About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Advancement of Measurement Technologies for Harsh Environments
|
| Presentation Title |
Digital Twin Development of a High-Temperature Molten Salt System |
| Author(s) |
Xingang Zhao, Vineet Kumar, Wesley C. Williams, William Gurecky |
| On-Site Speaker (Planned) |
Xingang Zhao |
| Abstract Scope |
This presentation introduces a preliminary digital twin (DT) developed for the Facility to Alleviate Salt Technology Risks (FASTR), a molten salt testbed operating at temperatures up to 725°C. The DT combines physics-based modeling with data-driven techniques to enable real-time monitoring, simulation, and prediction under harsh thermal conditions. We applied dynamic mode decomposition to generate reduced-order models from simulation data and developed a Modelica–ONNX interface to integrate neural networks directly into system simulations. These capabilities allow the DT to transform high-frequency sensor data into actionable insights for process optimization and predictive maintenance. Hardware-in-the-loop integration has also been demonstrated, supporting virtual experimentation when the physical loop was offline. This talk will highlight how hybrid modeling and machine learning can bridge the gap between raw sensor data and autonomous decision-making in high-temperature systems, aligning with ongoing efforts to modernize instrumentation and controls in extreme environments. |