Commit 7ae8e2b3 authored by Michael Kuron's avatar Michael Kuron
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minor documentation fix for Shan-Chen

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%% 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")
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
$\vec{F}_A(\vec{x})=-\psi(\rho_A(\vec{x}))g_{AA}\sum\limits_{i=1}^{19}w_i\psi(\rho_A(\vec{x}+\vec{c}_i))\vec{c}_i$
$\vec{F}_A(\vec{x})=-\psi(\rho_A(\vec{x}))g_{AA}\sum\limits_{i=1}^{q}w_i\psi(\rho_A(\vec{x}+\vec{c}_i))\vec{c}_i$
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',
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)
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.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
%% 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
......
%% Cell type:markdown id: tags:
# Shan-Chen Two-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 9.3.3 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 # domain size
omega_a = 1. # relaxation rate of first component
omega_b = 1. # relaxation rate of second component
# interaction strength
g_aa = 0.
g_ab = g_ba = 6.
g_bb = 0.
rho0 = 1.
stencil = get_stencil("D2Q9")
weights = get_weights(stencil, c_s_sq=sp.Rational(1,3))
```
%% Cell type:markdown id: tags:
## Data structures
We allocate two sets of PDF's, one for each phase. Additionally, for each phase there is one field to store its density and velocity.
To run the simulation on GPU, change the `default_target` to gpu
%% Cell type:code id: tags:
``` python
dim = len(stencil[0])
dh = ps.create_data_handling((N, ) * dim, periodicity=True, default_target='cpu')
src_a = dh.add_array('src_a', values_per_cell=len(stencil))
dst_a = dh.add_array_like('dst_a', 'src_a')
src_b = dh.add_array('src_b', values_per_cell=len(stencil))
dst_b = dh.add_array_like('dst_b', 'src_b')
ρ_a = dh.add_array('rho_a')
ρ_b = dh.add_array('rho_b')
u_a = dh.add_array('u_a', values_per_cell=dh.dim)
u_b = dh.add_array('u_b', values_per_cell=dh.dim)
```
%% Cell type:markdown id: tags:
## Force & combined velocity
The two LB methods are coupled using a force term. Its symbolic representation is created in the next cells.
The force value is not written to a field, but directly evaluated inside the collision kernel.
%% Cell type:markdown id: tags:
The force between the two components is
$\vec{F}_k(\vec{x})=-\psi(\rho_k(\vec{x}))\sum\limits_{k^\prime\in\{A,B\}}g_{kk^\prime}\sum\limits_{i=1}^{19}w_i\psi(\rho_{k^\prime}(\vec{x}+\vec{c}_i))\vec{c}_i$
$\vec{F}_k(\vec{x})=-\psi(\rho_k(\vec{x}))\sum\limits_{k^\prime\in\{A,B\}}g_{kk^\prime}\sum\limits_{i=1}^{q}w_i\psi(\rho_{k^\prime}(\vec{x}+\vec{c}_i))\vec{c}_i$
for $k\in\{A,B\}$
and 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_a = zero_vec
for factor, ρ in zip([g_aa, g_ab], [ρ_a, ρ_b]):
force_a += sum((psi(ρ[d]) * w_d * sp.Matrix(d)
for d, w_d in zip(stencil, weights)),
zero_vec) * psi(ρ_a.center) * -1 * factor
force_b = zero_vec
for factor, ρ in zip([g_ba, g_bb], [ρ_a, ρ_b]):
force_b += sum((psi(ρ[d]) * w_d * sp.Matrix(d)
for d, w_d in zip(stencil, weights)),
zero_vec) * psi(ρ_b.center) * -1 * factor
```
%% Cell type:markdown id: tags:
The barycentric velocity, which is used in place of the individual components' velocities in the equilibrium distribution and Guo force term, is
$\vec{u}=\frac{1}{\rho_a+\rho_b}\left(\rho_a\vec{u}_a+\frac{1}{2}\vec{F}_a+\rho_b\vec{u}_b+\frac{1}{2}\vec{F}_b\right)$.
%% Cell type:code id: tags:
``` python
u_full = [(ρ_a.center * u_a(i) + force_a[i]/2 +
ρ_b.center * u_b(i) + force_b[i]/2) / (ρ_a.center + ρ_b.center)
for i in range(dh.dim)]
```
%% Cell type:markdown id: tags:
## Kernels
%% Cell type:code id: tags:
``` python
collision_a = create_lb_update_rule(stencil=stencil,
relaxation_rate=omega_a,
compressible=True,
velocity_input=u_full, density_input=ρ_a,
force_model='guo',
force=force_a,
kernel_type='collide_only',
optimization={'symbolic_field': src_a})
collision_b = create_lb_update_rule(stencil=stencil,
relaxation_rate=omega_b,
compressible=True,
velocity_input=u_full, density_input=ρ_b,
force_model='guo',
force=force_b,
kernel_type='collide_only',
optimization={'symbolic_field': src_b})
```
%% Cell type:code id: tags:
``` python
stream_a = create_stream_pull_with_output_kernel(collision_a.method, src_a, dst_a,
{'density': ρ_a, 'velocity': u_a})
stream_b = create_stream_pull_with_output_kernel(collision_b.method, src_b, dst_b,
{'density': ρ_b, 'velocity': u_b})
opts = {'cpu_openmp': 1, # number of threads when running on CPU
'target': dh.default_target}
stream_a_kernel = ps.create_kernel(stream_a, **opts).compile()
stream_b_kernel = ps.create_kernel(stream_b, **opts).compile()
collision_a_kernel = ps.create_kernel(collision_a, **opts).compile()
collision_b_kernel = ps.create_kernel(collision_b, **opts).compile()
```
%% Cell type:markdown id: tags:
## Initialization
%% Cell type:code id: tags:
``` python
init_a = macroscopic_values_setter(collision_a.method, velocity=(0, 0),
pdfs=src_a.center_vector, density=ρ_a.center)
init_b = macroscopic_values_setter(collision_b.method, velocity=(0, 0),
pdfs=src_b.center_vector, density=ρ_b.center)
init_a_kernel = ps.create_kernel(init_a, ghost_layers=0).compile()
init_b_kernel = ps.create_kernel(init_b, ghost_layers=0).compile()
```
%% Cell type:code id: tags:
``` python
def init():
dh.fill(ρ_a.name, 0.1, slice_obj=ps.make_slice[:, :0.5])
dh.fill(ρ_a.name, 0.9, slice_obj=ps.make_slice[:, 0.5:])
dh.fill(ρ_b.name, 0.9, slice_obj=ps.make_slice[:, :0.5])
dh.fill(ρ_b.name, 0.1, slice_obj=ps.make_slice[:, 0.5:])
dh.run_kernel(init_a_kernel)
dh.run_kernel(init_b_kernel)
dh.fill(u_a.name, 0.0)
dh.fill(u_b.name, 0.0)
```
%% Cell type:markdown id: tags:
## Timeloop
%% Cell type:code id: tags:
``` python
sync_pdfs = dh.synchronization_function([src_a.name, src_b.name])
sync_ρs = dh.synchronization_function([ρ_a.name, ρ_b.name])
def time_loop(steps):
dh.all_to_gpu()
for i in range(steps):
sync_ρs() # collision kernel evaluates force values, that depend on neighboring ρ's
dh.run_kernel(collision_a_kernel)
dh.run_kernel(collision_b_kernel)
sync_pdfs()
dh.run_kernel(stream_a_kernel)
dh.run_kernel(stream_b_kernel)
dh.swap(src_a.name, dst_a.name)
dh.swap(src_b.name, dst_b.name)
dh.all_to_cpu()
```
%% Cell type:code id: tags:
``` python
def plot_ρs():
plt.subplot(1,2,1)
plt.title("$\\rho_A$")
plt.scalar_field(dh.gather_array(ρ_a.name), vmin=0, vmax=2)
plt.colorbar()
plt.subplot(1,2,2)
plt.title("$\\rho_B$")
plt.scalar_field(dh.gather_array(ρ_b.name), vmin=0, vmax=2)
plt.colorbar()
```
%% Cell type:markdown id: tags:
## Run the simulation
### Initial state
%% Cell type:code id: tags:
``` python
init()
plot_ρs()
```
%% Output
%% 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;
const int nfluids = 2;
const double gA = 0;
```
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_a = np.array([0.133183, 0.0921801, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0921801, 0.133183, 0.719568, 1.05507, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 1.05507, 0.719568])
ref_b = np.array([0.719568, 1.05507, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 1.05507, 0.719568, 0.133183, 0.0921801, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0921801, 0.133183])
assert np.allclose(dh.gather_array(ρ_a.name)[0], ref_a)
assert np.allclose(dh.gather_array(ρ_b.name)[0], ref_b)
```
%% Cell type:markdown id: tags:
### Run the simulation until converged
%% Cell type:code id: tags:
``` python
init()
time_loop(1000)
plot_ρs()
```
%% Output
......
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