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

TL;DR

This paper introduces a statistical method to reconstruct three-dimensional microstructures of porous media from two-dimensional images, enabling accurate prediction of their macroscopic properties.

Contribution

It presents a novel technique that uses 2D microstructure images to generate 3D models matching key statistical functions, aiding property prediction.

Findings

Reconstructed models match original two-point correlation functions.

Predicted conductivity aligns well with known data.

Method effectively reproduces morphology and properties.

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.