Froning, D.; Hoppe, E.; Peters, R. The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers. Appl. Sci.2023, 13, 6981.
Froning, D.; Hoppe, E.; Peters, R. The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers. Appl. Sci. 2023, 13, 6981.
Froning, D.; Hoppe, E.; Peters, R. The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers. Appl. Sci.2023, 13, 6981.
Froning, D.; Hoppe, E.; Peters, R. The Applicability of Machine Learning Methods to the Characterization of Fibrous Gas Diffusion Layers. Appl. Sci. 2023, 13, 6981.
Abstract
Porous materials can be characterized by well-trained neural networks. In this study, fibrous paper-type gas diffusion layers were trained with artificial data created by a stochastic geometry model. The features of the data were calculated by means of transport simulations using the Lattice–Boltzmann method based on stochastic micro-structures. A convolutional neural network was developed that can predict the permeability and tortuosity of the material, through-plane and in-plane. The characteristics of real data, both uncompressed and compressed, were predicted. The data was represented by reconstructed images of different sizes and image resolutions. Image artifacts are also a source of potential errors in the prediction. The Kozeny–Carman trend was used to evaluate the prediction of permeability and tortuosity of compressed real data. Using this method, it was possible to decide if the predictions on compressed data were appropriate.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
