ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs.

Journal: Scientific reports
Published Date: Aug 18, 2025

Abstract

This paper presents a novel approach, ReactorNet, a machine learning framework leveraging thermal neutron flux imaging to enable real-time monitoring of pressurized water reactors (PWRs). By integrating EfficientNetB0 with a hybrid classification-regression architecture, the model accurately identifies control rod positions and operational parameters through thermal neutron flux patterns detected by ex-core sensors. Principal Component Analysis (PCA) and Clustering Analysis decode radial flux variations linked to rod movements, while simulations of a 2772-MW(th) PWR using TRITON FORTRAN validate the framework. This framework outperforms Vision Transformers and ResNet50, achieving superior multi-class accuracy (97.5%) and reduced the mean absolute error (MAE) of regression. Test-Time Augmentation and cross-validation mitigate data limitations, ensuring robustness. This work bridges AI and nuclear engineering, demonstrating EfficientNetB0's potential for precise, real-time reactor monitoring, enhancing operational safety and efficiency.

Authors

  • Ahmed Omar
    Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt. ahmed.omar@mu.edu.eg.
  • Mohamed K Elhadad
    Department of Computer Engineering and AI, Military Technical College, Kobry El-Kobbah, Cairo, Egypt.
  • Moamen G El-Samrah
    Nuclear Engineering Department, Military Technical College, Kobry El-Kobbah, Cairo, Egypt.
  • Tarek F Nagla
    Nuclear Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Tamer Mekkawy
    Avionics Department, Military Technical College, Kobry El-Kobbah, Cairo, Egypt.

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