Performance prediction and geometry optimization of ejector in PEMFC system using coupled CFD-BPNN and genetic algorithm

The ejector is a promising hydrogen recirculation device in proton exchange membrane fuel cell (PEMFC) systems. However, the limited entrainment performance of the ejector at wide operating conditions hinders its development and widespread application in PEMFC systems. To address this challenge and design a high-performance ejector adapted to the wide power range of PEMFC systems, a backpropagation neural network (BPNN) model with computational fluid dynamics (CFD) simulation data is developed. The sensitivity analysis of geometric parameters based on the CFD model reveals that the nozzle throat diameter and the mixing chamber diameter exert the most pronounced impact on the entrainment performance, with average influence rates of 24% and 57%, respectively. Furthermore, two advanced multi-objective genetic algorithms are applied to improve ejector performance. The linear weighted genetic algorithm (LWGA) method proves effective in elevating the overall ejector performance, achieving a remarkable enhancement in entrainment performance of up to 7.9%. On the other hand, the non-dominated sorting genetic algorithm (NSGA- II) method is favorable for expanding the operational power range of the ejector by 30%, as well as a 4.5% increase in overall ejector performance. This work provides a robust framework for designing and optimizing high-performance ejectors in high-power PEMFC systems.