发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出普渡大学1号反应堆数字孪生（PUR-1 DT），整合高保真物理与AI模型栈，实现反应堆实时同步状态估计、预测与控制，为核系统数字孪生功能开发奠定基础。
AI 中文摘要
数字孪生技术有望提升核系统的运行灵活性与响应能力。然而，为了提供决策支持、网络事件表征、状态估计、预测控制以及运行数据的实时动态处理，高效的数字孪生需整合多个模型（数据驱动及基于物理的模型）并具备可解释性，同时需在小于设施运行周期的时间内与物理设施保持双向同步。本研究提出普渡大学1号反应堆数字孪生（PUR-1 DT），这是一种网络-物理数字孪生，拥有完整的高保真基于物理及AI驱动的虚拟模型栈（包含中子学、热工水力学、点动力学），可通过双向通信及网络-物理测试台向反应堆提供闭环可解释诊断、预测、预测控制及动作推荐。我们在完整反应堆运行周期内验证了实时同步状态估计与短期预测能力，并开展一系列基准实验以验证精度与延迟。结果显示与实验结果吻合良好，为现实设施中数字孪生支持功能的进一步开发与实验演示奠定基础。
英文摘要
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.
