Nuclear Engineering and Technology

  • 제56권7호
  • /
  • Pages.2610-2624
  • /
  • 2024
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Operating condition optimization of liquid metal heat pipe using deep learning based genetic algorithm: Heat transfer performance

  • Ik Jae Jin (Department of Nuclear Engineering Ulsan National Institute of Science and Technology (UNIST)) ;
  • Dong Hun Lee (Department of Nuclear Engineering Ulsan National Institute of Science and Technology (UNIST)) ;
  • In Cheol Bang (Department of Nuclear Engineering Ulsan National Institute of Science and Technology (UNIST))
  • 투고 : 2023.09.25
  • 심사 : 2024.02.10
  • 발행 : 2024.07.25
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초록

Liquid metal heat pipes play a critical role in various high-temperature applications, with their optimization being pivotal to achieving optimal thermal performance. In this study, a deep learning based genetic algorithm is suggested to optimize the operating conditions of liquid metal heat pipes. The optimization performance was investigated in both single and multi-variable optimization schemes, considering the operating conditions of heat load, inclination angle, and filling ratio. The single-variable optimization indicated reasonable performance for various conditions, reinforcing the potential applicability of the optimization method across a broad spectrum of high-temperature industries. The multi-variable optimization revealed an almost congruent performance level to single-variable optimization, suggesting that the robustness of optimization method is not compromised with additional variables. Furthermore, the generalization performance of the optimization method was investigated by conducting an experimental investigation, proving a similar performance. This study underlines the potential of optimizing the operating condition of heat pipes, with significant consequences in sectors such as high temperature field, thereby offering a pathway to more efficient, cost-effective thermal solutions.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. 2021M2D2A1A03048950).

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