Researchers at the U.S. Department of Energy’s Argonne National Laboratory are using transformer-based models to speed up simulations of fluid dynamics in advanced nuclear reactors. The team is integrating transformer architectures into Argonne’s System Analysis Module (SAM), a simulation tool used to study advanced nuclear reactors, to improve turbulence modeling. By combining transformer-based models with turbulence simulations, the researchers aim to produce results that are both faster and more accurate than conventional approaches. The model has already demonstrated high accuracy in representing resistance to fluid flow and heat transfer. “With AI, we can be as accurate as the complex methods and as fast as the simple methods,” said Rui Hu, principle nuclear engineer and manager of the Safety and Engineering Analysis Department in Argonne’s Nuclear Science and Engineering Division (NSED). According to Argonne, AI-based models can produce simulation results almost instantaneously while maintaining the accuracy associated with more complex computational methods. The researchers’ next step is to apply the model to simulations of entire power plants, including the reactor, cooling, safety, auxiliary, and other supporting systems. The team is also exploring digital twins, virtual representations of physical systems that can operate and update in real time.