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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13794-7 |
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| author | Ahmed Omar Mohamed K. Elhadad Moamen G. El-Samrah Tarek F. Nagla Tamer Mekkawy |
| author_facet | Ahmed Omar Mohamed K. Elhadad Moamen G. El-Samrah Tarek F. Nagla Tamer Mekkawy |
| author_sort | Ahmed Omar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f97dba9143b54f919f0bf2202a0f3a2e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f97dba9143b54f919f0bf2202a0f3a2e2025-08-20T03:07:20ZengNature PortfolioScientific Reports2045-23222025-08-0115111910.1038/s41598-025-13794-7ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRsAhmed Omar0Mohamed K. Elhadad1Moamen G. El-Samrah2Tarek F. Nagla3Tamer Mekkawy4Nuclear Engineering Department, Military Technical CollegeDepartment of Computer Engineering and AI, Military Technical CollegeNuclear Engineering Department, Military Technical CollegeNuclear Engineering Department, Faculty of Engineering, Alexandria UniversityAvionics Department, Military Technical CollegeAbstract 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.https://doi.org/10.1038/s41598-025-13794-7Pressurized water reactor (PWR)Nuclear reactor safetyControl rod predictionEfficientNetB0Vision transformer (ViT)AI-driven reactor operations |
| spellingShingle | Ahmed Omar Mohamed K. Elhadad Moamen G. El-Samrah Tarek F. Nagla Tamer Mekkawy ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs Scientific Reports Pressurized water reactor (PWR) Nuclear reactor safety Control rod prediction EfficientNetB0 Vision transformer (ViT) AI-driven reactor operations |
| title | ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs |
| title_full | ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs |
| title_fullStr | ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs |
| title_full_unstemmed | ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs |
| title_short | ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs |
| title_sort | reactornet based on machine learning framework to identify control rod position for real time monitoring in pwrs |
| topic | Pressurized water reactor (PWR) Nuclear reactor safety Control rod prediction EfficientNetB0 Vision transformer (ViT) AI-driven reactor operations |
| url | https://doi.org/10.1038/s41598-025-13794-7 |
| work_keys_str_mv | AT ahmedomar reactornetbasedonmachinelearningframeworktoidentifycontrolrodpositionforrealtimemonitoringinpwrs AT mohamedkelhadad reactornetbasedonmachinelearningframeworktoidentifycontrolrodpositionforrealtimemonitoringinpwrs AT moamengelsamrah reactornetbasedonmachinelearningframeworktoidentifycontrolrodpositionforrealtimemonitoringinpwrs AT tarekfnagla reactornetbasedonmachinelearningframeworktoidentifycontrolrodpositionforrealtimemonitoringinpwrs AT tamermekkawy reactornetbasedonmachinelearningframeworktoidentifycontrolrodpositionforrealtimemonitoringinpwrs |