Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel
Ensuring the integrity of spent nuclear fuel (SNF) is essential for nuclear non-proliferation efforts. While detecting gross defects is relatively straightforward, identifying partial defects remain challenging. This study proposes a Bayesian inference method implemented by our newly developed Yonse...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Nuclear Engineering and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573325002475 |
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| author | Hojik Kim Hyung-Joo Choi Woojin Kim Seungmin Lee Chul Hee Min Sung-Woo Kwak |
| author_facet | Hojik Kim Hyung-Joo Choi Woojin Kim Seungmin Lee Chul Hee Min Sung-Woo Kwak |
| author_sort | Hojik Kim |
| collection | DOAJ |
| description | Ensuring the integrity of spent nuclear fuel (SNF) is essential for nuclear non-proliferation efforts. While detecting gross defects is relatively straightforward, identifying partial defects remain challenging. This study proposes a Bayesian inference method implemented by our newly developed Yonsei Single-photon Emission Computed Tomography version 2 (YSECT.v.2) for verifying partial defects in SNF. Unlike traditional SNF defect detection algorithms that estimate specific values, the proposed method estimates distributions, thus providing belief in the estimates. Using the Monte Carlo (MC) method, we simulated partial defect scenarios and evaluated the proposed method's effectiveness against maximum-likelihood expectation-maximization (MLEM) across various defect patterns, ratios, and heterogeneous burnup conditions. The results indicate that the proposed technique reliably detects nuclear material diversion with high confidence. |
| format | Article |
| id | doaj-art-e5e69d93457f464e9355142205a5e8f8 |
| institution | Kabale University |
| issn | 1738-5733 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nuclear Engineering and Technology |
| spelling | doaj-art-e5e69d93457f464e9355142205a5e8f82025-08-20T03:50:48ZengElsevierNuclear Engineering and Technology1738-57332025-10-01571010367910.1016/j.net.2025.103679Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuelHojik Kim0Hyung-Joo Choi1Woojin Kim2Seungmin Lee3Chul Hee Min4Sung-Woo Kwak5Department of SMR Development, Korea Hydro & Nuclear Power (KHNP), Republic of KoreaDepartment of Radiation Convergence Engineering, Yonsei University, Republic of KoreaDepartment of Safeguards, Korea Institute of Nuclear Nonproliferation and Control (KINAC), Republic of KoreaDepartment of Safeguards, Korea Institute of Nuclear Nonproliferation and Control (KINAC), Republic of KoreaDepartment of Radiation Convergence Engineering, Yonsei University, Republic of Korea; Corresponding author.Department of Safeguards, Korea Institute of Nuclear Nonproliferation and Control (KINAC), Republic of Korea; Corresponding author.Ensuring the integrity of spent nuclear fuel (SNF) is essential for nuclear non-proliferation efforts. While detecting gross defects is relatively straightforward, identifying partial defects remain challenging. This study proposes a Bayesian inference method implemented by our newly developed Yonsei Single-photon Emission Computed Tomography version 2 (YSECT.v.2) for verifying partial defects in SNF. Unlike traditional SNF defect detection algorithms that estimate specific values, the proposed method estimates distributions, thus providing belief in the estimates. Using the Monte Carlo (MC) method, we simulated partial defect scenarios and evaluated the proposed method's effectiveness against maximum-likelihood expectation-maximization (MLEM) across various defect patterns, ratios, and heterogeneous burnup conditions. The results indicate that the proposed technique reliably detects nuclear material diversion with high confidence.http://www.sciencedirect.com/science/article/pii/S1738573325002475Spent nuclear fuelPartial defectBayesian inferenceGamma emission tomographyMonte Calro |
| spellingShingle | Hojik Kim Hyung-Joo Choi Woojin Kim Seungmin Lee Chul Hee Min Sung-Woo Kwak Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel Nuclear Engineering and Technology Spent nuclear fuel Partial defect Bayesian inference Gamma emission tomography Monte Calro |
| title | Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel |
| title_full | Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel |
| title_fullStr | Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel |
| title_full_unstemmed | Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel |
| title_short | Uncertainty analysis based on Bayesian inference for partial defect verification of PWR spent nuclear fuel |
| title_sort | uncertainty analysis based on bayesian inference for partial defect verification of pwr spent nuclear fuel |
| topic | Spent nuclear fuel Partial defect Bayesian inference Gamma emission tomography Monte Calro |
| url | http://www.sciencedirect.com/science/article/pii/S1738573325002475 |
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