Statistical reconstruction of three-dimensional porous media from two-dimensional images

Abstract

A method of modelling the three-dimensional microstructure of random isotropic two-phase materials is proposed. The information required to implement the technique can be obtained from two-dimensional images of the microstructure. The reconstructed models share two-point correlation and chord-distribution functions with the original composite. The method is designed to produce models for computationally and theoretically predicting the effective macroscopic properties of random materials (such as electrical and thermal conductivity, permeability and elastic moduli). To test the method we reconstruct the morphology and predict the conductivity of the well known overlapping sphere model. The results are in very good agreement with data for the original model.

11 pages, 12 figures

References in corpus (3)

• Transport properties of heterogeneous materials derived from Gaussian random fields: Bounds and Simulation
• Structure-Property Correlations in Model Composite Materials
• Transport and Elastic Properties of Fractal Media

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• A Two-Stage Reconstruction of Microstructures with Arbitrarily Shaped Inclusions
• Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model
• A New Framework for Interfacial Statistics: Exact n-Point Correlations of Gaussian Level Sets
• Novel Hierarchical Correlation Functions for Quantitative Representation of Complex Heterogeneous Materials and Microstructural Evolution