Study on the linkages between microstructure and permeability of porous media using pore network and BP neural network

Abstract

In applying porous media air bearings (PMABs), designing the pore microstructure of porous media to obtain the desired permeability is challenging. The key parameters in this design are to map the pore microstructure characteristics to permeability and adapt to manufacturing process with the characteristics. For this purpose, a framework is proposed to characterize pore microstructure with morphology descriptor and predict permeability. 3D digital images of porous media are obtained using X-ray micro-computed tomography and various image construction techniques. The complex pore microstructure of porous media is represented with a pore network. Permeability is calculated based on the pore network. Sixteen pore microstructure morphology descriptors are initially calculated to characterize pore microstructure. A back-propagation neural network (BPNN) is built to learn the correlation between morphology descriptors and permeability. Pearson correlation coefficient (PCC) and feature importance scores of morphology descriptors are obtained based on the dataset and trained BPNN. The results demonstrate that the prediction performance of BPNN is excellent. The following six morphology descriptors (porosity, coordination number, average pore diameter, average throat diameter, average pore throat ratio, average throat length) are reserved to characterize pore microstructure. Finally, two types of pore microstructure are designed with the help of knowledge obtained by this research.