{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source file for the documentation in the [jupyter notebook](SoundTransmissionThroughGaps.ipynb) and converted to markdown by `jupyter nbconvert SoundTransmissionThroughGaps.ipynb --to=markdown --output=README`.\n",
"All source files of this example can be downloaded from the [userdocu source-code repository](https://gitlab.com/openCFS/userdocu/-/archive/master/userdocu-master.zip?path=docs/Applications/Singlefield/Acoustics/SoundTransmissionThroughGaps).\n",
"\n",
"Sound Transmission Trough Gaps\n",
"============================\n",
"\n",
"## Problem Definition\n",
"\n",
"This example illustrates the use of openCFS to evaluate an acoustic wave passing through a periodic interface. The goal is to extract the reflection and transmission coefficients dependent on the incoming wave's frequency and angle of incidence.\n",
"\n",
"The problem is simulated in a 2D domain. The simulated domain represents only a small section of the real domain in y-Direction. Therefore a periodic boundary condition is used on the top and bottom boundary. This leads to the following domain, which has to be simulated. As a soundhard interface, several discs are choosen in this first simulation.\n",
"\n",
"![full_domain](Files/full_domain.png)\n",
"\n",
"The transmission coefficient is defined as\n",
"\n",
"$$ \\tau=\\frac{P_{trans}}{P_{inc}}, $$\n",
"\n",
"and the reflection coefficient as\n",
"\n",
"$$ r=\\frac{P_{refl}}{P_{inc}}. $$\n",
"\n",
"Therefore the calculation of the Transmittet Acoustic Power $P_{trans}$, the Reflected Acoustic Power $P_{refl}$ and the Incoming Acoustic Power $P_{inc}$ has to be performed to obtain both coefficients.\n",
"\n",
"$P_{trans}$ can be evaluated by calculating the power after the interface in the domain. Using openCFS post-processing, the Power can be calculated as a Surface Region Result `acouPower`.\n",
"\n",
"For calculating $P_{inc}$, a second domain, the \"excitation domain\" is simulated, which only represents the excitation side of the full problem, thus allowing to obtain only the incoming acoustic field. The acoustic power calculated within this domain represents the incoming power. Here the power is also calculated as a Surface Region Result `acouPower`. \n",
"However, since the incoming wave is a plane acoustic wave and the relation $v_{\\mathrm{a}}=\\frac{p_{\\mathrm{a}}}{\\rho \\cdot c}$ holds, this power can easily be calculated analytically. With the definition of the acoustic power $P_{\\mathrm{a}}=\\int_{\\Gamma} \\mathbf{I}_{\\mathrm{a}} \\cdot \\mathbf{n} \\mathrm{d} \\Gamma^{\\prime}$. This leads to the equation for the power of the incoming as\n",
"\n",
"$$ P_{inc}=\\frac{p_{\\mathrm{a}}^2}{2 \\cdot \\rho \\cdot c}\\cdot cos(\\alpha) \\cdot L. $$\n",
"\n",
"Here $p_{\\mathrm{a}}$ is the amplitude of the applied acoustic pressure, $\\rho$ is the density of air, $c$ the speed of sound, $\\alpha$ the angle of the applied wave field and $L$ the heigth of the simulated domain.\n",
"\n",
"![incoming_domain](Files/incoming_domain.png)\n",
"\n",
"Obtaining $P_{refl}$ is the most difficult part, since it can't be done directly via openCFS. When simulating the entire domain, the acoustic field on the incoming side of the domain represents the reflected wave superimposed with the incoming wave. Therefore, the wavefield obtained by simulating the excitation domain has to be subtracted from the full simulations' wavefield. This provides the reflected acoustic field which susequently allows the calculation of the reflected power. \n",
"With the acoustic fields for both domains obtained via openCFS, the following substraction and calculation of $P_{refl}$ is done in Python.\n",
"\n",
"\n",
"## Meshing\n",
"\n",
"The mesh input file is generated with [gmsh](https://gmsh.info/) and subsequently, the mesh file is produced with the command \n",
"```XML\n",
"gmsh mesh_input.geo -2 -v 0 -format msh2 -o mesh.msh\n",
"```\n",
"The provided files are: \n",
"- The mesh input file for the full domain [geometry_total.geo](Files/geometry_discs.geo) \n",
"- The mesh input file for the excitation domain [geometry_excitation.geo](Files/geometry_incoming.geo) \n",
"\n",
"## XML Simulation Files\n",
"\n",
"Now two XML files for harmonic acoustic analysis are set up. One for simulating the incoming acoustic field, hence the excitation domain, provided [here](Files/incoming_simulation.xml), and one for simulating the entire domain with the interface, provided [here](Files/full_simulation.xml). In addition to that, the XML [material file](Files/mat_accou.xml) is required.\n",
"\n",
"The angle of incidence $\\alpha$ can be chosen in the XML file by adjusting this value\n",
"\n",
"```XML\n",
"\n",
"```\n",
"\n",
"The simulated frequencies for the harmonic analysis are calculated within the XML file. Due to the periodic boundary condition, the frequencies for the harmonic analysis can not be chosen arbitrarily. They are calculated, depending on the angle of incidence $\\alpha$, the height of the domain and the speed of sound $c$. With these frequencies, a plane wave field can be produced in combination with the periodicity of the domain's boundary. Additionally an if-condition is implemented when calculating the frequencies, to directly set the frequencies in the case of $\\alpha=0°$.\n",
"\n",
"```XML\n",
"\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\t\n",
"\n",
"```\n",
"\n",
"For the subtraction of the incoming wave field from the entire wave field, it suffices to only execute this for one line in the domain and then calculate the power along this line. Therefore, in both simulated domains, a sensor array is set up at the same position to calculate the acoustic pressure and the velocity along a vertical line in the domain. \n",
"\n",
"```XML\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"```\n",
"\n",
"The power of the incoming acoustic wave is directly calculated by using the openCFS post-processing in the excitation domain. By the same approach, the transmitted acoustic power is calculated using the entire domain.\n",
"\n",
"## Python Post-processing\n",
"\n",
"The calculation of acoustic power reflected by the interface is carried out in Python. Note that much of the coding is done using pandas dataframes. This allows for a comprehensible code without the excessive use of comments. This code is provided [here](Files/Calculate_Coefficients.ipynb).\n",
"\n",
"First, some libraries are imported."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Functions\n",
"\n",
"Now the functions, which are defined [functions.py](Files/functions.py) are imported.\n",
"\n",
"The first function, `calculate_power`, reads the text files, created by the openCFS sensor arrays. Therefore the function needs the information of the angle of incidence and the name of the simulation in order to find the right files. Moreover the number of frequencies which were simulated in openCFS have to be provided. For each frequency the sensor array creates a separate file, hence this information is needed to know how many files have to be read. After reading the files, the velocities and the pressures of the excitation domain are subtracted from the entire domain's to obtain only the values for the reflected acoustic field. With these resulting values the acoustic intensity and subsequently the reflected acoustic power are calculated and returned.\n",
"\n",
"The function `read_inc_power` reads the power of the incoming acoustic field, which is calculated with openCFS directly by using the excitation domain and stored in a text file. The angle of incidence has to be passed over to this function as well, to choose the right file to read the data from. In addition to the power, the frequency for each simulation is stored in the same file, which is read and saved by the function as well.\n",
"\n",
"The last function, `read_trans_power`, similar to the second function, is created to read and store the power transmitted through the interface."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from functions import calculate_power\n",
"from functions import read_inc_power\n",
"from functions import read_trans_power"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Main Program\n",
"\n",
"Now all functions are defined and the main part of the code starts. First, the information on the performed simulations has to be defined. Simulations for five angles of incidence (60°, 30°, 0°, -30°, 60°) with ten simulated frequencies per angle, have been carried out. The performed simulations were stored by the name \"Discs\"."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# defining which simulation to process\n",
"simulation=\"Discs\" # for getting the coefficients of the original domain (discs as iterface)\n",
"angles_str = [\"60\", \"30\", \"00\", \"m30\", \"m60\"] # angles of incidence which were simulated (m for minus)\n",
"freq_num=10 # number of frequencies which were simulated"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next step some empty dataframes and arrays are defined, needed in the following parts of the code."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# vector for storing dataframe of the calculation for reflected power\n",
"results_dataframes = [None] * len(angles_str) \n",
"\n",
"# empty dataframes for power calculation \n",
"df_Power_ref = pd.DataFrame()\n",
"df_Power_inc = pd.DataFrame()\n",
"df_Power_trans = pd.DataFrame()\n",
"df_Power_balance = pd.DataFrame()\n",
"df_Coefficients = pd.DataFrame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following this, a for-loop over all angles of incidence is created. In this loop, the reflected, transmitted and incoming powers are calculated with the predefined functions and stored in the dataframes.\n",
"\n",
"With the determined powers, a power balance around the interface is set up. This step is not necessary for the calculation of the coefficients, but is a good way of validating the data obtained up until now. Since the interface in the performed simulation is soundhard, the power balance $P_{inc}=P_{refl}+P_{trans}$ has to be fulfilled. The relative error of this balance is calculated and used for validating the obtained powers.\n",
"\n",
"The last step performed in the loop is the calculation of the transmission coefficient and the reflection coefficient."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# for loop over all simulated incidence angles\n",
"for index, angle in enumerate(angles_str):\n",
" \n",
" # calcualting power reflected from interface\n",
" [df,Power_ref]=calculate_power(angle,simulation,freq_num) \n",
" results_dataframes[index]=df \n",
" df_Power_ref[angle]=np.concatenate(Power_ref)\n",
" \n",
" # reading the incoming power\n",
" [Power_inc,frequency]=read_inc_power(angle)\n",
" df_Power_inc['freq_'+angle]=frequency\n",
" df_Power_inc[angle]=Power_inc\n",
" \n",
" # reading the transmitted power\n",
" [Power_trans]=read_trans_power(angle,simulation)\n",
" df_Power_trans['freq_'+angle]=frequency\n",
" df_Power_trans[angle]=Power_trans\n",
"\n",
" # check the power balance (for soundhard interface: P_inc=P_refl+P_trans)\n",
" df_Power_balance['balance_'+angle]=df_Power_inc[angle]-df_Power_trans[angle]-df_Power_ref[angle]\n",
" df_Power_balance['Error_in_%_'+angle]=abs(df_Power_balance['balance_'+angle]/df_Power_inc[angle])*100 \n",
"\n",
" # calculate transmission and reflection coefficients\n",
" df_Coefficients['Trans_coef_'+angle]=df_Power_trans[angle]/df_Power_inc[angle]\n",
" df_Coefficients['Refl_coef_'+angle]=df_Power_ref[angle]/df_Power_inc[angle]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot Coefficients\n",
"\n",
"For showing the obtained coefficients two plots are created. The first shows the **transmission coefficients** depending on the angle of incidence and the excitation frequency.\n",
"\n",
"This plot shows, that the calculated coefficients are nearly the same when simply the sign of the incidence angle is switched. Considering the interface being symmetrical along the x-axis, this is an expected result. In fact, the transmission coefficients should be exactly the same. \n",
"The encountered small deviations might be due to the postprocessing procedure and indicate the overall error that can be expected from it. \n",
"\n",
"By simply looking at the interface, one can assume, that the transmitted power has a maximum by an incoming wavefront parallel to the interface and that this transmitted power decreases with a rising angle of incidence. This presumption is also confirmed by the calculated transmission coefficient, which on average has a maximum at an angle of incidence of $\\alpha=0°$."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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",
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