Turbulent Channel (Reynolds 180)#
The simulation of a periodic turbulent channel is also used for validation of a multilevel domain. A friction Reynolds number of 180 is fixed for both cases. The pressure gradient is estabilished through a constant body force.
from nassu.cfg.model import ConfigScheme
filename = "tests/validation/cases/04_turbulent_channel_flow.nassu.yaml"
sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)
The multilevel setup tries to reproduce a DNS using a optimized amount of grid nodes.
sim_cfg_mb = next(
sim_cfg
for (name, _), sim_cfg in sim_cfgs.items()
if sim_cfg.name == "periodicTurbulentChannelMultilevel"
)
sim_cfgs_use = {"mb": sim_cfg_mb}
Functions to use for turbulence channel processing
import pandas as pd
import numpy as np
import pathlib
from nassu.cfg.schemes.simul import SimulationConfigs
from vtk.util.numpy_support import vtk_to_numpy
from tests.validation.notebooks import common
def get_experimental_profiles(reynolds_tau: float) -> dict[str, pd.DataFrame]:
files_tau: dict[float, dict[str, str]] = {
142: {
"ux": "Turbulent_channel/Re_tau_142/u_avg.csv",
"ux_rms": "Turbulent_channel/Re_tau_142/u_rms.csv",
"uy_rms": "Turbulent_channel/Re_tau_142/v_rms.csv",
"uz_rms": "Turbulent_channel/Re_tau_142/w_rms.csv",
},
520: {
"ux": "Turbulent_channel/Re_tau_520/u_avg.csv",
"ux_rms": "Turbulent_channel/Re_tau_142/u_rms.csv",
},
}
files_get = files_tau[reynolds_tau]
vals_exp: dict[str, pd.DataFrame] = {}
for name, comp_file in files_get.items():
filename = pathlib.Path("tests/validation/comparison") / comp_file
df = pd.read_csv(filename, delimiter=",")
vals_exp[name] = df
return vals_exp
def get_height_scale(sim_cfg: SimulationConfigs, reynolds_tau: float, u_ref: float) -> float:
kin_visc = sim_cfg.models.LBM.kinematic_viscosity
return u_ref / kin_visc
reader_output = {}
for name, sim_cfg in sim_cfgs_use.items():
stats_export = sim_cfg.output.stats["default"]
last_step = stats_export.interval.get_all_process_steps(sim_cfg.n_steps)[-1]
reader = stats_export.read_vtm_export(last_step)
reader_output[sim_cfg.name] = reader.GetOutput()
def get_macr_compressed(
sim_cfg: SimulationConfigs, macr_name: str, is_2nd_order: bool
) -> np.ndarray:
global reader, reader_output
output_use = reader_output[sim_cfg.name]
macr_name_read = macr_name if not is_2nd_order else f"{macr_name}_2nd"
ds = sim_cfg.domain.domain_size
p0 = np.array((ds.x // 2, 0, ds.z // 2))
p1 = np.array((ds.x // 2, ds.y - 1, ds.z // 2))
n_points = ds.y
pos = np.linspace(p0, p1, num=n_points, endpoint=True)
norm_pos = (pos[:, 1] + 0.5) / (len(pos) + 1)
# Sum 0.5 because data is cell centered in vtm
line = common.create_line(p0 + 0.5, p1 + 0.5, n_points - 1)
probe_filter = common.probe_over_line(line, output_use)
probed_data = vtk_to_numpy(probe_filter.GetOutput().GetPointData().GetArray(macr_name_read))
return np.array([norm_pos, probed_data])
Results#
The average velocity profile is shown for the case. It can be a good approximation from reference results for the multilevel case.
import matplotlib.pyplot as plt
# Despite the reynolds value being 180, the experimental data for 142 and 180 is pretty much the same
reynolds_tau = 142
u_ref = 0.00529
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(6, 5)
for i, sim_cfg in enumerate(sim_cfgs_use.values()):
height_scale = get_height_scale(sim_cfg, reynolds_tau, u_ref)
analytical_values = get_experimental_profiles(reynolds_tau)
macr_compr = get_macr_compressed(sim_cfg, "ux", is_2nd_order=False)
y = macr_compr[0].copy()
ux_avg = macr_compr[1].copy()
ux_avg /= u_ref
y *= height_scale * sim_cfg.domain.domain_size.y
exp_y = analytical_values["ux"]["y+"]
exp_ux_avg = analytical_values["ux"]["u/u*"]
ax.plot(y, ux_avg, "--k", label=f"N={sim_cfg.domain.domain_size.x}")
ax.plot(exp_y, exp_ux_avg, "ok", label="Experimental", fillstyle="none")
ax.set_title("Multilevel")
ax.legend()
ax.set_xlim(1, 160)
ax.set_xscale("symlog")
The results of the \({\mathrm{u_{rms}}}\) velocity profiles shown below also indicate a good representation with the multilevel approach.
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(6, 5)
vel_name_map = {"ux": "u", "uy": "v", "uz": "w"}
for i, sim_cfg in enumerate(sim_cfgs_use.values()):
for macr_name, marker, color in [("ux", "o", "k"), ("uy", "^", "r"), ("uz", "s", "b")]:
macr_compr_rms = get_macr_compressed(sim_cfg, macr_name, is_2nd_order=True)
macr_compr_avg = get_macr_compressed(sim_cfg, macr_name, is_2nd_order=False)
y = macr_compr_rms[0].copy()
vel_2nd = macr_compr_rms[1].copy()
vel_avg = macr_compr_avg[1].copy()
vel_rms = (vel_2nd - vel_avg**2) ** 0.5
vel_rms /= u_ref
y *= height_scale * sim_cfg.domain.domain_size.y
# Remove wall value
vel_rms = vel_rms[1:]
y = y[1:]
name_vel = vel_name_map[macr_name]
df = analytical_values[f"{macr_name}_rms"]
exp_y = df["y+"]
exp_rms = df[f"{name_vel}'/u*"]
ax.plot(y, vel_rms, f"x{color}", label=f"Nassu {name_vel.upper()}")
ax.plot(
exp_y, exp_rms, f"{marker}{color}", label=f"Exp. {name_vel.upper()}", fillstyle="none"
)
ax.set_title("Multilevel")
ax.legend()
ax.set_xlim(0, 80)
Version#
sim_info = sim_cfg.output.read_info()
nassu_commit = sim_info["commit"]
nassu_version = sim_info["version"]
print("Version:", nassu_version)
print("Commit hash:", nassu_commit)
Version: 1.6.17
Commit hash: 09c9337ca174e12ce2cfa6eb39867054691a2742
Configuration#
from IPython.display import Code
Code(filename=filename)
simulations:
- name: periodicTurbulentChannel
save_path: ./tests/validation/results/04_turbulent_channel_flow/
n_steps: 600000
report:
frequency: 1000
domain:
# u* = 0.00493 / l = 64 / ETT = l/u* = 12,984
# Re_tau = 142
# y+ = 2.0
domain_size:
x: 512
y: 128
z: 128
block_size: 8
data:
divergence: { frequency: 50 }
instantaneous:
default: { interval: { frequency: 200000 }, macrs: [rho, u, S] }
statistics:
interval: { frequency: 100, start_step: 300000 }
macrs_1st_order: [rho, u]
macrs_2nd_order: [u]
exports:
default: { interval: { frequency: 50000 } }
models:
precision:
default: single
LBM:
tau: 0.5074
F:
x: 4.72e-7
y: 0
z: 0
vel_set: D3Q27
coll_oper: RRBGK
initialization:
vtm_filename: "../nassuArtifacts/macrs/turbulent_channel.vtm"
engine:
name: CUDA
BC:
periodic_dims: [true, false, true]
BC_map:
- pos: N
BC: RegularizedHWBB
wall_normal: N
order: 1
- pos: S
BC: RegularizedHWBB
wall_normal: S
order: 1
- name: periodicTurbulentChannelMultilevel
save_path: ./tests/validation/results/04_turbulent_channel_flow/
n_steps: 200000
# u* = 0.00525 / l = 48 / ETT = l/u* = 9,150
# Re_tau = 180
# y+ = 3.75 (lvl 0)
report:
frequency: 1000
domain:
domain_size:
x: 384
y: 96
z: 96
block_size: 8
refinement:
static:
default:
volumes_refine:
- start: [0, 0, 0]
end: [384, 8, 96]
lvl: 1
is_abs: true
- start: [0, 88, 0]
end: [384, 96, 96]
lvl: 1
is_abs: true
data:
divergence: { frequency: 50 }
instantaneous:
default: { interval: { frequency: 200000 }, macrs: [rho, u, S] }
start_simul:
{ interval: { end_step: 2000, frequency: 500 }, macrs: [rho, u, S] }
statistics:
interval: { frequency: 50, start_step: 50000 }
macrs_1st_order: [rho, u]
macrs_2nd_order: [u]
exports:
default: { interval: { frequency: 50000 } }
models:
precision:
default: single
LBM:
tau: 0.5042
F:
x: 5.75e-7
y: 0
z: 0
vel_set: D3Q27
coll_oper: RRBGK
initialization:
vtm_filename: "../nassuArtifacts/macrs/turbulent_channel.vtm"
engine:
name: CUDA
BC:
periodic_dims: [true, false, true]
BC_map:
- pos: N
BC: RegularizedHWBB
wall_normal: N
order: 1
- pos: S
BC: RegularizedHWBB
wall_normal: S
order: 1