Argonne's Digital Twins Enhance Safety and Efficiency in Advanced Nuclear Reactors

Argonne's Virtual Models Pave the Way for Advanced Nuclear Reactors LEMONT, Ill. -- Digital twins, which are virtual replicas of real-world systems, are revolutionizing various industries by enhancing monitoring, prediction, and management capabilities. At the U.S. Department of Energy’s (DOE) Argonne National Laboratory, researchers have harnessed this technology to improve the efficiency, reliability, and safety of nuclear reactors. By leveraging advanced computer models and artificial intelligence (AI), these digital twins can predict reactor behavior in real-time, providing critical insights to operators. "Digital twin technology represents a significant leap in our ability to understand and manage advanced nuclear reactors. It allows us to predict and respond to changes with the speed and precision needed to ensure optimal performance," said Rui Hu, a principal nuclear engineer at Argonne. Digital twins enable scientists to simulate and predict the behavior of small modular reactors and microreactors under a variety of conditions. The Argonne team has successfully applied their methodology to two different types of reactors: the now-inactive Experimental Breeder Reactor II (EBR-II) and a new design, the generic Fluoride-salt-cooled High-temperature Reactor (gFHR). The EBR-II digital twin was used as a test case to validate the accuracy of the simulation models. At the heart of this technology lies graph neural networks (GNNs), a type of AI designed to process data structured in graph form. GNNs excel at recognizing intricate patterns and connections within systems, making them ideal for modeling the complex interactions of reactor components. By incorporating the physical layout of reactor systems and fundamental laws of physics, GNN-based digital twins offer a precise and reliable virtual representation of the real-world reactors. To train the GNN and conduct uncertainty quantification, the researchers relied on the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility. This high-performance computing resource played a crucial role in validating the models and ensuring their robustness. One of the primary advantages of GNN-based digital twins is their speed. Traditional simulations can take hours or even days to produce results, whereas digital twins can rapidly predict reactor behavior during various scenarios, such as fluctuations in power output or changes in cooling system performance. This is achieved through training on data generated by Argonne’s System Analysis Module (SAM), a sophisticated tool for analyzing advanced nuclear reactors. Once trained, the model can make accurate predictions using limited real-time sensor data, leading to better planning and decision-making, and ultimately reducing maintenance and operational costs. Moreover, digital twins can continuously monitor reactors to detect anomalies or unexpected behavior. When such issues arise, the system can recommend corrective actions to maintain safety and operational integrity. This capability not only enhances reactor safety but also contributes to more efficient and cost-effective operations. The adoption of digital twin technology at Argonne offers several benefits over conventional methods. By comprehensively understanding how all reactor parts interact, digital twins provide more reliable predictions. They are particularly useful for emergency planning, informed decision-making, and even the possibility of autonomous reactor operation in the future. This innovation is a crucial step towards the safe, reliable, and efficient deployment of advanced nuclear reactors, promising to extend the lifespan of components and minimize costs. In summary, Argonne National Laboratory’s digital twin technology, powered by graph neural networks and high-performance computing, is transforming the way we approach nuclear reactor management. It represents a significant advancement that could lead to safer, more efficient, and more cost-effective nuclear energy solutions.