Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper intro...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001162 |
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| author | James Daniell Kazuma Kobayashi Ayodeji Alajo Syed Bahauddin Alam |
| author_facet | James Daniell Kazuma Kobayashi Ayodeji Alajo Syed Bahauddin Alam |
| author_sort | James Daniell |
| collection | DOAJ |
| description | The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state from the Missouri University of Science and Technology Reactor (MSTR) with noise-enhanced simulated data, our approach achieves remarkable accuracy (96% classification, 2.3% MAPE). The incorporation of simulated data with noise significantly improves the model’s generalization capabilities, mitigating the risk of overfitting. Designed as a digital twin supporting system, this framework integrates real-time, synchronized predictions of reactor state transitions, enabling dynamic operational monitoring and optimization. This innovative solution not only enables rapid and precise prediction of reactor behavior but also has the potential to revolutionize nuclear reactor operations, facilitating enhanced safety protocols, optimized performance, and streamlined decision-making processes. By aligning data-driven insights with the principles of digital twins, this work lays the groundwork for adaptable and scalable solutions for advanced reactors. |
| format | Article |
| id | doaj-art-ace5ee50beea4617a3fbd3e0774289c6 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-ace5ee50beea4617a3fbd3e0774289c62025-01-27T04:22:18ZengElsevierEnergy and AI2666-54682025-01-0119100450Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power predictionJames Daniell0Kazuma Kobayashi1Ayodeji Alajo2Syed Bahauddin Alam3Nuclear Engineering and Radiation Science, Missouri University of Science and Technology, Rolla, 65409, MO, USAGrainger College of Engineering, Nuclear, Plasma, & Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA; National Center for Supercomputing Applications, Urbana, IL, USANuclear Engineering and Radiation Science, Missouri University of Science and Technology, Rolla, 65409, MO, USAGrainger College of Engineering, Nuclear, Plasma, & Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA; National Center for Supercomputing Applications, Urbana, IL, USA; Corresponding author at: Grainger College of Engineering, Nuclear, Plasma, & Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state from the Missouri University of Science and Technology Reactor (MSTR) with noise-enhanced simulated data, our approach achieves remarkable accuracy (96% classification, 2.3% MAPE). The incorporation of simulated data with noise significantly improves the model’s generalization capabilities, mitigating the risk of overfitting. Designed as a digital twin supporting system, this framework integrates real-time, synchronized predictions of reactor state transitions, enabling dynamic operational monitoring and optimization. This innovative solution not only enables rapid and precise prediction of reactor behavior but also has the potential to revolutionize nuclear reactor operations, facilitating enhanced safety protocols, optimized performance, and streamlined decision-making processes. By aligning data-driven insights with the principles of digital twins, this work lays the groundwork for adaptable and scalable solutions for advanced reactors.http://www.sciencedirect.com/science/article/pii/S2666546824001162Digital twinMachine learningNeural networkPredictionNuclear systems |
| spellingShingle | James Daniell Kazuma Kobayashi Ayodeji Alajo Syed Bahauddin Alam Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction Energy and AI Digital twin Machine learning Neural network Prediction Nuclear systems |
| title | Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction |
| title_full | Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction |
| title_fullStr | Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction |
| title_full_unstemmed | Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction |
| title_short | Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction |
| title_sort | digital twin centered hybrid data driven multi stage deep learning framework for enhanced nuclear reactor power prediction |
| topic | Digital twin Machine learning Neural network Prediction Nuclear systems |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001162 |
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