计算流体力学
遗传算法
人工神经网络
质子交换膜燃料电池
替代模型
计算机科学
拉丁超立方体抽样
采样(信号处理)
算法
流量(数学)
功率(物理)
模拟
工程类
人工智能
燃料电池
机器学习
机械
数学
化学工程
物理
热力学
滤波器(信号处理)
统计
航空航天工程
计算机视觉
蒙特卡罗方法
作者
Zeting Yu,Lei Xia,Guoping Xu,Changjiang Wang,Daohan Wang
标识
DOI:10.1016/j.ijhydene.2022.08.077
摘要
This study proposes a systematic methodology for improving PEMFC's performance combining computational fluid dynamic (CFD), artificial neural network (ANN), and intelligent optimization algorithms. Firstly, a three-dimensional (3-D) multiphase PEMFC CFD model with 3-D fine-mesh flow field is developed. Then the key structural features of the fine-mesh flow field are extracted as optimization decision variables, and the sampling points are selected by using the Latin hypercube sampling (LHS) experimental method. The power density and oxygen uniformity index of sampling points are calculated by CFD modeling to form the database, which is used to train the artificial neural network (ANN) surrogate model. Finally, the single-objective optimization (SOO) and multi-objective optimization (MOO) are implemented by using genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II), respectively. It was found that using trained ANN surrogate models can get a high prediction precision. The maximum power density of SOO is increased by 7.546% than that of base case and is 0.562% larger than that of MOO case. However, the overall pressure drop in cathode flow field of SOO case is greater than that of MOO case and the base case. Furthermore, the oxygen concentration, the oxygen uniformity index and the water removal capacity of MOO case are better than that of SOO case. It is recommended that the improved flow field structure optimized by MOO is more beneficial to improve the overall performance of PEMFC.
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