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.flake8
View file @ 32380aab
[flake8]
max-line-length=120
exclude=lbmpy/plot.py
lbmpy/session.py
ignore = W293 W503 W291 E741
exclude=src/lbmpy/plot.py
src/lbmpy/session.py
ignore = W293 W503 W291 C901 E741
.gitattributes
View file @ 32380aab
lbmpy/_version.py export-subst
src/lbmpy/_version.py export-subst
.gitignore
View file @ 32380aab
__pycache__
.ipynb_checkpoints
.coverage
.coverage*
*.pyc
*.vti
/build
/html_doc
/dist
/*.egg-info
*.egg-info
.cache
_build
/.idea
......@@ -15,14 +15,15 @@ _local_tmp
**/pylintrc
*.bak
*.tmp
/lbmpy_tests/db
/tests/db
doc/bibtex.json
/db
/lbmpy/phasefield/simplex_projection.*.so
/lbmpy/phasefield/simplex_projection.c
/src/lbmpy/phasefield/simplex_projection.*.so
/src/lbmpy/phasefield/simplex_projection.c
# macOS
**/.DS_Store
*.uuid
# benchmark database
/lbmpy_tests/benchmark/db
\ No newline at end of file
/tests/benchmark/db
\ No newline at end of file
.gitlab-ci.yml
View file @ 32380aab
stages:
- pretest
- test
- nightly
- docs
- deploy
# -------------------------- Templates ------------------------------------------------------------------------------------
# Base configuration for jobs meant to run at every commit
.every-commit:
rules:
- if: $CI_PIPELINE_SOURCE != "schedule"
# Configuration for jobs meant to run on each commit to pycodegen/pystencils/master
.every-commit-master:
rules:
- if: '$CI_PIPELINE_SOURCE != "schedule" && $CI_PROJECT_PATH == "pycodegen/lbmpy" && $CI_COMMIT_BRANCH == "master"'
# Base configuration for jobs meant to run at a schedule
.scheduled:
rules:
- if: $CI_PIPELINE_SOURCE == "schedule"
# -------------------------- Pre Tests --------------------------------------------------------------------------------
# Normal test - runs on every commit all but "long run" tests
tests-and-coverage:
stage: pretest
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/full
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/full:cupy12.3
script:
# - pip install sympy --upgrade
- export NUM_CORES=$(nproc --all)
......@@ -22,41 +39,62 @@ tests-and-coverage:
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- env
- pip list
- py.test -v -n $NUM_CORES --cov-report html --cov-report term --cov=. -m "not longrun" --junitxml=report.xml
- py.test -v -n $NUM_CORES --cov-report html --cov-report xml --cov-report term --cov=. -m "not longrun" --junitxml=report.xml
- python3 -m coverage xml
tags:
- docker
- cuda11
- AVX
coverage: /Total coverage:\s\d+.\d+\%/
artifacts:
when: always
paths:
- coverage_report
reports:
cobertura: coverage.xml
coverage_report:
coverage_format: cobertura
path: coverage.xml
junit: report.xml
# Normal test with longruns
tests-and-coverage-with-longrun:
stage: test
when: manual
allow_failure: true
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/full
script:
# - pip install sympy --upgrade
- export NUM_CORES=$(nproc --all)
- mkdir -p ~/.config/matplotlib
- echo "backend:template" > ~/.config/matplotlib/matplotlibrc
- mkdir public
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- env
- pip list
- py.test -v -n $NUM_CORES
tags:
- docker
- cuda11
- AVX
minimal-conda:
stage: pretest
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/minimal_conda
script:
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- python setup.py quicktest
- pip install -e .
- python quicktest.py
tags:
- docker
# Linter for code formatting
flake8-lint:
stage: pretest
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/full
script:
- flake8 lbmpy
- flake8 src/lbmpy
tags:
- docker
- cuda11
......@@ -66,9 +104,7 @@ flake8-lint:
# pipeline with latest python version
latest-python:
stage: test
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/latest_python
before_script:
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
......@@ -88,61 +124,35 @@ latest-python:
reports:
junit: report.xml
# Nightly test - runs "long run" jobs only
test-longrun:
stage: test
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/full
script:
- env
- pip list
- export NUM_CORES=$(nproc --all)
- mkdir -p ~/.config/matplotlib
- echo "backend:template" > ~/.config/matplotlib/matplotlibrc
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- py.test -v -n $NUM_CORES --cov-report html --cov-report term --cov=. --junitxml=report.xml
tags:
- docker
- cuda11
- AVX
artifacts:
when: always
paths:
- coverage_report
reports:
junit: report.xml
# Minimal tests in windows environment
minimal-windows:
stage: test
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
tags:
- win
script:
- export NUM_CORES=$(nproc --all)
- export MPLBACKEND=Agg
- source /cygdrive/c/Users/build/Miniconda3/Scripts/activate
- source activate pystencils
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- python -c "import numpy"
- pip install sympy==1.9
- py.test -v -m "not (notebook or longrun)"
#minimal-windows:
# stage: test
# except:
# variables:
# - $ENABLE_NIGHTLY_BUILDS
# tags:
# - win
# script:
# - export NUM_CORES=$(nproc --all)
# - export MPLBACKEND=Agg
# - source /cygdrive/c/Users/build/Miniconda3/Scripts/activate
# - source activate pystencils
# - pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
# - python -c "import numpy"
# - pip install sympy==1.9
# - py.test -v -m "not (notebook or longrun)"
minimal-sympy-master:
stage: test
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/minimal_conda
before_script:
- pip install -e .
script:
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- python -m pip install --upgrade git+https://github.com/sympy/sympy.git
- pip list
- python setup.py quicktest
- python quicktest.py
allow_failure: true
tags:
- docker
......@@ -150,9 +160,7 @@ minimal-sympy-master:
ubuntu:
stage: test
except:
variables:
- $ENABLE_NIGHTLY_BUILDS
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/ubuntu
before_script:
# - apt-get -y remove python3-sympy
......@@ -165,7 +173,7 @@ ubuntu:
- echo "backend:template" > ~/.config/matplotlib/matplotlibrc
- env
- pip3 list
- pytest-3 -v -n $NUM_CORES -m "not longrun" --junitxml=report.xml
- pytest -v -n $NUM_CORES -m "not longrun" --junitxml=report.xml
tags:
- docker
- cuda11
......@@ -201,7 +209,7 @@ pycodegen-integration:
- make -j $NUM_CORES MicroBenchmarkGpuLbm LbCodeGenerationExample
- cd apps/benchmarks/UniformGridGPU
- make -j $NUM_CORES
- cd ../UniformGridGenerated
- cd ../UniformGridCPU
- make -j $NUM_CORES
tags:
......@@ -209,11 +217,44 @@ pycodegen-integration:
- cuda11
- AVX
# -------------------- Scheduled Tasks --------------------------------------------------------------------------
nightly-sympy:
stage: nightly
extends: .scheduled
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/latest_python
before_script:
- pip install -e .
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
- pip install --upgrade --pre sympy
script:
- env
- pip list
- export NUM_CORES=$(nproc --all)
- mkdir -p ~/.config/matplotlib
- echo "backend:template" > ~/.config/matplotlib/matplotlibrc
- mkdir public
- pytest -v -n $NUM_CORES -m "not longrun" --junitxml=report.xml
tags:
- docker
- AVX
- cuda
artifacts:
when: always
reports:
junit: report.xml
# -------------------- Documentation and deploy ------------------------------------------------------------------------
build-documentation:
stage: test
stage: docs
needs: []
extends: .every-commit
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/documentation
before_script:
- pip install -e .
script:
- export PYTHONPATH=`pwd`
- pip install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
......@@ -229,7 +270,9 @@ build-documentation:
pages:
image: i10git.cs.fau.de:5005/pycodegen/pycodegen/full
extends: .every-commit-master
stage: deploy
needs: ["tests-and-coverage", "build-documentation"]
script:
- ls -l
- mv coverage_report html_doc
......@@ -239,5 +282,3 @@ pages:
- public
tags:
- docker
only:
- master@pycodegen/lbmpy
COPYING.txt
View file @ 32380aab
------------------------ Important ---------------------------------
lbmpy is under the following GNU AGPLv3 license.
This license holds for the sources of lbmpy itself as well
as for all kernels generated with lbmpy i.e.
the output of lbmpy is also protected by the GNU AGPLv3 license.
----------------------------------------------------------------------
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
......
MANIFEST.in
View file @ 32380aab
include README.md
include COPYING.txt
include AUTHORS.txt
include CONTRIBUTING.md
global-include *.pyx
include versioneer.py
include lbmpy/_version.py
include CHANGELOG.md
README.md
View file @ 32380aab
......@@ -71,6 +71,7 @@ Many thanks go to the [contributors](https://i10git.cs.fau.de/pycodegen/lbmpy/-/
If you use lbmpy in a publication, please cite the following articles:
Overview:
- F. Hennig et al, Advanced Automatic Code Generation for Multiple Relaxation-Time Lattice Boltzmann Methods. SIAM Journal on Scientific Computing, 2023. https://doi.org/10.1137/22M1531348 ([Preprint](https://arxiv.org/abs/2211.02435))
- M. Bauer et al, lbmpy: Automatic code generation for efficient parallel lattice Boltzmann methods. Journal of Computational Science, 2021. https://doi.org/10.1016/j.jocs.2020.101269 ([Preprint](https://arxiv.org/abs/2001.11806))
Multiphase:
......
binder/environment.yml
View file @ 32380aab
......@@ -7,7 +7,7 @@
# conda env create -f conda_environment_user.yml
# . activate pystencils
#
# If you have CUDA installed and want to use your GPU, uncomment the last line to install pycuda
# If you have CUDA or ROCm installed and want to use your GPU, uncomment the last line to install cupy
#
# ----------------------------------------------------------------------------------------------------------------------
......@@ -33,4 +33,4 @@ dependencies:
- pyevtk # VTK output for serial simulations
- blitzdb # file-based No-SQL database to store simulation results
- pystencils
#- pycuda # add this if you have CUDA installed
#- cupy # add this if you have CUDA or ROCm installed
conftest.py
View file @ 32380aab
......@@ -19,9 +19,10 @@ try:
import pyximport
pyximport.install(language_level=3)
from lbmpy.phasefield.simplex_projection import simplex_projection_2d # NOQA
except ImportError:
pass
from lbmpy.phasefield.simplex_projection import simplex_projection_2d # NOQA
SCRIPT_FOLDER = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, os.path.abspath('lbmpy'))
......@@ -42,33 +43,35 @@ def add_path_to_ignore(path):
collect_ignore = [os.path.join(SCRIPT_FOLDER, "doc", "conf.py"),
os.path.join(SCRIPT_FOLDER, "doc", "img", "mb_discretization", "maxwell_boltzmann_stencil_plot.py")]
add_path_to_ignore('pystencils_tests/benchmark')
add_path_to_ignore('_local_tmp')
try:
import pycuda
import cupy
except ImportError:
collect_ignore += [os.path.join(SCRIPT_FOLDER, "lbmpy_tests/test_cpu_gpu_equivalence.py")]
collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_cpu_gpu_equivalence.py")]
try:
import waLBerla
except ImportError:
collect_ignore += [os.path.join(SCRIPT_FOLDER, "lbmpy_tests/test_datahandling_parallel.py")]
collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_datahandling_parallel.py")]
try:
import blitzdb
except ImportError:
collect_ignore += [os.path.join(SCRIPT_FOLDER, "lbmpy_tests/benchmark"),
collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/benchmark"),
os.path.join(SCRIPT_FOLDER,
"lbmpy_tests/full_scenarios/kida_vortex_flow/scenario_kida_vortex_flow.py")]
"tests/full_scenarios/kida_vortex_flow/scenario_kida_vortex_flow.py"),
os.path.join(SCRIPT_FOLDER, "tests/full_scenarios/shear_wave/scenario_shear_wave.py"),
os.path.join(SCRIPT_FOLDER, "tests/test_json_serializer.py"),
os.path.join(SCRIPT_FOLDER, "src/lbmpy/db.py")]
if platform.system().lower() == 'windows':
collect_ignore += [os.path.join(SCRIPT_FOLDER, "lbmpy_tests/test_quicktests.py")]
collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_quicktests.py")]
sver = sympy.__version__.split(".")
if int(sver[0]) == 1 and int(sver[1]) < 2:
add_path_to_ignore('lbmpy_tests/phasefield')
collect_ignore += [os.path.join(SCRIPT_FOLDER, "lbmpy_tests/test_n_phase_boyer_noncoupled.ipynb")]
add_path_to_ignore('tests/phasefield')
collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_n_phase_boyer_noncoupled.ipynb")]
collect_ignore += [os.path.join(SCRIPT_FOLDER, 'setup.py')]
......@@ -104,18 +107,25 @@ class IPyNbTest(pytest.Item):
# disable matplotlib output
exec("import matplotlib.pyplot as p; "
"p.close('all'); "
"p.switch_backend('Template')", global_dict)
# in notebooks there is an implicit plt.show() - if this is not called a warning is shown when the next
# plot is created. This warning is suppressed here
# Also animations cannot be shown, which also leads to a warning.
exec("import warnings;"
"warnings.filterwarnings('ignore', 'Adding an axes using the same arguments as a previous.*');",
"warnings.filterwarnings('ignore', 'Adding an axes using the same arguments as a previous.*');"
"warnings.filterwarnings('ignore', 'Animation was deleted without rendering anything.*');",
global_dict)
with tempfile.NamedTemporaryFile() as f:
f.write(self.code.encode())
f.flush()
runpy.run_path(f.name, init_globals=global_dict, run_name=self.name)
# Close any open figures
exec("import matplotlib.pyplot as p; "
"p.close('all')", global_dict)
class IPyNbFile(pytest.File):
def collect(self):
......@@ -138,10 +148,19 @@ class IPyNbFile(pytest.File):
pass
def pytest_collect_file(path, parent):
glob_exprs = ["*demo*.ipynb", "*tutorial*.ipynb", "test_*.ipynb"]
if any(path.fnmatch(g) for g in glob_exprs):
if pytest_version >= 50403:
return IPyNbFile.from_parent(fspath=path, parent=parent)
else:
return IPyNbFile(path, parent)
if pytest_version >= 70000:
# Since pytest 7.0, usage of `py.path.local` is deprecated and `pathlib.Path` should be used instead
import pathlib
def pytest_collect_file(file_path: pathlib.Path, parent):
glob_exprs = ["*demo*.ipynb", "*tutorial*.ipynb", "test_*.ipynb"]
if any(file_path.match(g) for g in glob_exprs):
return IPyNbFile.from_parent(path=file_path, parent=parent)
else:
def pytest_collect_file(path, parent):
glob_exprs = ["*demo*.ipynb", "*tutorial*.ipynb", "test_*.ipynb"]
if any(path.fnmatch(g) for g in glob_exprs):
if pytest_version >= 50403:
return IPyNbFile.from_parent(fspath=path, parent=parent)
else:
return IPyNbFile(path, parent)
doc/conf.py
View file @ 32380aab
......@@ -26,14 +26,14 @@ templates_path = ['_templates']
source_suffix = '.rst'
master_doc = 'index'
copyright = f'{datetime.datetime.now().year}, Martin Bauer, Markus Holzer'
copyright = f'{datetime.datetime.now().year}, Martin Bauer, Markus Holzer, Frederik Hennig'
author = 'Martin Bauer, Markus Holzer, Frederik Hennig'
# The short X.Y version (including .devXXXX, rcX, b1 suffixes if present)
version = re.sub(r'(\d+\.\d+)\.\d+(.*)', r'\1\2', lbmpy.__version__)
version = re.sub(r'(\.dev\d+).*?$', r'\1', version)
# The full version, including alpha/beta/rc tags.
release = lbmpy.__version__
language = None
language = 'en'
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
default_role = 'any'
pygments_style = 'sphinx'
......
doc/img/Boundary.pdf 0 → 100644
View file @ 32380aab
File added
doc/img/Boundary.svg 0 → 100644
View file @ 32380aab
<?xml version="1.0" encoding="UTF-8"?>
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="190.286" height="190.833" viewBox="0 0 190.286 190.833">
<defs>
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:maxdepth: 2
sphinx/tutorials.rst
sphinx/methods.rst
sphinx/boundary_conditions.rst
sphinx/forcemodels.rst
sphinx/api.rst
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%% Cell type:code id: tags:
 
``` python
import pytest
pytest.importorskip('pycuda')
pytest.importorskip('cupy')
```
 
%% Output
 
<module 'pycuda' from '/home/markus/miniconda3/envs/pystencils/lib/python3.8/site-packages/pycuda/__init__.py'>
<module 'cupy' from '/home/markus/.local/lib/python3.11/site-packages/cupy/__init__.py'>
 
%% Cell type:code id: tags:
 
``` python
from lbmpy.session import *
from pystencils import Target
```
 
%% Cell type:markdown id: tags:
 
# Tutorial 01: Running pre-defined scenarios
 
 
*lbmpy* is a module to do Lattice Boltzmann simulations in Python.
 
In this tutorial you will get a broad overview of *lbmpy*'s features. We will run some of the included scenarios that come with *lbmpy*, like a channel flow and a lid driven cavity. This tutorial uses the simple, high-level API of *lbmpy*, while the following tutorials go into the low-level details.
 
The only prerequisite for this tutorial is basic Python and [numpy](http://www.numpy.org/) knowledge.
 
 
> #### What's special about *lbmpy* ?
> The LBM kernels (i.e. the functions that do all the computations) are not written in Python. Instead *lbmpy* generates optimized C or CUDA code for these kernels and compiles it using the *pystencils* module. In that way we get very fast LBM kernels, a lot faster than pure Python implementations and probably also faster than handwritten C kernels. This sounds complicated, but we don't have to care about all this background work, since all compiled kernels are available as Python functions again. Thus *lbmpy* can be used just like any other Python package.
 
 
## Lid Driven Cavity
 
We start by simulating a fluid in a rectangular box, where one wall (the lid) is moving. This is called a 'lid driven cavity'. At the stationary walls *no-slip* boundary conditions are set, which enforce zero velocity at the wall. At the lid there is a *velocity bounce back (UBB)* boundary condition, which sets zero normal velocity and a prescribed tangential velocity.
 
We don't have to set up all these boundary conditions manually since there is a function ``create_lid_driven_cavity`` that does all the work for us. This function takes the tangential velocity of the lid, which drives the flow. It is given in lattice units and to get a stable simulation it should be smaller than 0.1. The `relaxation_rate` determines the viscosity of the fluid: Small relaxation rates correspond to high viscosity. The `relaxation_rate` has to be between 0 and 2.
 
%% Cell type:code id: tags:
 
``` python
ldc_scenario = create_lid_driven_cavity(domain_size=(80,50), lid_velocity=0.01, relaxation_rate=1.95)
ldc_scenario.method
```
 
%% Output
 
<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x7fe7a8828d30>
 
%% Cell type:markdown id: tags:
 
The *run* method of the scenario runs the specified amount of time steps. When you run the next cell, 2000 time steps are executed and the velocity field is plotted. You can run the cell multiple times to see a time evolution.
 
%% Cell type:code id: tags:
 
``` python
ldc_scenario.run(2000)
plt.figure(dpi=200)
plt.vector_field(ldc_scenario.velocity_slice(), step=2);
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
### Variations to experiment with:
- simulate with a higher ``relaxation_rate`` (i.e. higher [Reynolds number](https://en.wikipedia.org/wiki/Reynolds_number)), keep in mind that the ``relaxation_rate`` has to be smaller than 2. You might have to increase (``domain_size``) to keep the simulation stable and run more time steps to get to the stationary solution. You also might want to increase the ``step`` parameter for the plot, to reduce the number of arrows.
- run a 3D simulation by adding a third dimension size to ``domain_size``. The ``velocity`` property of the scenario is now a 3D field that has to be sliced before it can be plotted, e.g. ``ldc_scenario.velocity[:, :, 10, 0:2]`` generates a slice at ``z=10`` and plot the ``x`` and ``y`` component of the velocity.
 
%% Cell type:markdown id: tags:
 
## Fully periodic flow
 
Another simple scenario is a box with periodic boundary conditions in all directions. We initialize a non-zero initial velocity field, which is decaying over time due to viscous effects and the absence of driving forces or boundary conditions. In this example we initialize a shear flow where in one stripe the fluid is moving to the left, and everywhere else to the right. We perturbe this initial velocity field with random noise to get an instable shear layer.
 
%% Cell type:code id: tags:
 
``` python
width, height = 200, 60
velocity_magnitude = 0.05
init_vel = np.zeros((width,height,2))
# fluid moving to the right everywhere...
init_vel[:, :, 0] = velocity_magnitude
# ...except at a stripe in the middle, where it moves left
init_vel[:, height//3 : height//3*2, 0] = -velocity_magnitude
# small random y velocity component
init_vel[:, :, 1] = 0.1 * velocity_magnitude * np.random.rand(width,height)
 
plt.figure(dpi=200)
plt.vector_field(init_vel, step=4);
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
With this initial velocity field we create a simulation scenario:
 
%% Cell type:code id: tags:
 
``` python
shear_flow_scenario = create_fully_periodic_flow(initial_velocity=init_vel, relaxation_rate=1.97)
```
 
%% Cell type:code id: tags:
 
``` python
shear_flow_scenario.run(500)
plt.figure(dpi=200)
plt.vector_field(shear_flow_scenario.velocity[:, :])
```
 
%% Output
 
<matplotlib.quiver.Quiver at 0x7fe7a80e2bb0>
 
 
%% Cell type:markdown id: tags:
 
Instead of plotting a single point in time we create an animation. For this we first have to
define an update function that runs a few time steps and returns the field to plot.
This function is called ``iterations`` times, then the animation stops.
To cancel the animation while it is running, hit the stop button in the IPython menu bar.
 
%% Cell type:code id: tags:
 
``` python
# def next_frame():
# shear_flow_scenario.run(50)
# return shear_flow_scenario.velocity[:, :]
# plt.figure(dpi=200)
# display_animation(plt.vector_field_animation(next_frame, step=2), iterations=50)
```
 
%% Cell type:markdown id: tags:
 
Vortices are created between the two layers. This phenomenon is called [Kelvin-Helmholz Instability](https://en.wikipedia.org/wiki/Kelvin%E2%80%93Helmholtz_instability). For a better visualization of the vortices we can plot the [vorticity](https://en.wikipedia.org/wiki/Vorticity) of velocity field:
 
%% Cell type:code id: tags:
 
``` python
plt.figure(dpi=200)
plt.scalar_field(vorticity_2d(shear_flow_scenario.velocity[:, :]));
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
### Variations to experiment with:
- make an animation of the vorticity
- increase the ``relaxation_rate``. What is the maximum relaxation rate you can get before the simulation gets unstable?
- use an entropic method to get to higher relaxation rates:
 
```
entropic_shear_flow_scenario = create_fully_periodic_flow(initial_velocity=init_vel, method='trt-kbc-n4',
entropic=True, compressible=True)
entropic_shear_flow_scenario.kernel_params['omega_0'] = 1.999
```
 
%% Cell type:markdown id: tags:
 
## Channel
 
In the last part of this tutorial you learn how to modify the boundary handling of scenarios.
Therefor we set up a channel flow and place some objects into it.
 
The channel will be driven by a constant body force e.g. gravity which acts in x direction. Along the flow direction periodic boundary conditions are used whereas the walls are modeled with a *noslip* boundary condition.
 
%% Cell type:code id: tags:
 
``` python
channel_scenario = create_channel(domain_size=(300, 100), force=1e-7, initial_velocity=(0.025, 0),
relaxation_rate=1.97,
config=CreateKernelConfig(target=Target.GPU))
```
 
%% Cell type:code id: tags:
 
``` python
channel_scenario._lbmKernels[0].ast
```
 
%% Output
 
KernelFunction kernel([_data_force_driven_channel_pdfSrc, _data_force_driven_channel_pdfTmp])
 
%% Cell type:markdown id: tags:
 
As in the last scenario, we specify an initial velocity here. Instead of passing a velocity value for every cell we specify here a constant for the complete domain.
 
%% Cell type:code id: tags:
 
``` python
channel_scenario.run(10000)
plt.figure(dpi=200)
plt.vector_field(channel_scenario.velocity[:, :], step=4);
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
This is a 2D [Poiseuille flow](https://en.wikipedia.org/wiki/Hagen%E2%80%93Poiseuille_equation) where a parabolic profile of the x velocity is expected for the stationary case.
 
%% Cell type:code id: tags:
 
``` python
vel_profile = channel_scenario.velocity[0.5, :, 0]
plt.figure(dpi=200)
plt.plot(vel_profile);
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
The stationary state is not yet reached, you can run more time steps and see how the profile gets closer to a parabola.
 
 
### Modifying boundaries
 
Lets first view the current boundary configuration:
 
%% Cell type:code id: tags:
 
``` python
def draw_boundary_setup():
fig = plt.figure(figsize=(10.0, 3.0), dpi=200)
plt.boundary_handling(channel_scenario.boundary_handling)
plt.axis('off');
 
draw_boundary_setup()
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
In above plot you can see that the *no-slip* boundaries at the top and bottom of the channel, otherwise the channel is empty.
 
Since an empty channel is pretty boring, lets put an obstacle in it. We start with the simplest option: a rectangular, solid block. The rectangle is specified as a slice, similar to advanced *numpy* indexing. The ``make_slice`` function can also take ``float`` as indices for specifying the slice relative to the domain size. The following cell puts an obstacle into the domain that has one third of the channel height.
Additionally we have to pass a function that defines what should happen at the boundary (here ``noSlip``).
By default, the name of the function is also the boundary name.
 
%% Cell type:code id: tags:
 
``` python
from lbmpy.boundaries import NoSlip
 
wall = NoSlip()
channel_scenario.boundary_handling.set_boundary(wall, make_slice[0.2:0.25, 0:0.333])
```
 
%% Output
 
2
 
%% Cell type:markdown id: tags:
 
When plotting the boundary handling again, we see the rectangular obstacle:
 
%% Cell type:code id: tags:
 
``` python
draw_boundary_setup()
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
When setting and plotting boundaries the domain is actually 2 cells larger than originally specified. These so called 'ghost layer' slices are one cell thick and are automatically added at each domain boundary. They can be used to set boundaries and are also used for communication when running distributed memory parallel simulations.
When specifying the slice, keep in mind that the domain is actually slightly larger. When plotting the simulation results the ghost layers are automatically removed.
 
To convert a cell back to ``domain``, the same method can be used. To demonstrate this, we cut a piece out of the obstacle:
 
%% Cell type:code id: tags:
 
``` python
channel_scenario.boundary_handling.set_boundary('domain', make_slice[0.2:0.235, 0.0333:0.3])
fig = plt.figure(figsize=(10.0, 3.0), dpi=200)
plt.boundary_handling(channel_scenario.boundary_handling)
plt.axis('off');
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
To add non-rectangular obstacles one can also pass a mask array, which is ``True`` for cells where the boundary should be set. This is demonstrated in the next cell, where a sphere is placed in the channel.
 
%% Cell type:code id: tags:
 
``` python
def set_sphere(x, y):
shape = channel_scenario.domain_size
mid = (0.5 * shape[0], 0.5 * shape[1])
radius = 13
return (x-mid[0])**2 + (y-mid[1])**2 < radius**2
 
channel_scenario.boundary_handling.set_boundary(wall, mask_callback=set_sphere)
draw_boundary_setup()
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
Now, we run the simulation to see the flow aroung the obstacles:
 
%% Cell type:code id: tags:
 
``` python
channel_scenario.run(10000)
plt.figure(dpi=200)
plt.vector_field_magnitude(channel_scenario.velocity[:,:]);
```
 
%% Output
 
 
%% Cell type:markdown id: tags:
 
### Variations to experiment with:
 
- increase the Reynolds number. You might also have to increase the resolution, and/or use a more advanced method like cumulant or entropic stabilization
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%% Cell type:markdown id: tags:
# The conservative Allen-Cahn model for high Reynolds number, two phase flow with large-density and viscosity constrast
%% Cell type:code id: tags:
``` python
from pystencils.session import *
from lbmpy.session import *
from pystencils.simp import sympy_cse
from pystencils.boundaries import BoundaryHandling
from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle
from lbmpy.phasefield_allen_cahn.kernel_equations import *
from lbmpy.phasefield_allen_cahn.parameter_calculation import calculate_parameters_rti, AllenCahnParameters
from lbmpy.advanced_streaming import LBMPeriodicityHandling
from lbmpy.boundaries import NoSlip, LatticeBoltzmannBoundaryHandling
```
%% Cell type:markdown id: tags:
If `pycuda` is installed the simulation automatically runs on GPU
If `cupy` is installed the simulation automatically runs on GPU
%% Cell type:code id: tags:
``` python
try:
import pycuda
import cupy
except ImportError:
pycuda = None
cupy = None
gpu = False
target = ps.Target.CPU
print('No pycuda installed')
print('No cupy installed')
if pycuda:
if cupy:
gpu = True
target = ps.Target.GPU
```
%% Output
No pycuda installed
%% Cell type:markdown id: tags:
The conservative Allen-Cahn model (CACM) for two-phase flow is based on the work of Fakhari et al. (2017) [Improved locality of the phase-field lattice-Boltzmann model for immiscible fluids at high density ratios](http://dx.doi.org/10.1103/PhysRevE.96.053301). The model can be created for two-dimensional problems as well as three-dimensional problems, which have been described by Mitchell et al. (2018) [Development of a three-dimensional
phase-field lattice Boltzmann method for the study of immiscible fluids at high density ratios](http://dx.doi.org/10.1103/PhysRevE.96.053301). Furthermore, cascaded lattice Boltzmann methods can be combined with the model which was described in [A cascaded phase-field lattice Boltzmann model for the simulation of incompressible, immiscible fluids with high density contrast](http://dx.doi.org/10.1016/j.camwa.2019.08.018)
The CACM is suitable for simulating highly complex two phase flow problems with high-density ratios and high Reynolds numbers. In this tutorial, an overview is provided on how to derive the model with lbmpy. For this, the model is defined with two LBM populations. One for the interface tracking, which we call the phase-field LB step and one for recovering the hydrodynamic properties. The latter is called the hydrodynamic LB step.
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## Geometry Setup
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 can be performed in either 2D- or 3D-space. For 2D simulations, only the D2Q9 stencil is supported. For 3D simulations, the D3Q15, D3Q19 and the D3Q27 stencil are supported. Note here that the cascaded LBM can not be derived for D3Q15 stencils.
%% Cell type:code id: tags:
``` python
stencil_phase = LBStencil(Stencil.D2Q9)
stencil_hydro = LBStencil(Stencil.D2Q9)
assert(len(stencil_phase[0]) == len(stencil_hydro[0]))
dimensions = len(stencil_phase[0])
```
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Definition of the domain size
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``` python
# domain
L0 = 256
domain_size = (L0, 4 * L0)
```
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## Parameter definition
The next step is to calculate all parameters which are needed for the simulation. In this example, a Rayleigh-Taylor instability test case is set up. The parameter calculation for this setup is already implemented in lbmpy and can be used with the dimensionless parameters which describe the problem.
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``` python
# time step
timesteps = 8000
# reference time
reference_time = 4000
# calculate the parameters for the RTI
parameters = calculate_parameters_rti(reference_length=L0,
reference_time=reference_time,
density_heavy=1.0,
capillary_number=0.44,
reynolds_number=3000,
atwood_number=0.998,
peclet_number=1000,
density_ratio=1000,
viscosity_ratio=100)
```
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This function returns a `AllenCahnParameters` class. It is struct like class holding all parameters for the conservative Allen Cahn model:
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``` python
parameters
```
%% Output
<lbmpy.phasefield_allen_cahn.parameter_calculation.AllenCahnParameters at 0x126d30cd0>
<lbmpy.phasefield_allen_cahn.parameter_calculation.AllenCahnParameters at 0x1329b88b0>
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## Fields
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/). This object holds all fields and manages the kernel runs.
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``` python
# create a datahandling object
dh = ps.create_data_handling((domain_size), periodicity=(True, False), parallel=False, default_target=target)
# pdf fields. g is used for hydrodynamics and h for the interface tracking
g = dh.add_array("g", values_per_cell=len(stencil_hydro))
dh.fill("g", 0.0, ghost_layers=True)
h = dh.add_array("h",values_per_cell=len(stencil_phase))
dh.fill("h", 0.0, ghost_layers=True)
g_tmp = dh.add_array("g_tmp", values_per_cell=len(stencil_hydro))
dh.fill("g_tmp", 0.0, ghost_layers=True)
h_tmp = dh.add_array("h_tmp",values_per_cell=len(stencil_phase))
dh.fill("h_tmp", 0.0, ghost_layers=True)
# velocity field
u = dh.add_array("u", values_per_cell=dh.dim)
dh.fill("u", 0.0, ghost_layers=True)
# phase-field
C = dh.add_array("C")
dh.fill("C", 0.0, ghost_layers=True)
C_tmp = dh.add_array("C_tmp")
dh.fill("C_tmp", 0.0, ghost_layers=True)
```
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As a next step the relaxation time is stated in a symbolic form. It is calculated via interpolation.
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``` python
rho_L = parameters.symbolic_density_light
rho_H = parameters.symbolic_density_heavy
density = rho_L + C.center * (rho_H - rho_L)
body_force = [0, 0, 0]
body_force[1] = parameters.symbolic_gravitational_acceleration * density
```
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## Definition of the lattice Boltzmann methods
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For both LB steps, a weighted orthogonal MRT (WMRT) method is used. It is also possible to change the method to a simpler SRT scheme or a more complicated CLBM scheme. The CLBM scheme can be obtained with `Method.CENTRAL_MOMENT`. Note here that the hydrodynamic LB step is formulated as an incompressible velocity-based LBM. Thus, the velocity terms can not be removed from the equilibrium in the central moment space.
%% Cell type:code id: tags:
``` python
w_c = parameters.symbolic_omega_phi
config_phase = LBMConfig(stencil=stencil_phase, method=Method.MRT, compressible=True,
delta_equilibrium=False,
force=sp.symbols("F_:2"), velocity_input=u,
weighted=True, relaxation_rates=[0, w_c, w_c, 1, 1, 1, 1, 1, 1],
output={'density': C_tmp}, kernel_type='stream_pull_collide')
output={'density': C_tmp})
method_phase = create_lb_method(lbm_config=config_phase)
method_phase
```
%% Output
<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x126d44ee0>
<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x1346b22e0>
%% Cell type:code id: tags:
``` python
omega = parameters.omega(C)
config_hydro = LBMConfig(stencil=stencil_hydro, method=Method.MRT, compressible=False,
weighted=True, relaxation_rates=[omega, 1, 1, 1],
force=sp.symbols("F_:2"),
output={'velocity': u}, kernel_type='collide_stream_push')
force=sp.symbols("F_:2"), output={'velocity': u})
method_hydro = create_lb_method(lbm_config=config_hydro)
method_hydro
```
%% Output
<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x126d22eb0>
<lbmpy.methods.momentbased.momentbasedmethod.MomentBasedLbMethod at 0x137772a30>
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## Initialization
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The probability distribution functions (pdfs) are initialised with the equilibrium distribution for the LB methods.
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``` python
h_updates = initializer_kernel_phase_field_lb(method_phase, C, u, h, parameters)
g_updates = initializer_kernel_hydro_lb(method_hydro, 1, u, g)
h_init = ps.create_kernel(h_updates, target=dh.default_target, cpu_openmp=True).compile()
g_init = ps.create_kernel(g_updates, target=dh.default_target, cpu_openmp=True).compile()
```
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Following this, the phase field is initialised directly in python.
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``` python
# initialize the domain
def Initialize_distributions():
Nx = domain_size[0]
Ny = domain_size[1]
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]
y = np.zeros_like(block.midpoint_arrays[1])
y[:, :] = block.midpoint_arrays[1]
y -= 2 * L0
tmp = 0.1 * Nx * np.cos((2 * np.pi * x) / Nx)
init_values = 0.5 + 0.5 * np.tanh((y - tmp) / (parameters.interface_thickness / 2))
block["C"][:, :] = init_values
block["C_tmp"][:, :] = init_values
if gpu:
dh.all_to_gpu()
dh.run_kernel(h_init, **parameters.symbolic_to_numeric_map)
dh.run_kernel(g_init)
```
%% Cell type:code id: tags:
``` python
Initialize_distributions()
plt.scalar_field(dh.gather_array(C.name))
```
%% Output
<matplotlib.image.AxesImage at 0x1417f86d0>
<matplotlib.image.AxesImage at 0x137ad6bb0>
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## Source Terms
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For the Allen-Cahn LB step, the Allen-Cahn equation needs to be applied as a source term. Here, a simple forcing model is used which is directly applied in the moment space:
$$
F_i^\phi (\boldsymbol{x}, t) = \Delta t \frac{\left[1 - 4 \left(\phi - \phi_0\right)^2\right]}{\xi} w_i \boldsymbol{c}_i \cdot \frac{\nabla \phi}{|{\nabla \phi}|},
$$
where $\phi$ is the phase-field, $\phi_0$ is the interface location, $\Delta t$ it the timestep size $\xi$ is the interface width, $\boldsymbol{c}_i$ is the discrete direction from stencil_phase and $w_i$ are the weights. Furthermore, the equilibrium needs to be shifted:
$$
\bar{h}^{eq}_\alpha = h^{eq}_\alpha - \frac{1}{2} F^\phi_\alpha
$$
The hydrodynamic force is given by:
$$
F_i (\boldsymbol{x}, t) = \Delta t w_i \frac{\boldsymbol{c}_i \boldsymbol{F}}{\rho c_s^2},
$$
where $\rho$ is the interpolated density and $\boldsymbol{F}$ is the source term which consists of the pressure force
$$
\boldsymbol{F}_p = -p^* c_s^2 \nabla \rho,
$$
the surface tension force:
$$
\boldsymbol{F}_s = \mu_\phi \nabla \phi
$$
and the viscous force term:
$$
F_{\mu, i}^{\mathrm{MRT}} = - \frac{\nu}{c_s^2 \Delta t} \left[\sum_{\beta} c_{\beta i} c_{\beta j} \times \sum_{\alpha} \Omega_{\beta \alpha}(g_\alpha - g_\alpha^{\mathrm{eq}})\right] \frac{\partial \rho}{\partial x_j}.
$$
In the above equations $p^*$ is the normalised pressure which can be obtained from the zeroth order moment of the hydrodynamic distribution function $g$. The lattice speed of sound is given with $c_s$ and the chemical potential is $\mu_\phi$. Furthermore, the viscosity is $\nu$ and $\Omega$ is the moment-based collision operator. Note here that the hydrodynamic equilibrium is also adjusted as shown above for the phase-field distribution functions.
For CLBM methods the forcing is applied directly in the central moment space. This is done with the `CentralMomentMultiphaseForceModel`. Furthermore, the GUO force model is applied here to be consistent with [A cascaded phase-field lattice Boltzmann model for the simulation of incompressible, immiscible fluids with high density contrast](http://dx.doi.org/10.1016/j.camwa.2019.08.018). Here we refer to equation D.7 which can be derived for 3D stencils automatically with lbmpy.
%% Cell type:code id: tags:
``` python
force_h = interface_tracking_force(C, stencil_phase, parameters)
hydro_force = hydrodynamic_force(g, C, method_hydro, parameters, body_force)
hydro_force = hydrodynamic_force(C, method_hydro, parameters, body_force)
```
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## Definition of the LB update rules
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The update rule for the phase-field LB step is defined as:
$$
h_i (\boldsymbol{x} + \boldsymbol{c}_i \Delta t, t + \Delta t) = h_i(\boldsymbol{x}, t) + \Omega_{ij}^h(\bar{h_j}^{eq} - h_j)|_{(\boldsymbol{x}, t)} + F_i^\phi(\boldsymbol{x}, t).
$$
In our framework the pull scheme is applied as streaming step. Furthermore, the update of the phase-field is directly integrated into the kernel. As a result of this, a second temporary phase-field is needed.
%% Cell type:code id: tags:
``` python
lbm_optimisation = LBMOptimisation(symbolic_field=h, symbolic_temporary_field=h_tmp)
allen_cahn_update_rule = create_lb_update_rule(lbm_config=config_phase,
lbm_optimisation=lbm_optimisation)
allen_cahn_update_rule = add_interface_tracking_force(allen_cahn_update_rule, force_h)
ast_kernel = ps.create_kernel(allen_cahn_update_rule, target=dh.default_target, cpu_openmp=True)
kernel_allen_cahn_lb = ast_kernel.compile()
```
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The update rule for the hydrodynmaic LB step is defined as:
$$
g_i (\boldsymbol{x} + \boldsymbol{c}_i \Delta t, t + \Delta t) = g_i(\boldsymbol{x}, t) + \Omega_{ij}^g(\bar{g_j}^{eq} - g_j)|_{(\boldsymbol{x}, t)} + F_i(\boldsymbol{x}, t).
$$
Here, the push scheme is applied which is easier due to the data access required for the viscous force term. Furthermore, the velocity update is directly done in the kernel.
%% Cell type:code id: tags:
``` python
force_Assignments = hydrodynamic_force_assignments(g, u, C, method_hydro, parameters, body_force)
force_Assignments = hydrodynamic_force_assignments(u, C, method_hydro, parameters, body_force)
lbm_optimisation = LBMOptimisation(symbolic_field=g, symbolic_temporary_field=g_tmp)
hydro_lb_update_rule = create_lb_update_rule(lbm_config=config_hydro,
lbm_optimisation=lbm_optimisation)
hydro_lb_update_rule = add_hydrodynamic_force(hydro_lb_update_rule, force_Assignments, C, g, parameters)
hydro_lb_update_rule = add_hydrodynamic_force(hydro_lb_update_rule, force_Assignments, C, g, parameters,
config_hydro)
ast_kernel = ps.create_kernel(hydro_lb_update_rule, target=dh.default_target, cpu_openmp=True)
kernel_hydro_lb = ast_kernel.compile()
```
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## Boundary Conditions
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As a last step suitable boundary conditions are applied
%% Cell type:code id: tags:
``` python
# periodic Boundarys for g, h and C
periodic_BC_C = dh.synchronization_function(C.name, target=dh.default_target, optimization = {"openmp": True})
periodic_BC_g = LBMPeriodicityHandling(stencil=stencil_hydro, data_handling=dh, pdf_field_name=g.name,
streaming_pattern='push')
streaming_pattern=config_hydro.streaming_pattern)
periodic_BC_h = LBMPeriodicityHandling(stencil=stencil_phase, data_handling=dh, pdf_field_name=h.name,
streaming_pattern='pull')
streaming_pattern=config_phase.streaming_pattern)
# No slip boundary for the phasefield lbm
bh_allen_cahn = LatticeBoltzmannBoundaryHandling(method_phase, dh, 'h',
target=dh.default_target, name='boundary_handling_h',
streaming_pattern='pull')
streaming_pattern=config_phase.streaming_pattern)
# No slip boundary for the velocityfield lbm
bh_hydro = LatticeBoltzmannBoundaryHandling(method_hydro, dh, 'g' ,
target=dh.default_target, name='boundary_handling_g',
streaming_pattern='push')
streaming_pattern=config_hydro.streaming_pattern)
contact_angle = BoundaryHandling(dh, C.name, stencil_hydro, target=dh.default_target)
contact = ContactAngle(90, parameters.interface_thickness)
wall = NoSlip()
if dimensions == 2:
bh_allen_cahn.set_boundary(wall, make_slice[:, 0])
bh_allen_cahn.set_boundary(wall, make_slice[:, -1])
bh_hydro.set_boundary(wall, make_slice[:, 0])
bh_hydro.set_boundary(wall, make_slice[:, -1])
contact_angle.set_boundary(contact, make_slice[:, 0])
contact_angle.set_boundary(contact, make_slice[:, -1])
else:
bh_allen_cahn.set_boundary(wall, make_slice[:, 0, :])
bh_allen_cahn.set_boundary(wall, make_slice[:, -1, :])
bh_hydro.set_boundary(wall, make_slice[:, 0, :])
bh_hydro.set_boundary(wall, make_slice[:, -1, :])
contact_angle.set_boundary(contact, make_slice[:, 0, :])
contact_angle.set_boundary(contact, make_slice[:, -1, :])
bh_allen_cahn.prepare()
bh_hydro.prepare()
contact_angle.prepare()
```
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## Full timestep
%% Cell type:code id: tags:
``` python
# definition of the timestep for the immiscible fluids model
def timeloop():
# Solve the interface tracking LB step with boundary conditions
periodic_BC_h()
bh_allen_cahn()
dh.run_kernel(kernel_allen_cahn_lb, **parameters.symbolic_to_numeric_map)
dh.swap("C", "C_tmp")
# Solve the hydro LB step with boundary conditions
periodic_BC_g()
bh_hydro()
dh.run_kernel(kernel_hydro_lb, **parameters.symbolic_to_numeric_map)
dh.swap("C", "C_tmp")
# apply the three phase-phase contact angle
contact_angle()
# periodic BC of the phase-field
periodic_BC_C()
# solve the hydro LB step with boundary conditions
dh.run_kernel(kernel_hydro_lb, **parameters.symbolic_to_numeric_map)
periodic_BC_g()
bh_hydro()
# field swaps
dh.swap("h", "h_tmp")
dh.swap("g", "g_tmp")
```
%% Cell type:code id: tags:
``` python
Initialize_distributions()
frames = 300
steps_per_frame = (timesteps//frames) + 1
if 'is_test_run' not in globals():
def run():
for i in range(steps_per_frame):
timeloop()
if gpu:
dh.to_cpu("C")
return dh.gather_array(C.name)
animation = plt.scalar_field_animation(run, frames=frames, rescale=True)
set_display_mode('video')
res = display_animation(animation)
else:
timeloop()
res = None
res
```
%% Output
<IPython.core.display.HTML object>
%% Cell type:markdown id: tags:
Note that the video is played for 10 seconds while the simulation time is only 2 seconds!
......