A Real-Time Digital Twin Simulation Platform for the AHPR1000 Safety System Test Facility

To address the high risk of high-temperature and high-pressure physical testing, the long testing cycle, and the low efficiency of control logic integration in integrated safety system tests for advanced pressurized water reactors, this study proposes a real-time digital twin simulation platform for the AHPR1000 integrated safety system test facility based on closed-loop coupling between a mechanistic model and a virtual distributed control system (DCS). According to the requirements of steady-state operation, accident transient simulation, control logic verification, and operating condition reproduction, a full-scope thermal-hydraulic model covering 14 subsystems, including the reactor coolant system, pressurizer system, main steam system, feedwater system, safety injection system, and residual heat removal system, was established. A large-scale tag mapping, data interaction, and operating state management mechanism was further developed between the model side and the virtual DCS side. The platform supports real-time monitoring and control of more than 3000 tags and provides functions such as freeze/unfreeze, reset, snapshot saving, online parameter tuning, and multi-scenario switching. The steady-state validation results show that the platform reproduces key thermal-hydraulic parameters with good accuracy and can operate continuously and stably for more than 100 h. The transient validation results, taking a 2-inch cold-leg small-break loss-of-coolant accident as an example, demonstrate that the platform can reasonably reproduce key processes including primary loop depressurization, safety system actuation, tank level variation, and accident mitigation. The results indicate that the proposed method can effectively balance system-level physical fidelity, real-time performance, and closed-loop control integration requirements, and can provide technical support for safety system testing, control strategy verification, operator training, and digital twin applications in advanced nuclear power systems.