The 10th Asian Symposium on Computational Heat Transfer and Fluid Flow ( ASCHT2025)

Abstract
Metal foam rib flow fields (MRFF) characterized by their inherent porous architecture demonstrates significant advantages in mass transfer compared to traditional flow fields, while also enabling the fabrication of integrated porous electrodes. This study presents a refined MRFF structure with a gradient porosity distribution, to optimize the reactant flow and water removal within the electrodes of proton exchange membrane fuel cells (PEMFCs). The coupled effects of five associated structural and operational parameters on PEMFC performance were systematically analyzed using response surface methodology (RSM). Furthermore, to improve the comprehensive performance of PEMFC, the five parameters were optimized using a multi-objective optimization (MOO) algorithm with oxygen concentration uniformity, average membrane water content, and net power output as the evaluation metrics. Derived from the three-dimensional PEMFC model, the dataset used for optimization was employed to develop the surrogate model using artificial neural network (ANN). Moreover, the Pareto front was obtained using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal solution within the solution set was selected using the technique for order preference by similarity to an ideal solution (TOPSIS). Relative to the reference case, enhancements of 10.74%, 1.42%, and 34.73% were achieved in the three performance indicators by the optimized design. Through systematic analysis, this article reveals how structural-operational parameter interactions affect MRFF-integrated PEMFC performance. The proposed optimization methodology and results provide valuable insights for enhancing PEMFC performance and refining operational control strategies.
 

PEMFC, Metal foam, Gradient porosity, Response surface methodology, Multi-objective optimization