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Seismic performance enhancement of small modular reactors using optimal design of inerter-based vibration absorbers

  • Shuaijun Zhang (Yantai Research Institute, Harbin Engineering University) ;
  • Xiaoyu Ji (Yantai Research Institute, Harbin Engineering University) ;
  • Gangling Hou (Yantai Research Institute, Harbin Engineering University) ;
  • Ding Xu (Yantai Research Institute, Harbin Engineering University) ;
  • Chengyu Yang (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University) ;
  • Xuesong Cai (Yantai Research Institute, Harbin Engineering University)
  • 투고 : 2024.05.02
  • 심사 : 2024.06.16
  • 발행 : 2024.11.25

초록

Nuclear power plant (NPP) structures face significant challenges in ensuring structural resilience subjected to earthquake motions. The inerter-based vibration absorbers (IVAs) have been proven to control structural vibration, but they have not been applied in small modular reactor (SMR) structures. In this study, the optimal IVA parameters are investigated for SMR structures, which focus on reducing the seismic response of the first floor and fifth floor. By using a genetic algorithm (GA), the IVAs parameters are optimized to minimize the storey displacement variance and storey acceleration variance. Besides, the robustness analysis is performed to ensure the optimized parameters are practical. The various ground motions are selected for time history analysis (THA) to illustrate the seismic response of SMR equipped with the optimal IVAs. The results of THA demonstrate that the optimal IVAs enhance the seismic performance of the SMR compared with traditional SMR structures. By positioning optimal IVAs, significant mitigation of both storey displacement and storey acceleration are achieved. This study explores to a deeper understanding of IVAs' efficacy in SMR structures, which is beneficial to the practical use of IVAs in SMR structures.

키워드

과제정보

This work was supported by Guangdong Zhuhai LNG Expansion Program Phase II EPC Project Excellence Initiative Program (China National Offshore Oil Corporation) (Grant No. Z2023LGENT1210), and Heilongjiang Provincial Key Research and Development Plan funded by Heilongjiang Province, China (Department of Science and Technology) (Grant No. 2022ZX01A14).

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