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Thermo-mechanical optimization of thermoelectric generators using deep learning artificial intelligence algorithms fed with verified finite element simulation data

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  • Maduabuchi, Chika

Abstract

The rising levels of global warming in the environment owing to emissions from fossil-fuel-based engines has increased the search for efficient clean energy systems. Thermoelectric generators (TEGs) standout as a promising energy conversion device which can directly convert heat to electricity. Several optimization studies have been carried out on these devices to improve their power generation rate and efficiencies while guaranteeing long lifespan. However, the limitations of finite element methods (FEMs) in easily providing optimization guidelines at a fast rate has hindered the manufacture of TEGs with high thermo-mechanical performance.

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  • 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).
    • Handle: RePEc:eee:appene:v:315:y:2022:i:c:s0306261922003622
    DOI: 10.1016/j.apenergy.2022.118943
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Alexander Vargas-Almeida & Miguel Angel Olivares-Robles & Andres Alfonso Andrade-Vallejo, 2023. "Design of Thermoelectric Generators and Maximum Electrical Power Using Reduced Variables and Machine Learning Approaches," Energies, MDPI, vol. 16(21), pages 1-27, October.
    2. Xu, Aoqi & Xie, Changjun & Xie, Liping & Zhu, Wenchao & Xiong, Binyu & Gooi, Hoay Beng, 2024. "Performance prediction and optimization of annular thermoelectric generators based on a comprehensive surrogate model," Energy, Elsevier, vol. 290(C).
    3. Wang, Z.H. & Ma, Y.J. & Tang, G.H. & Zhang, Hu & Ji, F. & Sheng, Q., 2023. "Integration of thermal insulation and thermoelectric conversion embedded with phase change materials," Energy, Elsevier, vol. 278(C).
    4. 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.
    5. Chika Maduabuchi & Chinedu Nsude & Chibuoke Eneh & Emmanuel Eke & Kingsley Okoli & Emmanuel Okpara & Christian Idogho & Bryan Waya & Catur Harsito, 2023. "Renewable Energy Potential Estimation Using Climatic-Weather-Forecasting Machine Learning Algorithms," Energies, MDPI, vol. 16(4), pages 1-20, February.
    6. 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).
    7. Sabina-Cristiana Necula, 2023. "Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review," Energies, MDPI, vol. 16(22), pages 1-24, November.
    8. 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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