.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/00-fluent/tyler_sofrin_modes.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_examples_00-fluent_tyler_sofrin_modes.py>`
to download the full example code.
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_examples_00-fluent_tyler_sofrin_modes.py:
.. _ref_ts_mode_calculator:
Tyler-Sofrin Compressor Modes Post-Processing
---------------------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 30-76
Objective
~~~~~~~~~
This example demonstrates PyFluent API's for
* Read a case and data file
* Create monitor points to calculate Fourier coefficients
* Write Fourier coefficients to a file
* Tyler-Sofrin mode Plot using the matplotlib library
Background
~~~~~~~~~~
Tyler and Sofrin (1961) demonstrated that interactions between a rotor and a
stator result in an infinite set of spinning modes. Each Tyler-Sofrin (TS)
mode exhibits an m-lobed pattern and rotates at a speed given by the
following equation:
:math:`\text{speed} = \frac{BnΩ}{m}`
Where:
* m is the Tyler-Sofrin mode number, defined as 'm = nB + kV'
* n is the impeller frequency harmonic
* k is the vane harmonic
* B is the number of rotating blades
* V is the number of stationary vanes
* Ω is the Rotor shaft speed, rad/s
Example:
* 8-blade rotor interacting with a 6-vane stator
* 2-lobed pattern turning at (8)(1)/(2) = 4 times shaft speed
Example Table
~~~~~~~~~~~~~
.. image:: ../../_static/ExampleTable.jpg
:alt: Example Table
Tyler-Sofrin Modes
~~~~~~~~~~~~~~~~~~
.. image:: ../../_static/TSmode.jpg
:alt: Tyler-Sofrin Modes
.. GENERATED FROM PYTHON SOURCE LINES 79-99
Example Note: Discrete Fourier Transform (DFT)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ In order to calculate the pressure related to each TS-mode, extend the
simulation and perform the DFT of pressure at the desired blade passing
frequency harmonics.
+ Disable the Hanning windowing (specifically for periodic flows like
this one) to avoid getting half the expected magnitudes for periodic flows.
Make sure to set the windowing parameter to 'None' when specifying
the Discrete Fourier Transform (DFT) in the graphical user interface (GUI).
+ The DFT data will only be accurate if the sampling is done across the
entire specified sampling period.
.. note::
The .cas/.dat file provided with this example is for demonstration purposes only.
A finer mesh is necessary for accurate acoustic analysis. This example uses data
sets generated with Ansys Fluent V2023R2.
.. GENERATED FROM PYTHON SOURCE LINES 101-103
Post-Processing Implementation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. GENERATED FROM PYTHON SOURCE LINES 106-108
Import required libraries/modules
=====================================================================================
.. GENERATED FROM PYTHON SOURCE LINES 108-118
.. code-block:: Python
import math
from pathlib import Path
import random
import matplotlib.pyplot as plt
import numpy as np
import ansys.fluent.core as pyfluent
from ansys.fluent.core import examples
.. GENERATED FROM PYTHON SOURCE LINES 119-126
Specifying save path
=====================================================================================
save_path can be specified as:
+ Path("E:/", "pyfluent-examples-tests") or
+ Path("E:/pyfluent-examples-tests") in a Windows machine for example, or
+ Path("~/pyfluent-examples-tests") in Linux.
.. GENERATED FROM PYTHON SOURCE LINES 126-129
.. code-block:: Python
save_path = Path(pyfluent.EXAMPLES_PATH)
.. GENERATED FROM PYTHON SOURCE LINES 130-132
Downloading cas/dat file
=====================================================================================
.. GENERATED FROM PYTHON SOURCE LINES 132-144
.. code-block:: Python
import_filename = examples.download_file(
"axial_comp_fullWheel_DFT_23R2.cas.h5",
"pyfluent/examples/Tyler-Sofrin-Modes-Compressor",
save_path=save_path,
)
examples.download_file(
"axial_comp_fullWheel_DFT_23R2.dat.h5",
"pyfluent/examples/Tyler-Sofrin-Modes-Compressor",
save_path=save_path,
)
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Launch Fluent session and print Fluent version
=====================================================================================
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.. code-block:: Python
session = pyfluent.launch_fluent(
ui_mode="gui", processor_count=4, product_version="25.1.0"
)
print(session.get_fluent_version())
.. GENERATED FROM PYTHON SOURCE LINES 153-158
Reading case and data file
=====================================================================================
.. note::
The dat file should correspond to the already completed DFT simulation.
.. GENERATED FROM PYTHON SOURCE LINES 158-161
.. code-block:: Python
session.file.read(file_type="case-data", file_name=import_filename)
.. GENERATED FROM PYTHON SOURCE LINES 162-171
Define User constant/variables
=====================================================================================
.. note::
The variable names should match the ones written from the DFT and can be
identified by manually examining the solution variables as shown below:
.. image:: ../../_static/var_names.jpg
:alt: variable names
.. GENERATED FROM PYTHON SOURCE LINES 171-187
.. code-block:: Python
varname = [
"mean-static-pressure-dataset",
"dft-static-pressure_10.00kHz-ta",
"dft-static-pressure-1_21.43kHz-ta",
"dft-static-pressure-2_30.00kHz-ta",
]
n_mode = [0, 1, 2, 3] # Impeller frequency harmonics
r = 0.082 # meters
z = -0.037 # meters
d_theta = 5 # degrees
m_max = 50 # maximum TS mode number
# Plot will be from -m_max to +m_max, incremented by m_inc
m_inc = 2 # TS mode increment
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Create monitor points
=====================================================================================
.. GENERATED FROM PYTHON SOURCE LINES 190-197
.. code-block:: Python
for angle in range(0, 360, d_theta):
x = math.cos(math.radians(angle)) * r
y = math.sin(math.radians(angle)) * r
pt_name = "point-" + str(angle)
session.results.surfaces.point_surface[pt_name] = {}
session.results.surfaces.point_surface[pt_name].point = [x, y, z]
.. GENERATED FROM PYTHON SOURCE LINES 198-200
Compute Fourier coefficients at each monitor point (An, Bn)
=====================================================================================
.. GENERATED FROM PYTHON SOURCE LINES 200-240
.. code-block:: Python
An = np.zeros((len(varname), int(360 / d_theta)))
Bn = np.zeros((len(varname), int(360 / d_theta)))
for angle_ind, angle in enumerate(range(0, 360, d_theta)):
for n_ind, variable in enumerate(varname):
if len(variable) >= 4 and variable[:4] == "mean":
session.solution.report_definitions.surface["mag-report"] = {
"report_type": "surface-vertexavg",
"surface_names": ["point-" + str(angle)],
"field": str(variable),
}
mag = session.solution.report_definitions.compute(
report_defs=["mag-report"]
)
mag = mag[0]["mag-report"][0]
An[n_ind][angle_ind] = mag
Bn[n_ind][angle_ind] = 0
else:
session.solution.report_definitions.surface["mag-report"] = {
"report_type": "surface-vertexavg",
"surface_names": ["point-" + str(angle)],
"field": str(variable) + "-mag",
}
mag = session.solution.report_definitions.compute(
report_defs=["mag-report"]
)
mag = mag[0]["mag-report"][0]
session.solution.report_definitions.surface["phase-report"] = {
"report_type": "surface-vertexavg",
"surface_names": ["point-" + str(angle)],
"field": str(variable) + "-phase",
}
phase = session.solution.report_definitions.compute(
report_defs=["phase-report"]
)
phase = phase[0]["phase-report"][0]
An[n_ind][angle_ind] = mag * math.cos(phase)
Bn[n_ind][angle_ind] = -mag * math.sin(phase)
.. GENERATED FROM PYTHON SOURCE LINES 241-247
Write Fourier coefficients to file
=====================================================================================
.. note::
This step is only required if data is to be processed with other standalone
tools. Update the path to the file accordingly.
.. GENERATED FROM PYTHON SOURCE LINES 247-266
.. code-block:: Python
fourier_coefficients_file = Path(save_path, "FourierCoefficients.txt")
with open(fourier_coefficients_file, "w") as f:
f.write("n theta An Bn \n")
for n_ind, variable in enumerate(varname):
for ind, x in enumerate(An[n_ind, :]):
f.write(
str(n_mode[n_ind])
+ ","
+ str(ind * d_theta)
+ ","
+ str(An[n_ind, ind])
+ ","
+ str(Bn[n_ind, ind])
+ "\n"
)
.. GENERATED FROM PYTHON SOURCE LINES 267-274
Calculate Resultant Pressure Field
=====================================================================================
Create list of m values based on m_max and m_inc
.. image:: ../../_static/TS_formulas.jpg
:alt: variable names
.. GENERATED FROM PYTHON SOURCE LINES 274-301
.. code-block:: Python
m_mode = range(-m_max, m_max + m_inc, m_inc)
# Initialize solution matrices with zeros
Anm = np.zeros((len(varname), len(m_mode)))
Bnm = np.zeros((len(varname), len(m_mode)))
Pnm = np.zeros((len(varname), len(m_mode)))
for n_ind, variable in enumerate(varname): # loop over n modes
for m_ind, m in enumerate(m_mode): # loop over m modes
for angle_ind, angle in enumerate(
np.arange(0, math.radians(360), math.radians(d_theta))
): # loop over all angles, in radians
Anm[n_ind][m_ind] += An[n_ind][angle_ind] * math.cos(m * angle) - Bn[n_ind][
angle_ind
] * math.sin(m * angle)
Bnm[n_ind][m_ind] += An[n_ind][angle_ind] * math.sin(m * angle) + Bn[n_ind][
angle_ind
] * math.cos(m * angle)
Anm[n_ind][m_ind] = Anm[n_ind][m_ind] / (2 * math.pi) * math.radians(d_theta)
Bnm[n_ind][m_ind] = Bnm[n_ind][m_ind] / (2 * math.pi) * math.radians(d_theta)
Pnm[n_ind][m_ind] = math.sqrt(Anm[n_ind][m_ind] ** 2 + Bnm[n_ind][m_ind] ** 2)
# P_00 is generally orders of magnitude larger than that of other modes.
# Giving focus to other modes by setting P_00 equal to zero
Pnm[0][int(len(m_mode) / 2)] = 0
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Plot Tyler-Sofrin modes
=====================================================================================
.. GENERATED FROM PYTHON SOURCE LINES 305-319
.. code-block:: Python
fig = plt.figure()
ax = plt.axes(projection="3d")
ax.set_xlabel("Tyler-Sofrin Mode, m")
ax.set_ylabel("Imp Freq Harmonic, n")
ax.set_zlabel("Pnm [Pa]")
plt.yticks(n_mode)
for n_ind, n in enumerate(n_mode):
x = m_mode
y = np.full(Pnm.shape[1], n)
z = Pnm[n_ind]
rgb = (random.random(), random.random(), random.random())
ax.plot3D(x, y, z, c=rgb)
plt.show()
.. GENERATED FROM PYTHON SOURCE LINES 320-324
Tyler-Sofrin modes
=====================================================================================
.. image:: ../../_static/ts_modes.png
:alt: Tyler-Sofrin modes
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Close the session
=====================================================================================
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.. code-block:: Python
session.exit()
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References
=====================================================================================
[1] J.M. Tyler and T. G. Sofrin, Axial Flow Compressor Noise Studies,1961 Manly
Memorial Award.
.. _sphx_glr_download_examples_00-fluent_tyler_sofrin_modes.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: tyler_sofrin_modes.ipynb <tyler_sofrin_modes.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: tyler_sofrin_modes.py <tyler_sofrin_modes.py>`
.. container:: sphx-glr-download sphx-glr-download-zip
:download:`Download zipped: tyler_sofrin_modes.zip <tyler_sofrin_modes.zip>`
.. only:: html
.. rst-class:: sphx-glr-signature
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