Data for the paper: "Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches"

These are the data files for the paper:

Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches

authored by: Hani Elmestikawy,  Julia Reuter, Fabien Evrard, Sanaz Mostaghim, Berend van Wachem.

The datasets encapsulate the estimated hydrodynamic forces acting on random periodic arrangements of spherical particles in Stokes regime.

Description of the dataThe forces are computed using the method of periodic regularized Stokeslets. Each file includes datasets from multiple realizations at a given  volume fraction, $\varepsilon_\mathrm{p}$. The files are in CSV format with the filename including the volume fraction and the total number of particles in all realizations at the given volume fraction. For example, the file data_VF_0.25_N_2775.csv includes datasets from multiple realizations at $\varepsilon_\mathrm{p}=0.25$ with total number of particles 2775. Each row in a file contains the information about an individual particle such as:- The hydrodynamic force acting on the particle- The relative positions of the nearest 110 neighbors (sorted from nearest to furthest).

In the datasets, the reported hydrodynamic force acting of each individual particle is normalized by the Stokes drag defined as: $$F_\text{St} = 3 \pi \mu d_\mathrm{p}  u_o,$$ where  - $\mu$ is the dynamic viscosity of the fluid- $d_\mathrm{p}$ is the particle diameter- $u_o$ is the superficial velocity.

The description of each column in the CSV file follows:

Column
Description

#
Particle id within all the datasets in this file

instance
simulation instance that this particle belongs to

seed
random seed used to generate the random arrangement

phi
mean particle volume fraction of the arrangement [\-]

d_particle
particle diameter [m]

mu
dynamic viscosity of the fluid [kg/(m s)]

u
superficial fluid velocity in the x-direction (mean flow direction) [m/s]

v
superficial fluid velocity in the y-direction [m/s]

w
superficial fluid velocity in the z-direction [m/s]

fx/stokes
hydrodynamic force in the x-direction normalized by $F_\text{St}$ (drag force) [\-]

fy/stokes
hydrodynamic force in the y-direction normalized by $F_\text{St}$ [\-]

fz/stokes
hydrodynamic force in the z-direction normalized by $F_\text{St}$ [\-]

norm_rel_dist_i
relative distance of the $i$-th neighbor normalized by $d_\mathrm{p}$ [\-]

norm_rel_x_i
relative x-coordinate of the $i$-th neighbor normalized by $d_\mathrm{p}$ [\-]

norm_rel_y_i
relative y-coordinate of the $i$-th neighbor normalized by $d_\mathrm{p}$ [\-]

norm_rel_z_i
relative z-coordinate of the $i$-th neighbor normalized by $d_\mathrm{p}$ [\-]

Reading the datasetsTo read the datasets, a simple python code, read_dset.py is included, where we read one of the datasets and the mean drag as an example.

Acknowledgments

The authors gratefully acknowledge the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 466092867 within the Priority Programme SPP 2331: Machine Learning in Chemical Engineering.