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Nuclear reactors are critical to the U.S. Navy missions of maintaining global reach and maritime dominance. These systems provide the power and propulsion for U.S. Navy vessels, including submarines and aircraft carriers. Yet designing and building new nuclear propulsion plants can take decades. Researchers at the Software Engineering Institute at Carnegie Mellon are applying new AI models and methods that will significantly reduce the time needed for the design and construction of new nuclear propulsion plants while maintaining high safety and security standards.

Why it matters: As global threats evolve and naval missions grow more energy-intensive, the Navy needs next-generation propulsion systems that are safe, adaptable and efficient to maintain energy resilience, fleet readiness and technological superiority. Accelerating the design and deployment of programs for strategic advantage requires technologies and practices that can solve difficult AI, software and cybersecurity challenges.

Catch up quick: To meet energy and national defense demands, the U.S. needs to deploy Naval nuclear reactors more rapidly at a lower cost. Doing so will require bringing new technology online at an unprecedented rate. The Software Engineering Institute (SEI) at ��ɫ����TV is assisting the Naval Nuclear Laboratory (NNL) in accelerating propulsion plant design, analysis, verification and build to reduce the total design and build time of a propulsion plant from several decades to several years. Yet accelerating one element of plant development without others would create critical chokepoints and dramatically slow progress. The Department of Defense needs a comprehensive approach to ensure that all aspects of plant design and construction are aligned with new processes augmented by artificial intelligence (AI), in particular machine learning (ML).

Challenge: Using ML to improve U.S. Navy nuclear reactors

The safe and reliable operation of U.S. Navy nuclear power reactors is a national defense imperative. The SEI collaborates with the NNL to advance the use of AI, especially ML, to improve the safety, resilience, and assurance of operations of these plants.

The way forward: 

• Integrate ML to design better reactor plants: The SEI is assisting the NNL in developing a unified ML-centric propulsion plant design process. This applied research introduces new tools and processes that more efficiently create engineering process maps and ultimately accelerates propulsion plant design, analysis, verification, and build. This process will also reduce the time required for major reactor refueling overhauls by a factor of years.
• Use ML to detect reactor anomalies faster and avoid incidents: Naval nuclear propulsion plants, which are generally reliable, can experience anomalies that could lead to unexpected shutdowns, reduced power and hazardous conditions. The SEI is collaborating with the NNL to develop ML models to more quickly detect and resolve anomalies that could lead to unexpected shutdowns or critical safety failures in naval nuclear propulsion plants as well as minimize the likelihood of future anomalies. This effort will also deliver new operator interface concepts to promote trust in ML applications. The SEI and NNL also recognize the critical need to identify and analyze cybersecurity risks in all phases of reactor design and operations.

The bottom line: Opportunities exist to foster synergy between investments and innovations in major national security initiatives and the frontier of energy — and Carnegie Mellon is actively seizing those opportunities.