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Deep neural networks for quick and precise geometry optimization of segmented thermoelectric generators

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  • Maduabuchi, Chika
  • Eneh, Chibuoke
  • Alrobaian, Abdulrahman Abdullah
  • Alkhedher, Mohammad

Abstract

To solve the problems of the current optimization methods for solar segmented thermoelectric generator performance based on numerical methods, this paper applied deep neural networks to optimize the device geometry for improved thermo-mechanical performance. The motivation for using the deep neural network is to overcome the lengthy computational time and very high computational energy required by the traditional numerical method in optimizing the segmented thermoelectric generator performance. The numerical model is built using ANSYS software and the effects of temperature dependency in the 4 thermoelectric materials are considered to ensure result accuracy. Furthermore, 16 possible geometry parameters which were previously not considered, encompassing the individual and combined segment's heights and cross-sectional areas are optimized to find which set of parameters are the best in maximizing the device performance. The deep neural network is a regressive multilayer perceptron with network hyperparameters comprising 2 hidden layers with 5 neurons per layer. The training process is governed by the Levenberg-Marquardt standard backpropagation algorithm to minimize the mean squared error and maximize the regression correlation between the neural network forecasted outputs and the numerical-generated dataset. The most significant contribution of the proposed deep neural network is that it was able to quickly and accurately forecast the device performance in just 10 s, which was 2880 times faster than the conventional numerical-based optimization approach. Additionally, the optimized device had a maximum efficiency of 18%, which was 78% higher than that of the unoptimized device. Also, the thermal stress of the optimized device was 73% less than that of the unoptimized device design, indicating an extension in the device mechanical reliability and service lifetime. The results reported in this paper will accelerate the ease at which efficient, long-lasting segmented thermoelectric generators are manufactured by harnessing the power of artificial intelligence.

Suggested Citation

  • Maduabuchi, Chika & Eneh, Chibuoke & Alrobaian, Abdulrahman Abdullah & Alkhedher, Mohammad, 2023. "Deep neural networks for quick and precise geometry optimization of segmented thermoelectric generators," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s036054422202775x
    DOI: 10.1016/j.energy.2022.125889
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    References listed on IDEAS

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    Cited by:

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    7. He, Hongxi & Xie, Yongchuan & Zuo, Qingsong & Chen, Wei & Shen, Zhuang & Ma, Ying & Zhang, Hehui & Zhu, Guohui & Ouyang, Yixuan, 2024. "Optimization analysis for thermoelectric performance improvement of biconical segmented annular thermoelectric generator," Energy, Elsevier, vol. 306(C).
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    9. Wen, Xin & Ji, Jie & Li, Zhaomeng & Yao, Tingting, 2023. "Proposing of a novel PV/T module in series with a ST+TE module to pursue a round-the-clock continuous energy output," Energy, Elsevier, vol. 285(C).

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