Sensitivity Analysis of Plutonium Production Potential in the Research Reactor Using Monte Carlo‐Based Neutron Transport Solver
Hyoeun Lee, Eunhyun Ryu, Yonhong Jeong, Jaehyun Cho
IF 4.2
International Journal of Energy Research
The Yongbyon reactor in North Korea represents a significant global security threat because of its potential for plutonium production, which can be utilized in nuclear weapons. The nuclear tests conducted at the Yongbyon research reactor from 2006 to 2017 highlight the necessity for accurate assessments of its plutonium production capabilities. This study estimated the plutonium production potential of the Yongbyon reactor to be ~51 kg, based on its operational history and analysis using the Monte Carlo code for advanced reactor design (McCARD) code. Sensitivity analysis indicates that the most critical variable for predicting plutonium production capacity is the integrated thermal power release data from the reactor. Factors such as the temperature of fuel and coolant, and the number of neutron samples in the McCARD have a negligible impact (less than 1%) on the estimates of plutonium production. Regardless of how diverse the history of thermal power is, or what value the maximum power reaches (20 or 25 MWt), the integrated thermal energy consistently determines the amount of plutonium produced, emphasizing its significance in the analysis.
Exhaustive simulation approach for severe accident risk in nuclear power plants: OPR-1000 full-power internal events
Jaehyun Cho, Sang Hun Lee, Young Suk Bang, Suwon Lee, Soo Yong Park
IF 11
Reliability Engineering & System Safety
The estimation of severe accident risks in nuclear power plants (NPPs) is significant to confirm the safety level of NPPs and to reinforce their vulnerable points. Severe accident risks can be quantified by Level 2 probabilistic safety assessment (PSA), which traditionally applies a grouping feature to handle the tremendous numbers of accident scenarios. Accordingly, risk information is likely to be lost in the process of grouping similar scenarios and in the treatment of many scenarios with one representative scenario. To obtain more comprehensive risk information including source term behaviors and plant responses during severe accidents, this study suggests an exhaustive simulation approach with new software that helps to automatically generate a large number of input data for an accident simulation code, and performs an application study using PSA models from OPR-1000 full-power internal events. Only a three-day run time was required to simulate all the severe accident scenarios, totaling 690 scenarios, using a commercial computer. The application study revealed that the conventional grouping approach can either underestimate or overestimate overall NPP risk depending on the selection of the representative scenario.
Sensitivity Analysis of Plutonium Production Potential in the Research Reactor Using Monte Carlo‐Based Neutron Transport Solver
Hyoeun Lee, Eunhyun Ryu, Yonhong Jeong, Jaehyun Cho
IF 4.2
International Journal of Energy Research
The Yongbyon reactor in North Korea represents a significant global security threat because of its potential for plutonium production, which can be utilized in nuclear weapons. The nuclear tests conducted at the Yongbyon research reactor from 2006 to 2017 highlight the necessity for accurate assessments of its plutonium production capabilities. This study estimated the plutonium production potential of the Yongbyon reactor to be ~51 kg, based on its operational history and analysis using the Monte Carlo code for advanced reactor design (McCARD) code. Sensitivity analysis indicates that the most critical variable for predicting plutonium production capacity is the integrated thermal power release data from the reactor. Factors such as the temperature of fuel and coolant, and the number of neutron samples in the McCARD have a negligible impact (less than 1%) on the estimates of plutonium production. Regardless of how diverse the history of thermal power is, or what value the maximum power reaches (20 or 25 MWt), the integrated thermal energy consistently determines the amount of plutonium produced, emphasizing its significance in the analysis.
Exhaustive simulation approach for severe accident risk in nuclear power plants: OPR-1000 full-power internal events
Jaehyun Cho, Sang Hun Lee, Young Suk Bang, Suwon Lee, Soo Yong Park
IF 11
Reliability Engineering & System Safety
The estimation of severe accident risks in nuclear power plants (NPPs) is significant to confirm the safety level of NPPs and to reinforce their vulnerable points. Severe accident risks can be quantified by Level 2 probabilistic safety assessment (PSA), which traditionally applies a grouping feature to handle the tremendous numbers of accident scenarios. Accordingly, risk information is likely to be lost in the process of grouping similar scenarios and in the treatment of many scenarios with one representative scenario. To obtain more comprehensive risk information including source term behaviors and plant responses during severe accidents, this study suggests an exhaustive simulation approach with new software that helps to automatically generate a large number of input data for an accident simulation code, and performs an application study using PSA models from OPR-1000 full-power internal events. Only a three-day run time was required to simulate all the severe accident scenarios, totaling 690 scenarios, using a commercial computer. The application study revealed that the conventional grouping approach can either underestimate or overestimate overall NPP risk depending on the selection of the representative scenario.
A Fast Variance Reduction Technique for Efficient Radiation Shielding Calculations in Nuclear Reactors
Sung‐Hyun Jo, Sang‐Hwan Kim, Jaehyun Cho
IF 3.2
Energies
The increasing demand for cleaner and more sustainable energy sources has sparked significant interest in small modular reactors (SMRs). Due to their compact and modular design, SMRs pose unique challenges in radiation shielding, requiring a more refined approach. This study focuses on developing a new variance reduction technique (VRT) for radiation shielding analysis, specifically tailored for SMRs, to address the limitations of traditional methods such as surface source write/surface source read (SSW/SSR). The proposed VRT supports multi-threading and enhances computational efficiency by redefining source particles using a two-step method. The analysis is conducted using the Monte Carlo radiation transport code, MCNP6, and the effectiveness of the new VRT is evaluated through sensitivity analyses across various energy and directional divisions.
Machine learning-based categorization of source terms for risk assessment of nuclear power plants
Kyungho Jin, Jaehyun Cho, Sung-yeop Kim
IF 2.6
Nuclear Engineering and Technology
In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.
