The PUR-1 Cyber-Physical Digital Twin
2026-08-31 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and ScienceMachine Learning
AI summaryⓘ
The authors developed a digital twin for a nuclear reactor that combines detailed physics models and AI to mirror the reactor's behavior in real-time. This digital twin can estimate the reactor’s current state, predict short-term changes, and suggest actions, all while communicating directly with the physical reactor. They tested their system throughout a full operational cycle and found that its predictions matched real experimental data well. This work shows promise for using digital twins to improve monitoring and control of nuclear facilities.
Digital TwinNuclear ReactorNeutronicsThermal-hydraulicsPoint KineticsState EstimationPredictive ControlCyber-physical SystemsReal-time SynchronizationExplainable AI
Authors
Vasileios Theos, Jonah Lau, Konstantinos Gkouliaras, Zachery Dahm, Konstantinos Vasili, Noah Fillgrove, William Richards, True Miller, Brian Jowers, Stylianos Chatzidakis
Abstract
Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decision support, cyber event characterization, state estimation, predictive control, and real-time dynamic processing of operational data, however, an efficient digital twin needs to integrate multiple models (data-driven as well as physics-based) with explainability while at the same time maintain two-way synchronization with the physical facility at a time constant less than its operational cycle. In this work, we present the Purdue University Reactor One Digital Twin (PUR-1 DT), a cyber-physical digital twin with a complete high-fidelity physics-based and AI-driven virtual model stack (neutronics, thermal-hydraulics, point kinetics) which provides closed-loop explainable diagnostics, forecasting, predictive control, and action recommendation back to the reactor via two-way communications and a cyber-physical testbed. We demonstrate real-time synchronized state estimation and short-term forecasting over a full reactor operational cycle and conduct a series of benchmarking experiments to validate accuracy and latency. Our results show good agreement with experimental results and lay the groundwork for further development and experimental demonstration of DT-enabled functionalities in real-world facilities.