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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies

Authors: van Wachem, Berend; Chéron, Victor; Elmestikawy, Hani;

Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies

Abstract

#Data linked to the manuscript: Microstructure-based prediction of hydrodynamic forces in stationary particle assemblies #Corresponding author: Berend.van.Wachem@gmail.com #Acknowleddgements This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 422037413 - TRR 287. This funding is gratefully acknowledged. #Files - Minkowski tensors and Lagrangian forces computed for all simulations stored in the .csv files - Python script to read Minkowski tensors and Lagrangian forces stored in the .csv files - One flow configuration results with hdf fields files at mean flow Reynolds number 100 and solid volume fraction 0.5. #SimulationResult Note to open the simulation results: The Eulerian fields and Lagrangian particles information are stored in HDF5 files. The relevant fileds are: #Eulerian: - Velocity vector - Pressure - AMR Level - Other information relevant for the coupling between the IBM particles and the fluid #Lagrangian: - Particles coordinates - Particles diameter - Particles hydrodynamic force components - Particles torque components - Particles velocity components - Particles rotational velocity components A .xmf file is provided to read the simulation results in paraview. Only the initial state and the converged state of the simulation are provided due to storage limits.

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 422037413 - TRR 287.

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Keywords

Particle-resolved direct numerical simulation (PR-DNS)

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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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influence
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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