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

Wei Sun, Pengfei Wen (), Sijie Zhu and Pengcheng Zhai
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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

Energies, 2024, vol. 17, issue 9, 1-13

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.

Keywords: thermoelectric generator; muti-objective optimization; neural network; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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