Abstract
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.
Recommended Citation
J. Daniell et al., "Digital Twin-centered Hybrid Data-driven Multi-stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction," Energy and AI, vol. 19, article no. 100450, Elsevier, Jan 2025.
The definitive version is available at https://doi.org/10.1016/j.egyai.2024.100450
Department(s)
Nuclear Engineering and Radiation Science
Publication Status
Open Access
Keywords and Phrases
Digital twin; Machine learning; Neural network; Nuclear systems; Prediction
International Standard Serial Number (ISSN)
2666-5468
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2025
Comments
U.S. Nuclear Regulatory Commission, Grant OAC-1919789