Commit b1ba7bd7 authored by Frederik Hennig's avatar Frederik Hennig
Browse files

Forgot to save the test notebook.

parent 115b0b08
Pipeline #27520 failed with stage
in 17 minutes and 57 seconds
%% Cell type:code id: tags:
``` python
from lbmpy.session import *
from lbmpy.boundaries.boundaries_in_kernel import update_rule_with_push_boundaries
from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter
from collections import OrderedDict
from time import perf_counter
```
%% Cell type:markdown id: tags:
# Version 1: compile-in boundaries
%% Cell type:code id: tags:
``` python
domain_size = (32, 32, 32)
relaxation_rate = 1.8
time_steps = 100
lid_velocity = 0.05
```
%% Cell type:code id: tags:
``` python
dh = create_data_handling(domain_size, default_target='cpu')
pdfs = dh.add_array('pdfs', values_per_cell=19)
u = dh.add_array('u', values_per_cell=len(domain_size))
streaming_pattern = 'aa'
```
%% Cell type:code id: tags:
``` python
boundaries = OrderedDict((
((0, 1, 0), UBB([lid_velocity, 0, 0])),
((1, 0, 0), NoSlip()),
((-1, 0, 0), NoSlip()),
((0, -1, 0), NoSlip()),
((0, 0, 1), NoSlip()),
((0, 0, -1), NeumannByCopy()),
((0, 0, -1), NoSlip()),
))
opt = {'symbolic_field': pdfs, 'cse_global': False, 'cse_pdfs': True}
cr_even = create_lb_collision_rule(stencil="D3Q19", relaxation_rate=relaxation_rate, compressible=False, optimization=opt)
cr_odd = create_lb_collision_rule(stencil="D3Q19", relaxation_rate=relaxation_rate, compressible=False, optimization=opt)
update_rule_aa_even = update_rule_with_push_boundaries(cr_even, pdfs, boundaries, streaming_pattern, Timestep.EVEN)
update_rule_aa_odd = update_rule_with_push_boundaries(cr_odd, pdfs, boundaries, streaming_pattern, Timestep.ODD)
getter_assignments = macroscopic_values_getter(update_rule_aa_even.method, velocity=u.center_vector,
pdfs=pdfs, density=None,
streaming_pattern=streaming_pattern,
previous_timestep=Timestep.EVEN)
getter_kernel = ps.create_kernel(getter_assignments, target=dh.default_target).compile()
even_kernel = ps.create_kernel(update_rule_aa_even, target=dh.default_target, ghost_layers=1).compile()
odd_kernel = ps.create_kernel(update_rule_aa_odd, target=dh.default_target, ghost_layers=1).compile()
```
%% Cell type:code id: tags:
``` python
def init():
dh.fill(pdfs.name, 0, ghost_layers=True)
def aa_time_loop(steps=100):
assert steps % 2 == 0, "Works only for an even number of time steps"
dh.all_to_gpu()
for i in range(steps // 2):
dh.run_kernel(odd_kernel)
dh.run_kernel(even_kernel)
dh.run_kernel(getter_kernel)
dh.all_to_cpu()
```
%% Cell type:code id: tags:
``` python
init()
aa_time_loop(time_steps)
vel_version1 = dh.gather_array(u.name, ghost_layers=False).copy()
plt.vector_field_magnitude(vel_version1[:, :, domain_size[2]//2, :])
plt.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7fa8b1205490>
<matplotlib.colorbar.Colorbar at 0x7f9d260364c0>
%%%% Output: display_data
![](data:image/png;base64,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%% Cell type:markdown id: tags:
# Version 2: Normal boundary handling
%% Cell type:code id: tags:
``` python
ldc = create_lid_driven_cavity(domain_size, relaxation_rate=relaxation_rate, lid_velocity=lid_velocity)
ldc.run(time_steps)
vel_version2 = ldc.velocity[:, :, :, :]
plt.vector_field_magnitude(vel_version2[:, :, domain_size[2]//2, :])
plt.colorbar()
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7fa8b15ff7f0>
<matplotlib.colorbar.Colorbar at 0x7f9d2158a520>
%%%% Output: display_data
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