Journal
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 47, Issue 82, Pages 35038-35054Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2022.08.077
Keywords
Proton exchange membrane fuel cell; Structural optimization; Latin hypercube sampling; Artificial neural network; Computational fluid dynamic; Genetic algorithm
Categories
Funding
- Shandong Provincial Natural Science Foundation of China
- National Natural Science Foundation of China
- [ZR2019MEE045]
- [62192753]
Ask authors/readers for more resources
This study proposes a systematic methodology combining CFD, ANN, and intelligent optimization algorithms to improve the performance of PEMFC. The improved flow field structure optimized by multi-objective optimization can enhance the maximum power density and oxygen uniformity of PEMFC. The use of trained ANN surrogate models achieves high prediction precision.
This study proposes a systematic methodology for improving PEMFC's performance combining computational fluid dynamic (CFD), artificial neural network (ANN), and intel-ligent 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.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available