PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Heejong Yoo Co-author(s): Gyunyoung Heo (gheo@khu.ac.kr) *Corresponding author
| Lessons-learned of using Monte Carlo method with importance sampling in fault tree quantification |
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| In the quantitative evaluation of fault trees (FTs) and event trees (ETs) during a level 1 PSA, ETs are easily calculated by the product of each probability, while FTs need additional evaluation techniques to deal with Boolean logic. FTs used in nuclear engineering are usually classified as large FTs, which causes difficulty in calculating the top event probability when using the conventional methods. Other problems that could arise is that all conventional methods use minimal cut sets, which needs additional process of gaining the minimal cut sets. Validation issues are also present due to the fact that widely used methods are all using minimal cut sets, leading to the need of developing methods that is free of minimal cut sets. While there were some attempts to gain the top event probability of FTs by using the Monte Carlo method, which could be free from using minimal cut sets by setting a different algorithm, the time and computational cost for using the Monte Carlo method is always the main issue. In order to reduce computational resource and having its strong point in variance reduction, the most frequently used application for the Monte Carlo method is importance sampling. This paper suggests an algorithm of implying importance sampling, a general method used to reduce the cost for the Monte Carlo method, in order to quantify FTs, and show both the application and limitations of importance sampling. An example FT is given in the paper to show the application and algorithm of Monte Carlo method and to imply importance sampling for the quantification process. |
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Author and Presentation Info
Lead Author Name: Heejong Yoo (kreacher@khu.ac.kr)
Bio: Heejong Yoo, currently a Master's candidate, received the B.S. degree in Nuclear Engineering, Kyung Hee University, Gyeonggi-do, Republic of Korea, in 2021. His research interests are the multi-unit probabilistic safety assessments and cyber security for nuclear power plants. His recent activites include studies for the combination of site operation states in multi-unit PSA, analyzing PSA results with Monte Carlo method with importance sampling, emergency response in nuclear emergency situations, and cyber security for nuclear power plants.
Country: South Korea
Company: Kyung Hee University
Job Title: Graduate Researcher
Bio: Heejong Yoo, currently a Master's candidate, received the B.S. degree in Nuclear Engineering, Kyung Hee University, Gyeonggi-do, Republic of Korea, in 2021. His research interests are the multi-unit probabilistic safety assessments and cyber security for nuclear power plants. His recent activites include studies for the combination of site operation states in multi-unit PSA, analyzing PSA results with Monte Carlo method with importance sampling, emergency response in nuclear emergency situations, and cyber security for nuclear power plants.
Country: South Korea
Company: Kyung Hee University
Job Title: Graduate Researcher