No Cover Image

Journal article 572 views 17 downloads

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

PhD student Fu, Min Wang Orcid Logo, Dunhui Xiao, Shan Zhong Orcid Logo, Xiangyun Ge Orcid Logo, Minglu Wu, Ben Evans Orcid Logo

Computer Methods in Applied Mechanics and Engineering, Volume: 410, Start page: 116049

Swansea University Authors: PhD student Fu, Ben Evans Orcid Logo

Abstract

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often requ...

Full description

Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63170
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. This paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.
College: Faculty of Science and Engineering
Funders: EPSRC, United Kingdom grant: PURIFY (E P/V 000756/1) and Swansea University Impact Fund, United Kingdom, and we also would like to acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government.
Start Page: 116049