A machine learning based method to generate random packed isotropic porous media with desired porosity and permeability

TL;DR

This paper presents a novel machine learning-based method for generating random packed isotropic porous media with specific porosity and permeability, enabling precise control over these properties for various applications.

Contribution

The study introduces a new approach combining digital porous media generation with CNN-based permeability prediction to achieve targeted permeability and porosity.

Findings

The CNN model accurately predicts permeability of generated media.

The method efficiently produces porous media matching desired properties.

The approach shortens the time to generate media with specific permeability.

Abstract

Porous materials are used in many fields, including energy industry, agriculture, medical industry, etc. The generation of digital porous media facilitates the fabrication of real porous media and the analysis of their properties. The past random digital porous media generation methods are unable to generate a porous medium with a specific permeability. A new method is proposed in the present study, which can generate the random packed isotropic porous media with specific porosity and permeability. Firstly, the process of generating the random packed isotropic porous media is detailed. Secondly, the permeability of the generated porous media is calculated with the multi-relaxation time (MRT) lattice Boltzmann method (LBM), which is prepared for the training of convolutional neural network (CNN). Thirdly, 3000 samples on the microstructure of porous media and their permeabilities are used to train the CNN model. The trained model is very effective in predicting the permeability of a porous medium. Finally, our method is elaborated and the choice of target permeability in this method is discussed. With the support of a powerful computer, a porous medium that satisfies the error condition of porosity and permeability can be generated in a short time.