Artificial intelligence based structural optimization of solid oxide fuel cell with three-dimensional reticulated trapezoidal flow field

Abstract

Three-dimensional Reticulated trapezoidal flow field (RTFF) is promising in improving the performance and durability of the solid oxide fuel cell (SOFC). However, the structural complexity makes it challenging for the geometry configuration of the splitter and mixer. To this end, an intelligent optimization framework is proposed by coupling artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II), in order to maximize the net power density and oxygen uniformity simultaneously. The ANN prediction model is trained to obtain the computationally efficient surrogate model of the computational fluid dynamics (CFD) numerical simulation. NSGA-II is used for the multi-objective optimization of the RTFF structural parameters. The results illustrate that the prediction model is of high prediction precision and generalization capability. In comparison to SOFC with conventional parallel flow fields (CPFF), the degree of the performance improvement of SOFC with optimized RTFF depends on the working condition, i.e., fuel and air flow rates and operating temperatures. The SOFC with the optimal RTFF achieves a higher molar concentration of oxygen and a more uniform distribution of oxygen and current density than the CPFF SOFC. The proposed optimization framework provides an efficient design method for the development of the next-generation SOFC flow field.