Abstract With the increasing demand for hydrogen fuel cell vehicles, Roots hydrogen circulation pumps play a crucial role in ensuring efficiency and reliability of hydrogen fuel cell systems. Traditional optimization methods for Roots pumps struggle to balance multiple objectives effectively during parameter optimization. This research integrates Gaussian Process Regression (GPR) with Particle Swarm Optimization (PSO), applying this optimization framework to the multi-objective parameter optimization of a Roots hydrogen recirculation pump. Flow rate and power were selected as optimization objectives, while optimization variables—including diameter–distance ratio, three different clearances, and rotating speed—were investigated. A training database was constructed using greedy algorithm optimized Latin hypercube sampling. And GPR was validated as surrogate model compared with radial basic function. Multi-objective particle swarm optimization (PSO) was then applied to obtain Pareto solutions, from which the TOPSIS method selected the optimal solution. The optimized rotor achieved a 24% increase in flow rate, a 21% reduction in flow pulsation, and stable shaft power. Flow field analysis confirmed reduced leakage and vortex formation, improving volumetric efficiency and flow stability. The PSO–GPR optimization method used in this research demonstrates high accuracy and reliability, providing an effective approach for enhancing hydrogen circulation pump design and offering valuable guidance for future fuel cell system applications.
Liu et al. (Fri,) studied this question.