Crack path analysis of spent nuclear fuel cladding using the strain energy-based Dijkstra algorithm

• Published in: Nuclear engineering and design Vol. 429; p. 113661
• Main Authors: Baik, Jee A, Kim, Jung Jin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: Crack path; Dijkstra algorithm; Finite element analysis; Spent nuclear fuel; Strain energy; Crack path; Spent nuclear fuel; Finite element analysis; Strain energy; Dijkstra algorithm
• ISSN: 0029-5493
• DOI: 10.1016/j.nucengdes.2024.113661

[Display omitted] •Proposed method uses strain energy-based Dijkstra algorithm for crack prediction.•Achieves 92.78% accuracy in predicting crack initiation in spent fuel cladding.•Predicted crack paths are within 200 μm of actual paths.•Finite element analysis quantifies resistance changes, enhancing prediction reliability.•Enhances safety assessments and management strategies for nuclear fuel. The integrity of spent fuel cladding is crucial for preventing the release of radioactive materials, which pose significant risks to public safety and the environment. However, accurately predicting cracks in cladding tubes remains a challenge. This study proposes a novel method for predicting crack paths in spent nuclear fuel cladding tubes using the Dijkstra algorithm, based on strain energy. In this method, cladding images are segmented into cladding and hydride pixels, followed by a finite element analysis to calculate the strain energy. The Dijkstra algorithm utilizes this strain energy data from hydrides to predict crack paths in areas with low resistance to loading. The predicted path exhibited an accuracy of 92.78 % with respect to the initiation point of the actual crack path and was located within 200 μm of the actual crack path. The proposed method demonstrates a higher similarity to the actual crack path than conventional image-based methods. These results suggest that the safety assessment of spent nuclear fuel can be enhanced, enabling the development of effective management strategies for spent nuclear fuel.