Performance-based design of 2D gas diffusion layer microstructure using denoising diffusion probabilistic model

Abstract

This paper proposes a performance-based microstructure design methodology specifically tailored for gas diffusion layers (GDLs). The generative machine learning denoising diffusion probabilistic model (DDPM) is trained and used for performance-based microstructure design. This study demonstrates significant progress in performance-based design by incorporating DDPM into the microstructure design process, offering a promising approach for GDL microstructure design. This methodology provides the advantage of generating 2D microstructures with desired permeability and volume fractions. Moreover, it is not limited to a specific performance criterion, making it adaptable to target other metrics. To train the DDPM, a 2D GDL microstructure dataset is constructed using the Lattice Boltzmann Method (LBM) for permeability estimation. Subsequently, we employ the DDPM with U-net architecture, which leverages positional encoding to learn the microstructure’s volume fraction and permeability effectively. The input label pair of permeability and volume fraction is generated considering the inherent relationship between these two parameters to ensure the generation of meaningful microstructures. This relationship is supposed to ensure that the resulting microstructures align with realistic and physically meaningful characteristics. The simulated performance results obtained from the generated microstructures using the proposed methodology demonstrate a strong consistency with the targeted performance objectives.