Experiment-based simulation of a cross-flow large-scale SOFC model

Abstract

The multi-physical field full-coupling simulation of solid oxide fuel cell (SOFC) stack requires huge computational resources. Repeated iteration of highly non-linear calculation is easy to cause oscillation and lead to solution failure. At present, the simulation of SOFC stack models mainly focuses on the co-flow condition and counter-flow condition models. Most of them are simplified models that simplify the stack scale or physical field. In this paper, a SOFC decoupling model based on machine learning is established, and the full three-dimensional and multi-physical fields of the cross-flow large-scale SOFC stack are simulated. The model is divided into three parts for calculation, unit cell model, alternative mapping model, and cross-flow large-scale SOFC stack model. The alternative mapping model obtained by the BP neural network algorithm replaces the nonlinear multi-physics equations in the traditional model. Compared with the traditional method, the decoupling model can greatly reduce the computing resources and improve the stability of computing. In this paper, the experimental data of the single cell and the 30-layer stack are used to calibrate and verify the simulation results of stack. Studying the performance of the SOFC stack under different parameter conditions. Temperature, flow uniformity, gas mole fraction, and voltage distribution in the SOFC stack under different inlet flow rates and stack currents are obtained. Obtaining the output power and fuel utilization rate of the stack under different working conditions.