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논문 기본 정보

자료유형
학술저널
저자정보
Siddharth Suman (Independent Researcher)
저널정보
한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제52권 제11호
발행연도
2020.11
수록면
2,565 - 2,571 (7page)
DOI
https://doi.org/10.1016/j.net.2020.04.025

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초록· 키워드

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Background: Understanding the behaviour of nuclear fuel claddings by conducting burst test on singlecladding tube under simulated loss-of-coolant accident conditions and developing theoretical cumempirical predictive computer codes have been the focus of several investigations. The developed burstcriterion (a) assumes symmetrical deformation of cladding tube in contrast to experimental observation(b) interpolates the properties of Zircaloy-4 cladding in mixed aþ b phase (c) does not account forazimuthal temperature variations. In order to overcome all these drawbacks of burst criterion, it isreasoned that artificial intelligence technique may be a better option to predict the burst parameters. Methods: Artificial neural network models based on feedforward backpropagation algorithm with logsigtransfer function are developed. Results: Neural network architecture of 2-4-4-3, that is model with two hidden layers having four nodesin each layer is found to be the most suitable. The mean, maximum, and minimum prediction errors forthis optimised model are 0.82%, 19.62%, and 0.004%, respectively. Conclusion: The burst stress, burst temperature, and burst strain obtained from burst criterion haveaverage deviation of 19%, 12%, and 53% respectively whereas the developed neural network modelpredicted these parameters with average deviation of 6%, 2%, and 8%, respectively.

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