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Functional regression-based fluid permeability prediction in monodisperse sphere packings from isotropic two-point correlation functions
RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. Chalmers University of Technology, Sweden.
RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience. Chalmers University of Technology, Sweden.
2017 (English)In: Computational materials science, ISSN 0927-0256, E-ISSN 1879-0801, Vol. 134, p. 126-131Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 134, p. 126-131
Keywords [en]
Correlation functions, Functional regression, Granular materials, Permeability, Sphere packings, Flow of fluids, Forecasting, Packing, Regression analysis, Spheres, Volume fraction, Correlation function, Microstructural descriptors, Microstructural heterogeneity, Non-parametric statistics, Permeability prediction, Two point correlation functions, Mechanical permeability
National Category
Condensed Matter Physics Materials Chemistry
Identifiers
URN: urn:nbn:se:ri:diva-30877DOI: 10.1016/j.commatsci.2017.03.042Scopus ID: 2-s2.0-85017093852OAI: oai:DiVA.org:ri-30877DiVA, id: diva2:1139010
Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2017-11-17Bibliographically approved

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CiteExportLink to record
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