Data-driven multi-objective optimization of flow field header design for PEM fuel cells

Optimizing reactant distribution in flow field plates is critical for proton exchange membrane (PEM) fuel cell performance. In this study, cathode flow field header designs are explored using a hybrid framework that integrates computational fluid dynamics (CFD), artificial neural networks (ANN), and the non-dominated sorting genetic algorithm II (NSGA-II). Twenty-seven CFD simulations, generated by varying header design, including its porosity (the ratio of fluid area with obstacles to that without obstacles), header size, and obstacle size, provide the dataset for training a multi-input multi-output surrogate ANN model, whereas the number and dimensions of flow channels in the active area are fixed to isolate header effects, with identical inlet and outlet headers to reduce design complexity, maintain symmetry, and ensure consistence and comparability. The trained model achieves high accuracy (R 2 = 0.999) and enables rapid evaluation of design alternatives. Multi-objective optimization through NSGA-II yields a Pareto front balancing flow uniformity and pressure drop. The optimized design achieves flow uniformity >92 % with a pressure drop of ∼1900 Pa, closely matching the CFD simulation outcomes. This integrated, data-driven approach lowers computational cost, accelerates header design exploration, and offers a practical pathway for advancing PEM fuel cell technology toward commercialization. • CFD-ANN-NSGA-II framework optimizes PEM fuel cell cathode header design. • Dataset of 27 CFD cases trains a high-accuracy ANN surrogate model (R 2 = 0.999). • NSGA-II optimization balances flow uniformity (>92 %) and pressure drop (∼1900 Pa). • Knee-point solution shows <1 % error compared to CFD validation results. • Developed framework cuts computational cost and aids scalable PEM fuel cell design.