AI-Assisted Forecasting of a Mitigated Multiple Steam Generator Tube Rupture Scenario in a Typical Nuclear Power Plant

AI-Assisted Forecasting of a Mitigated Multiple Steam Generator Tube Rupture Scenario in a Typical Nuclear Power Plant

This study is focused on developing a machine learning (ML) meta-model to predict the progression of a multiple steam generator tube rupture (MSGTR) accident in the APR1400 reactor. The accident was simulated using the thermal–hydraulic code RELAP5/SCDAPSIM/MOD3.4. The model incorporates a mitigatio...

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Bibliographic Details
Main Authors: Sonia Spisak, Aya Diab
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Energies
Subjects:
multiple steam generator tube rupture
operator actions
mitigation
design extension conditions
machine learning
Online Access:https://www.mdpi.com/1996-1073/18/2/250
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Summary:This study is focused on developing a machine learning (ML) meta-model to predict the progression of a multiple steam generator tube rupture (MSGTR) accident in the APR1400 reactor. The accident was simulated using the thermal–hydraulic code RELAP5/SCDAPSIM/MOD3.4. The model incorporates a mitigation strategy executed through operator interventions. Following this, uncertainty quantification employing the Best Estimate Plus Uncertainty (BEPU) methodology was undertaken by coupling RELAP5/SCDAPSIM/MOD3.4 with the statistical software, DAKOTA 6.14.0. The analysis concentrated on critical safety parameters, including Reactor Coolant System (RCS) pressure and temperature, as well as reactor vessel upper head (RVUH) void fraction. These simulations generated a comprehensive dataset, which served as the foundation for training three ML architectures: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional LSTM (CNN+LSTM). Among these models, the CNN+LSTM hybrid configuration demonstrated superior performance, excelling in both predictive accuracy and computational efficiency. To bolster the model’s transparency and interpretability, Integrated Gradients (IGs)—an advanced Explainable AI (XAI) technique—was applied, elucidating the contribution of input features to the model’s predictions and enhancing its trustworthiness.
ISSN:1996-1073

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