References
- Hanai T, Shinaki S, Toyoshima K, et al. Development of the multi-site/unit risk assessment method based on continuous Markov Monte Carlo method and Bayesian networks: (1) modeling method of interactive accident scenarios with a multi-site/unit PRA (ASRAM2020-1023). Proceeding ASRAM2020; Nov.30–Dec.2 2020; online.
- Jang S, Yamaguchi A, Hanai T, et al. Development of the multi-site/unit risk assessment method based on continuous Markov Monte Carlo method and Bayesian networks: (2) assessment of mitigation strategy using concept of FLEX (ASRAM2020-1031). Proceeding ASRAM2020, Nov.30–Dec.2 2020; online.
- Sawada K, Endo T, Yamamoto A, et al. “Study on multi-unit/multi-source PRA considering correlation between multiple safety systems using the continuous Markov chain Monte Carlo method (ASRAM2020-1027). Proceeding ASRAM2020; Nov.30–Dec.2 2020; online.
- Yoo H, Jin K, Heo G Estimation of site operating states for multi-unit nuclear power plants using Monte-Carlo method (ASRAM2020-1052). Proceeding ASRAM2020; Nov.30–Dec.2 2020; online.
- Mandelli D, Parisi C, Alfonsi A, et al. Multi-unit dynamic PRA. Reliab Eng Syst Saf. 2019;185:303–317. doi: 10.1016/j.ress.2018.12.029
- Yang J-E. Multi-unit risk assessment of nuclear power plants: Current status and issues. Nucl Eng Technol. 2018;50(8):1199–1209. doi: 10.1016/j.net.2018.09.010
- Kim IS, Jang M, Kim SR. Holistic approach to multi-unit site risk assessment: status and issues. Nucl Eng Technol. 2017;49(2):286–294. doi: 10.1016/j.net.2017.01.003
- Mandelli D, Alfonsi A, Wang C, et al. Mutual integration of classical and dynamic PRA. Nucl Technol. 2021;207(3):363–375. doi: 10.1080/00295450.2020.1776030
- Mandelli D, Parisi C, Anderson N, et al. Dynamic PRA methods to evaluate the impact on accident progression of accident tolerant fuels. Nucl Technol. 2021;207(3):389–405. doi: 10.1080/00295450.2020.1794234
- Christian R, Shah AUA, Kang HG. Dynamic PRA-Based estimation of PWR coping time using a surrogate model for accident tolerant fuel. Nucl Technol. 2021;207(3):376–388. doi: 10.1080/00295450.2020.1777035
- Smidts C, Devooght J. Probabilistic reactor dynamics—II: A Monte Carlo study of a fast reactor transient. Nucl Sci Eng. 1992;111(3):241–256. doi: 10.13182/NSE92-A23938
- Yabuchi S, Takata T, Yamaguchi A. Quantification of event sequence chronology using a continuous MCMC method for level 2 PSA of a nuclear power plant. Proceeding of the probabilistic safety assessment and management, Vol. 9; 2008 May 18–23; Hong Kong.
- Shinzaki S, Takata T, Yamaguchi A.Quantification of severe accident scenarios in level 2 PSA of nuclear power plant with continuous Markov chain model and Monte Carlo method. In: Proceeding of the probabilistic safety assessment and management 10; 2010 June 7–11; Seattle, USA.
- Shinzaki S, Takata T, Yamaguchi A Dynamic scenario quantification based on continuous Markov Monte Carlo method with meta-model of thermal hydraulics for level 2 PSA. Proceeding of the 17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics; 2011 Sept 25–30; Toronto, Canada.
- Jang S, Yamaguchi A. Dynamic scenario quantification for level 2 PRA of sodium-cooled fast reactor based on continuous Markov chain and Monte Carlo method coupled with meta-model of thermal–hydraulic analysis. J Nucl Sci Technol. 2018;55(8):850–858. doi: 10.1080/00223131.2018.1445564
- Takata T, Azuma E. Event sequence assessment of deep snow in sodium-cooled fast reactor based on continuous Markov chain Monte Carlo method with plant dynamics analysis. J Nucl Sci Technol. 2016;53(11):1749–1757. doi: 10.1080/00223131.2016.1155508
- Jankovsky ZK, Haskin TC, Denman MR. How to ADAPT [Internet]. 2018 [cited 2023 Apr 21]. p. SAND20186660, 1457610. Report No.: SAND20186660, 1457610. Available from: SAND20186660, 1457610. Report No.: SAND20186660, 1457610. Available from: http://www.osti.gov/servlets/purl/1457610/
- Kloos M, Peschke J. MCDET: A probabilistic dynamics method combining Monte Carlo simulation with the discrete dynamic event tree approach. Nucl Sci Eng. 2006;153(2):137–156. doi: 10.13182/NSE06-A2601
- Sawada K, Yamamoto A, Endo T, et al. Application of continuous Markov-chain Monte-Carlo method to multi-unit risk evaluations considering interdependence of accident progression among multiple units. J Nucl Sci Technol. 2021;58(12):1308–1317. doi: 10.1080/00223131.2021.1940341
- Mandelli D, Ma Z, Parisi C, et al. Measuring risk importance in a dynamic PRA framework. Proceeding of PSA2017; 2017 Sep 24-28; Pittsburgh, PA.
- Kschwendt H, Rief H. TIMOC-A general purpose Monte Carlo code for stationary and time dependent neutron transport. Ispra (Italy): Commission of the European Communities; 1970. p EUR4519e.
- Gelbard EM, Rief H, Spanier J. MARC-A multigroup Monte Carlo programme for the calculation of capture probabilities. Pittsburgh (US): U.S. Atomic Energy Commission; 1962. pp. WAPD-TM–273.
- EPRI. Modular accident analysis program 5 (MAAP5) applications guidance: desktop reference for using MAAP5 software-phase 3 report. 2017.
- Sawada K Application of Continuous Markov Process Monte Carlo Methods to Multi-Unit Risk Assessment [ Master’s Thesis]. Nagoya University, 2021. [in Japanese]