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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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Data for the paper: Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows

Authors: Chéron, Victor; Evrard, Fabien; van Wachem, Berend;

Data for the paper: Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows

Abstract

These are the data files for the paper: Drag, lift and torque correlations for axi-symmetric rod-like non-spherical particles in locally linear shear flows authored by: Victor Chéron, Fabien Evrard, and Berend van Wachem #Files Temporally averaged drag, lift and torque coefficients are written in .txt files stored in the folder ResultsCoefficients. Python scripts used to derive the correlations are stored in the folder PythonScripts. Results of an example simulation are provided in the folder SimulationResults. A Python script with the final correlations of the manuscript is also included. #ResultsCoefficients The .txt files are split per coefficient, aspect ratio and shear rate, which can be identified by the name of the .txt file The results obtained for the torque coefficient of the particle of aspect ratio 2.5 for a uniform flow configuration are given in the file: ### Uniform-Torque-Angles-Size2-5.txt The results obtained for the lift coefficient of the particle of aspect ratio 10 for a shear rate 0.2 configuration are given in the file: ### Shear02-Lift-Angles-Size10.txt In these files, the results are ordered per orientation angle and particle Reynolds number. #PythonScripts The python scripts for processing the data are split among three main functions in two files: - Getter.py (reads the .txt files storing the coefficients - separate functions for the drag, lift and torque coefficients - as well as fill up the missing points for spherical particles using the Kurose and Komori correlations) - generalmain (calls the Getter.py function). The Getter.py is called from the generalmain.py file. (run python3 generalmain.py). This will return a 1D column vector ordering the variables used to derive the correlations: - Coefficients - Reynolds number - Orientation Angle - Shear rate - Aspect ratio - Additional coefficients. The Python script ManuscriptCorrelations.py has the correlations as derived in the manuscript. This routine can be used to query the correlations, for plotting them or using them in a simulation. #SimulationResults Simulation results of one case are provided: - Aspect ratio 2.5, particle Reynolds number 200, orientation angle 90, Shear rate 0.2 The fields and particles information are stored in hdf5 file format. A .xmf wrapper file is provided to read the simulation results in paraview. Data up to 40 seconds of real time are provided due to storage limits. This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 448292913.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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