TY - JOUR

T1 - Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities

AU - Ayodeji, Abiodun

AU - Amidu, Muritala Alade

AU - Olatubosun, Samuel Abiodun

AU - Addad, Yacine

AU - Ahmed, Hafiz

PY - 2022/9/1

Y1 - 2022/9/1

N2 - Deep learning algorithms provide plausible benefits for efficient prediction and analysis of nuclear reactor safety phenomena. However, research works that discuss the critical challenges with deep learning models from the reactor safety perspective are limited. This article presents the state-of-the-art in deep learning application in nuclear reactor safety analysis, and the inherent limitations in deep learning models. In addition, critical issues such as deep learning model explainability, sensitivity and uncertainty constraints, model reliability, and trustworthiness are discussed from the nuclear safety perspective, and robust solutions to the identified issues are also presented. As a major contribution, a deep feedforward neural network is developed as a surrogate model to predict turbulent eddy viscosity in Reynolds-averaged Navier–Stokes (RANS) simulation. Further, the deep feedforward neural network performance is compared with the conventional Spalart Allmaras closure model in the RANS turbulence closure simulation. In addition, the Shapely Additive Explanation (SHAP) and the local interpretable model-agnostic explanations (LIME) APIs are introduced to explain the deep feedforward neural network predictions. Finally, exciting research opportunities to optimize deep learning-based reactor safety analysis are presented.

AB - Deep learning algorithms provide plausible benefits for efficient prediction and analysis of nuclear reactor safety phenomena. However, research works that discuss the critical challenges with deep learning models from the reactor safety perspective are limited. This article presents the state-of-the-art in deep learning application in nuclear reactor safety analysis, and the inherent limitations in deep learning models. In addition, critical issues such as deep learning model explainability, sensitivity and uncertainty constraints, model reliability, and trustworthiness are discussed from the nuclear safety perspective, and robust solutions to the identified issues are also presented. As a major contribution, a deep feedforward neural network is developed as a surrogate model to predict turbulent eddy viscosity in Reynolds-averaged Navier–Stokes (RANS) simulation. Further, the deep feedforward neural network performance is compared with the conventional Spalart Allmaras closure model in the RANS turbulence closure simulation. In addition, the Shapely Additive Explanation (SHAP) and the local interpretable model-agnostic explanations (LIME) APIs are introduced to explain the deep feedforward neural network predictions. Finally, exciting research opportunities to optimize deep learning-based reactor safety analysis are presented.

KW - Deep learning

KW - Machine learning

KW - Modeling and simulation

KW - Nuclear reactor safety

KW - Reliability

KW - Sensitivity analysis

KW - Uncertainty quantification

U2 - https://doi.org/10.1016/j.pnucene.2022.104339

DO - https://doi.org/10.1016/j.pnucene.2022.104339

M3 - Article

VL - 151

JO - Progress in Nuclear Energy

JF - Progress in Nuclear Energy

SN - 0149-1970

M1 - 104339

ER -