Working to advance the nuclear renaissance

2026-04-03 · Source: MIT News - Artificial intelligence · Field: Energy & Utilities — Nuclear Energy & Advanced Technologies, Artificial Intelligence & Machine Learning, Engineering & Applied Sciences · Depth: Intermediate, medium

Summary

MIT Assistant Professor Dean Price, from the Department of Nuclear Science and Engineering, advocates for expanding nuclear power in the United States, which currently operates 94 reactors providing nearly 20 percent of the nation's electricity. Price's research focuses on multiphysics modeling to understand interacting processes like neutronics and thermal hydraulics within reactor cores, crucial for predicting behavior under varying conditions. While established for 1,000 MW light water reactors, these models are less developed for advanced designs such as small modular reactors (SMRs, 20-300 MW) and microreactors (1-20 MW). Price is exploring artificial intelligence and machine learning to reduce the computational burden of these simulations, aiming to provide similar insights without solving complex nonlinear equations. His work, which began at MIT in September 2025, seeks to integrate AI into reactor design and operational control to enhance safety and economics, without directly interfacing with safety-critical systems.

Key takeaway

For nuclear engineers and research scientists focused on advanced reactor development, integrating AI into multiphysics modeling offers a path to significantly reduce computational overhead and accelerate design cycles. Your teams should explore machine learning applications for predicting reactor core behavior and optimizing operational control, ensuring AI's role remains supportive of established safety frameworks rather than replacing them. This approach can lead to safer, more economical, and flexible nuclear energy solutions.

Key insights

AI can significantly reduce computational costs in nuclear reactor design and operation by modeling complex physical interactions.

Principles

• Multiphysics modeling reveals critical process interactions.
• AI augments established nuclear safety procedures.
• Advanced reactors require new modeling approaches.

Method

Dean Price's research applies AI and machine learning to multiphysics modeling, correlating neutronics and thermal hydraulics to predict reactor behavior and optimize design without solving burdensome nonlinear differential equations.

In practice

• Use AI for novel reactor design assistance.
• Apply ML to predict fuel temperature distributions.
• Integrate AI for intelligent control decisions.

Topics

• Nuclear Energy
• Artificial Intelligence
• Multiphysics Modeling
• Advanced Reactors
• Reactor Design

Best for: AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT News - Artificial intelligence.