Functional regression-based fluid permeability prediction in monodisperse sphere packings from isotropic two-point correlation functions
Artikel i vetenskaplig tidskrift, 2017

We study fluid permeability in random sphere packings consisting of impermeable monodisperse hard spheres. Several different pseudo-potential models are used to obtain varying degrees of microstructural heterogeneity. Systematically varying solid volume fraction and degree of heterogeneity, virtual screening of more than 10,000 material structures is performed, simulating fluid flow using a lattice Boltzmann framework and computing the permeability. We develop a well-performing functional regression model for permeability prediction based on using isotropic two-point correlation functions as microstructural descriptors. The performance is good over a large range of solid volume fractions and degrees of heterogeneity, and to our knowledge this is the first attempt at using two-point correlation functions as functional predictors in a nonparametric statistics/machine learning context for permeability prediction.

Correlation functions

Granular materials

Sphere packings

Functional regression

Permeability

Författare

Magnus Röding

SuMo Biomaterials

Peter Svensson

RISE Research Institutes of Sweden

Niklas Lorén

SuMo Biomaterials

Chalmers, Fysik, Eva Olsson Group

Computational Materials Science

0927-0256 (ISSN)

Vol. 134 126-131

Ämneskategorier

Annan fysik

Styrkeområden

Materialvetenskap

DOI

10.1016/j.commatsci.2017.03.042

Mer information

Senast uppdaterat

2020-08-18