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

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

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 accuratel...

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Bibliographic Details
Main Authors: Ahmed Omar, Mohamed K. Elhadad, Moamen G. El-Samrah, Tarek F. Nagla, Tamer Mekkawy
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Pressurized water reactor (PWR)
Nuclear reactor safety
Control rod prediction
EfficientNetB0
Vision transformer (ViT)
AI-driven reactor operations
Online Access:https://doi.org/10.1038/s41598-025-13794-7
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https://doi.org/10.1038/s41598-025-13794-7

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