The PUR-1 Cyber-Physical Digital Twin

2026-08-30

AI Summary

该研究通过开发一种集成高保真物理模型和AI驱动的虚拟模型堆栈的PUR-1 DT，解决了核系统中实时状态估计、预测控制等问题。

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.

Problem

• digital twin
• nuclear systems
• operational flexibility
• real-time synchronization
• model integration

Innovation

• cyber-physical digital twin
• high-fidelity physics-based models
• AI-driven virtual model stack
• closed-loop explainable diagnostics
• two-way synchronization