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Volume 43 Issue S2
Dec.  2022
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Yu Pei, Hou Ting, Zhao Weiguang. Evaluation of Component Cooling System based on Optimization Algorithm in HPR1000[J]. Nuclear Power Engineering, 2022, 43(S2): 1-6. doi: 10.13832/j.jnpe.2022.S2.0001
Citation: Yu Pei, Hou Ting, Zhao Weiguang. Evaluation of Component Cooling System based on Optimization Algorithm in HPR1000[J]. Nuclear Power Engineering, 2022, 43(S2): 1-6. doi: 10.13832/j.jnpe.2022.S2.0001

Evaluation of Component Cooling System based on Optimization Algorithm in HPR1000

doi: 10.13832/j.jnpe.2022.S2.0001
  • Received Date: 2022-08-18
  • Rev Recd Date: 2022-10-07
  • Publish Date: 2022-12-31
  • Aiming at the problems of large design margin and low winter supply water temperature of cold chain systems such as equipment cooling water in power plants, an innovative design method is provided, which can be used to determine the system configuration and operation scheme in the overall design stage. Firstly, based on the basic principle of system heat balance, a thermal evaluation model is established, and an economic evaluation model is established based on the guiding ideology of improving economy as much as possible under safety and operating conditions. Then, based on the basic principle of optimization design, the multi-objective optimization algorithm and analysis program are developed. Finally, the above program is used to deal with the multi-objective optimization problems in the system design, and finally the system scheme evaluation is realized. The optimization evaluation results of the cooling water system of HPR1000 equipment are given, which significantly improves the economy of the power plant. It breaks through the traditional single-line design process which relies too much on design experience and is subject to insufficient quantitative analysis. In the adjustment of multi-discipline and multi-variable design scheme and parameter optimization, software decision-making is used to assist manual decision-making to shorten the design time and improve the accuracy.

     

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  • [1]
    李丙栋. 超多目标演化算法及其应用研究[D]. 合肥: 中国科学技术大学, 2017: 1-35.
    [2]
    TIAN Y, WANG H D, ZHANG X Y, et al. Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization[J]. Complex & Intelligent Systems, 2017, 3(4): 247-263.
    [3]
    YUAN Y, XU H, WANG B, et al. A new dominance relation-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(1): 16-37. doi: 10.1109/TEVC.2015.2420112
    [4]
    刘益萍. 高维多目标进化优化理论与方法[D]. 徐州: 中国矿业大学, 2017: 9-17.
    [5]
    过晓芳. 超多目标优化问题的几种进化算法研究[D]. 西安: 西安电子科技大学, 2015: 1-9.
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