Coupled thickness optimization of PEM, CL, and GDL in Two-Phase PEMFCs using OpenFOAM-Based 3D CFD and Sensitivity-Guided ANN–GA surrogate modeling
6
Issued Date
2026-05-01
Resource Type
eISSN
25901745
Scopus ID
2-s2.0-105037950310
Journal Title
Energy Conversion and Management X
Volume
30
Rights Holder(s)
SCOPUS
Bibliographic Citation
Energy Conversion and Management X Vol.30 (2026)
Suggested Citation
Seanglumlert C., Boekfah A., Kamma P., Loksupapaiboon K., Chaiworapuek W., Punurai W., Promtong M., Suvanjumrat C. Coupled thickness optimization of PEM, CL, and GDL in Two-Phase PEMFCs using OpenFOAM-Based 3D CFD and Sensitivity-Guided ANN–GA surrogate modeling. Energy Conversion and Management X Vol.30 (2026). doi:10.1016/j.ecmx.2026.101912 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116704
Title
Coupled thickness optimization of PEM, CL, and GDL in Two-Phase PEMFCs using OpenFOAM-Based 3D CFD and Sensitivity-Guided ANN–GA surrogate modeling
Corresponding Author(s)
Other Contributor(s)
Abstract
Proton exchange membrane fuel cell (PEMFC) performance is governed by tightly coupled electrochemical reactions and multi-phase transport processes occurring within the membrane electrode assembly (MEA). In particular, the thicknesses of the proton exchange membrane (PEM), catalyst layer (CL), and gas diffusion layer (GDL) jointly control protonic resistance, catalyst utilization, oxygen accessibility, and liquid water evacuation. However, most available studies still optimize MEA layers independently, despite the fact that realistic PEMFC operation is dominated by nonlinear coupling between charge transport and two-phase water dynamics. In this work, a high-fidelity three-dimensional two-phase PEMFC model was developed in OpenFOAM using the openFuelCell2 framework to resolve multi-species transport, electrochemical kinetics, conjugate heat transfer, and multiphase water transport across all MEA layers. A systematic multi-parameter dataset was generated by simultaneously varying PEM thickness (25–200 μm), CL thickness (3–20 μm), and GDL thickness (150–350 μm) under fixed operating conditions. To enable efficient exploration of the nonlinear high-dimensional design space, a surrogate-assisted artificial neural network–genetic algorithm (ANN–GA) optimization framework was constructed. The ANN surrogate was trained using CFD-generated polarization data and subsequently coupled with a real-coded GA to maximize power density. The integrated optimization identified an optimal MEA configuration of t<inf>PEM</inf> = 25 μm, t<inf>CL</inf> = 14.0037 μm, and t<inf>GDL</inf> = 167.6356 μm, achieving a maximum predicted power density of 0.3785 W/cm<sup>2</sup> at 0.8733 A/cm<sup>2</sup>. High-fidelity CFD verification yielded 0.3748 W/cm<sup>2</sup>, corresponding to a deviation below 1.01%, confirming the reliability of the surrogate-driven optimization. Mechanistic analysis reveals that the optimum arises from the combined minimization of protonic ohmic losses (thin PEM), enhancement of electrochemically active reaction volume without severe oxygen diffusion limitation (moderate CL), and reduction of cathode-side mass transport resistance while suppressing excessive liquid water accumulation (thin-to-moderate GDL). The proposed CFD–ANN–GA framework provides a computationally scalable pathway for high-dimensional MEA structural optimization and offers quantitative design guidance for next-generation PEMFC development under two-phase operating conditions.