Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Design optimization of a nuclear main steam safety valve based on an E-AHF ensemble surrogate model

  • Chaoyong Zong (School of Mechanical Engineering, Dalian University of Technology) ;
  • Maolin Shi (School of Agricultural Engineering, Jiangsu University) ;
  • Qingye Li (School of Mechanical Engineering, Dalian University of Technology) ;
  • Fuwen Liu (School of Mechanical Engineering, Dalian University of Technology) ;
  • Weihao Zhou (School of Mechanical Engineering, Dalian University of Technology) ;
  • Xueguan Song (School of Mechanical Engineering, Dalian University of Technology)
  • Received : 2022.03.16
  • Accepted : 2022.06.18
  • Published : 2022.11.25
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Abstract

Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 52075068), Natural Science Foundation of Jiangsu Province (BK20210777) and Funding of Jiangsu University (20JDG068).

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