Researchers at Idaho National Laboratory (INL) recently performed their first digital twin test of the Microreactor Agile Non-nuclear Experimental Testbed (MAGNET) and captured the demonstration in a video posted July 14. The digital twin—a virtual representation of a microreactor—was built using advancements in remote monitoring, autonomous control, and predictive capabilities that could help lower operating costs of microreactor technologies and enhance their safety.
MAGNET: Researchers built a virtual model of MAGNET using sensor data and open-source technologies to create a consistent flow of information and permit real-time data sharing under different operating conditions.
They found that the digital twin successfully predicted future heat pipe temperatures and detected trends toward unfavorable threshold temperatures. The virtual model could then autonomously control the heat pipe by adjusting its temperature. The researchers used a separate computer system to capture a 3D model of the heat pipe, along with sensor temperatures.
Toward deployment: “The success of this digital twin could revolutionize the advanced nuclear industry,” said Jeren Browning, a digital engineering researcher at INL supporting the MAGNET project. “Autonomous control is a significant cost-savings and safety feature that will enable new microreactors to come online more readily and quickly meet regulatory standards.”
MAGNET was developed through the Department of Energy’s Microreactor Program to help national labs, universities, and industry partners test microreactor technologies. As a nonnuclear test bed, MAGNET uses electrical heating elements and heat pipes to simulate the behavior and main components of microreactor concepts.
MAGNET’s twin: More information about MAGNET’s digital twin is available from DICE—the Digital Innovation Center of Excellence—a network for digital engineering that operates virtually out of INL to coordinate digital engineering for both nuclear and nonnuclear energy systems under development to detect and prevent problems before they impact the schedule and budget of a major project.
According to DICE, MAGNET’s digital twin uses the DeepLynx data warehouse and MOOSE multiphysics framework to combine sensor data gathering, multiphysics simulation, machine-learning forecasting, and asset control via integration to the MAGNET Data Acquisition System. Users can view the status of MAGNET, its twin, predictions, and asset control via a user dashboard.
Once validated within a nonnuclear environment, the digital twin framework could be enhanced and deployed in conjunction with power-producing microreactors to deliver benefits such as reduced operations cost, predictive maintenance, forecasting of potential problems or anomalies, and remote monitoring.