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Round robin analysis of vessel failure probabilities for PTS events in Korea

  • Jhung, Myung Jo (Department of Nuclear Safety Research, Korea Institute of Nuclear Safety) ;
  • Oh, Chang-Sik (Department of Nuclear Safety Research, Korea Institute of Nuclear Safety) ;
  • Choi, Youngin (Department of Nuclear Safety Research, Korea Institute of Nuclear Safety) ;
  • Kang, Sung-Sik (Department of Nuclear Safety Research, Korea Institute of Nuclear Safety) ;
  • Kim, Maan-Won (Mechanical Engineering Lab., Central Research Institute, Korea Hydro & Nuclear Power Co., Ltd) ;
  • Kim, Tae-Hyeon (Mechanical Engineering Lab., Central Research Institute, Korea Hydro & Nuclear Power Co., Ltd) ;
  • Kim, Jong-Min (Safety Materials Technology Development Division, Korea Atomic Energy Research Institute) ;
  • Kim, Min Chul (Safety Materials Technology Development Division, Korea Atomic Energy Research Institute) ;
  • Lee, Bong Sang (Safety Materials Technology Development Division, Korea Atomic Energy Research Institute) ;
  • Kim, Jong-Min (Mechanical Engineering Group, KEPCO Engineering & Construction Co., Inc.) ;
  • Kim, Kyuwan (Mechanical Engineering Group, KEPCO Engineering & Construction Co., Inc.)
  • Received : 2019.12.04
  • Accepted : 2020.01.26
  • Published : 2020.08.25

Abstract

Round robin analyses for vessel failure probabilities due to PTS events are proposed for plant-specific analyses of all types of reactors developed in Korea. Four organizations, that are responsible for regulation, operation, research and design of the nuclear power plant in Korea, participated in the round robin analysis. The vessel failure probabilities from the probabilistic fracture mechanics analyses are calculated to assure the structural integrity of the reactor pressure vessel during transients that are expected to initiate PTS events. The failure probabilities due to various parameters are compared with each other. All results are obtained based on several assumptions about material properties, flaw distribution data, and transient data such as pressure, temperature, and heat transfer coefficient. The realistic input data can be used to obtain more realistic failure probabilities. The various results presented in this study will be helpful not only for benchmark calculations, result comparisons, and verification of PFM codes developed but also as a contribution to knowledge management for the future generation.

Keywords

References

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