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<p>Nuclear fuel assemblies (FA) become high-level radioactive waste known as spent nuclear fuel (SNF) after several years of operation in nuclear reactors. Currently, a considerable portion of SNF is temporarily stored in sealed stainless-steel dry storage canisters. Handling, storage or transportation events (normal operations or accidents) can cause potential damage to the FAs inside the canisters. Damage to FAs inside the canisters needs to be identified for safety purposes during storage or before and after transportation. Due to the difficulty of a visual inspection of the sealed canisters, non-destructive evaluation (NDE) is critical to identify the potential internal damage. In this study, a high-fidelity finite element (FE) model was used to simulate different levels of FA damage. Wasserstein generative adversarial networks (WGAN) were developed to learn and generate noise using data from previously conducted experiments, which was then added to the numerically obtained frequency response functions (FRF) to bridge the gap between experimental and numerical domains. The noisy computational data were analyzed by a multi-task extreme gradient boosting (XGBoost) model to identify the damage level and location. The XGBoost achieved macro-F1 scores of 0.998 and 0.900 for damage detection and localization tasks in the FE dataset and perfect scores of 1.0 for the same in the experimental dataset. The results demonstrate that the machine learning (ML)-aided NDE method was successful in identifying various damage modes within SNF canisters even in the presence of noise levels observed in actual large-scale experiments.</p>

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Effect of experimental noise on internal damage detection of sealed spent nuclear fuel canisters

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月28日

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  • Anna Arcaro,
  • Bora Gencturk,
  • Roger Ghanem,
  • Bozhou Zhuang

摘要

Nuclear fuel assemblies (FA) become high-level radioactive waste known as spent nuclear fuel (SNF) after several years of operation in nuclear reactors. Currently, a considerable portion of SNF is temporarily stored in sealed stainless-steel dry storage canisters. Handling, storage or transportation events (normal operations or accidents) can cause potential damage to the FAs inside the canisters. Damage to FAs inside the canisters needs to be identified for safety purposes during storage or before and after transportation. Due to the difficulty of a visual inspection of the sealed canisters, non-destructive evaluation (NDE) is critical to identify the potential internal damage. In this study, a high-fidelity finite element (FE) model was used to simulate different levels of FA damage. Wasserstein generative adversarial networks (WGAN) were developed to learn and generate noise using data from previously conducted experiments, which was then added to the numerically obtained frequency response functions (FRF) to bridge the gap between experimental and numerical domains. The noisy computational data were analyzed by a multi-task extreme gradient boosting (XGBoost) model to identify the damage level and location. The XGBoost achieved macro-F1 scores of 0.998 and 0.900 for damage detection and localization tasks in the FE dataset and perfect scores of 1.0 for the same in the experimental dataset. The results demonstrate that the machine learning (ML)-aided NDE method was successful in identifying various damage modes within SNF canisters even in the presence of noise levels observed in actual large-scale experiments.