Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (6): 76-82.doi: 10.6040/j.issn.1672-3961.0.2024.333

• 能动工程——热管理专题 • Previous Articles    

Optimisation of finned tube structure based on BP neural network and genetic algorithm

SHAO Mengwei1, YUAN Shifei2, ZHOU Hongzhi2, WANG Naihua2*   

  1. SHAO Mengwei1, YUAN Shifei2, ZHOU Hongzhi2, WANG Naihua2*(1. Shandong Shuilongwang Scicence and Technology Co., Ltd., Jinan 250301, Shandong, China;
    2. Institute of Thermal Science and Technology, Shandong University, Jinan 250061, Shandong, China
  • Published:2025-12-22

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Abstract: To enhance the comprehensive performance of the heat pipe heat exchanger, the finned tube structure in a thermosyphon evaporator for nuclear power plant passive cooling systems was optimized using the hybrid back propagation(BP)neural network prediction model and non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ). Six structural parameters, such as fin thickness, spacing, height, transverse tube pitch, longitudinal tube pitch, and aspect ratio, were selected as design variables. Predictive models for the Nusselt number, pressure drop, and minimum cross-sectional airflow velocity were developed, enabling multi-objective optimization targeting maximum heat transfer and minimum flow resistance. The optimal configuration(1 mm fin thickness, 6 mm spacing, 5 mm height, 70 mm transverse tube pitch, 75 mm longitudinal tube pitch, 1.4 aspect ratio)resulted in a 25.86% increase in the heat factor, a 17.96% reduction in the flow resistance, and a 35.24% improvement in the overall performance coefficient. These results validated the effectiveness of the combined BP neural network and genetic algorithm for heat pipe design, providing both critical parameters and theoretical guidance for engineering optimization of nuclear power plant passive cooling systems.

Key words: BP neural network, non-dominated sorting genetic algorithm, passive cooling system, thermosyphon, multi-objective optimization

CLC Number: 

  • TK172
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