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Geometrical Optimization of Segmented Thermoelectric Generators (TEGs) Based on Neural Network and Multi-Objective Genetic Algorithm

Author

Listed:
  • Wei Sun

    (Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Wuhan University of Technology, Wuhan 430070, China)

  • Pengfei Wen

    (Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Wuhan University of Technology, Wuhan 430070, China)

  • Sijie Zhu

    (Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Wuhan University of Technology, Wuhan 430070, China)

  • Pengcheng Zhai

    (Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Wuhan University of Technology, Wuhan 430070, China
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology, Wuhan 430070, China)

Abstract

In this study, a neural network and a multi-objective genetic algorithm were used to optimize the geometric parameters of segmented thermoelectric generators (TEGs) with trapezoidal legs, including the cold end width of thermoelectric (TE) legs ( W c ), the ratios of cold-segmented length to the total lengths of the n- and p-legs ( S n , c and S p , c ), and the width ratios of the TE legs between the hot end and the cold end of the n- and p-legs ( K n and K p ). First, a neural network with high prediction accuracy was trained based on 5000 sets of parameters and the corresponding output power values of the TEGs obtained from finite element simulations. Then, based on the trained neural network, the multi-objective genetic algorithm was applied to optimize the geometric parameters of the segmented TEGs with the objectives of maximizing the output power ( P ) and minimizing the semiconductor volume ( V ). The optimal geometric parameters for different semiconductor volumes were obtained, and their variations were analyzed. The results indicated that the optimal S n , c , S p , c , K n , and K p remained almost unchanged when V increased from 52.8 to 216.2 mm 3 for different semiconductor volumes. This work provides practical guidance for the design of segmented TEGs with trapezoidal legs.

Suggested Citation

  • Wei Sun & Pengfei Wen & Sijie Zhu & Pengcheng Zhai, 2024. "Geometrical Optimization of Segmented Thermoelectric Generators (TEGs) Based on Neural Network and Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 17(9), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2094-:d:1384351
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    References listed on IDEAS

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    1. Wang, Yancheng & Shi, Yaoguang & Mei, Deqing & Chen, Zichen, 2018. "Wearable thermoelectric generator to harvest body heat for powering a miniaturized accelerometer," Applied Energy, Elsevier, vol. 215(C), pages 690-698.
    2. Ge, Ya & Liu, Zhichun & Sun, Henan & Liu, Wei, 2018. "Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm," Energy, Elsevier, vol. 147(C), pages 1060-1069.
    3. Zhu, Yuxiao & Newbrook, Daniel W. & Dai, Peng & de Groot, C.H. Kees & Huang, Ruomeng, 2022. "Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator," Applied Energy, Elsevier, vol. 305(C).
    4. Shittu, Samson & Li, Guiqiang & Zhao, Xudong & Ma, Xiaoli, 2020. "Review of thermoelectric geometry and structure optimization for performance enhancement," Applied Energy, Elsevier, vol. 268(C).
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