Optimization of SOFC stack gas distribution structure based on BP Neural network and CFD

• Published in: E3S Web of Conferences Vol. 245; p. 3007
• Main Authors: Zhang, Yuhao, Xiong, Xingyu, Wu, Xin, Song, Zhonghui, Xue, Zhenzhong
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2021
• Subjects: Back propagation networks; Buffers; CAD; Chambers; Computer aided design; Fuel technology; Heat loss; Mathematical models; Neural networks; Nuclear fuels; Optimization; Outlet flow; Solid oxide fuel cells; Surface area
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202124503007

The flow field distribution of solid oxide fuel cells significantly affects the performance of the stack. The flow uniformity can be improved and the power generation efficiency can be improved by optimizing the gas distribution structure of the stack. Based on the simplified 6kW stack model, the stack gas distribution structure with two-stage buffer cavity was designed, and the stack model was numerically simulated by ANSYS Fluent software. The BP neural network model, which can predict the uniformity of the outlet of the integrated stack, is established successfully. The parameters of the gas distribution structure are analyzed and optimized by using the orthogonal test and BP neural network. The results show that at the same time considering pile distribution structure under the condition of surface area and uniformity, when the first stage inlet buffer chamber depth is 40 mm, the channel width is 40 mm, the secondary inlet buffer chamber depth is 80 mm, can effectively reduce the electric pile distribution structure, surface area, to reduce heat loss, at the same time guarantee the integrated electric reactor outlet flow uniformity of more than 96%, greatly improves the efficiency of power generation.