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Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator

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  • Zhu, Yuxiao
  • Newbrook, Daniel W.
  • Dai, Peng
  • de Groot, C.H. Kees
  • Huang, Ruomeng

Abstract

The ever-increasing demand for renewable energy and zero carbon dioxide emission have been the driving force for the development of thermoelectric generators with better power generation performance. Alongside with the effort to discover thermoelectric materials with higher figure-of-merit, the geometrical and structural optimisation of thermoelectric generators are also essential for maximized power generation and efficiency. This work demonstrates for the first time the application of artificial neural network, a deep learning technique, in forward modelling the maximum power generation and efficiency of a thermoelectric generator and its application in the generator design and optimisation. After training using a dataset containing 5000 3-D finite element method based simulations, the artificial neural networks with 5 layers and 400 neurons per layer demonstrate extremely high prediction accuracy over 98% and are able to operate under both constant temperature difference and heat flux conditions while taking into account of the contact electrical resistance, surface heat transfer and other thermoelectric effects. Coupling with genetic algorithm, the trained artificial neural networks can optimise the leg height, leg width, fill factor and interconnect height of the thermoelectric generator for different operating and contact resistance conditions. With almost identical optimised values obtained, our neural networks can realise geometrical optimisation within 40 s for each operating condition, which is averagely over 1,000 times faster than the optimisation performed by finite element method. The up-front computational time for the neural network can be recovered when more than 2 optimisations are needed. The successful application of this data-driven approach in this work clearly represents a new and cost-effective avenue for conducting system level design and optimisation of thermoelectric generators and other energy harvesting technologies.

Suggested Citation

  • Zhu, Yuxiao & Newbrook, Daniel W. & Dai, Peng & de Groot, C.H. Kees & Huang, Ruomeng, 2022. "Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s030626192101134x
    DOI: 10.1016/j.apenergy.2021.117800
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    1. Zhu, Lei & Li, Huaqi & Chen, Sen & Tian, Xiaoyan & Kang, Xiaoya & Jiang, Xinbiao & Qiu, Suizheng, 2020. "Optimization analysis of a segmented thermoelectric generator based on genetic algorithm," Renewable Energy, Elsevier, vol. 156(C), pages 710-718.
    2. Liu, Zhichun & Zhu, Shiping & Ge, Ya & Shan, Feng & Zeng, Lingping & Liu, Wei, 2017. "Geometry optimization of two-stage thermoelectric generators using simplified conjugate-gradient method," Applied Energy, Elsevier, vol. 190(C), pages 540-552.
    3. Gou, Xiaolong & Xiao, Heng & Yang, Suwen, 2010. "Modeling, experimental study and optimization on low-temperature waste heat thermoelectric generator system," Applied Energy, Elsevier, vol. 87(10), pages 3131-3136, October.
    4. Shittu, Samson & Li, Guiqiang & Zhao, Xudong & Ma, Xiaoli, 2020. "Review of thermoelectric geometry and structure optimization for performance enhancement," Applied Energy, Elsevier, vol. 268(C).
    5. Chen, Wei-Hsin & Chiou, Yi-Bin, 2020. "Geometry design for maximizing output power of segmented skutterudite thermoelectric generator by evolutionary computation," Applied Energy, Elsevier, vol. 274(C).
    6. Chen, Wei-Hsin & Lin, Yi-Xian & Wang, Xiao-Dong & Lin, Yu-Li, 2019. "A comprehensive analysis of the performance of thermoelectric generators with constant and variable properties," Applied Energy, Elsevier, vol. 241(C), pages 11-24.
    7. Ge, Ya & Liu, Zhichun & Sun, Henan & Liu, Wei, 2018. "Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm," Energy, Elsevier, vol. 147(C), pages 1060-1069.
    8. Chen, Wei-Hsin & Wu, Po-Hua & Lin, Yu-Li, 2018. "Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 209(C), pages 211-223.
    9. Anh Tuan Duong & Van Quang Nguyen & Ganbat Duvjir & Van Thiet Duong & Suyong Kwon & Jae Yong Song & Jae Ki Lee & Ji Eun Lee & SuDong Park & Taewon Min & Jaekwang Lee & Jungdae Kim & Sunglae Cho, 2016. "Achieving ZT=2.2 with Bi-doped n-type SnSe single crystals," Nature Communications, Nature, vol. 7(1), pages 1-6, December.
    10. Suter, C. & Jovanovic, Z.R. & Steinfeld, A., 2012. "A 1kWe thermoelectric stack for geothermal power generation – Modeling and geometrical optimization," Applied Energy, Elsevier, vol. 99(C), pages 379-385.
    11. Gangjian Tan & Fengyuan Shi & Shiqiang Hao & Li-Dong Zhao & Hang Chi & Xiaomi Zhang & Ctirad Uher & Chris Wolverton & Vinayak P. Dravid & Mercouri G. Kanatzidis, 2016. "Non-equilibrium processing leads to record high thermoelectric figure of merit in PbTe–SrTe," Nature Communications, Nature, vol. 7(1), pages 1-9, November.
    12. Li-Dong Zhao & Shih-Han Lo & Yongsheng Zhang & Hui Sun & Gangjian Tan & Ctirad Uher & C. Wolverton & Vinayak P. Dravid & Mercouri G. Kanatzidis, 2014. "Ultralow thermal conductivity and high thermoelectric figure of merit in SnSe crystals," Nature, Nature, vol. 508(7496), pages 373-377, April.
    13. Shen, Zu-Guo & Wu, Shuang-Ying & Xiao, Lan & Yin, Gang, 2016. "Theoretical modeling of thermoelectric generator with particular emphasis on the effect of side surface heat transfer," Energy, Elsevier, vol. 95(C), pages 367-379.
    14. Chen, Wei-Hsin & Huang, Shih-Rong & Lin, Yu-Li, 2015. "Performance analysis and optimum operation of a thermoelectric generator by Taguchi method," Applied Energy, Elsevier, vol. 158(C), pages 44-54.
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    4. Zhixing Li & Mimi Tian & Yafei Zhao & Zhao Zhang & Yuxi Ying, 2021. "Development of an Integrated Performance Design Platform for Residential Buildings Based on Climate Adaptability," Energies, MDPI, vol. 14(24), pages 1-44, December.
    5. 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).
    6. 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).
    7. R. S. Varun Kumar & M. D. Alsulami & I. E. Sarris & B. C. Prasannakumara & Saurabh Rana, 2023. "Backpropagated Neural Network Modeling for the Non-Fourier Thermal Analysis of a Moving Plate," Mathematics, MDPI, vol. 11(2), pages 1-32, January.
    8. 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.
    9. 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).
    10. Nidhal Ben Khedher & Fatih Selimefendigil & Lioua Kolsi & Walid Aich & Lotfi Ben Said & Ismail Boukholda, 2022. "Performance Optimization of a Thermoelectric Device by Using a Shear Thinning Nanofluid and Rotating Cylinder in a Cavity with Ventilation Ports," Mathematics, MDPI, vol. 10(7), pages 1-20, March.
    11. Xuan, Zhiwei & Ge, Minghui & Zhao, Chenyang & Li, Yanzhe & Wang, Shixue & Zhao, Yulong, 2024. "Effect of nonuniform solar radiation on the performance of solar thermoelectric generators," Energy, Elsevier, vol. 290(C).
    12. 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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