Optimization of a Membraneless Microfluidic Fuel Cell with a Double-Bridge Flow Channel

In this work, a design optimization study was conducted to improve the performance of a membraneless microfluidic fuel cell with a double-bridge cross-section of the flow channel. Governing equations including Navier–Stokes, mass-transport, and Butler–Volmer equations were solved numerically to analyze the electrochemical phenomena and evaluate the performance of the fuel cells. Optimization was performed to maximize the peak power density using a genetic algorithm combined with a surrogate model constructed by radial basis neural network. Two sub-channel widths of the flow channel were selected as design variables for the optimization. As a result, a large increase in the inner channel width and a small decrease in the outer channel width effectively increased the peak power density of the MMFC. The optimal design increased the peak power density by 57.6% compared to the reference design.