Published December 5, 2024
| Version 2.0
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Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows
Creators
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1.
Otto-von-Guericke University Magdeburg
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2.
Polytechnique Montréal
Description
# Data repository for the paper
# _Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows_
Corresponding author:
Berend.van.Wachem@multiflow.org
This repository consists of the data and exemplary python scripts for the paper "Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows" by Christian Gorges, Victor Chéron, Anjali Chopra, Fabian Denner and Berend van Wachem. The data stored in this repository have the following data format:
- .csv files consisting the raw data of the simulations used for the coefficient plots in the results' chapter of the paper
- .py files containing python scripts serving as examples on how to use and plot the raw data of the .csv files and the correlations
The main folders of this repository are named as the non-spherical particle shapes (Oblate, Prolate, Rod-like) and a folder with the data on which the correlations are based.
The folders named after the non-spherical particle shapes contain the raw simulation data. For instance, the Oblate folder contains the individual .csv files of all simulations of the oblate spheroid for all Reynolds numbers, Mach numbers, and angles of attack.
The folder Correlations/ consists of the temporally averaged drag, lift and torque coefficients, which are written in .csv files and stored in the folder ResultsCoefficients/, as well as Python scripts for plotting the correlations.
The naming style of the raw data files and the subfolders for each section is explained in the following:
The file names of the .csv files within the particle shape folders consist of the Reynolds number, followed by the Mach number and the angle of attack. For example "log_Re100M2_0_alpha_90.csv" consists of the data for a Reynolds number of 100, a Mach number of 2.0 and an angle of attack of 90 degrees. The content in the .csv files is given as: "%f,%f,%f,%f\n" which corresponds to "Physical time, drag coefficient, lift coefficient, torque coefficient". The first row in each file gives the headers of each column.
The .csv files in the folder Correlations/ResultsCoefficients/ are split per coefficient, shape, and particle Reynolds numbers, which can be identified by the name of the .csv file. For instance, the results obtained for the lift coefficient of
the prolate spheroid particle for at a particle Reynolds numbers 100 for all orientation angles and Mach numbers are given in the file:
"Prolate_100_CL.csv". In these files, the results are ordered per orientation angle (rows) and Mach
number (column).
The python scripts have been tested with Python 3.11.5.
PlotCoefficients.py is an example python script to read the .csv files and plot the aerodynamic force coefficients as it is done in the results section of the paper.
The python scripts in the directory Correlations/ are split in three main functions in two files:
- Getter.py (read the .csv files storing the coefficients - separate functions
for the drag, lift and torque coefficients)
- ManuscriptCorrelation.py with all the correlations derived in this work for an
effective implementation in any solver, and a plotting function to have visual
representation of the correlations.
- generalmain.py (calls Getter and Plotter)
The Getter is called from the generalmain.py file. (run python3 generalmain.py) so that all coefficients can be gathered in a 3D array.
First dimension : Reynolds number
Second dimension : Orientation angle
Third dimension : Mach number
The user just needs to give the absolute path to the folder ResultsCoefficients/.
This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 447633787.
Files
DATA-CompressibleCorrelationsData.zip
Files
(71.1 MB)
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Additional details
Funding
- Deutsche Forschungsgemeinschaft
- Behaviour and modelling of non-spherical particles in compressible flows 447633787
Dates
- Updated
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2024-12-05Some modifications in results