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Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Author

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  • Ravi Anant Kishore

    (Center for Energy Harvesting Materials and Systems (CEHMS), Virginia Tech, Blacksburg, VA 24061, USA)

  • Roop L. Mahajan

    (Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
    Institute for Critical Technology and Applied Science (ICTAS), 325 Stanger Street, Blacksburg, VA 24061, USA)

  • Shashank Priya

    (Center for Energy Harvesting Materials and Systems (CEHMS), Virginia Tech, Blacksburg, VA 24061, USA
    Materials Research Institute, Penn State, University Park, PA 16802, USA)

Abstract

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

Suggested Citation

  • Ravi Anant Kishore & Roop L. Mahajan & Shashank Priya, 2018. "Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator," Energies, MDPI, vol. 11(9), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2216-:d:165601
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    2. Maduabuchi, Chika, 2022. "Thermo-mechanical optimization of thermoelectric generators using deep learning artificial intelligence algorithms fed with verified finite element simulation data," Applied Energy, Elsevier, vol. 315(C).
    3. 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.
    4. 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).
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    6. 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).
    7. Tae Young Kim, 2021. "Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks," Energies, MDPI, vol. 14(9), pages 1-14, April.

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