Nuclear power stands as America's largest source of carbon-free electricity, outperforming solar and wind combined, positioning it as a crucial component in global climate change mitigation strategies. However, with the U.S. nuclear infrastructure aging rapidly, facility operators face mounting pressure to optimize operational efficiency to remain competitive against conventional coal and natural gas plants.
The reactor core represents one of the most critical areas for cost reduction, where nuclear fission occurs. Through strategic positioning of fuel rods that drive these reactions, plants can achieve significantly reduced fuel consumption and maintenance requirements. While nuclear engineers have spent decades refining layout designs to extend the lifespan of expensive fuel rods through traditional trial-and-error methods, artificial intelligence now emerges as a transformative force in this domain.
Researchers from MIT and Exelon have demonstrated that by gamifying the reactor design process, AI systems can generate dozens of optimal configurations that extend fuel rod lifespan by approximately 5%. This breakthrough translates to estimated annual savings of $3 million for a typical nuclear power plant, according to the research team. Beyond cost savings, the AI system identifies superior solutions more rapidly than human engineers and enables swift design modifications within a safe, simulated environment. These groundbreaking findings have been published in the prestigious journal Nuclear Engineering and Design.
"This revolutionary technology holds universal applicability across all nuclear reactors worldwide," notes the study's senior author, Koroush Shirvan, an assistant professor in MIT's Department of Nuclear Science and Engineering. "By enhancing the economic viability of nuclear energy—which currently generates 20% of U.S. electricity—we can contribute to limiting global carbon emissions while attracting top talent to this vital clean energy sector."
Within standard reactors, fuel rods are arranged in grid-like assemblies based on their uranium and gadolinium oxide content, resembling strategic placement of chess pieces. In this configuration, radioactive uranium drives reactions while rare-earth gadolinium serves as a moderating influence. The optimal layout achieves perfect equilibrium between these competing forces to maximize reaction efficiency. Traditional algorithmic approaches have shown limited success in improving upon human-designed layouts, particularly given the astronomical number of possible configurations in a typical 100-rod assembly.
The research team explored whether deep reinforcement learning—an AI technique that has demonstrated superhuman proficiency in complex games like chess and Go—could accelerate the optimization process. This cutting-edge approach combines deep neural networks, which excel at identifying patterns within massive datasets, with reinforcement learning, which associates learning processes with reward signals such as winning games or achieving high scores.
In this application, researchers trained their AI agent to position fuel rods according to specific constraints, awarding points for each advantageous placement. These carefully selected constraints embody decades of expert knowledge grounded in fundamental physics principles. For instance, the AI agent might earn points by strategically placing low-uranium rods along assembly edges to moderate reactions, distributing gadolinium "control" rods to ensure consistent burn levels, and maintaining the number of control rods within the optimal range of 16-18.
"Once we establish the fundamental rules, the neural networks begin making remarkably intelligent decisions," explains lead author Majdi Radaideh, a postdoc in Shirvan's lab. "The system avoids wasting time on random experimentation. It was fascinating to observe the AI learning to navigate the design process with human-like strategic thinking."
While reinforcement learning has enabled AI systems to master increasingly complex games at or above human performance levels, the technology's practical applications in real-world scenarios remain relatively unexplored. This research demonstrates the potentially transformative impact of reinforcement learning on critical industrial challenges.
"This study represents an exciting demonstration of transferring AI gaming techniques to solve practical, real-world problems," says co-author Joshua Joseph, a research scientist at the MIT Quest for Intelligence. "The implications extend far beyond nuclear energy, potentially revolutionizing how we approach complex engineering challenges across multiple industries."
Exelon is currently testing a beta version of this AI system in virtual environments simulating assemblies within boiling water reactors, as well as approximately 200 assemblies within pressurized water reactors—the most prevalent reactor type globally. As America's largest nuclear operator with 21 reactors across the United States, Exelon could potentially implement this system within operational facilities within one to two years, according to company representatives.
The research team additionally included Isaac Wolverton, an MIT senior who participated through the Undergraduate Research Opportunities Program; Nicholas Roy and Benoit Forget of MIT; and James Tusar and Ugi Otgonbaatar of Exelon.
