Overcoming a recent impasse in the application of artificial neural networks as solid oxide fuel cells simulator with computational topology  

作　　者：Grzegorz Brus 

机构地区：[1]AGH University of Krakow,Faculty of Energy and Fuels,Department of Fundamental Research in Energy Engineering,30 Mickiewicza Ave.,30059 Krakow,Poland

出　　处：《Energy and AI》2023年第4期451-462,共12页能源与人工智能（英文）

摘　　要：In recent years,the solid oxide fuel cell(SOFC)scientific community has invested continuous efforts to employ artificial intelligence methods to design and develop new energy systems.It is crucial to gain a better understanding of the microscale phenomena that occur in the electrodes.In this review,we present a literature review of the field,discussing the limitations of including microstructural data in existing research and possible research directions to overcome them.This review focuses on a particular research area that uses artificial neural networks(ANNs)to predict the performance of SOFCs.Herein,we show that neural networks are used not only to conform to the newest trends but also for improving the design and providing a better understanding of microscale phenomena that occur in the electrodes.The review concludes by highlighting topological data analysis as a promising area of research that can incorporate detailed microstructure characterization in ANNs for performance prediction.

关 键 词：Solid oxide fuel cells Artificial neural networks Mathematical modeling Topological data analysis 

分 类 号：TG6[金属学及工艺—金属切削加工及机床]