Working to advance the nuclear renaissance

In the United States, nuclear energy plays a significant role in the national energy landscape, with 94 reactors currently in operation, more than any other country globally. Collectively, these reactors contribute nearly 20 percent of the nation's electricity supply. This achievement is recognized as a major milestone by Dean Price, an assistant professor at MIT's Department of Nuclear Science and Engineering (NSE) and the Atlantic Richfield Career Development Professor in Energy Studies. However, Price argues that more can be achieved from nuclear energy, particularly as the search for alternatives to fossil fuel-based power intensifies. His commitment to advancing nuclear technology stems from a desire to ensure that it meets the growing energy demands of the nation.

Price emphasizes the critical role of nuclear engineers in maintaining the infrastructure that supports carbon-free energy generation in the United States. He notes that the community of nuclear engineers is relatively small and tightly-knit, fostering a collaborative environment. This camaraderie is a motivating factor for Price, who has dedicated his career to the advancement of nuclear reactor design, aiming to enhance safety, economic viability, and reliability.

Research Focus and Innovations

Price's journey into nuclear engineering began during his undergraduate studies at the University of Illinois Urbana at Champaign, where he focused on the safety of storage methods for spent reactor fuel rods. His research confirmed that the current storage practices are safe, although the long-term disposal of these fuel casks remains unresolved. This early work laid the foundation for his subsequent research endeavors.

After transitioning to graduate studies at the University of Michigan in 2020, Price shifted his focus to multiphysics modeling, a complex area of study that examines the interplay between various physical processes within a nuclear reactor. This modeling approach contrasts with traditional methods that analyze individual processes in isolation. Two critical processes in this context are neutronics, which deals with neutron behavior and nuclear fission, and thermal hydraulics, which pertains to the cooling mechanisms that manage the heat generated during fission.

Price explains that multiphysics modeling can reveal how the heat produced in a reactor influences neutron behavior, which is essential for understanding reactor operations. For instance, the temperature of the fuel is vital when adjusting power levels or making operational changes. By correlating neutronics with thermal properties, Price's research aims to enhance predictive capabilities regarding reactor behavior under varying conditions.

While multiphysics modeling for existing light water reactors is well established, the methodologies for advanced reactors, such as small modular reactors (SMRs) and microreactors, are still developing. Price focuses on these advanced systems due to their potential for safer, more cost-effective, and flexible energy production.

The Role of Artificial Intelligence

One of the significant challenges in multiphysics modeling is the computational intensity required to solve complex nonlinear equations, often necessitating the use of supercomputers. To address this issue, Price is exploring the integration of artificial intelligence (AI) to streamline the modeling process. AI excels at identifying patterns within data, enabling it to uncover correlations between critical variables without the need to solve intricate equations.

For example, Price notes that AI could predict fuel temperature and the three-dimensional temperature distribution within a reactor core based on the reactor's power level. This capability could significantly reduce computational costs and enhance modeling efficiency.

Price envisions several applications where AI could be particularly beneficial, including the design of innovative reactor types. He emphasizes that AI would complement existing safety frameworks rather than replace them, ensuring that safety-critical systems remain robust while leveraging AI to fill knowledge gaps.

By utilizing machine learning models trained on extensive datasets, researchers can gain deeper insights into the relationships between essential physical processes. This understanding can lead to improved design decisions in the early stages of reactor development and facilitate more intelligent control strategies for safe and economical reactor operations.

Price's overarching goal is to harness the potential of AI to transform the nuclear industry, a field he believes is ripe for innovation. He feels well-positioned at MIT to drive advancements in nuclear technology while also nurturing the next generation of leaders in this critical sector.

In his teaching role, Price aims to instill the same passion for nuclear engineering that he experienced as a student. By fostering a supportive and engaging environment, he hopes to inspire his students to pursue their ambitions in nuclear science and engineering, ultimately contributing to the future of energy generation in the United States.