Commit 1a97248c authored by Frederik Hennig's avatar Frederik Hennig
Browse files

Still buggy periodic pipe test case

parent 06124196
Pipeline #27231 failed with stage
in 27 minutes and 6 seconds
%% Cell type:code id: tags:
```
%load_ext autoreload
%autoreload 2
```
%% Cell type:code id: tags:
```
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
from pystencils.datahandling import create_data_handling
from pystencils import create_kernel
from pystencils.slicing import slice_from_direction, make_slice
from pystencils.plot import scalar_field, vector_field, vector_field_magnitude
from lbmpy.creationfunctions import create_lb_collision_rule, create_lb_function
from lbmpy.macroscopic_value_kernels import flexible_macroscopic_values_getter, flexible_macroscopic_values_setter
from lbmpy.stencils import get_stencil
from lbmpy.plot import boundary_handling as plot_boundaries
from lbmpy.advanced_streaming import PeriodicityHandling, FlexibleLBMBoundaryHandling, FlexibleNoSlip
from lbmpy.advanced_streaming.utility import is_inplace
from numpy.testing import assert_allclose
from test_periodic_pipe_with_force import PeriodicPipeFlow
```
%% Cell type:code id: tags:
```
stencil = get_stencil('D3Q27')
pull_flow = PeriodicPipeFlow(stencil, 'pull')
push_flow = PeriodicPipeFlow(stencil, 'push')
```
%% Cell type:code id: tags:
```
push_flow.init()
```
%% Cell type:markdown id: tags:
After exactly 32 iterations, the entire domain collapses to `NaN`. This is weird.
$$
32 = 2^5
$$
This only happens if another `PeriodicPipeFlow` object was created *before* the active object.
%% Cell type:code id: tags:
```
push_flow.run(31)
u = push_flow.get_trimmed_velocity_array()
plot = scalar_field(u[30,:,:,0])
plt.colorbar(plot)
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7f0f5c7771c0>
%%%% Output: display_data
![](data:image/png;base64,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%% Cell type:code id: tags:
```
push_flow.run(1)
u = push_flow.get_trimmed_velocity_array()
plot = scalar_field(u[30,:,:,0])
plt.colorbar(plot)
```
%%%% Output: execute_result
<matplotlib.colorbar.Colorbar at 0x7f0f547843d0>
%%%% Output: display_data
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%% Cell type:code id: tags:
```
%load_ext autoreload
%autoreload 2
```
%% Cell type:code id: tags:
```
import numpy as np
import sympy as sp
from pystencils.datahandling import create_data_handling
from pystencils import create_kernel
from pystencils.plot import scalar_field, vector_field, vector_field_magnitude
from lbmpy.creationfunctions import create_lb_collision_rule, create_lb_function
from lbmpy.macroscopic_value_kernels import flexible_macroscopic_values_getter, flexible_macroscopic_values_setter
from lbmpy.stencils import get_stencil
from lbmpy.advanced_streaming import PeriodicityHandling
from lbmpy.advanced_streaming.utility import is_inplace
from numpy.testing import assert_allclose
```
%% Cell type:code id: tags:
```
stencil = get_stencil('D2Q9')
q = len(stencil)
dim = len(stencil[0])
streaming_pattern = 'esotwist'
streaming_pattern = 'push'
inplace = is_inplace(streaming_pattern)
timesteps = ['even', 'odd'] if inplace else ['both']
zeroth_timestep = timesteps[0]
```
%% Cell type:markdown id: tags:
## Data Handling and Fields
%% Cell type:code id: tags:
```
domain_size = (30,) * dim
periodicity = (True,) * dim
dh = create_data_handling(domain_size=domain_size, periodicity=periodicity)
pdfs = dh.add_array('pdfs', q)
if not inplace:
pdfs_tmp = dh.add_array_like('pdfs_tmp', pdfs.name)
```
%% Cell type:markdown id: tags:
## LBM Collision Rule and Update Kernel(s)
%% Cell type:code id: tags:
```
method_params = {
'stencil' : stencil,
'method' : 'srt',
'relaxation_rate' : 1.0
}
optimization = {
'symbolic_field' : pdfs,
'target' : 'cpu'
}
if not inplace:
optimization['symbolic_temporary_field'] = pdfs_tmp
lb_collision = create_lb_collision_rule(optimization=optimization, **method_params)
lb_method = lb_collision.method
lb_kernels = []
if inplace:
lb_kernels.append(create_lb_function(collision_rule=lb_collision,
optimization=optimization,
kernel_type=streaming_pattern + '_even',
**method_params))
lb_kernels.append(create_lb_function(collision_rule=lb_collision,
optimization=optimization,
kernel_type=streaming_pattern + '_odd',
**method_params))
else:
if streaming_pattern == 'pull':
kernel_type = 'stream_pull_collide'
elif streaming_pattern == 'push':
kernel_type = 'collide_stream_push'
lb_kernels.append(create_lb_function(collision_rule=lb_collision,
optimization=optimization,
kernel_type=kernel_type,
**method_params))
```
%% Cell type:code id: tags:
```
from lbmpy.creationfunctions import create_lb_update_rule
push_update = create_lb_update_rule(
collision_rule=lb_collision, optimization=optimization, kernel_type='collide_stream_push', **method_params)
esotwist_update = create_lb_update_rule(
collision_rule=lb_collision, optimization=optimization, kernel_type='esotwist_even', **method_params)
```
%% Cell type:code id: tags:
```
push_update.main_assignments[1]
```
%%%% Output: execute_result
$\displaystyle {{pdfs_tmp}_{(0,1)}^{1}} \leftarrow {{pdfs}_{(0,0)}^{1}} + rr_{0} \left(- {{pdfs}_{(0,0)}^{1}} + \frac{f_{eq common}}{9} + \frac{u_{1}^{2}}{2} + \frac{u_{1}}{3}\right)$
Assignment(pdfs_tmp_N^1, pdfs_C^1 + rr_0*(-pdfs_C^1 + f_eq_common/9 + u_1**2/2 + u_1/3))
%% Cell type:code id: tags:
```
esotwist_update.main_assignments[1]
```
%%%% Output: execute_result
$\displaystyle {{pdfs}_{(0,1)}^{2}} \leftarrow rr_{0} \left(\frac{f_{eq common}}{9} + \frac{u_{1}^{2}}{2} + \frac{u_{1}}{3} - \xi_{8}\right) + \xi_{8}$
Assignment(pdfs_N^2, rr_0*(f_eq_common/9 + u_1**2/2 + u_1/3 - xi_8) + xi_8)
%% Cell type:markdown id: tags:
...damnit!
`pystencils` does different algebraic modifications to the update equations depending on the streaming pattern. Floating-point addition is not associative! This causes minor numerical differences between the streaming patterns, as visible in the test case.
%% Cell type:markdown id: tags:
## Macroscopic Values
%% Cell type:code id: tags:
```
density = 1.0
density_field = dh.add_array('rho', 1)
u_x = 0.01
velocity = (u_x, u_x)
velocity_field = dh.add_array('u', dim)
setter = flexible_macroscopic_values_setter(
lb_method, density, velocity, pdfs,
streaming_pattern=streaming_pattern, previous_timestep=zeroth_timestep)
setter_kernel = create_kernel(setter, ghost_layers=1).compile()
getter_kernels = []
for t in timesteps:
getter = flexible_macroscopic_values_getter(
lb_method, density_field, velocity_field, pdfs,
streaming_pattern=streaming_pattern, previous_timestep=t)
getter_kernels.append(create_kernel(getter, ghost_layers=1).compile())
```
%% Cell type:markdown id: tags:
## Periodicity Handler
%% Cell type:code id: tags:
```
periodicity_handler = PeriodicityHandling(
stencil, dh, pdfs.name,
streaming_pattern=streaming_pattern, zeroth_timestep=zeroth_timestep)
```
%% Cell type:markdown id: tags:
## Initialization and Time Loop
%% Cell type:code id: tags:
```
t_modulus = 0
def init():
global t_modulus
t_modulus = 0
dh.run_kernel(setter_kernel)
def one_step():
global t_modulus
# Periodicty
periodicity_handler(timesteps[t_modulus])
# Macroscopic Values
dh.run_kernel(getter_kernels[t_modulus])
# Here, the next time step begins
t_modulus = (t_modulus + 1) % len(timesteps)
# LBM Step
dh.run_kernel(lb_kernels[t_modulus])
# Field Swaps
if not inplace:
dh.swap(pdfs.name, pdfs_tmp.name)
```
%% Cell type:markdown id: tags:
## Simulation
%% Cell type:code id: tags:
```
init()
```
%% Cell type:code id: tags:
```
for i in range(100):
one_step()
```
%% Cell type:code id: tags:
```
u = dh.gather_array(velocity_field.name)
u_ref = np.full(domain_size + (dim,), u_x)
assert_allclose(u, u_ref)
```
%% Cell type:code id: tags:
```
vector_field(u)
```
%%%% Output: execute_result
&lt;matplotlib.quiver.Quiver at 0x7f6f243b9040&gt;
%%%% Output: display_data
![](data:image/png;base64,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......
......@@ -146,8 +146,11 @@ def test_fully_periodic_flow(stencil, streaming_pattern):
# Equal to the steady-state velocity field up to numerical errors
assert_allclose(u, u_ref)
# Exactly equal to other streaming patterns!
# Flow must be equal up to numerical error for all streaming patterns
global all_results
for prev_pattern, prev_u in all_results.items():
assert_array_equal(
u, prev_u, err_msg=f'Velocity field for {streaming_pattern} differed from {prev_pattern}!')
for key, prev_u in all_results.items():
if key[0] == stencil:
prev_pattern = key[1]
assert_allclose(
u, prev_u, err_msg=f'Velocity field for {streaming_pattern} differed from {prev_pattern}!')
all_results[(stencil, streaming_pattern)] = u
This source diff could not be displayed because it is too large. You can view the blob instead.
import numpy as np
import sympy as sp
from pystencils.datahandling import create_data_handling
from pystencils import create_kernel
from pystencils.slicing import make_slice
from lbmpy.creationfunctions import create_lb_collision_rule, create_lb_function
from lbmpy.macroscopic_value_kernels import flexible_macroscopic_values_getter, flexible_macroscopic_values_setter
from lbmpy.stencils import get_stencil
from lbmpy.advanced_streaming import PeriodicityHandling, FlexibleNoSlip, FlexibleLBMBoundaryHandling
from lbmpy.advanced_streaming.utility import is_inplace, streaming_patterns
import pytest
from numpy.testing import assert_allclose
all_results = dict()
class PeriodicPipeFlow:
def __init__(self, stencil, streaming_pattern):
# Stencil
self.stencil = stencil
self.q = len(self.stencil)
self.dim = len(self.stencil[0])
# Streaming
self.streaming_pattern = streaming_pattern
self.inplace = is_inplace(self.streaming_pattern)
self.timesteps = ['even', 'odd'] if self.inplace else ['both']
self.between_timesteps = ['even_to_odd', 'odd_to_even'] if self.inplace else ['both']
self.zeroth_timestep = self.timesteps[0]
# Domain, Data Handling and PDF fields
self.pipe_length = 60
self.pipe_radius = 15
self.domain_size = (self.pipe_length, ) + (2 * self.pipe_radius,) * (self.dim - 1)
self.periodicity = (True, ) + (False, ) * (self.dim - 1)
self.force = (0.0001, ) + (0.0,) * (self.dim - 1)
self.dh = create_data_handling(domain_size=self.domain_size, periodicity=self.periodicity)
self.pdfs = self.dh.add_array('pdfs', self.q)
if not self.inplace:
self.pdfs_tmp = self.dh.add_array_like('pdfs_tmp', self.pdfs.name)
# LBM Streaming and Collision
method_params = {
'stencil': stencil,
'method': 'srt',
'relaxation_rate': 1.0,
'force_model': 'guo',
'force': self.force
}
optimization = {
'symbolic_field': self.pdfs,
'target': 'cpu'
}
if not self.inplace:
optimization['symbolic_temporary_field'] = self.pdfs_tmp
self.lb_collision = create_lb_collision_rule(optimization=optimization, **method_params)
self.lb_method = self.lb_collision.method
self.lb_kernels = []
if self.inplace:
self.lb_kernels.append(create_lb_function(collision_rule=self.lb_collision,
optimization=optimization,
kernel_type=self.streaming_pattern + '_even',
**method_params))
self.lb_kernels.append(create_lb_function(collision_rule=self.lb_collision,
optimization=optimization,
kernel_type=self.streaming_pattern + '_odd',
**method_params))
else:
if self.streaming_pattern == 'pull':
kernel_type = 'stream_pull_collide'
elif self.streaming_pattern == 'push':
kernel_type = 'collide_stream_push'
self.lb_kernels.append(create_lb_function(collision_rule=self.lb_collision,
optimization=optimization,
kernel_type=kernel_type,
**method_params))
# Macroscopic Values
self.density = 1.0
self.density_field = self.dh.add_array('rho', 1)
u_x = 0.0
self.velocity = (u_x,) * self.dim
self.velocity_field = self.dh.add_array('u', self.dim)
setter = flexible_macroscopic_values_setter(
self.lb_method, self.density, self.velocity, self.pdfs,
streaming_pattern=self.streaming_pattern, previous_timestep=self.zeroth_timestep)
self.init_kernel = create_kernel(setter, ghost_layers=1).compile()
self.getter_kernels = []
for t in self.timesteps:
getter = flexible_macroscopic_values_getter(
self.lb_method, self.density_field, self.velocity_field, self.pdfs,
streaming_pattern=self.streaming_pattern, previous_timestep=t)
self.getter_kernels.append(create_kernel(getter, ghost_layers=1).compile())
# Periodicity
self.periodicity_handler = PeriodicityHandling(
stencil, self.dh, self.pdfs.name,
streaming_pattern=self.streaming_pattern, zeroth_timestep=self.zeroth_timestep)
# Boundary Handling
self.noslip = FlexibleNoSlip()
self.bh = FlexibleLBMBoundaryHandling(self.lb_method, self.dh, self.pdfs.name,
streaming_pattern=self.streaming_pattern)
self.bh.set_boundary(boundary_obj=self.noslip, mask_callback=self.mask_callback)
self.t_modulus = 0
def mask_callback(self, x, y, z=None):
y = y - self.pipe_radius
z = z - self.pipe_radius if z is not None else 0
return np.sqrt(y**2 + z**2) >= self.pipe_radius
def init(self):
self.t_modulus = 0
self.dh.run_kernel(self.init_kernel)
def step(self):
# Boundaries
self.bh(between_timesteps=self.between_timesteps[self.t_modulus])
# Periodicty
self.periodicity_handler(self.timesteps[self.t_modulus])
# Macroscopic Values
self.dh.run_kernel(self.getter_kernels[self.t_modulus])
# Here, the next time step begins
self.t_modulus = (self.t_modulus + 1) % len(self.timesteps)