Journal of sustainable Energy Systems

Journal of sustainable Energy Systems

Particle Swarm Optimization of Dovetail Cross-Section Flow Channels in Proton Exchange Membrane Fuel Cells: A Multi-Objective Framework for Performance Enhancement

Document Type : Original Article

Authors
1 Mechanical Engineering, Energy Conversion, Alborz campus, University of Tehran, Tehran, Iran.
2 School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
10.22059/ses.2026.416931.1264
Abstract
In this paper, a comprehensive framework for optimizing the particle swarm optimization algorithm in proton exchange membrane fuel cells with a dovetail channel cross-section is presented. The main goal of this research is to present a multi-objective method for simultaneous optimization that includes increasing the current density, energy output, and water management efficiency, along with reducing pressure drop and parasitic losses. The input variables of the optimization model include two categories: (1) operating conditions (inlet velocity 1.0 to 3.0 m/s, operating temperature 323 to 353 K, operating pressure 1 to 3 atmospheres, and relative humidity 40 to 100%) and (2) channel geometry (channel depth, upper surface width 0.5 to 2.0 mm, lower surface width 0.8 to 2.5 mm, contact angle 5 to 30 degrees, and contact surface width 0.5 to 2.0 mm). The performance evaluation criteria include current density, voltage characteristics, power density, pressure drop, reactant distribution uniformity, and water management efficiency. The results show that the optimized dovetail channel design increased the average current density to 1215.3 A/m2, which is equivalent to a 24.8% improvement over the baseline. The power density also increased from 118.5 to 152.3 W/m2 and the water management efficiency from 77.8% to 92.3%. These improvements were achieved while the pressure drop increased by only 29.8%.
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Articles in Press, Accepted Manuscript
Available Online from 05 August 2026