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The accurate and efficient modeling of nuclear reactor transients is crucialfor ensuring safe and optimal reactor operation. Traditional physics-basedmodels, while valuable, can be computationally intensive and may not fullycapture the complexities of real-world reactor behavior. This paper introducesa novel hybrid digital twin-focused multi-stage deep learning framework thataddresses these limitations, offering a faster and more robust solution forpredicting the final steady-state power of reactor transients. By leveraging acombination of feed-forward neural networks with both classification andregression stages, and training on a unique dataset that integrates real-worldmeasurements of reactor power and controls state from the Missouri Universityof Science and Technology Reactor (MSTR) with noise-enhanced simulated data,our approach achieves remarkable accuracy (96% classification, 2.3% MAPE). Theincorporation of simulated data with noise significantly improves the model'sgeneralization capabilities, mitigating the risk of overfitting. Designed as adigital twin supporting system, this framework integrates real-time,synchronized predictions of reactor state transitions, enabling dynamicoperational monitoring and optimization. This innovative solution not onlyenables rapid and precise prediction of reactor behavior but also has thepotential to revolutionize nuclear reactor operations, facilitating enhancedsafety protocols, optimized performance, and streamlined decision-makingprocesses. By aligning data-driven insights with the principles of digitaltwins, this work lays the groundwork for adaptable and scalable solutions innuclear system management.

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction