Pore-scale analysis of clamping force effects on microstructure and transport properties of PEMFC gas diffusion layers
Ning Zhang, Wenshang Chen, Qihao Deng, Ben Chen
Abstract
• The GDL compression process is simulated using a random reconstruction algorithm coupled with a finite element model. • A sensitivity analysis of compression speed on GDL behavior is conducted. • The stress and strain of GDL under compression are analyzed at the pore-scale level. • The effects of compression on GDL pore structure and mass transfer properties are investigated. Appropriate clamping force ensures good contact between the membrane electrode assembly (MEA) and the bipolar plates, reducing contact resistance and thereby improving the efficiency of electrochemical reactions and the cell performance. This study systematically investigates the influence of clamping force on the microstructure and transport properties of gas diffusion layers (GDLs) through an integrated approach combining advanced characterization techniques and numerical simulations. The GDLs were generated by reconstruction algorithm, and finite element models were developed to simulate GDL deformation under varying clamping forces. The compressed GDLs were processed using Boolean operations to extract fluid domains for establishing mass transport models. The results demonstrated that 0.3 m/s represents an optimal compression speed for achieving quasi-static conditions, and with the increase of compression ratio, significant reduction in GDL porosity (from 0.78 to 0.7) and average pore size (from 25.9 μm to 17.8 μm), while pore size distribution shifting toward smaller pores. Furthermore, enhanced stress concentrations, particularly at fiber intersections, with stress values reaching 200 MPa at 30 % compression ratio; and altered transport properties, including reduced liquid water permeation velocity and localized oxygen starvation under high compression ratio. These results highlight the profound impact of compression on GDL microstructure and transport properties, providing critical insights for optimizing fuel cell design and performance.