methods"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Central-Moment-Based Method\n",
+ " </th>\n",
+ " <td>Stencil: D2Q9</td>\n",
+ " <td>Zero-Centered Storage: ✗</td>\n",
+ " <td>Force Model: Guo</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Continuous Hydrodynamic Maxwellian Equilibrium\n",
+ " </th>\n",
+ " <td rowspan=\"2\" style=\"width: 50%; text-align: center\">\n",
+ " $f (\\rho, \\left( u_{0}, \\ u_{1}\\right), \\left( v_{0}, \\ v_{1}\\right)) \n",
+ " = \\frac{3 \\rho e^{- \\frac{3 \\left(- u_{0} + v_{0}\\right)^{2}}{2} - \\frac{3 \\left(- u_{1} + v_{1}\\right)^{2}}{2}}}{2 \\pi}$\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>Compressible: ✓</td>\n",
+ " <td>Deviation Only: ✗</td>\n",
+ " <td>Order: 2</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr> <th colspan=\"3\" style=\"text-align: left\"> Relaxation Info </th> </tr>\n",
+ " <tr>\n",
+ " <th>Central Moment</th>\n",
+ " <th>Eq. Value </th>\n",
+ " <th>Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2 \\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\rho}{9}$</td>\n",
+ " <td style=\"border:none\">$\\frac{1.0}{3.0 M_{m} + 0.5}$</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod at 0x7faa38901550>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "config_phase = LBMConfig(stencil=stencil_phase, method=Method.CENTRAL_MOMENT,\n",
+ " compressible=True, zero_centered=False,\n",
+ " relaxation_rates=[w_c, ] * stencil_phase.Q,\n",
+ " force=sp.symbols(f\"F_:{stencil_phase.D}\"),\n",
+ " output={'density': C_tmp}, \n",
+ " velocity_input=u)\n",
+ "\n",
+ "method_phase = create_lb_method(config_phase)\n",
+ "method_phase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Central-Moment-Based Method\n",
+ " </th>\n",
+ " <td>Stencil: D2Q9</td>\n",
+ " <td>Zero-Centered Storage: ✓</td>\n",
+ " <td>Force Model: Guo</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Continuous Hydrodynamic Maxwellian Equilibrium\n",
+ " </th>\n",
+ " <td rowspan=\"2\" style=\"width: 50%; text-align: center\">\n",
+ " $f (\\rho, \\left( u_{0}, \\ u_{1}\\right), \\left( v_{0}, \\ v_{1}\\right)) \n",
+ " = \\frac{3 \\delta_{\\rho} e^{- \\frac{3 v_{0}^{2}}{2} - \\frac{3 v_{1}^{2}}{2}}}{2 \\pi} + \\frac{3 e^{- \\frac{3 \\left(- u_{0} + v_{0}\\right)^{2}}{2} - \\frac{3 \\left(- u_{1} + v_{1}\\right)^{2}}{2}}}{2 \\pi}$\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>Compressible: ✗</td>\n",
+ " <td>Deviation Only: ✗</td>\n",
+ " <td>Order: 2</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr> <th colspan=\"3\" style=\"text-align: left\"> Relaxation Info </th> </tr>\n",
+ " <tr>\n",
+ " <th>Central Moment</th>\n",
+ " <th>Eq. Value </th>\n",
+ " <th>Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$- \\delta_{\\rho} u_{0}$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$- \\delta_{\\rho} u_{1}$</td>\n",
+ " <td style=\"border:none\">$0.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$\\delta_{\\rho} u_{0} u_{1}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2}{2 {C}_{(0,0)} \\left(\\tau_{H} - \\tau_{L}\\right) + 2 \\tau_{L} + 1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\delta_{\\rho} u_{0}^{2} - \\delta_{\\rho} u_{1}^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2}{2 {C}_{(0,0)} \\left(\\tau_{H} - \\tau_{L}\\right) + 2 \\tau_{L} + 1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\delta_{\\rho} u_{0}^{2} + \\delta_{\\rho} u_{1}^{2} + \\frac{2 \\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2}{2 {C}_{(0,0)} \\left(\\tau_{H} - \\tau_{L}\\right) + 2 \\tau_{L} + 1}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y$</td>\n",
+ " <td style=\"border:none\">$- \\frac{\\delta_{\\rho} u_{1}}{3}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y^{2}$</td>\n",
+ " <td style=\"border:none\">$- \\frac{\\delta_{\\rho} u_{0}}{3}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\delta_{\\rho} u_{0}^{2}}{3} + \\frac{\\delta_{\\rho} u_{1}^{2}}{3} + \\frac{\\rho}{9}$</td>\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod at 0x7faa35715c10>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "config_hydro = LBMConfig(stencil=stencil_hydro, method=Method.CENTRAL_MOMENT,\n",
+ " compressible=False,\n",
+ " force=sp.symbols(f\"F_:{stencil_hydro.D}\"),\n",
+ " output={'velocity': u},\n",
+ " relaxation_rates=[omega, omega, 1, 1])\n",
+ "\n",
+ "\n",
+ "method_hydro = create_lb_method(config_hydro)\n",
+ "method_hydro"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Central-Moment-Based Method\n",
+ " </th>\n",
+ " <td>Stencil: D2Q9</td>\n",
+ " <td>Zero-Centered Storage: ✗</td>\n",
+ " <td>Force Model: None</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr>\n",
+ " <th colspan=\"3\" style=\"text-align: left\">\n",
+ " Continuous Hydrodynamic Maxwellian Equilibrium\n",
+ " </th>\n",
+ " <td rowspan=\"2\" style=\"width: 50%; text-align: center\">\n",
+ " $f (\\rho, \\left( u_{0}, \\ u_{1}\\right), \\left( v_{0}, \\ v_{1}\\right)) \n",
+ " = \\frac{3 \\rho e^{- \\frac{3 \\left(- u_{0} + v_{0}\\right)^{2}}{2} - \\frac{3 \\left(- u_{1} + v_{1}\\right)^{2}}{2}}}{2 \\pi}$\n",
+ " </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>Compressible: ✓</td>\n",
+ " <td>Deviation Only: ✗</td>\n",
+ " <td>Order: 2</td>\n",
+ " </tr>\n",
+ " </table>\n",
+ " \n",
+ " <table style=\"border:none; width: 100%\">\n",
+ " <tr> <th colspan=\"3\" style=\"text-align: left\"> Relaxation Info </th> </tr>\n",
+ " <tr>\n",
+ " <th>Central Moment</th>\n",
+ " <th>Eq. Value </th>\n",
+ " <th>Relaxation Rate</th>\n",
+ " </tr>\n",
+ " <tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$1$</td>\n",
+ " <td style=\"border:none\">$\\rho$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{3 \\kappa_{H} \\left(\\frac{{C}_{(0,0)}}{2} + \\frac{1}{2}\\right) + 3 \\kappa_{L} \\left(\\frac{1}{2} - \\frac{{C}_{(0,0)}}{2}\\right) + \\frac{1}{2}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{3 \\kappa_{H} \\left(\\frac{{C}_{(0,0)}}{2} + \\frac{1}{2}\\right) + 3 \\kappa_{L} \\left(\\frac{1}{2} - \\frac{{C}_{(0,0)}}{2}\\right) + \\frac{1}{2}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{3 \\kappa_{H} \\left(\\frac{{C}_{(0,0)}}{2} + \\frac{1}{2}\\right) + 3 \\kappa_{L} \\left(\\frac{1}{2} - \\frac{{C}_{(0,0)}}{2}\\right) + \\frac{1}{2}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{3 \\kappa_{H} \\left(\\frac{{C}_{(0,0)}}{2} + \\frac{1}{2}\\right) + 3 \\kappa_{L} \\left(\\frac{1}{2} - \\frac{{C}_{(0,0)}}{2}\\right) + \\frac{1}{2}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} - y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$\\frac{1}{3 \\kappa_{H} \\left(\\frac{{C}_{(0,0)}}{2} + \\frac{1}{2}\\right) + 3 \\kappa_{L} \\left(\\frac{1}{2} - \\frac{{C}_{(0,0)}}{2}\\right) + \\frac{1}{2}}$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} + y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{2 \\rho}{3}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x y^{2}$</td>\n",
+ " <td style=\"border:none\">$0$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "<tr style=\"border:none\">\n",
+ " <td style=\"border:none\">$x^{2} y^{2}$</td>\n",
+ " <td style=\"border:none\">$\\frac{\\rho}{9}$</td>\n",
+ " <td style=\"border:none\">$1.0$</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "<lbmpy.methods.momentbased.centralmomentbasedmethod.CentralMomentBasedLbMethod at 0x7faa3b7b4490>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "config_thermal = LBMConfig(stencil=stencil_thermal, method=Method.CENTRAL_MOMENT,\n",
+ " compressible=True, zero_centered=False, relaxation_rate=w_t,\n",
+ " output={'density': temperature}, velocity_input=u)\n",
+ "\n",
+ "method_thermal = create_lb_method(lbm_config=config_thermal)\n",
+ "method_thermal"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initialization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "h_updates = initializer_kernel_phase_field_lb(method_phase, C, u, h, parameters)\n",
+ "g_updates = initializer_kernel_hydro_lb(method_hydro, 1.0, u, g)\n",
+ "f_updates = pdf_initialization_assignments(method_thermal, temperature, u, f)\n",
+ "\n",
+ "h_init = ps.create_kernel(h_updates, target=dh.default_target, cpu_openmp=True).compile()\n",
+ "g_init = ps.create_kernel(g_updates, target=dh.default_target, cpu_openmp=True).compile()\n",
+ "f_init = ps.create_kernel(f_updates, target=dh.default_target, cpu_openmp=True).compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Initialisation of the phase-field, as well as the temperature array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# initialize the domain\n",
+ "def Initialize_distributions():\n",
+ " Nx = domain_size[0]\n",
+ " Ny = domain_size[1]\n",
+ " \n",
+ " for block in dh.iterate(ghost_layers=True, inner_ghost_layers=False):\n",
+ " x = np.zeros_like(block.midpoint_arrays[0])\n",
+ " x[:, :] = block.midpoint_arrays[0] \n",
+ " y = np.zeros_like(block.midpoint_arrays[1])\n",
+ " y[:, :] = block.midpoint_arrays[1]\n",
+ " \n",
+ " tmp = np.sqrt((x - midpoint[0])**2 + (y - midpoint[1])**2)\n",
+ " init_values = 0.5 - 0.5 * np.tanh(2.0 * (tmp - Radius)/ parameters.interface_thickness)\n",
+ " block[\"C\"][:, :] = init_values\n",
+ " block[\"C_tmp\"][:, :] = init_values\n",
+ " \n",
+ " if gpu:\n",
+ " dh.all_to_gpu() \n",
+ " \n",
+ " dh.run_kernel(h_init, **parameters.symbolic_to_numeric_map)\n",
+ " dh.run_kernel(g_init)\n",
+ " dh.run_kernel(f_init)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "force_h = force_h = interface_tracking_force(C, stencil_phase, parameters)\n",
+ "hydro_force = hydrodynamic_force(C, method_hydro, parameters, body_force=[0, 0, 0],\n",
+ " temperature_field=temperature)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Heat source acting on the temperature field"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "counters = [LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(stencil_hydro.D)]\n",
+ "\n",
+ "xs = 181\n",
+ "ys = 21\n",
+ "ws = 6\n",
+ "ds = 8\n",
+ "Qs = 0.2\n",
+ "\n",
+ "nominator = ((counters[0] - xs)**2 + (counters[1] - ys)**2)\n",
+ "term = Qs * sp.exp(-2 * nominator / (ws**2) )\n",
+ "heat_soure = sp.Piecewise((term, nominator <= ds**2), (0.0, True))\n",
+ "\n",
+ "weights = method_thermal.weights\n",
+ "heat_terms = list()\n",
+ "\n",
+ "for i in range(len(stencil_thermal)):\n",
+ " heat_terms.append(weights[i] * heat_soure)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Definition of the LB update rules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lbm_optimisation = LBMOptimisation(symbolic_field=h, symbolic_temporary_field=h_tmp)\n",
+ "allen_cahn_lb = create_lb_update_rule(lbm_config=config_phase,\n",
+ " lbm_optimisation=lbm_optimisation)\n",
+ "\n",
+ "allen_cahn_lb = add_interface_tracking_force(allen_cahn_lb, force_h)\n",
+ "\n",
+ "ast_allen_cahn_lb = ps.create_kernel(allen_cahn_lb, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_allen_cahn_lb = ast_allen_cahn_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "force_Assignments = hydrodynamic_force_assignments(u, C, method_hydro, parameters,\n",
+ " body_force=[0, 0, 0], temperature_field=temperature)\n",
+ "\n",
+ "lbm_optimisation = LBMOptimisation(symbolic_field=g, symbolic_temporary_field=g_tmp)\n",
+ "hydro_lb_update_rule = create_lb_update_rule(lbm_config=config_hydro,\n",
+ " lbm_optimisation=lbm_optimisation)\n",
+ "hydro_lb_update_rule = add_hydrodynamic_force(hydro_lb_update_rule, force_Assignments, C, g,\n",
+ " parameters, config_hydro) \n",
+ "\n",
+ "ast_hydro_lb = ps.create_kernel(hydro_lb_update_rule, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_hydro_lb = ast_hydro_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lbm_optimisation = LBMOptimisation(symbolic_field=f, symbolic_temporary_field=f_tmp)\n",
+ "\n",
+ "thermal_lb_update_rule = create_lb_update_rule(lbm_config=config_thermal,\n",
+ " lbm_optimisation=lbm_optimisation)\n",
+ "\n",
+ "main_assignments = thermal_lb_update_rule.main_assignments\n",
+ "\n",
+ "for i in range(len(stencil_thermal)):\n",
+ " main_assignments[i] = ps.Assignment(main_assignments[i].lhs, main_assignments[i].rhs + heat_terms[i])\n",
+ "\n",
+ "ast_thermal_lb = ps.create_kernel(thermal_lb_update_rule, target=dh.default_target, cpu_openmp=True)\n",
+ "kernel_thermal_lb = ast_thermal_lb.compile()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Boundary Conditions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "periodic_BC_C = dh.synchronization_function(C.name, target=dh.default_target, optimization={\"openmp\": True})\n",
+ "# periodic_BC_T = dh.synchronization_function(temperature.name, target=dh.default_target, optimization={\"openmp\": True})\n",
+ "\n",
+ "periodic_BC_g = LBMPeriodicityHandling(stencil=stencil_hydro, data_handling=dh, pdf_field_name=g.name)\n",
+ "periodic_BC_h = LBMPeriodicityHandling(stencil=stencil_phase, data_handling=dh, pdf_field_name=h.name)\n",
+ "\n",
+ "# No slip boundary for the phasefield lbm\n",
+ "bh_allen_cahn = LatticeBoltzmannBoundaryHandling(method_phase, dh, h.name,\n",
+ " target=dh.default_target, name='boundary_handling_h')\n",
+ "\n",
+ "# No slip boundary for the velocityfield lbm\n",
+ "bh_hydro = LatticeBoltzmannBoundaryHandling(method_hydro, dh, \"g\",\n",
+ " target=dh.default_target, name='boundary_handling_g')\n",
+ "\n",
+ "bh_thermal = LatticeBoltzmannBoundaryHandling(method_thermal, dh, f.name,\n",
+ " target=dh.default_target, name='boundary_handling_f')\n",
+ "\n",
+ "contact_angle = BoundaryHandling(dh, C.name, LBStencil(Stencil.D2Q9), target=dh.default_target)\n",
+ "contact = ContactAngle(contact_angle_degree, parameters.interface_thickness)\n",
+ "\n",
+ "wall = NoSlip()\n",
+ "\n",
+ "contact_angle.set_boundary(contact, make_slice[:, 0])\n",
+ "contact_angle.set_boundary(contact, make_slice[:, -1])\n",
+ "\n",
+ "bh_allen_cahn.set_boundary(wall, make_slice[:, 0])\n",
+ "bh_allen_cahn.set_boundary(wall, make_slice[:, -1])\n",
+ "\n",
+ "bh_hydro.set_boundary(wall, make_slice[:, 0])\n",
+ "bh_hydro.set_boundary(UBB((u_max, 0)), make_slice[:, -1])\n",
+ "\n",
+ "bh_thermal.set_boundary(DiffusionDirichlet(T_c, u), make_slice[:, 0])\n",
+ "bh_thermal.set_boundary(DiffusionDirichlet(T_c, u), make_slice[:, -1])\n",
+ "bh_thermal.set_boundary(NeumannByCopy(), make_slice[0, :])\n",
+ "bh_thermal.set_boundary(NeumannByCopy(), make_slice[-1, :])\n",
+ "\n",
+ "bh_allen_cahn.prepare()\n",
+ "bh_hydro.prepare()\n",
+ "bh_thermal.prepare()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Full timestep"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def Temp_update():\n",
+ " # periodic_BC_f()\n",
+ " bh_thermal()\n",
+ " dh.run_kernel(kernel_thermal_lb, **parameters.symbolic_to_numeric_map)\n",
+ " dh.swap(f.name, f_tmp.name)\n",
+ " # periodic_BC_T()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# definition of the timestep for the immiscible fluids model\n",
+ "def timeloop():\n",
+ " # Solve the interface tracking LB step with boundary conditions\n",
+ " periodic_BC_h()\n",
+ " bh_allen_cahn() \n",
+ " dh.run_kernel(kernel_allen_cahn_lb, **parameters.symbolic_to_numeric_map)\n",
+ " # Solve the hydro LB step with boundary conditions\n",
+ " periodic_BC_g()\n",
+ " bh_hydro()\n",
+ " dh.run_kernel(kernel_hydro_lb, **parameters.symbolic_to_numeric_map)\n",
+ " \n",
+ " dh.swap(C.name, C_tmp.name)\n",
+ " # periodic BC of the phase-field\n",
+ " periodic_BC_C()\n",
+ " contact_angle()\n",
+ " \n",
+ " # Update the temperature field\n",
+ " Temp_update()\n",
+ " \n",
+ " # field swaps\n",
+ " dh.swap(\"h\", \"h_tmp\")\n",
+ " dh.swap(\"g\", \"g_tmp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <source 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type=\"video/mp4\">\n",
+ " Your browser does not support the video tag.\n",
+ "</video>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Initialize_distributions()\n",
+ "\n",
+ "if 'is_test_run' not in globals():\n",
+ " # initial temperature field is gathered by running the thermal step 1s of physical time\n",
+ " for i in range(0, parameters.reference_time):\n",
+ " Temp_update() \n",
+ "\n",
+ " def run():\n",
+ " dh.to_cpu(C.name)\n",
+ " phase_field = dh.gather_array(C.name)\n",
+ " for i in range (int(parameters.reference_time / 25)):\n",
+ " timeloop()\n",
+ " return phase_field\n",
+ "\n",
+ " animation = plt.scalar_field_animation(run, frames=int(25 * simulation_time), rescale=True)\n",
+ " set_display_mode('video')\n",
+ " res = display_animation(animation)\n",
+ "else:\n",
+ " timeloop(10)\n",
+ " res = None\n",
+ "res"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " if gpu:\n",
+ " dh.all_to_cpu()\n",
+ "\n",
+ " plt.scalar_field(dh.gather_array(C.name))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " if gpu:\n",
+ " dh.all_to_cpu()\n",
+ "\n",
+ " plt.scalar_field(dh.gather_array(temperature.name))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "if 'is_test_run' not in globals():\n",
+ " if gpu:\n",
+ " dh.all_to_cpu()\n",
+ "\n",
+ " plt.vector_field_magnitude(dh.gather_array(u.name))"
+ ]
+ },
+ {
+ "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": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/doc/sphinx/tutorials.rst b/doc/sphinx/tutorials.rst
index d5874500c809f0718a444c969fd63dee3bb0c234..7d8e40912a1723c4bdf16353355569ae5932f0f1 100644
--- a/doc/sphinx/tutorials.rst
+++ b/doc/sphinx/tutorials.rst
@@ -4,6 +4,10 @@ Tutorials
All tutorials are automatically created by Jupyter Notebooks.
You can open the notebooks directly to play around with the code examples.
+=================
+Basics
+=================
+
.. toctree::
:maxdepth: 1
@@ -13,18 +17,68 @@ You can open the notebooks directly to play around with the code examples.
/notebooks/03_tutorial_lbm_formulation.ipynb
/notebooks/04_tutorial_cumulant_LBM.ipynb
/notebooks/05_tutorial_nondimensionalization_and_scaling.ipynb
+
+===================
+Turbulence modeling
+===================
+
+.. toctree::
+ :maxdepth: 1
+
/notebooks/06_tutorial_modifying_method_smagorinsky.ipynb
+
+=================
+Thermal flows
+=================
+
+.. toctree::
+ :maxdepth: 1
+
/notebooks/07_tutorial_thermal_lbm.ipynb
+ /notebooks/demo_thermalized_lbm.ipynb
+
+=================
+Multiphase flows
+=================
+
+.. toctree::
+ :maxdepth: 1
+
/notebooks/08_tutorial_shanchen_twophase.ipynb
/notebooks/09_tutorial_shanchen_twocomponent.ipynb
/notebooks/10_tutorial_conservative_allen_cahn_two_phase.ipynb
+
+========================
+Thermocapillary flows
+========================
+
+.. toctree::
+ :maxdepth: 1
+
+ /notebooks/12_Thermocapillary_flows_heated_channel.ipynb
+ /notebooks/13_Thermocapillary_flows_droplet_motion.ipynb
+
+===================
+Non Newtonian flow
+===================
+
+.. toctree::
+ :maxdepth: 1
+
/notebooks/11_tutorial_Non_Newtonian_Flow.ipynb
+
+=================
+Diverse
+=================
+
+.. toctree::
+ :maxdepth: 1
+
/notebooks/demo_stencils.ipynb
/notebooks/demo_streaming_patterns.ipynb
/notebooks/demo_create_method_from_scratch.ipynb
/notebooks/demo_moments_cumulants_and_maxwellian_equilibrium.ipynb
/notebooks/demo_automatic_chapman_enskog_analysis.ipynb
/notebooks/demo_interpolation_boundary_conditions.ipynb
- /notebooks/demo_thermalized_lbm.ipynb
/notebooks/demo_shallow_water_lbm.ipynb
/notebooks/demo_theoretical_background_generic_equilibrium_construction.ipynb
diff --git a/lbmpy/advanced_streaming/communication.py b/lbmpy/advanced_streaming/communication.py
index 9c7dc4ca16af5ddb71dc0e147015af3c9bbde411..aa12f5d4a4ddebb48868b2a9491e68774ecdbb01 100644
--- a/lbmpy/advanced_streaming/communication.py
+++ b/lbmpy/advanced_streaming/communication.py
@@ -163,6 +163,7 @@ def periodic_pdf_copy_kernel(pdf_field, src_slice, dst_slice,
src_slice = src_slice[:-1]
dst_slice = dst_slice[:-1]
+ # TODO this is the domain_size with GL
if domain_size is None:
domain_size = pdf_field.spatial_shape
diff --git a/lbmpy/boundaries/boundaryconditions.py b/lbmpy/boundaries/boundaryconditions.py
index 70438e3bbcb85191f0b1d80b1b3b76c44101964b..d0829e04385042af94d3c9415f32785a918ed0e3 100644
--- a/lbmpy/boundaries/boundaryconditions.py
+++ b/lbmpy/boundaries/boundaryconditions.py
@@ -4,7 +4,7 @@ from warnings import warn
from pystencils import Assignment, Field
from pystencils.simp.assignment_collection import AssignmentCollection
from pystencils.stencil import offset_to_direction_string, direction_string_to_offset, inverse_direction
-from pystencils.sympyextensions import get_symmetric_part, simplify_by_equality
+from pystencils.sympyextensions import get_symmetric_part, simplify_by_equality, scalar_product
from pystencils.typing import create_type, TypedSymbol
from lbmpy.advanced_streaming.utility import AccessPdfValues, Timestep
@@ -806,23 +806,46 @@ class FixedDensity(LbBoundary):
# end class FixedDensity
-
class DiffusionDirichlet(LbBoundary):
- """Boundary condition for advection-diffusion problems that fixes the concentration at the obstacle.
+ """Concentration boundary which is used for concentration or thermal boundary conditions of convection-diffusion
+ equation Base on https://doi.org/10.1103/PhysRevE.85.016701.
Args:
- concentration: value of the concentration which should be set.
+ concentration: can either be a constant, an access into a field, or a callback function.
+ The callback functions gets a numpy record array with members, 'x','y','z', 'dir' (direction)
+ and 'concentration' which has to be set to the desired velocity of the corresponding link
+ velocity_field: if velocity field is given the boundary value is approximated by using the discrete equilibrium.
name: optional name of the boundary.
+ data_type: data type of the concentration value. default is double
"""
- def __init__(self, concentration, name=None, data_type='double'):
+ def __init__(self, concentration, velocity_field=None, name=None, data_type='double'):
if name is None:
- name = "Diffusion Dirichlet " + str(concentration)
+ name = "DiffusionDirichlet"
self.concentration = concentration
self._data_type = data_type
+ self.concentration_is_callable = callable(self.concentration)
+ self.velocity_field = velocity_field
super(DiffusionDirichlet, self).__init__(name)
+ @property
+ def additional_data(self):
+ """ In case of the UBB boundary additional data is a velocity vector. This vector is added to each cell to
+ realize velocity profiles for the inlet."""
+ if self.concentration_is_callable:
+ return [('concentration', create_type(self._data_type))]
+ else:
+ return []
+
+ @property
+ def additional_data_init_callback(self):
+ """Initialise additional data of the boundary. For an example see
+ `tutorial 02 <https://pycodegen.pages.i10git.cs.fau.de/lbmpy/notebooks/02_tutorial_boundary_setup.html>`_
+ or lbmpy.geometry.add_pipe_inflow_boundary"""
+ if self.concentration_is_callable:
+ return self.concentration
+
def get_additional_code_nodes(self, lb_method):
"""Return a list of code nodes that will be added in the generated code before the index field loop.
@@ -832,15 +855,34 @@ class DiffusionDirichlet(LbBoundary):
Returns:
list containing LbmWeightInfo
"""
- return [LbmWeightInfo(lb_method, self._data_type)]
+ if self.velocity_field:
+ return [LbmWeightInfo(lb_method, self._data_type), NeighbourOffsetArrays(lb_method.stencil)]
+ else:
+ return [LbmWeightInfo(lb_method, self._data_type)]
def __call__(self, f_out, f_in, dir_symbol, inv_dir, lb_method, index_field):
assert lb_method.conserved_quantity_computation.zero_centered_pdfs is False, \
"DiffusionDirichlet only works for methods with normal pdfs storage -> set zero_centered=False"
weight_info = LbmWeightInfo(lb_method, self._data_type)
w_dir = weight_info.weight_of_direction(dir_symbol, lb_method)
- return [Assignment(f_in(inv_dir[dir_symbol]),
- 2 * w_dir * self.concentration - f_out(dir_symbol))]
+
+ if self.concentration_is_callable:
+ concentration = index_field[0]('concentration')
+ else:
+ concentration = self.concentration
+
+ if self.velocity_field:
+ neighbour_offset = NeighbourOffsetArrays.neighbour_offset(dir_symbol, lb_method.stencil)
+ u = self.velocity_field
+ cs = sp.Rational(1, 3)
+
+ equilibrium = (1 + scalar_product(neighbour_offset, u.center_vector)**2 / (2 * cs**4)
+ - scalar_product(u.center_vector, u.center_vector) / (2 * cs**2))
+ else:
+ equilibrium = sp.Rational(1, 1)
+
+ result = [Assignment(f_in(inv_dir[dir_symbol]), 2.0 * w_dir * concentration * equilibrium - f_out(dir_symbol))]
+ return result
# end class DiffusionDirichlet
diff --git a/lbmpy/boundaries/boundaryhandling.py b/lbmpy/boundaries/boundaryhandling.py
index 437a755ae6cf0a6d8b1a092cb17ad7950939e2fe..ed086d1f501666232a93e3663f350fd4aecc419d 100644
--- a/lbmpy/boundaries/boundaryhandling.py
+++ b/lbmpy/boundaries/boundaryhandling.py
@@ -12,8 +12,8 @@ from lbmpy.advanced_streaming.utility import is_inplace, Timestep, AccessPdfValu
class LatticeBoltzmannBoundaryHandling(BoundaryHandling):
"""
- Enables boundary handling for LBM simulations with advanced streaming patterns.
- For the in-place patterns AA and EsoTwist, two kernels are generated for a boundary
+ Enables boundary handling for LBM simulations with advanced streaming patterns.
+ For the in-place patterns AA and EsoTwist, two kernels are generated for a boundary
object and the right one selected depending on the time step.
"""
diff --git a/lbmpy/creationfunctions.py b/lbmpy/creationfunctions.py
index 79050dbbd39685496c60e6d82436ab61e0c0f6a4..b0497e6bbfe91b17504cddd1fa7531acc7e5e0d4 100644
--- a/lbmpy/creationfunctions.py
+++ b/lbmpy/creationfunctions.py
@@ -284,6 +284,11 @@ class LBMConfig:
Symbolic field where the density is read from. If `None` is given the density is calculated inplace from
with zeroth order moment.
"""
+ conserved_moments: bool = True
+ """
+ If lower order moments are conserved or not. If velocity or density input is set the lower order moments are not
+ conserved anymore.
+ """
kernel_type: Union[str, Type[PdfFieldAccessor]] = 'default_stream_collide'
"""
@@ -470,6 +475,9 @@ class LBMConfig:
force_model_class = force_model_dict[self.force_model.name.lower()]
self.force_model = force_model_class(force=self.force[:self.stencil.D])
+ if self.density_input or self.velocity_input:
+ self.conserved_moments = False
+
@dataclass
class LBMOptimisation:
@@ -791,12 +799,15 @@ def create_lb_method(lbm_config=None, **params):
method = create_trt(lbm_config.stencil, relaxation_rates[0], relaxation_rates[1], **common_params)
elif lbm_config.method == Method.MRT:
method = create_mrt_orthogonal(lbm_config.stencil, relaxation_rates, weighted=lbm_config.weighted,
- nested_moments=lbm_config.nested_moments, **common_params)
+ nested_moments=lbm_config.nested_moments,
+ conserved_moments=lbm_config.conserved_moments, **common_params)
elif lbm_config.method == Method.CENTRAL_MOMENT:
method = create_central_moment(lbm_config.stencil, relaxation_rates,
- nested_moments=lbm_config.nested_moments, **common_params)
+ nested_moments=lbm_config.nested_moments,
+ conserved_moments=lbm_config.conserved_moments, **common_params)
elif lbm_config.method == Method.MRT_RAW:
- method = create_mrt_raw(lbm_config.stencil, relaxation_rates, **common_params)
+ method = create_mrt_raw(lbm_config.stencil, relaxation_rates,
+ conserved_moments=lbm_config.conserved_moments, **common_params)
elif lbm_config.method in (Method.TRT_KBC_N1, Method.TRT_KBC_N2, Method.TRT_KBC_N3, Method.TRT_KBC_N4):
if lbm_config.stencil.D == 2 and lbm_config.stencil.Q == 9:
dim = 2
@@ -821,13 +832,14 @@ def create_lb_method(lbm_config=None, **params):
relaxation_rates = FOURTH_ORDER_RELAXATION_RATE_SYMBOLS
if lbm_config.nested_moments is not None:
- method = create_cumulant(
- lbm_config.stencil, relaxation_rates, lbm_config.nested_moments, **cumulant_params)
+ method = create_cumulant(lbm_config.stencil, relaxation_rates, lbm_config.nested_moments,
+ conserved_moments=lbm_config.conserved_moments, **cumulant_params)
else:
method = create_with_default_polynomial_cumulants(lbm_config.stencil, relaxation_rates, **cumulant_params)
elif lbm_config.method == Method.MONOMIAL_CUMULANT:
- method = create_with_monomial_cumulants(lbm_config.stencil, relaxation_rates, **cumulant_params)
+ method = create_with_monomial_cumulants(lbm_config.stencil, relaxation_rates,
+ conserved_moments=lbm_config.conserved_moments, **cumulant_params)
else:
raise ValueError("Failed to create LB method. Please use lbmpy.enums.Method for the creation")
diff --git a/lbmpy/forcemodels.py b/lbmpy/forcemodels.py
index 650ece1359046ad054aa50e8559f8cb0ff530e9f..e517491e3a0bbc2b62181489d66a82576114fef8 100644
--- a/lbmpy/forcemodels.py
+++ b/lbmpy/forcemodels.py
@@ -327,20 +327,20 @@ class He(AbstractForceModel):
.. math::
- F (\mathbf{c})
- = \frac{1}{\rho c_s^2}
- \mathbf{F} \cdot ( \mathbf{c} - \mathbf{u} )
+ F (\mathbf{c})
+ = \frac{1}{\rho c_s^2}
+ \mathbf{F} \cdot ( \mathbf{c} - \mathbf{u} )
f^{\mathrm{eq}} (\mathbf{c})
the following analytical expresson for the monomial raw moments of the force is found:
.. math::
- m_{\alpha\beta\gamma}^{F, \mathrm{He}}
- = \frac{1}{\rho c_s^2} \left(
- F_x m^{\mathrm{eq}}_{\alpha+1,\beta,\gamma}
- + F_y m^{\mathrm{eq}}_{\alpha,\beta+1,\gamma}
- + F_z m^{\mathrm{eq}}_{\alpha,\beta,\gamma+1}
+ m_{\alpha\beta\gamma}^{F, \mathrm{He}}
+ = \frac{1}{\rho c_s^2} \left(
+ F_x m^{\mathrm{eq}}_{\alpha+1,\beta,\gamma}
+ + F_y m^{\mathrm{eq}}_{\alpha,\beta+1,\gamma}
+ + F_z m^{\mathrm{eq}}_{\alpha,\beta,\gamma+1}
- m^{eq}_{\alpha\beta\gamma} ( \mathbf{F} \cdot \mathbf{u} )
\right)
"""
diff --git a/lbmpy/methods/creationfunctions.py b/lbmpy/methods/creationfunctions.py
index e7440bd82a81b68c457db4b298ed404e9728c891..01cd85c61fdb0d1b71efaaf280d57d0df420c02b 100644
--- a/lbmpy/methods/creationfunctions.py
+++ b/lbmpy/methods/creationfunctions.py
@@ -304,7 +304,7 @@ def create_trt_with_magic_number(stencil, relaxation_rate, magic_number=sp.Ratio
relaxation_rate_odd_moments=rr_odd, **kwargs)
-def create_mrt_raw(stencil, relaxation_rates, continuous_equilibrium=True, **kwargs):
+def create_mrt_raw(stencil, relaxation_rates, continuous_equilibrium=True, conserved_moments=True, **kwargs):
r"""
Creates a MRT method using non-orthogonalized moments.
@@ -318,6 +318,7 @@ def create_mrt_raw(stencil, relaxation_rates, continuous_equilibrium=True, **kwa
relaxation_rates: relaxation rates (inverse of the relaxation times) for each moment
continuous_equilibrium: determines if the discrete or continuous maxwellian equilibrium is
used to compute the equilibrium moments.
+ conserved_moments: If lower order moments are conserved or not.
Returns:
:class:`lbmpy.methods.momentbased.MomentBasedLbMethod` instance
"""
@@ -325,7 +326,7 @@ def create_mrt_raw(stencil, relaxation_rates, continuous_equilibrium=True, **kwa
check_and_set_mrt_space(CollisionSpace.RAW_MOMENTS)
moments = get_default_moment_set_for_stencil(stencil)
nested_moments = [(c,) for c in moments]
- rr_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments, stencil.D)
+ rr_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments, stencil.D, conserved_moments)
if continuous_equilibrium:
return create_with_continuous_maxwellian_equilibrium(stencil, rr_dict, **kwargs)
else:
@@ -333,7 +334,7 @@ def create_mrt_raw(stencil, relaxation_rates, continuous_equilibrium=True, **kwa
def create_central_moment(stencil, relaxation_rates, nested_moments=None,
- continuous_equilibrium=True, fraction_field=None, **kwargs):
+ continuous_equilibrium=True, conserved_moments=True, fraction_field=None, **kwargs):
r"""
Creates moment based LB method where the collision takes place in the central moment space.
@@ -346,6 +347,8 @@ def create_central_moment(stencil, relaxation_rates, nested_moments=None,
nested_moments: a list of lists of modes, grouped by common relaxation times.
continuous_equilibrium: determines if the discrete or continuous maxwellian equilibrium is
used to compute the equilibrium moments.
+ conserved_moments: If lower order moments are conserved or not.
+ fraction_field: fraction field for the PSM method
Returns:
:class:`lbmpy.methods.momentbased.CentralMomentBasedLbMethod` instance
"""
@@ -367,7 +370,7 @@ def create_central_moment(stencil, relaxation_rates, nested_moments=None,
if not nested_moments:
nested_moments = cascaded_moment_sets_literature(stencil)
- rr_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments, stencil.D)
+ rr_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments, stencil.D, conserved_moments)
if fraction_field is not None:
relaxation_rates_modifier = (1.0 - fraction_field.center)
rr_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments, stencil.D,
@@ -459,7 +462,7 @@ def create_trt_kbc(dim, shear_relaxation_rate, higher_order_relaxation_rate, met
def create_mrt_orthogonal(stencil, relaxation_rates, continuous_equilibrium=True, weighted=None,
- nested_moments=None, **kwargs):
+ nested_moments=None, conserved_moments=True, **kwargs):
r"""
Returns an orthogonal multi-relaxation time model for the stencils D2Q9, D3Q15, D3Q19 and D3Q27.
These MRT methods are just one specific version - there are many MRT methods possible for all these stencils
@@ -480,6 +483,7 @@ def create_mrt_orthogonal(stencil, relaxation_rates, continuous_equilibrium=True
nested_moments: a list of lists of modes, grouped by common relaxation times. If this argument is not provided,
Gram-Schmidt orthogonalization of the default modes is performed. The default modes equal the
raw moments except for the separation of the shear and bulk viscosity.
+ conserved_moments: If lower order moments are conserved or not.
"""
continuous_equilibrium = _deprecate_maxwellian_moments(continuous_equilibrium, kwargs)
check_and_set_mrt_space(CollisionSpace.RAW_MOMENTS)
@@ -511,7 +515,8 @@ def create_mrt_orthogonal(stencil, relaxation_rates, continuous_equilibrium=True
nested_moments[2] = shear_moments
nested_moments.insert(3, bulk_moment)
- moment_to_relaxation_rate_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments, stencil.D)
+ moment_to_relaxation_rate_dict = _get_relaxation_info_dict(relaxation_rates, nested_moments,
+ stencil.D, conserved_moments)
if continuous_equilibrium:
return create_with_continuous_maxwellian_equilibrium(stencil,
@@ -522,8 +527,7 @@ def create_mrt_orthogonal(stencil, relaxation_rates, continuous_equilibrium=True
# ----------------------------------------- Cumulant method creators ---------------------------------------------------
-
-def create_cumulant(stencil, relaxation_rates, cumulant_groups, fraction_field=None, **kwargs):
+def create_cumulant(stencil, relaxation_rates, cumulant_groups, conserved_moments=True, fraction_field=None, **kwargs):
r"""Creates a cumulant-based lattice Boltzmann method.
Args:
@@ -535,12 +539,13 @@ def create_cumulant(stencil, relaxation_rates, cumulant_groups, fraction_field=N
that the force is applied correctly to the momentum groups
cumulant_groups: Nested sequence of polynomial expressions defining the cumulants to be relaxed. All cumulants
within one group are relaxed with the same relaxation rate.
+ conserved_moments: If lower order moments are conserved or not.
kwargs: See :func:`create_with_continuous_maxwellian_equilibrium`
Returns:
:class:`lbmpy.methods.cumulantbased.CumulantBasedLbMethod` instance
"""
- cumulant_to_rr_dict = _get_relaxation_info_dict(relaxation_rates, cumulant_groups, stencil.D)
+ cumulant_to_rr_dict = _get_relaxation_info_dict(relaxation_rates, cumulant_groups, stencil.D, conserved_moments)
if fraction_field is not None:
relaxation_rates_modifier = (1.0 - fraction_field.center)
@@ -557,7 +562,7 @@ def create_cumulant(stencil, relaxation_rates, cumulant_groups, fraction_field=N
**kwargs)
-def create_with_monomial_cumulants(stencil, relaxation_rates, **kwargs):
+def create_with_monomial_cumulants(stencil, relaxation_rates, conserved_moments=True, **kwargs):
r"""Creates a cumulant lattice Boltzmann model using the given stencil's canonical monomial cumulants.
Args:
@@ -567,6 +572,7 @@ def create_with_monomial_cumulants(stencil, relaxation_rates, **kwargs):
used for determine the viscosity of the simulation. All other cumulants are relaxed with one.
If a cumulant force model is provided the first order cumulants are relaxed with two to ensure
that the force is applied correctly to the momentum groups
+ conserved_moments: If lower order moments are conserved or not.
kwargs: See :func:`create_cumulant`
Returns:
@@ -575,10 +581,10 @@ def create_with_monomial_cumulants(stencil, relaxation_rates, **kwargs):
# Get monomial moments
cumulants = get_default_moment_set_for_stencil(stencil)
cumulant_groups = [(c,) for c in cumulants]
- return create_cumulant(stencil, relaxation_rates, cumulant_groups, **kwargs)
+ return create_cumulant(stencil, relaxation_rates, cumulant_groups, conserved_moments, **kwargs)
-def create_with_default_polynomial_cumulants(stencil, relaxation_rates, **kwargs):
+def create_with_default_polynomial_cumulants(stencil, relaxation_rates, conserved_moments=True, **kwargs):
r"""Creates a cumulant lattice Boltzmann model based on the default polynomial set of :cite:`geier2015`.
Args: See :func:`create_cumulant`.
@@ -588,10 +594,11 @@ def create_with_default_polynomial_cumulants(stencil, relaxation_rates, **kwargs
"""
# Get polynomial groups
cumulant_groups = cascaded_moment_sets_literature(stencil)
- return create_cumulant(stencil, relaxation_rates, cumulant_groups, **kwargs)
+ return create_cumulant(stencil, relaxation_rates, cumulant_groups, conserved_moments, **kwargs)
-def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim, relaxation_rates_modifier=None):
+def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim,
+ conserved_moments=True, relaxation_rates_modifier=None):
r"""Creates a dictionary where each moment is mapped to a relaxation rate.
Args:
@@ -601,6 +608,7 @@ def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim, relaxation_
in the moment group.
nested_moments: list of lists containing the moments.
dim: dimension
+ conserved_moments: If lower order moments are conserved or not.
"""
result = OrderedDict()
@@ -630,12 +638,18 @@ def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim, relaxation_
if len(relaxation_rates) == 1:
for group in nested_moments:
for moment in group:
- if get_order(moment) <= 1:
- result[moment] = 0.0
- elif is_shear_moment(moment, dim):
- result[moment] = relaxation_rates[0]
+ if conserved_moments:
+ if get_order(moment) <= 1:
+ result[moment] = 0.0
+ elif is_shear_moment(moment, dim):
+ result[moment] = relaxation_rates[0]
+ else:
+ result[moment] = 1.0
else:
- result[moment] = 1.0
+ if is_shear_moment(moment, dim) or get_order(moment) <= 1:
+ result[moment] = relaxation_rates[0]
+ else:
+ result[moment] = 1.0
# if relaxation rate for each moment is specified they are all used
if len(relaxation_rates) == number_of_moments:
@@ -659,15 +673,25 @@ def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim, relaxation_
rr = next(rr_iter)
next_rr = False
for moment in group:
- if get_order(moment) <= 1:
- result[moment] = 0.0
- elif is_shear_moment(moment, dim):
- result[moment] = shear_rr
- elif is_bulk_moment(moment, dim):
- result[moment] = bulk_rr
+ if conserved_moments:
+ if get_order(moment) <= 1:
+ result[moment] = 0.0
+ elif is_shear_moment(moment, dim):
+ result[moment] = shear_rr
+ elif is_bulk_moment(moment, dim):
+ result[moment] = bulk_rr
+ else:
+ next_rr = True
+ result[moment] = rr
else:
- next_rr = True
- result[moment] = rr
+ if is_shear_moment(moment, dim) or get_order(moment) <= 1:
+ result[moment] = shear_rr
+ elif is_bulk_moment(moment, dim):
+ result[moment] = bulk_rr
+ else:
+ next_rr = True
+ result[moment] = rr
+
except StopIteration:
raise ValueError("Not enough relaxation rates are specified. You can either specify one relaxation rate, "
"which is used as the shear relaxation rate. In this case, conserved moments are "
diff --git a/lbmpy/phasefield_allen_cahn/analytical.py b/lbmpy/phasefield_allen_cahn/analytical.py
index 1fb5b9e244e53c1d7209f2573be87683b48eebc1..72dd886ffe95218d775650cc3c47b97264c62ae6 100644
--- a/lbmpy/phasefield_allen_cahn/analytical.py
+++ b/lbmpy/phasefield_allen_cahn/analytical.py
@@ -1,3 +1,6 @@
+import numpy as np
+from math import sinh, cosh, cos, sin, pi
+
def analytic_rising_speed(gravitational_acceleration, bubble_diameter, viscosity_gas):
r"""
@@ -10,3 +13,77 @@ def analytic_rising_speed(gravitational_acceleration, bubble_diameter, viscosity
"""
result = -(gravitational_acceleration * bubble_diameter * bubble_diameter) / (12.0 * viscosity_gas)
return result
+
+
+def analytical_solution_microchannel(reference_length, length_x, length_y,
+ kappa_top, kappa_bottom,
+ t_h, t_c, t_0,
+ reference_surface_tension, dynamic_viscosity_light_phase,
+ transpose=True):
+ """
+ https://www.sciencedirect.com/science/article/pii/S0021999113005986
+ """
+ l_ref = reference_length
+ sigma_t = reference_surface_tension
+
+ kstar = kappa_top / kappa_bottom
+ mp = (l_ref // 2) - 1
+
+ w = pi / l_ref
+ a = mp * w
+ b = mp * w
+
+ f = 1.0 / (kstar * sinh(b) * cosh(a) + sinh(a) * cosh(b))
+ g = sinh(a) * f
+
+ h = (sinh(a) ** 2 - a ** 2) * (sinh(b) ** 2 - b ** 2) / \
+ ((sinh(b) ** 2 - b ** 2) * (sinh(2.0 * a) - 2.0 * a)
+ + (sinh(a) ** 2 - a ** 2) * (sinh(2.0 * b) - 2.0 * b))
+
+ Ca1 = sinh(a) ** 2 / (sinh(a) ** 2 - a ** 2)
+ Ca2 = -1.0 * mp * a / (sinh(a) ** 2 - a ** 2)
+ Ca3 = (2 * a - sinh(2 * a)) / (2.0 * (sinh(a) ** 2 - a ** 2))
+
+ Cb1 = sinh(b) ** 2 / (sinh(b) ** 2 - b ** 2)
+ Cb2 = -1.0 * mp * b / (sinh(b) ** 2 - b ** 2)
+ Cb3 = (-2 * b + sinh(2 * b)) / (2.0 * (sinh(b) ** 2 - b ** 2))
+
+ umax = -1.0 * (t_0 * sigma_t / dynamic_viscosity_light_phase) * g * h
+ jj = 0
+ xx = np.linspace(-l_ref - 0.5, l_ref - 0.5, length_x)
+ yy = np.linspace(-mp, mp, length_y)
+ u_x = np.zeros([len(xx), len(yy)])
+ u_y = np.zeros([len(xx), len(yy)])
+ t_a = np.zeros([len(xx), len(yy)])
+ tt = t_c - t_h
+ nom = kstar * t_c * mp + t_h * mp
+ denom = mp + kstar * mp
+ for y in yy:
+ ii = 0
+ for x in xx:
+ swx = sin(w * x)
+ cwx = cos(w * x)
+
+ if y > 0:
+ tmp1 = ((Ca1 + w * (Ca2 + Ca3 * y)) * cosh(w * y) + (Ca3 + w * Ca1 * y) * sinh(w * y))
+ tmp2 = (Ca1 * y * cosh(w * y) + (Ca2 + Ca3 * y) * sinh(w * y))
+
+ t_a[ii, jj] = (tt * y + nom) / denom + t_0 * f * sinh(a - y * w) * cwx
+ u_x[ii, jj] = umax * tmp1 * swx
+ u_y[ii, jj] = -w * umax * tmp2 * cwx
+
+ elif y <= 0:
+ tmp3 = (sinh(a) * cosh(w * y) - kstar * sinh(w * y) * cosh(a))
+ tmp4 = ((Cb1 + w * (Cb2 + Cb3 * y)) * cosh(w * y) + (Cb3 + w * Cb1 * y) * sinh(w * y))
+
+ t_a[ii, jj] = (kstar * tt * y + nom) / denom + t_0 * f * tmp3 * cwx
+ u_x[ii, jj] = umax * tmp4 * swx
+ u_y[ii, jj] = -w * umax * (Cb1 * y * cosh(w * y) + (Cb2 + Cb3 * y) * sinh(w * y)) * cwx
+
+ ii += 1
+ jj += 1
+ x, y = np.meshgrid(xx, yy)
+ if transpose:
+ return x, y, u_x.T, u_y.T, t_a.T
+ else:
+ return x, y, u_x, u_y, t_a
diff --git a/lbmpy/phasefield_allen_cahn/contact_angle.py b/lbmpy/phasefield_allen_cahn/contact_angle.py
index e6a16d153ca54e9c681e53d0438068d42d77970e..9f6264da9d06cfd817922102eccd3fd9665b65d9 100644
--- a/lbmpy/phasefield_allen_cahn/contact_angle.py
+++ b/lbmpy/phasefield_allen_cahn/contact_angle.py
@@ -1,7 +1,7 @@
import math
import sympy as sp
-from pystencils.astnodes import SympyAssignment
+from pystencils.astnodes import Block, Conditional, SympyAssignment
from pystencils.boundaries.boundaryhandling import BoundaryOffsetInfo
from pystencils.boundaries.boundaryconditions import Boundary
@@ -34,26 +34,29 @@ class ContactAngle(Boundary):
def __call__(self, field, direction_symbol, **kwargs):
neighbor = BoundaryOffsetInfo.offset_from_dir(direction_symbol, field.spatial_dimensions)
+ dist = TypedSymbol("h", self._data_type)
+ angle = TypedSymbol("a", self._data_type)
+ d = CastFunc(sum([x * x for x in neighbor]), self._data_type)
+ var = - dist * (4.0 / self._interface_width) * angle
+ tmp = 1 + var
+ else_branch = (tmp - sp.sqrt(tmp * tmp - 4.0 * var * field[neighbor])) / var - field[neighbor]
if field.index_dimensions == 0:
- if math.isclose(90, self._contact_angle, abs_tol=1e-5):
- return [SympyAssignment(field.center, field[neighbor])]
+ if isinstance(self._contact_angle, (int, float)):
+ result = [SympyAssignment(angle, math.cos(math.radians(self._contact_angle))),
+ SympyAssignment(dist, 0.5 * sp.sqrt(d)),
+ Conditional(sp.LessThan(var * var, 0.000001),
+ Block([SympyAssignment(field.center, field[neighbor])]),
+ Block([SympyAssignment(field.center, else_branch)]))]
+ return result
+ else:
+ result = [SympyAssignment(angle, sp.cos(self._contact_angle * (sp.pi / sp.Number(180)))),
+ SympyAssignment(dist, 0.5 * sp.sqrt(d)),
+ Conditional(sp.LessThan(var * var, 0.000001),
+ Block([SympyAssignment(field.center, field[neighbor])]),
+ Block([SympyAssignment(field.center, else_branch)]))]
+ return result
- dist = TypedSymbol("h", self._data_type)
- angle = TypedSymbol("a", self._data_type)
- tmp = TypedSymbol("tmp", self._data_type)
-
- result = [SympyAssignment(tmp, CastFunc(sum([x * x for x in neighbor]), self._data_type)),
- SympyAssignment(dist, 0.5 * sp.sqrt(tmp)),
- SympyAssignment(angle, math.cos(math.radians(self._contact_angle)))]
-
- var = - dist * (4.0 / self._interface_width) * angle
- tmp = 1 + var
- else_branch = (tmp - sp.sqrt(tmp * tmp - 4 * var * field[neighbor])) / var - field[neighbor]
- update = sp.Piecewise((field[neighbor], dist < 0.001), (else_branch, True))
-
- result.append(SympyAssignment(field.center, update))
- return result
else:
raise NotImplementedError("Contact angle only implemented for phase-fields which have a single "
"value for each cell")
diff --git a/lbmpy/phasefield_allen_cahn/derivatives.py b/lbmpy/phasefield_allen_cahn/derivatives.py
new file mode 100644
index 0000000000000000000000000000000000000000..de4e83179f762fdaa3d5bb7407fc3e46b57eff01
--- /dev/null
+++ b/lbmpy/phasefield_allen_cahn/derivatives.py
@@ -0,0 +1,94 @@
+from pystencils.fd.derivation import FiniteDifferenceStencilDerivation
+
+import sympy as sp
+
+
+def laplacian_symbolic(field, stencil):
+ r"""
+ Get a symbolic expression for the laplacian of a field.
+ Args:
+ field: the field on which the laplacian is applied to
+ stencil: stencil to derive the finite difference for the laplacian (2nd order isotropic)
+ """
+ lap = sp.simplify(0)
+ for i in range(stencil.D):
+ deriv = FiniteDifferenceStencilDerivation((i, i), stencil)
+ for j in range(stencil.D):
+ # assume the stencil is symmetric
+ deriv.assume_symmetric(dim=j, anti_symmetric=False)
+
+ # set weights for missing degrees of freedom in the calculation and assume the stencil is isotropic
+ if stencil.D == 2 and stencil.Q == 9:
+ res = deriv.get_stencil(isotropic=True)
+ lap += res.apply(field.center)
+ elif stencil.D == 2 and stencil.Q == 25:
+ deriv.set_weight((2, 0), sp.Rational(1, 10))
+
+ res = deriv.get_stencil(isotropic=True)
+ lap += res.apply(field.center)
+ elif stencil.D == 3 and stencil.Q == 15:
+ deriv.set_weight((0, 0, 0), sp.Rational(-32, 27))
+ res = deriv.get_stencil(isotropic=True)
+ lap += res.apply(field.center)
+ elif stencil.D == 3 and stencil.Q == 19:
+ res = deriv.get_stencil(isotropic=True)
+ lap += res.apply(field.center)
+ elif stencil.D == 3 and stencil.Q == 27:
+ deriv.set_weight((0, 0, 0), sp.Rational(-38, 27))
+ res = deriv.get_stencil(isotropic=True)
+ lap += res.apply(field.center)
+ else:
+ raise ValueError(f"stencil with {stencil.D} dimensions and {stencil.Q} entries is not supported")
+
+ return lap
+
+
+def isotropic_gradient_symbolic(field, stencil):
+ r"""
+ Get a symbolic expression for the isotropic gradient of the phase-field
+ Args:
+ field: the field on which the isotropic gradient is applied
+ stencil: stencil to derive the finite difference for the gradient (2nd order isotropic)
+ """
+ deriv = FiniteDifferenceStencilDerivation((0,), stencil)
+
+ deriv.assume_symmetric(0, anti_symmetric=True)
+ deriv.assume_symmetric(1, anti_symmetric=False)
+ if stencil.D == 3:
+ deriv.assume_symmetric(2, anti_symmetric=False)
+
+ # set weights for missing degrees of freedom in the calculation and assume the stencil is isotropic
+ # furthermore the stencils gets rotated to get the y and z components
+ if stencil.D == 2 and stencil.Q == 9:
+ res = deriv.get_stencil(isotropic=True)
+ grad = [res.apply(field.center), res.rotate_weights_and_apply(field.center, (0, 1)), 0]
+ elif stencil.D == 2 and stencil.Q == 25:
+ deriv.set_weight((2, 0), sp.Rational(1, 10))
+
+ res = deriv.get_stencil(isotropic=True)
+ grad = [res.apply(field.center), res.rotate_weights_and_apply(field.center, (0, 1)), 0]
+ elif stencil.D == 3 and stencil.Q == 15:
+ res = deriv.get_stencil(isotropic=True)
+ grad = [res.apply(field.center),
+ res.rotate_weights_and_apply(field.center, (0, 1)),
+ res.rotate_weights_and_apply(field.center, (1, 2))]
+ elif stencil.D == 3 and stencil.Q == 19:
+ deriv.set_weight((0, 0, 0), sp.sympify(0))
+ deriv.set_weight((1, 0, 0), sp.Rational(1, 6))
+
+ res = deriv.get_stencil(isotropic=True)
+ grad = [res.apply(field.center),
+ res.rotate_weights_and_apply(field.center, (0, 1)),
+ res.rotate_weights_and_apply(field.center, (1, 2))]
+ elif stencil.D == 3 and stencil.Q == 27:
+ deriv.set_weight((0, 0, 0), sp.sympify(0))
+ deriv.set_weight((1, 0, 0), sp.Rational(2, 9))
+
+ res = deriv.get_stencil(isotropic=True)
+ grad = [res.apply(field.center),
+ res.rotate_weights_and_apply(field.center, (0, 1)),
+ res.rotate_weights_and_apply(field.center, (1, 2))]
+ else:
+ raise ValueError(f"stencil with {stencil.D} dimensions and {stencil.Q} entries is not supported")
+
+ return grad
diff --git a/lbmpy/phasefield_allen_cahn/kernel_equations.py b/lbmpy/phasefield_allen_cahn/kernel_equations.py
index 8a357061cb20306f3abd5e37dcb8f43be79119af..e57b71e05e4cc16e8ed50332bf9a8e4e335c2fe0 100644
--- a/lbmpy/phasefield_allen_cahn/kernel_equations.py
+++ b/lbmpy/phasefield_allen_cahn/kernel_equations.py
@@ -1,10 +1,17 @@
-from pystencils.fd.derivation import FiniteDifferenceStencilDerivation
+from typing import Union
+
from pystencils import Assignment, AssignmentCollection, Field
+from pystencils.sympyextensions import scalar_product
from lbmpy import pdf_initialization_assignments
+from lbmpy.advanced_streaming.utility import get_accessor
+from lbmpy.creationfunctions import LBMConfig
+from lbmpy.fieldaccess import StreamPushTwoFieldsAccessor
from lbmpy.methods.abstractlbmethod import LbmCollisionRule
from lbmpy.utils import second_order_moment_tensor
-from lbmpy.phasefield_allen_cahn.parameter_calculation import AllenCahnParameters
+
+from lbmpy.phasefield_allen_cahn.derivatives import isotropic_gradient_symbolic, laplacian_symbolic
+from lbmpy.phasefield_allen_cahn.parameter_calculation import AllenCahnParameters, ThermocapillaryParameters
import sympy as sp
@@ -18,29 +25,7 @@ def chemical_potential_symbolic(phi_field, stencil, beta, kappa):
beta: coefficient related to surface tension and interface thickness
kappa: coefficient related to surface tension and interface thickness
"""
- lap = sp.simplify(0)
- for i in range(stencil.D):
- deriv = FiniteDifferenceStencilDerivation((i, i), stencil)
- for j in range(stencil.D):
- # assume the stencil is symmetric
- deriv.assume_symmetric(dim=j, anti_symmetric=False)
-
- # set weights for missing degrees of freedom in the calculation and assume the stencil is isotropic
- if stencil.Q == 9:
- res = deriv.get_stencil(isotropic=True)
- lap += res.apply(phi_field.center)
- elif stencil.Q == 15:
- deriv.set_weight((0, 0, 0), sp.Rational(-32, 27))
- res = deriv.get_stencil(isotropic=True)
- lap += res.apply(phi_field.center)
- elif stencil.Q == 19:
- res = deriv.get_stencil(isotropic=True)
- lap += res.apply(phi_field.center)
- else:
- deriv.set_weight((0, 0, 0), sp.Rational(-38, 27))
- res = deriv.get_stencil(isotropic=True)
- lap += res.apply(phi_field.center)
-
+ lap = laplacian_symbolic(phi_field, stencil)
# get the chemical potential
four = sp.Rational(4, 1)
one = sp.Rational(1, 1)
@@ -49,50 +34,6 @@ def chemical_potential_symbolic(phi_field, stencil, beta, kappa):
return mu
-def isotropic_gradient_symbolic(phi_field, stencil):
- r"""
- Get a symbolic expression for the isotropic gradient of the phase-field
- Args:
- phi_field: the phase-field on which the isotropic gradient is applied
- stencil: stencil to derive the finite difference for the gradient (2nd order isotropic)
- """
- deriv = FiniteDifferenceStencilDerivation((0,), stencil)
-
- deriv.assume_symmetric(0, anti_symmetric=True)
- deriv.assume_symmetric(1, anti_symmetric=False)
- if stencil.D == 3:
- deriv.assume_symmetric(2, anti_symmetric=False)
-
- # set weights for missing degrees of freedom in the calculation and assume the stencil is isotropic
- # furthermore the stencils gets rotated to get the y and z components
- if stencil.Q == 9:
- res = deriv.get_stencil(isotropic=True)
- grad = [res.apply(phi_field.center), res.rotate_weights_and_apply(phi_field.center, (0, 1)), 0]
- elif stencil.Q == 15:
- res = deriv.get_stencil(isotropic=True)
- grad = [res.apply(phi_field.center),
- res.rotate_weights_and_apply(phi_field.center, (0, 1)),
- res.rotate_weights_and_apply(phi_field.center, (1, 2))]
- elif stencil.Q == 19:
- deriv.set_weight((0, 0, 0), sp.sympify(0))
- deriv.set_weight((1, 0, 0), sp.Rational(1, 6))
-
- res = deriv.get_stencil(isotropic=True)
- grad = [res.apply(phi_field.center),
- res.rotate_weights_and_apply(phi_field.center, (0, 1)),
- res.rotate_weights_and_apply(phi_field.center, (1, 2))]
- else:
- deriv.set_weight((0, 0, 0), sp.sympify(0))
- deriv.set_weight((1, 0, 0), sp.Rational(2, 9))
-
- res = deriv.get_stencil(isotropic=True)
- grad = [res.apply(phi_field.center),
- res.rotate_weights_and_apply(phi_field.center, (0, 1)),
- res.rotate_weights_and_apply(phi_field.center, (1, 2))]
-
- return grad
-
-
def normalized_isotropic_gradient_symbolic(phi_field, stencil, fd_stencil=None):
r"""
Get a symbolic expression for the normalized isotropic gradient of the phase-field
@@ -134,11 +75,10 @@ def pressure_force(phi_field, lb_method, stencil, density_heavy, density_light,
return result
-def viscous_force(lb_velocity_field, phi_field, lb_method, tau, density_heavy, density_light, fd_stencil=None):
+def viscous_force(phi_field, lb_method, tau, density_heavy, density_light, fd_stencil=None):
r"""
Get a symbolic expression for the viscous force
Args:
- lb_velocity_field: hydrodynamic distribution function
phi_field: phase-field
lb_method: lattice boltzmann method used for hydrodynamics
tau: relaxation time of the hydrodynamic lattice boltzmann step
@@ -154,7 +94,7 @@ def viscous_force(lb_velocity_field, phi_field, lb_method, tau, density_heavy, d
iso_grad = sp.Matrix(isotropic_gradient_symbolic(phi_field, fd_stencil)[:stencil.D])
- f_neq = lb_velocity_field.center_vector - lb_method.get_equilibrium_terms()
+ f_neq = sp.Matrix(lb_method.pre_collision_pdf_symbols) - lb_method.get_equilibrium_terms()
stress_tensor = second_order_moment_tensor(f_neq, lb_method.stencil)
normal_stress_tensor = stress_tensor * iso_grad
@@ -169,17 +109,19 @@ def viscous_force(lb_velocity_field, phi_field, lb_method, tau, density_heavy, d
return [fmx, fmy, fmz]
-def surface_tension_force(phi_field, stencil, beta, kappa, fd_stencil=None):
+def surface_tension_force(phi_field, stencil, parameters, fd_stencil=None):
r"""
Get a symbolic expression for the surface tension force
Args:
phi_field: the phase-field on which the chemical potential is applied
stencil: stencil of the lattice Boltzmann step
- beta: coefficient related to surface tension and interface thickness
- kappa: coefficient related to surface tension and interface thickness
+ parameters: AllenCahnParameters
fd_stencil: stencil to derive the finite differences of the isotropic gradient and the laplacian of the phase
field. If it is not given the stencil of the LB method will be applied.
"""
+ beta = parameters.beta
+ kappa = parameters.kappa
+
if fd_stencil is None:
fd_stencil = stencil
@@ -188,18 +130,57 @@ def surface_tension_force(phi_field, stencil, beta, kappa, fd_stencil=None):
return [chemical_potential * x for x in iso_grad]
-def hydrodynamic_force(lb_velocity_field, phi_field, lb_method, parameters: AllenCahnParameters,
- body_force, fd_stencil=None):
+def thermocapillary_surface_tension_force(phi_field, temperature_field,
+ stencil, parameters, fd_stencil=None):
r"""
- Get a symbolic expression for the hydrodynamic force
+ Get a symbolic expression for the surface tension force
+ Args:
+ phi_field: the phase-field on which the chemical potential is applied
+ temperature_field: the temperature field which contains the temperature for each cell
+ stencil: stencil of the lattice Boltzmann step
+ parameters: AllenCahnParameters
+ fd_stencil: stencil to derive the finite differences of the isotropic gradient and the laplacian of the phase
+ field. If it is not given the stencil of the LB method will be applied.
+ """
+ if fd_stencil is None:
+ fd_stencil = stencil
+
+ sigma_ref = parameters.symbolic_sigma_ref
+ sigma_t = parameters.symbolic_sigma_t
+ tmp_ref = parameters.symbolic_tmp_ref
+ interface_thickness = parameters.symbolic_interface_thickness
+
+ sigma = sigma_ref + sigma_t * (temperature_field.center - tmp_ref)
+ beta = sp.Rational(12, 1) * (sigma / interface_thickness)
+ kappa = sp.Rational(3, 2) * sigma * interface_thickness
+
+ chemical_potential = chemical_potential_symbolic(phi_field, fd_stencil, beta, kappa)
+ gradient_phi = isotropic_gradient_symbolic(phi_field, fd_stencil)
+ gradient_temp = isotropic_gradient_symbolic(temperature_field, fd_stencil)
+ magnitude_phi = sum([x * x for x in gradient_phi])
+
+ dot_temperature_phase = scalar_product(gradient_temp, gradient_phi)
+ delta_s = sp.Rational(3, 2) * interface_thickness * sigma_t
+
+ return [chemical_potential * gp + delta_s * (magnitude_phi * gt - dot_temperature_phase * gp) for gp, gt in
+ zip(gradient_phi, gradient_temp)]
+
+
+def hydrodynamic_force(phi_field, lb_method,
+ parameters: Union[AllenCahnParameters, ThermocapillaryParameters],
+ body_force, fd_stencil=None,
+ temperature_field=None):
+ r"""
+ Get a symbolic expression for the hydrodynamic force. If a temperature field is provided the hydrodynamic force
+ for thermocapillary simulations is derived.
Args:
- lb_velocity_field: hydrodynamic distribution function
phi_field: phase-field
lb_method: Lattice boltzmann method used for hydrodynamics
parameters: AllenCahnParameters
body_force: force acting on the fluids. Usually the gravity
fd_stencil: stencil to derive the finite differences of the isotropic gradient and the laplacian of the phase
field. If it is not given the stencil of the LB method will be applied.
+ temperature_field: the temperature field which contains the temperature for each cell
"""
stencil = lb_method.stencil
@@ -211,12 +192,16 @@ def hydrodynamic_force(lb_velocity_field, phi_field, lb_method, parameters: Alle
tau_L = parameters.symbolic_tau_light
tau_H = parameters.symbolic_tau_heavy
tau = sp.Rational(1, 2) + tau_L + phi_field.center * (tau_H - tau_L)
- beta = parameters.beta
- kappa = parameters.kappa
fp = pressure_force(phi_field, lb_method, stencil, density_heavy, density_light, fd_stencil)
- fm = viscous_force(lb_velocity_field, phi_field, lb_method, tau, density_heavy, density_light, fd_stencil)
- fs = surface_tension_force(phi_field, stencil, beta, kappa, fd_stencil)
+ fm = viscous_force(phi_field, lb_method, tau, density_heavy, density_light, fd_stencil)
+
+ if temperature_field is None:
+ fs = surface_tension_force(phi_field, stencil, parameters, fd_stencil)
+ else:
+ assertion_string = "For thermocapillary ThermocapillaryParameters needs to be passed"
+ assert isinstance(parameters, ThermocapillaryParameters), assertion_string
+ fs = thermocapillary_surface_tension_force(phi_field, temperature_field, stencil, parameters, fd_stencil)
result = []
for i in range(stencil.D):
@@ -254,14 +239,14 @@ def interface_tracking_force(phi_field, stencil, parameters: AllenCahnParameters
return result
-def hydrodynamic_force_assignments(lb_velocity_field, velocity_field, phi_field, lb_method,
+def hydrodynamic_force_assignments(velocity_field, phi_field, lb_method,
parameters: AllenCahnParameters,
- body_force, fd_stencil=None, sub_iterations=2):
+ body_force, fd_stencil=None, sub_iterations=2,
+ temperature_field=None):
r"""
Get a symbolic expression for the hydrodynamic force
Args:
- lb_velocity_field: hydrodynamic distribution function
velocity_field: velocity
phi_field: phase-field
lb_method: Lattice boltzmann method used for hydrodynamics
@@ -270,6 +255,7 @@ def hydrodynamic_force_assignments(lb_velocity_field, velocity_field, phi_field,
fd_stencil: stencil to derive the finite differences of the isotropic gradient and the laplacian of the phase
field. If it is not given the stencil of the LB method will be applied.
sub_iterations: number of sub iterations for the hydrodynamic force
+ temperature_field: the temperature field which contains the temperature for each cell
"""
rho_L = parameters.symbolic_density_light
@@ -280,12 +266,13 @@ def hydrodynamic_force_assignments(lb_velocity_field, velocity_field, phi_field,
# method has to have a force model
symbolic_force = lb_method.force_model.symbolic_force_vector
- force = hydrodynamic_force(lb_velocity_field, phi_field, lb_method, parameters, body_force, fd_stencil=fd_stencil)
+ force = hydrodynamic_force(phi_field, lb_method, parameters, body_force, fd_stencil=fd_stencil,
+ temperature_field=temperature_field)
cqc = lb_method.conserved_quantity_computation
u_symp = cqc.velocity_symbols
- cqe = cqc.equilibrium_input_equations_from_pdfs(lb_velocity_field.center_vector)
+ cqe = cqc.equilibrium_input_equations_from_pdfs(lb_method.pre_collision_pdf_symbols)
cqe = cqe.new_without_subexpressions()
cqe_velocity = [eq.rhs for eq in cqe.main_assignments[1:]]
@@ -332,7 +319,7 @@ def add_interface_tracking_force(update_rule: LbmCollisionRule, force):
def add_hydrodynamic_force(update_rule: LbmCollisionRule, force, phi_field,
- hydro_pdfs, parameters: AllenCahnParameters):
+ hydro_pdfs, parameters: AllenCahnParameters, lbm_config: LBMConfig = None):
r"""
Adds the interface tracking force to a lattice Boltzmann update rule
Args:
@@ -341,29 +328,53 @@ def add_hydrodynamic_force(update_rule: LbmCollisionRule, force, phi_field,
phi_field: phase-field
hydro_pdfs: source field of the hydrodynamic PDFs
parameters: AllenCahnParameters
+ lbm_config: LBMConfig to determine the streaming scheme
"""
+ if lbm_config is None:
+ accessor = StreamPushTwoFieldsAccessor()
+ else:
+ accessor = get_accessor(lbm_config.streaming_pattern, lbm_config.timestep)
+ method = update_rule.method
+ reads = accessor.read(hydro_pdfs, method.stencil)
+
+ # First apply force according to Allen Cahn model
rho_L = parameters.symbolic_density_light
rho_H = parameters.symbolic_density_heavy
density = rho_L + phi_field.center * (rho_H - rho_L)
- method = update_rule.method
- symbolic_force = method.force_model.symbolic_force_vector
+ force_subs = {f: f / density for f in method.force_model.symbolic_force_vector}
+ update_rule = update_rule.new_with_substitutions(force_subs)
+ update_rule.subexpressions += force
+
+ # Then add missing conversed quantities that occur in the force terms
cqc = method.conserved_quantity_computation
- rho = cqc.density_deviation_symbol
+ density = cqc.density_symbol
+ density_deviation = cqc.density_deviation_symbol
+ free_symbols = update_rule.free_symbols
- force_subs = {f: f / density for f in symbolic_force}
+ if density_deviation in free_symbols:
+ t = cqc.output_equations_from_pdfs(reads,
+ {"density_deviation": density_deviation}).new_without_subexpressions()
+ update_rule.add_subexpression(t.main_assignments[0].rhs, t.main_assignments[0].lhs)
- update_rule = update_rule.subs(force_subs)
+ if density in free_symbols:
+ t = cqc.output_equations_from_pdfs(reads, {"density": density}).new_without_subexpressions()
+ update_rule.add_subexpression(t.main_assignments[0].rhs, t.main_assignments[0].lhs)
- update_rule.subexpressions += [Assignment(rho, sum(hydro_pdfs.center_vector))]
- update_rule.subexpressions += force
update_rule.topological_sort(sort_subexpressions=True, sort_main_assignments=False)
- return update_rule
+ subs_dict = {f: read for f, read in zip(method.pre_collision_pdf_symbols, reads)}
+
+ up = update_rule.new_with_substitutions(subs_dict)
+ result = LbmCollisionRule(lb_method=method, main_assignments=up.main_assignments,
+ subexpressions=up.subexpressions, simplification_hints=up.simplification_hints,
+ subexpression_symbol_generator=up.subexpression_symbol_generator)
+
+ return result
def initializer_kernel_phase_field_lb(lb_method, phi, velocity, ac_pdfs, parameters: AllenCahnParameters,
- fd_stencil=None):
+ fd_stencil=None, **kwargs):
r"""
Returns an assignment list for initializing the phase-field distribution functions
Args:
@@ -374,9 +385,10 @@ def initializer_kernel_phase_field_lb(lb_method, phi, velocity, ac_pdfs, paramet
parameters: AllenCahnParameters
fd_stencil: stencil to derive the finite differences of the isotropic gradient and the laplacian of the phase
field. If it is not given the stencil of the LB method will be applied.
+ kwargs: keyword arguments for pdf_initialization_assignments
"""
- h_updates = pdf_initialization_assignments(lb_method, phi, velocity, ac_pdfs)
+ h_updates = pdf_initialization_assignments(lb_method, phi, velocity, ac_pdfs, **kwargs)
force_h = interface_tracking_force(phi, lb_method.stencil, parameters,
fd_stencil=fd_stencil)
@@ -407,7 +419,7 @@ def initializer_kernel_phase_field_lb(lb_method, phi, velocity, ac_pdfs, paramet
return h_updates
-def initializer_kernel_hydro_lb(lb_method, pressure, velocity, hydro_pdfs):
+def initializer_kernel_hydro_lb(lb_method, pressure, velocity, hydro_pdfs, **kwargs):
r"""
Returns an assignment list for initializing the velocity distribution functions
Args:
@@ -415,11 +427,12 @@ def initializer_kernel_hydro_lb(lb_method, pressure, velocity, hydro_pdfs):
pressure: order parameter of the hydrodynamic LB step (pressure)
velocity: initial velocity
hydro_pdfs: source field of the hydrodynamic PDFs
+ kwargs: keyword arguments for pdf_initialization_assignments
"""
symbolic_force = lb_method.force_model.symbolic_force_vector
force_subs = {f: 0 for f in symbolic_force}
- g_updates = pdf_initialization_assignments(lb_method, pressure, velocity, hydro_pdfs)
+ g_updates = pdf_initialization_assignments(lb_method, pressure, velocity, hydro_pdfs, **kwargs)
g_updates = g_updates.new_with_substitutions(force_subs)
return g_updates
diff --git a/lbmpy/phasefield_allen_cahn/numerical_solver.py b/lbmpy/phasefield_allen_cahn/numerical_solver.py
new file mode 100644
index 0000000000000000000000000000000000000000..756fc43223342632dfb73206c8338d76524038c2
--- /dev/null
+++ b/lbmpy/phasefield_allen_cahn/numerical_solver.py
@@ -0,0 +1,121 @@
+from pystencils import Assignment, AssignmentCollection
+from pystencils.sympyextensions import scalar_product
+from pystencils.simp.subexpression_insertion import insert_constants
+
+from lbmpy.phasefield_allen_cahn.derivatives import isotropic_gradient_symbolic, laplacian_symbolic
+
+import sympy as sp
+
+VELOCITY_SYMBOLS = sp.symbols(f"u_:{3}")
+GRAD_T_SYMBOLS = sp.symbols(f"gratT_:{3}")
+GRAD_K_SYMBOLS = sp.symbols(f"gratK_:{3}")
+LAPLACIAN_SYMBOL = sp.Symbol("lap")
+
+
+def get_runge_kutta_update_assignments(stencil, phase_field, temperature_field, velocity_field, runge_kutta_fields,
+ conduction_h, conduction_l, heat_capacity_h, heat_capacity_l,
+ density, stabiliser=1):
+ dimensions = len(stencil[0])
+
+ grad_temperature = isotropic_gradient_symbolic(temperature_field, stencil)
+ lap_temperature = laplacian_symbolic(temperature_field, stencil)
+ grad_conduction = _get_conduction_gradient(stencil, phase_field, conduction_h, conduction_l)
+
+ grad_rk = [isotropic_gradient_symbolic(rk, stencil) for rk in runge_kutta_fields]
+ lap_rk = [laplacian_symbolic(rk, stencil) for rk in runge_kutta_fields]
+
+ dot_u_grad_t = scalar_product(VELOCITY_SYMBOLS[:dimensions], GRAD_T_SYMBOLS[:dimensions])
+ dot_grad_k_grad_t = scalar_product(GRAD_K_SYMBOLS[:dimensions], GRAD_T_SYMBOLS[:dimensions])
+
+ conduction = conduction_l + phase_field.center * sp.nsimplify(conduction_h - conduction_l)
+ conduction_times_lap = conduction * LAPLACIAN_SYMBOL
+
+ heat_capacity = heat_capacity_l + phase_field.center * sp.nsimplify(heat_capacity_h - heat_capacity_l)
+
+ rho_cp = 1.0 / (density * heat_capacity)
+ end_term = dot_grad_k_grad_t + conduction_times_lap
+
+ update_stage_1 = temperature_field.center + stabiliser * 0.5 * (-1.0 * dot_u_grad_t + rho_cp * end_term)
+ subexpressions_1 = _get_stage(dimensions, velocity_field, grad_temperature, grad_conduction, lap_temperature)
+ stage_1 = AssignmentCollection(main_assignments=[Assignment(runge_kutta_fields[0].center, update_stage_1)],
+ subexpressions=subexpressions_1)
+
+ if len(runge_kutta_fields) == 1:
+ update_stage_2 = temperature_field.center + stabiliser * (-1.0 * dot_u_grad_t + rho_cp * end_term)
+ subexpressions_2 = _get_stage(dimensions, velocity_field, grad_rk[0], grad_conduction, lap_rk[0])
+ stage_2 = AssignmentCollection(main_assignments=[Assignment(temperature_field.center, update_stage_2)],
+ subexpressions=subexpressions_2)
+
+ return [insert_constants(ac) for ac in [stage_1, stage_2]]
+
+ update_stage_2 = temperature_field.center + stabiliser * 0.5 * (-1.0 * dot_u_grad_t + rho_cp * end_term)
+ subexpressions_2 = _get_stage(dimensions, velocity_field, grad_rk[0], grad_conduction, lap_rk[0])
+ stage_2 = AssignmentCollection(main_assignments=[Assignment(runge_kutta_fields[1].center, update_stage_2)],
+ subexpressions=subexpressions_2)
+
+ update_stage_3 = temperature_field.center + stabiliser * 1.0 * (-1.0 * dot_u_grad_t + rho_cp * end_term)
+ subexpressions_3 = _get_stage(dimensions, velocity_field, grad_rk[1], grad_conduction, lap_rk[1])
+ stage_3 = AssignmentCollection(main_assignments=[Assignment(runge_kutta_fields[2].center, update_stage_3)],
+ subexpressions=subexpressions_3)
+
+ update_stage_4 = stabiliser * 1.0 * (-1.0 * dot_u_grad_t + rho_cp * end_term)
+ rk_update = 2.0 * runge_kutta_fields[0].center + 4.0 * runge_kutta_fields[1].center + 2.0 * runge_kutta_fields[
+ 2].center
+ update_stage_4 = (1.0 - 4.0 / 3.0) * temperature_field.center + (rk_update - update_stage_4) / 6.0
+ subexpressions_4 = _get_stage(dimensions, velocity_field, grad_rk[2], grad_conduction, lap_rk[2])
+ stage_4 = AssignmentCollection(main_assignments=[Assignment(temperature_field.center, update_stage_4)],
+ subexpressions=subexpressions_4)
+
+ return [insert_constants(ac) for ac in [stage_1, stage_2, stage_3, stage_4]]
+
+
+def get_initialiser_assignments(temperature_field, runge_kutta_fields):
+ result = []
+ for i in range(len(runge_kutta_fields)):
+ result.append(Assignment(runge_kutta_fields[i].center, temperature_field.center))
+
+ return result
+
+
+def _get_conduction_gradient(stencil, phase_field, conduction_h, conduction_l):
+ dimensions = len(stencil[0])
+ grad_phase = isotropic_gradient_symbolic(phase_field, stencil)
+
+ free_symbols = set()
+ for i in range(dimensions):
+ free_symbols.update(grad_phase[i].free_symbols)
+
+ subs_dict = {}
+
+ for f in free_symbols:
+ subs_dict[f] = interpolate_field_access(f, conduction_h, conduction_l)
+
+ result = list()
+ for i in range(dimensions):
+ eq = grad_phase[i].subs(subs_dict)
+ # replace very small numbers by zero
+ eq = eq.xreplace(dict([(n, 0) for n in eq.atoms(sp.Float) if abs(n) < 1e-16]))
+ result.append(eq)
+
+ return result
+
+
+def interpolate_field_access(field_access, upper, lower):
+ return lower + field_access * sp.nsimplify(upper - lower)
+
+
+def _get_stage(dimensions, velocity_field, gradient_t, gradient_k, laplacian):
+ result = list()
+
+ for i in range(dimensions):
+ result.append(Assignment(VELOCITY_SYMBOLS[i], velocity_field.center_vector[i]))
+
+ for i in range(dimensions):
+ result.append(Assignment(GRAD_T_SYMBOLS[i], gradient_t[i]))
+
+ for i in range(dimensions):
+ result.append(Assignment(GRAD_K_SYMBOLS[i], gradient_k[i]))
+
+ result.append(Assignment(LAPLACIAN_SYMBOL, laplacian))
+
+ return result
diff --git a/lbmpy/phasefield_allen_cahn/parameter_calculation.py b/lbmpy/phasefield_allen_cahn/parameter_calculation.py
index 72cc5a2ecb6d33cc69408a7d87be4e0bf5dd6a70..ead4c4b95eec2bd194d7be78e6bea71f681c7ef7 100644
--- a/lbmpy/phasefield_allen_cahn/parameter_calculation.py
+++ b/lbmpy/phasefield_allen_cahn/parameter_calculation.py
@@ -103,7 +103,8 @@ class AllenCahnParameters:
def symbolic_to_numeric_map(self):
return {t.name: self.parameter_map()[t] for t in self.parameter_map()}
- def _repr_html_(self):
+ @staticmethod
+ def _parameter_strings():
names = ("Density heavy phase",
"Density light phase",
"Relaxation time heavy phase",
@@ -113,7 +114,10 @@ class AllenCahnParameters:
"Interface thickness",
"Mobility",
"Surface tension")
+ return names
+ def _repr_html_(self):
+ names = self._parameter_strings()
table = """
<table style="border:none; width: 100%">
<tr {nb}>
@@ -140,6 +144,85 @@ class AllenCahnParameters:
return table.format(content=content, nb='style="border:none"')
+class ThermocapillaryParameters(AllenCahnParameters):
+ def __init__(self, density_heavy: float, density_light: float,
+ dynamic_viscosity_heavy: float, dynamic_viscosity_light: float,
+ surface_tension: float,
+ heat_conductivity_heavy: float, heat_conductivity_light: float,
+ mobility: float = 0.2,
+ gravitational_acceleration: float = 0.0, interface_thickness: int = 5,
+ sigma_ref: float = 5e-3, sigma_t: float = 2e-4, tmp_ref: float = 0,
+ velocity_wall: float = 0.0, reference_time: int = 10):
+
+ self.heat_conductivity_heavy = heat_conductivity_heavy
+ self.heat_conductivity_light = heat_conductivity_light
+ self.sigma_ref = sigma_ref
+ self.sigma_t = sigma_t
+ self.tmp_ref = tmp_ref
+ self.velocity_wall = velocity_wall
+ self.reference_time = reference_time
+
+ super(ThermocapillaryParameters, self).__init__(density_heavy, density_light,
+ dynamic_viscosity_heavy, dynamic_viscosity_light,
+ surface_tension, mobility,
+ gravitational_acceleration, interface_thickness)
+
+ @property
+ def symbolic_heat_conductivity_heavy(self):
+ return sp.Symbol("kappa_H")
+
+ @property
+ def symbolic_heat_conductivity_light(self):
+ return sp.Symbol("kappa_L")
+
+ @property
+ def symbolic_sigma_ref(self):
+ return sp.Symbol("sigma_ref")
+
+ @property
+ def symbolic_sigma_t(self):
+ return sp.Symbol("sigma_T")
+
+ @property
+ def symbolic_tmp_ref(self):
+ return sp.Symbol("T_ref")
+
+ def parameter_map(self):
+ result = {self.symbolic_density_heavy: self.density_heavy,
+ self.symbolic_density_light: self.density_light,
+ self.symbolic_tau_heavy: self.relaxation_time_heavy,
+ self.symbolic_tau_light: self.relaxation_time_light,
+ self.symbolic_omega_phi: self.omega_phi,
+ self.symbolic_gravitational_acceleration: self.gravitational_acceleration,
+ self.symbolic_interface_thickness: self.interface_thickness,
+ self.symbolic_mobility: self.mobility,
+ self.symbolic_surface_tension: self.surface_tension,
+ self.symbolic_heat_conductivity_heavy: self.heat_conductivity_heavy,
+ self.symbolic_heat_conductivity_light: self.heat_conductivity_light,
+ self.symbolic_sigma_ref: self.sigma_ref,
+ self.symbolic_sigma_t: self.sigma_t,
+ self.symbolic_tmp_ref: self.tmp_ref}
+ return result
+
+ @staticmethod
+ def _parameter_strings():
+ names = ("Density heavy phase",
+ "Density light phase",
+ "Relaxation time heavy phase",
+ "Relaxation time light phase",
+ "Relaxation rate Allen Cahn LB",
+ "Gravitational acceleration",
+ "Interface thickness",
+ "Mobility",
+ "Surface tension",
+ "Heat Conductivity Heavy",
+ "Heat Conductivity Light",
+ "Sigma Ref",
+ "Sigma T",
+ "Temperature Ref")
+ return names
+
+
def calculate_parameters_rti(reference_length=256,
reference_time=16000,
density_heavy=1.0,
@@ -170,8 +253,8 @@ def calculate_parameters_rti(reference_length=256,
"""
# calculate the gravitational acceleration and the reference velocity
- g = reference_length / (reference_time ** 2 * atwood_number)
- reference_velocity = math.sqrt(g * reference_length)
+ gravity_lattice_units = reference_length / (reference_time ** 2 * atwood_number)
+ reference_velocity = math.sqrt(gravity_lattice_units * reference_length)
dynamic_viscosity_heavy = (density_heavy * reference_velocity * reference_length) / reynolds_number
dynamic_viscosity_light = dynamic_viscosity_heavy / viscosity_ratio
@@ -187,7 +270,7 @@ def calculate_parameters_rti(reference_length=256,
dynamic_viscosity_light=dynamic_viscosity_light,
surface_tension=surface_tension,
mobility=mobility,
- gravitational_acceleration=-g)
+ gravitational_acceleration=-gravity_lattice_units)
return parameters
@@ -215,15 +298,15 @@ def calculate_dimensionless_rising_bubble(reference_time=18000,
viscosity_ratio: viscosity ratio of the heavier and the lighter fluid
"""
- bubble_diameter = bubble_radius * 2
- g = bubble_diameter / (reference_time ** 2)
+ bubble_d = bubble_radius * 2
+ gravity_lattice_units = bubble_d / (reference_time ** 2)
density_light = density_heavy / density_ratio
- dynamic_viscosity_heavy = (density_heavy * math.sqrt(g * bubble_diameter ** 3)) / reynolds_number
+ dynamic_viscosity_heavy = (density_heavy * math.sqrt(gravity_lattice_units * bubble_d ** 3)) / reynolds_number
dynamic_viscosity_light = dynamic_viscosity_heavy / viscosity_ratio
- surface_tension = (density_heavy - density_light) * g * bubble_diameter ** 2 / bond_number
+ surface_tension = (density_heavy - density_light) * gravity_lattice_units * bubble_d ** 2 / bond_number
# calculation of the Morton number
# Mo = gravitational_acceleration * dynamic_viscosity_heavy / (density_heavy * surface_tension ** 3)
@@ -232,5 +315,121 @@ def calculate_dimensionless_rising_bubble(reference_time=18000,
dynamic_viscosity_heavy=dynamic_viscosity_heavy,
dynamic_viscosity_light=dynamic_viscosity_light,
surface_tension=surface_tension,
- gravitational_acceleration=-g)
+ gravitational_acceleration=-gravity_lattice_units)
+ return parameters
+
+
+def calculate_parameters_taylor_bubble(reference_length=128,
+ reference_time=16000,
+ density_heavy=1.0,
+ diameter_outer=0.0254,
+ diameter_inner=0.0127):
+ r"""
+ Calculate the simulation parameters for a rising Taylor bubble in an annulus pipe. The calculation can be found in
+ 10.1016/S0009-2509(97)00210-8 by G. Das
+
+ Args:
+ reference_length: chosen reference length in lattice cells
+ reference_time: chosen reference time in latte timesteps
+ density_heavy: density of water in lattice units
+ diameter_outer: diameter of the outer tube
+ diameter_inner: diameter of the inner cylinder
+ """
+
+ # physical parameters #
+ water_rho = 998 # kg/m3
+ air_rho = 1.2047 # kg/m3
+ surface_tension = 0.07286 # kg/s2
+ water_mu = 1.002e-3 # kg/ms
+ air_mu = 1.8205e-5 # kg/ms
+ gravity = 9.81 # m/s2
+
+ # water_nu = water_mu / water_rho # m2/s
+ # air_nu = air_mu / air_rho # m2/s
+
+ diameter_fluid = diameter_outer - diameter_inner
+ diameter_ratio = diameter_outer / diameter_inner
+
+ inverse_viscosity_number = math.sqrt((water_rho - air_rho) * water_rho * gravity * diameter_fluid ** 3) / water_mu
+ bond_number = (water_rho - air_rho) * gravity * diameter_fluid ** 2 / surface_tension
+ # morton_number = gravity * water_mu ** 4 * (water_rho - air_rho) / (water_rho ** 2 * surface_tension ** 3)
+
+ diameter_lattice_untis = reference_length / diameter_ratio
+
+ density_light = 1.0 / (water_rho / air_rho)
+ diameter_fluid = reference_length - diameter_lattice_untis
+ gravity_lattice_units = diameter_fluid / reference_time ** 2
+
+ density_diff = density_heavy - density_light
+
+ grav_df_cube = gravity_lattice_units * diameter_fluid ** 3
+ water_mu_lattice_units = math.sqrt(density_diff * density_heavy * grav_df_cube) / inverse_viscosity_number
+ air_mu_lattice_units = water_mu_lattice_units / (water_mu / air_mu)
+
+ dynamic_viscosity_heavy = water_mu_lattice_units / density_heavy
+ dynamic_viscosity_light = air_mu_lattice_units / density_light
+
+ surface_tension_lattice_units = density_diff * gravity_lattice_units * diameter_fluid ** 2 / bond_number
+
+ parameters = AllenCahnParameters(density_heavy=density_heavy,
+ density_light=density_light,
+ dynamic_viscosity_heavy=dynamic_viscosity_heavy,
+ dynamic_viscosity_light=dynamic_viscosity_light,
+ surface_tension=surface_tension_lattice_units,
+ gravitational_acceleration=-gravity_lattice_units)
+ return parameters
+
+
+def calculate_parameters_droplet_migration(radius=32,
+ reynolds_number=0.16,
+ capillary_number=0.01,
+ marangoni_number=0.08,
+ peclet_number=1,
+ viscosity_ratio=1,
+ heat_ratio=1,
+ interface_width=4,
+ reference_surface_tension=5e-3,
+ height=None):
+ r"""
+ Calculate the simulation parameters moving droplet with a laser heat source. The calculation can be found in
+ https://doi.org/10.1016/j.jcp.2013.08.054 by Liu et al.
+
+ Args:
+ radius: radius of the droplet which functions as the reference length
+ reynolds_number: Reynolds number of the simulation
+ capillary_number: Capillary number of the simulation
+ marangoni_number: Marangoni number of the simulation
+ peclet_number: Peclet number of the simulation
+ viscosity_ratio: viscosity ratio between the two fluids
+ heat_ratio: ratio of the heat conductivity in the two fluids
+ interface_width: Resolution of cells for the interface
+ reference_surface_tension: reference surface tension
+ height: height of the simulation domain. If not defined it is asumed to be 2 * radius of the droplet.
+
+ """
+
+ if height is None:
+ height = 2 * radius
+
+ u_char = math.sqrt(reynolds_number * capillary_number * reference_surface_tension / radius)
+ gamma = u_char / radius
+ u_wall = gamma * height
+
+ kinematic_viscosity_heavy = radius * u_char / reynolds_number
+ kinematic_viscosity_light = kinematic_viscosity_heavy / viscosity_ratio
+
+ heat_conductivity_heavy = radius * u_char / marangoni_number
+ heat_conductivity_light = heat_conductivity_heavy / heat_ratio
+
+ mobility_of_interface = gamma * radius * interface_width / peclet_number
+
+ parameters = ThermocapillaryParameters(density_heavy=1.0,
+ density_light=1.0,
+ dynamic_viscosity_heavy=kinematic_viscosity_heavy,
+ dynamic_viscosity_light=kinematic_viscosity_light,
+ surface_tension=0.0,
+ heat_conductivity_heavy=heat_conductivity_heavy,
+ heat_conductivity_light=heat_conductivity_light,
+ mobility=mobility_of_interface,
+ velocity_wall=u_wall, reference_time=int(1.0 / gamma))
return parameters
diff --git a/lbmpy/phasefield_allen_cahn/phasefield_simplifications.py b/lbmpy/phasefield_allen_cahn/phasefield_simplifications.py
deleted file mode 100644
index 688661cd0961d2bd015ebae2a65b2337a0b70474..0000000000000000000000000000000000000000
--- a/lbmpy/phasefield_allen_cahn/phasefield_simplifications.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import sympy as sp
-
-from pystencils.simp import (
- SimplificationStrategy, apply_to_all_assignments,
- insert_aliases, insert_zeros, insert_constants)
-
-
-def create_phasefield_simplification_strategy(lb_method):
- s = SimplificationStrategy()
- expand = apply_to_all_assignments(sp.expand)
-
- s.add(expand)
-
- s.add(insert_zeros)
- s.add(insert_aliases)
- s.add(insert_constants)
- s.add(lambda ac: ac.new_without_unused_subexpressions())
-
- return s
diff --git a/lbmpy/stencils.py b/lbmpy/stencils.py
index 818d8f5551cbb67089fcde00d4c481e11c8a979c..635502a8a36d2512e485aaee8129dc31391dd65c 100644
--- a/lbmpy/stencils.py
+++ b/lbmpy/stencils.py
@@ -185,6 +185,10 @@ def _predefined_stencils(stencil: str, ordering: str):
(1, 0, 0), (-1, 0, 0), (0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1),
(1, 1, 1), (-1, -1, -1), (1, 1, -1), (-1, -1, 1),
(1, -1, 1), (-1, 1, -1), (-1, 1, 1), (1, -1, -1)),
+ 'fakhari': ((0, 0, 0),
+ (1, 0, 0), (-1, 0, 0), (0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1),
+ (1, 1, 1), (-1, -1, -1), (-1, 1, 1), (1, -1, -1),
+ (1, -1, 1), (-1, 1, -1), (1, 1, -1), (-1, -1, 1)),
},
'D3Q19': {
'walberla': ((0, 0, 0),
@@ -244,7 +248,6 @@ def _predefined_stencils(stencil: str, ordering: str):
(1, -1, -1)),
}
}
-
try:
return predefined_stencils[stencil][ordering]
except KeyError:
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_analytical.py b/lbmpy_tests/phasefield_allen_cahn/test_analytical.py
index 93065066138f8745737356fcb4fedc9c021f6947..a9f4d2ee1af9e35a8d344f6f075181bc0df18b53 100644
--- a/lbmpy_tests/phasefield_allen_cahn/test_analytical.py
+++ b/lbmpy_tests/phasefield_allen_cahn/test_analytical.py
@@ -1,7 +1,7 @@
import numpy as np
from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_dimensionless_rising_bubble, \
- calculate_parameters_rti
+ calculate_parameters_rti, calculate_parameters_taylor_bubble
from lbmpy.phasefield_allen_cahn.analytical import analytic_rising_speed
@@ -15,8 +15,8 @@ def test_analytical():
density_ratio=1000,
viscosity_ratio=100)
- np.isclose(parameters.density_light, 0.001, rtol=1e-05, atol=1e-08, equal_nan=False)
- np.isclose(parameters.gravitational_acceleration, -9.876543209876543e-08, rtol=1e-05, atol=1e-08, equal_nan=False)
+ assert np.isclose(parameters.density_light, 0.001)
+ assert np.isclose(parameters.gravitational_acceleration, -9.876543209876543e-08)
parameters = calculate_parameters_rti(reference_length=128,
reference_time=18000,
@@ -28,10 +28,22 @@ def test_analytical():
density_ratio=3,
viscosity_ratio=3)
- np.isclose(parameters.density_light, 1/3, rtol=1e-05, atol=1e-08, equal_nan=False)
- np.isclose(parameters.gravitational_acceleration, -3.9506172839506174e-07,
- rtol=1e-05, atol=1e-08, equal_nan=False)
- np.isclose(parameters.mobility, 0.0012234169653524492, rtol=1e-05, atol=1e-08, equal_nan=False)
-
- rs = analytic_rising_speed(1-6, 20, 0.01)
- np.isclose(rs, 16666.666666666668, rtol=1e-05, atol=1e-08, equal_nan=False)
+ assert np.isclose(parameters.density_light, 1/3)
+ assert np.isclose(parameters.gravitational_acceleration, -3.9506172839506174e-07)
+ assert np.isclose(parameters.mobility, 0.0012234169653524492)
+
+ rs = analytic_rising_speed(1 - 6, 20, 0.01)
+ assert np.isclose(rs, 16666.666666666668)
+
+ parameters = calculate_parameters_taylor_bubble(reference_length=192,
+ reference_time=36000,
+ density_heavy=1.0,
+ diameter_outer=0.0254,
+ diameter_inner=0.0127)
+
+ assert np.isclose(parameters.density_heavy, 1.0)
+ assert np.isclose(parameters.density_light, 0.001207114228456914)
+ assert np.isclose(parameters.dynamic_viscosity_heavy, 5.733727652152216e-05)
+ assert np.isclose(parameters.dynamic_viscosity_light, 0.0008630017037694861)
+ assert np.isclose(parameters.gravitational_acceleration, -7.407407407407407e-08)
+ assert np.isclose(parameters.surface_tension, 3.149857262258028e-05, rtol=1e-05)
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb b/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb
index 28a7086fd8781bc13f3b818df4308953b2e44a10..0c2d8a89fab9d55295b7fedffe19ac217f5e747a 100644
--- a/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb
+++ b/lbmpy_tests/phasefield_allen_cahn/test_phase_field_derivatives.ipynb
@@ -258,10 +258,10 @@
"metadata": {},
"outputs": [],
"source": [
- "a = hydrodynamic_force(g, C, lb_method, parameters, [0, 0, 0] , fd_stencil=None)\n",
- "b = hydrodynamic_force(g, C, lb_method, parameters, [0, 0, 0] , fd_stencil=LBStencil(Stencil.D3Q27))\n",
- "c = hydrodynamic_force(g, C, lb_method, parameters, [0, 0, 0] , fd_stencil=LBStencil(Stencil.D3Q19))\n",
- "d = hydrodynamic_force(g, C, lb_method, parameters, [0, 0, 0] , fd_stencil=LBStencil(Stencil.D3Q15))"
+ "a = hydrodynamic_force(C, lb_method, parameters, [0, 0, 0] , fd_stencil=None)\n",
+ "b = hydrodynamic_force(C, lb_method, parameters, [0, 0, 0] , fd_stencil=LBStencil(Stencil.D3Q27))\n",
+ "c = hydrodynamic_force(C, lb_method, parameters, [0, 0, 0] , fd_stencil=LBStencil(Stencil.D3Q19))\n",
+ "d = hydrodynamic_force(C, lb_method, parameters, [0, 0, 0] , fd_stencil=LBStencil(Stencil.D3Q15))"
]
},
{
@@ -297,8 +297,12 @@
"outputs": [],
"source": [
"pf = pressure_force(C, lb_method, stencil, 1, 0.1)\n",
- "vf = viscous_force(g, C, lb_method, tau, 1, 0.1)\n",
- "sf = surface_tension_force(C, stencil, beta, kappa)\n",
+ "vf = viscous_force(C, lb_method, tau, 1, 0.1)\n",
+ "sf = surface_tension_force(C, stencil, parameters)\n",
+ "\n",
+ "sf[0] = sf[0].subs(parameters.symbolic_to_numeric_map)\n",
+ "sf[1] = sf[1].subs(parameters.symbolic_to_numeric_map)\n",
+ "sf[2] = sf[2].subs(parameters.symbolic_to_numeric_map)\n",
"\n",
"assert sp.simplify(pf[0] + vf[0] + sf[0] - b[0]) == 0\n",
"assert sp.simplify(pf[1] + vf[1] + sf[1] - b[1]) == 0\n",
@@ -321,8 +325,8 @@
"lbm_config = LBMConfig(stencil=stencil, method=Method.MRT, relaxation_rates=[1/tau, 1, 1, 1, 1, 1])\n",
"lb_method = create_lb_method(lbm_config=lbm_config)\n",
"\n",
- "a = hydrodynamic_force(g, C, lb_method, parameters, [0, 0, 0] , fd_stencil=None)\n",
- "b = hydrodynamic_force(g, C, lb_method, parameters, [0, 0, 0] , fd_stencil=stencil)"
+ "a = hydrodynamic_force(C, lb_method, parameters, [0, 0, 0] , fd_stencil=None)\n",
+ "b = hydrodynamic_force(C, lb_method, parameters, [0, 0, 0] , fd_stencil=stencil)"
]
}
],
diff --git a/lbmpy_tests/phasefield_allen_cahn/test_runge_kutta_solver.py b/lbmpy_tests/phasefield_allen_cahn/test_runge_kutta_solver.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed3258341386f16e3e2115369e5a851875780284
--- /dev/null
+++ b/lbmpy_tests/phasefield_allen_cahn/test_runge_kutta_solver.py
@@ -0,0 +1,120 @@
+import numpy as np
+import pytest
+
+from pystencils import Assignment, create_kernel, create_data_handling
+
+from lbmpy.stencils import LBStencil, Stencil
+
+from lbmpy.phasefield_allen_cahn.analytical import analytical_solution_microchannel
+from lbmpy.phasefield_allen_cahn.numerical_solver import get_runge_kutta_update_assignments
+
+
+@pytest.mark.parametrize('solver', ["RK2", "RK4"])
+def test_runge_kutta_solver(solver):
+ stencil = LBStencil(Stencil.D2Q9)
+
+ L0 = 25
+ domain_size = (2 * L0, L0)
+
+ # time step
+ timesteps = 2000
+
+ rho_H = 1.0
+ rho_L = 1.0
+
+ mu_L = 0.25
+
+ W = 4
+
+ T_h = 20
+ T_c = 10
+ T_0 = 4
+
+ sigma_T = -5e-4
+
+ cp_h = 1
+ cp_l = 1
+ k_h = 0.2
+ k_l = 0.2
+
+ # create a datahandling object
+ dh = create_data_handling(domain_size, periodicity=(True, False))
+
+ u = dh.add_array("u", values_per_cell=dh.dim)
+ dh.fill("u", 0.0, ghost_layers=True)
+
+ C = dh.add_array("C", values_per_cell=1)
+ dh.fill("C", 0.0, ghost_layers=True)
+
+ temperature = dh.add_array("temperature", values_per_cell=1)
+ dh.fill("temperature", T_c, ghost_layers=True)
+
+ RK1 = dh.add_array("RK1", values_per_cell=1)
+ dh.fill("RK1", 0.0, ghost_layers=True)
+
+ rk_fields = [RK1, ]
+ init_RK = [Assignment(RK1.center, temperature.center), ]
+
+ if solver == "RK4":
+ RK2 = dh.add_array("RK2", values_per_cell=1)
+ dh.fill("RK2", 0.0, ghost_layers=True)
+
+ RK3 = dh.add_array("RK3", values_per_cell=1)
+ dh.fill("RK3", 0.0, ghost_layers=True)
+
+ rk_fields += [RK2, RK3]
+ init_RK += [Assignment(RK2.center, temperature.center),
+ Assignment(RK3.center, temperature.center)]
+
+ rho = rho_L + C.center * (rho_H - rho_L)
+
+ for block in dh.iterate(ghost_layers=True, inner_ghost_layers=False):
+ x = np.zeros_like(block.midpoint_arrays[0])
+ x[:, :] = block.midpoint_arrays[0]
+
+ normalised_x = np.zeros_like(x[:, 0])
+ normalised_x[:] = x[:, 0] - L0
+ omega = np.pi / L0
+ # bottom wall
+ block["temperature"][:, 0] = T_h + T_0 * np.cos(omega * normalised_x)
+ # top wall
+ block["temperature"][:, -1] = T_c
+
+ y = np.zeros_like(block.midpoint_arrays[1])
+ y[:, :] = block.midpoint_arrays[1] + (domain_size[1] // 2)
+
+ block["C"][:, :] = 0.5 + 0.5 * np.tanh((y - domain_size[1]) / (W / 2))
+
+ a = get_runge_kutta_update_assignments(stencil, C, temperature, u, rk_fields,
+ k_h, k_l, cp_h, cp_l, rho)
+
+ init_RK_kernel = create_kernel(init_RK, target=dh.default_target).compile()
+
+ temperature_update_kernel = []
+ for ac in a:
+ temperature_update_kernel.append(create_kernel(ac, target=dh.default_target).compile())
+
+ periodic_BC_T = dh.synchronization_function(temperature.name)
+
+ x, y, u_x, u_y, t_a = analytical_solution_microchannel(L0, domain_size[0], domain_size[1],
+ k_h, k_l,
+ T_h, T_c, T_0,
+ sigma_T, mu_L)
+
+ for i in range(0, timesteps + 1):
+ dh.run_kernel(init_RK_kernel)
+ for kernel in temperature_update_kernel:
+ dh.run_kernel(kernel)
+ periodic_BC_T()
+
+ num = 0.0
+ den = 0.0
+ T = dh.gather_array(temperature.name, ghost_layers=False)
+ for ix in range(domain_size[0]):
+ for iy in range(domain_size[1]):
+ num += (T[ix, iy] - t_a.T[ix, iy]) ** 2
+ den += (t_a.T[ix, iy]) ** 2
+
+ error = np.sqrt(num / den)
+
+ assert error < 0.02
diff --git a/lbmpy_tests/test_stencils.py b/lbmpy_tests/test_stencils.py
index ce8637261f0122850864164723d2d778a90adf3d..7635e7d053a6e3dfd013046f4458a1d14a0d2431 100644
--- a/lbmpy_tests/test_stencils.py
+++ b/lbmpy_tests/test_stencils.py
@@ -21,12 +21,17 @@ def get_all_stencils():
LBStencil(Stencil.D3Q27, 'walberla'),
LBStencil(Stencil.D2Q9, 'counterclockwise'),
-
LBStencil(Stencil.D2Q9, 'braunschweig'),
- LBStencil(Stencil.D3Q19, 'braunschweig'),
+ LBStencil(Stencil.D2Q9, 'uk'),
+
- LBStencil(Stencil.D3Q27, 'premnath'),
+ LBStencil(Stencil.D3Q15, 'premnath'),
+ LBStencil(Stencil.D3Q15, 'fakhari'),
+
+ LBStencil(Stencil.D3Q19, 'braunschweig'),
+ LBStencil(Stencil.D3Q19, 'premnath'),
+ LBStencil(Stencil.D3Q27, "premnath"),
LBStencil(Stencil.D3Q27, "fakhari"),
]
diff --git a/pytest.ini b/pytest.ini
index a841bb6f7cc35615a85d96079d575fe664ce826d..cd60af2d63cd4d6a32abcc033a4476fea587cb0e 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -43,7 +43,7 @@ exclude_lines =
if __name__ == .__main__.:
skip_covered = True
-fail_under = 88
+fail_under = 87
[html]
directory = coverage_report