Commit b04caa75 authored by Markus Holzer's avatar Markus Holzer Committed by Jan Hönig
Browse files

Dataclasses for method creation

parent 4e47c1eb
......@@ -143,7 +143,7 @@ ubuntu:
before_script:
- apt-get -y remove python3-sympy
- ln -s /usr/include/locale.h /usr/include/xlocale.h
- pip3 install `grep -Eo 'sympy[>=]+[0-9\.]+' setup.py | sed 's/>/=/g'`
# - pip3 install `grep -Eo 'sympy[>=]+[0-9\.]+' setup.py | sed 's/>/=/g'`
- pip3 install git+https://gitlab-ci-token:${CI_JOB_TOKEN}@i10git.cs.fau.de/pycodegen/pystencils.git@master#egg=pystencils
script:
- export NUM_CORES=$(nproc --all)
......
......@@ -16,11 +16,12 @@ It even comes with an integrated Chapman Enskog analysis based on sympy!
Common test scenarios can be set up quickly:
```python
from lbmpy.scenarios import create_channel
from pystencils import Target
from lbmpy.session import *
ch = create_channel(domain_size=(300,100, 100), force=1e-7, method="trt",
ch = create_channel(domain_size=(300, 100, 100), force=1e-7, method=Method.TRT,
equilibrium_order=2, compressible=True,
relaxation_rates=[1.97, 1.6], optimization={'target': 'gpu'})
relaxation_rates=[1.97, 1.6], optimization={'target': Target.GPU})
```
To find out more, check out the interactive [tutorial notebooks online with binder](https://mybinder.org/v2/gh/mabau/lbmpy/master?filepath=doc%2Fnotebooks).
......
......@@ -29,8 +29,8 @@ templates_path = ['_templates']
source_suffix = '.rst'
master_doc = 'index'
copyright = f'{datetime.datetime.now().year}, Martin Bauer'
author = 'Martin Bauer'
copyright = f'{datetime.datetime.now().year}, Martin Bauer, Markus Holzer'
author = 'Martin Bauer, Markus Holzer'
# 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)
......
This diff is collapsed.
%% Cell type:markdown id: tags:
# Shan-Chen Two-Phase Single-Component Lattice Boltzmann
%% Cell type:code id: tags:
``` python
from lbmpy.session import *
from lbmpy.updatekernels import create_stream_pull_with_output_kernel
from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter
from lbmpy.maxwellian_equilibrium import get_weights
```
%% Cell type:markdown id: tags:
This is based on section 9.3.2 of Krüger et al.'s "The Lattice Boltzmann Method", Springer 2017 (http://www.lbmbook.com).
Sample code is available at [https://github.com/lbm-principles-practice/code/](https://github.com/lbm-principles-practice/code/blob/master/chapter9/shanchen.cpp).
%% Cell type:markdown id: tags:
## Parameters
%% Cell type:code id: tags:
``` python
N = 64
omega_a = 1.
g_aa = -4.7
rho0 = 1.
stencil = get_stencil("D2Q9")
stencil = LBStencil(Stencil.D2Q9)
weights = get_weights(stencil, c_s_sq=sp.Rational(1,3))
```
%% Cell type:markdown id: tags:
## Data structures
%% Cell type:code id: tags:
``` python
dim = len(stencil[0])
dh = ps.create_data_handling((N,)*dim, periodicity=True, default_target='cpu')
src = dh.add_array('src', values_per_cell=len(stencil))
dst = dh.add_array_like('dst', 'src')
ρ = dh.add_array('rho')
```
%% Cell type:markdown id: tags:
## Force & combined velocity
%% Cell type:markdown id: tags:
The force on the fluid is
$\vec{F}_A(\vec{x})=-\psi(\rho_A(\vec{x}))g_{AA}\sum\limits_{i=1}^{q}w_i\psi(\rho_A(\vec{x}+\vec{c}_i))\vec{c}_i$
with
$\psi(\rho)=\rho_0\left[1-\exp(-\rho/\rho_0)\right]$.
%% Cell type:code id: tags:
``` python
def psi(dens):
return rho0 * (1. - sp.exp(-dens / rho0));
```
%% Cell type:code id: tags:
``` python
zero_vec = sp.Matrix([0] * dh.dim)
force = sum((psi(ρ[d]) * w_d * sp.Matrix(d)
for d, w_d in zip(stencil, weights)), zero_vec) * psi(ρ.center) * -1 * g_aa
```
%% Cell type:markdown id: tags:
## Kernels
%% Cell type:code id: tags:
``` python
collision = create_lb_update_rule(stencil=stencil,
relaxation_rate=omega_a,
compressible=True,
force_model='guo',
force=force,
kernel_type='collide_only',
lbm_config = LBMConfig(stencil=stencil, relaxation_rate=omega_a, compressible=True,
force_model=ForceModel.GUO, force=force, kernel_type='collide_only')
collision = create_lb_update_rule(lbm_config=lbm_config,
optimization={'symbolic_field': src})
stream = create_stream_pull_with_output_kernel(collision.method, src, dst, {'density': ρ})
opts = {'cpu_openmp': False,
'target': dh.default_target}
stream_kernel = ps.create_kernel(stream, **opts).compile()
collision_kernel = ps.create_kernel(collision, **opts).compile()
```
%% Cell type:markdown id: tags:
## Initialization
%% Cell type:code id: tags:
``` python
method_without_force = create_lb_method(stencil=stencil, relaxation_rate=omega_a, compressible=True)
method_without_force = create_lb_method(LBMConfig(stencil=stencil, relaxation_rate=omega_a, compressible=True))
init_assignments = macroscopic_values_setter(method_without_force, velocity=(0, 0),
pdfs=src.center_vector, density=ρ.center)
init_kernel = ps.create_kernel(init_assignments, ghost_layers=0).compile()
```
%% Cell type:code id: tags:
``` python
def init():
for x in range(N):
for y in range(N):
if (x-N/2)**2 + (y-N/2)**2 <= 15**2:
dh.fill(ρ.name, 2.1, slice_obj=[x,y])
else:
dh.fill(ρ.name, 0.15, slice_obj=[x,y])
dh.run_kernel(init_kernel)
```
%% Cell type:markdown id: tags:
## Timeloop
%% Cell type:code id: tags:
``` python
sync_pdfs = dh.synchronization_function([src.name])
sync_ρs = dh.synchronization_function([ρ.name])
def time_loop(steps):
dh.all_to_gpu()
for i in range(steps):
sync_ρs()
dh.run_kernel(collision_kernel)
sync_pdfs()
dh.run_kernel(stream_kernel)
dh.swap(src.name, dst.name)
dh.all_to_cpu()
```
%% Cell type:code id: tags:
``` python
def plot_ρs():
plt.figure(dpi=200)
plt.title("$\\rho$")
plt.scalar_field(dh.gather_array(ρ.name), vmin=0, vmax=2.5)
plt.colorbar()
```
%% Cell type:markdown id: tags:
## Run the simulation
### Initial state
%% Cell type:code id: tags:
``` python
init()
plot_ρs()
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
### Check the first time step against reference data
The reference data was obtained with the [sample code](https://github.com/lbm-principles-practice/code/blob/master/chapter9/shanchen.cpp) after making the following changes:
```c++
const int nsteps = 1000;
const int noutput = 1;
```
Remove the next cell if you changed the parameters at the beginning of this notebook.
%% Cell type:code id: tags:
``` python
init()
time_loop(1)
ref = np.array([0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.136756, 0.220324, 1.2382, 2.26247, 2.26183, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.1, 2.26183, 2.26247, 1.2382, 0.220324, 0.136756, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15])
assert np.allclose(dh.gather_array(ρ.name)[N//2], ref)
```
%% Cell type:markdown id: tags:
### Run the simulation until converged
%% Cell type:code id: tags:
``` python
init()
time_loop(1000)
plot_ρs()
```
%%%% Output: display_data
![](data:image/png;base64,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)
......
This diff is collapsed.
This diff is collapsed.
......@@ -5,6 +5,7 @@ API Reference
:maxdepth: 1
scenarios.rst
enums.rst
kernelcreation.rst
methodcreation.rst
stencils.rst
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment