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Predicting the Optimal Performance of a Concentrated Solar Segmented Variable Leg Thermoelectric Generator Using Neural Networks

Author

Listed:
  • Chika Maduabuchi

    (Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

  • Hassan Fagehi

    (Department of Mechanical Engineering, College of Engineering, Jazan University, Jazan 45142, Saudi Arabia)

  • Ibrahim Alatawi

    (Mechanical Engineering Department, Engineering College, University of Ha’il, Ha’il 81451, Saudi Arabia)

  • Mohammad Alkhedher

    (Department of Mechanical and Industrial Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates)

Abstract

The production of high-performing thermoelectrics is limited by the high computational energy and time required by the current finite element method solvers that are used to analyze these devices. This paper introduces a new concentrating solar thermoelectric generator made of segmented materials that have non-uniform leg geometry to provide high efficiency. After this, the optimum performance of the device is obtained using the finite element method conducted using ANSYS software. Finally, to solve the high energy and time requirements of the conventional finite element method, the data generated by finite elements are used to train a regressive artificial neural network with 10 neurons in the hidden layer. Results are that the power and efficiency obtained from the optimized device design are 3× and 2× higher than the original unoptimized device design. Furthermore, the developed neural network has a high accuracy of 99.95% in learning the finite element data. Finally, the neural network predicts the modified device performance about 800× faster than the conventional finite element method. Overall, the paper provides insights into how thermoelectric manufacturing companies can harness the power of artificial intelligence to design very high-performing devices while saving time and cost.

Suggested Citation

  • Chika Maduabuchi & Hassan Fagehi & Ibrahim Alatawi & Mohammad Alkhedher, 2022. "Predicting the Optimal Performance of a Concentrated Solar Segmented Variable Leg Thermoelectric Generator Using Neural Networks," Energies, MDPI, vol. 15(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6024-:d:892807
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    References listed on IDEAS

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    Cited by:

    1. Lan, Yuncheng & Lu, Junhui & Wang, Suilin, 2023. "Study of the geometry and structure of a thermoelectric leg with variable material properties and side heat dissipation based on thermodynamic, economic, and environmental analysis," Energy, Elsevier, vol. 282(C).
    2. Feng, Mengqi & Lv, Song & Deng, Jingcai & Guo, Ying & Wu, Yangyang & Shi, Guoqing & Zhang, Mingming, 2023. "An overview of environmental energy harvesting by thermoelectric generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    3. Alghamdi, Hisham & Maduabuchi, Chika & Okoli, Kingsley & Albaker, Abdullah & Alatawi, Ibrahim & Alghassab, Mohammed & Albalawi, Hind & Alkhedher, Mohammad, 2024. "Performance optimization of nanofluid-cooled photovoltaic-thermoelectric systems: A study on geometry configuration, steady-state and annual transient effects," Energy, Elsevier, vol. 296(C).
    4. Demeke, Wabi & Ryu, Byungki & Ryu, Seunghwa, 2024. "Machine learning-based optimization of segmented thermoelectric power generators using temperature-dependent performance properties," Applied Energy, Elsevier, vol. 355(C).

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