LAPSE:2023.22395
Published Article
LAPSE:2023.22395
A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm
March 24, 2023
Abstract
Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets.
Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets.
Record ID
Keywords
anode-supported solid oxide fuel cell, artificial neural network, multi-armed bandit algorithm, Optimization
Suggested Citation
Song C, Lee S, Gu B, Chang I, Cho GY, Baek JD, Cha SW. A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm. (2023). LAPSE:2023.22395
Author Affiliations
Song C: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Lee S: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea [ORCID]
Gu B: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Chang I: Department of Mechanical and Automotive Engineering, Won-Kwang University, 460 Iksan-daero, Iksan, Jeonbuk 54538, Korea
Cho GY: Department of Mechanical Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do 16890, Korea
Baek JD: Department of Automotive Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 38541, Korea
Cha SW: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Lee S: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea [ORCID]
Gu B: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Chang I: Department of Mechanical and Automotive Engineering, Won-Kwang University, 460 Iksan-daero, Iksan, Jeonbuk 54538, Korea
Cho GY: Department of Mechanical Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do 16890, Korea
Baek JD: Department of Automotive Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 38541, Korea
Cha SW: Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Journal Name
Energies
Volume
13
Issue
7
Article Number
E1621
Year
2020
Publication Date
2020-04-02
ISSN
1996-1073
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PII: en13071621, Publication Type: Journal Article
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LAPSE:2023.22395
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https://doi.org/10.3390/en13071621
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Mar 24, 2023
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