Reactor Geometry

Reactor geometry optimization is a crucial area of research aiming to improve reactor efficiency, safety, and economic viability. Current efforts leverage machine learning, particularly deep neural networks, Bayesian optimization, and reinforcement learning algorithms, to explore high-dimensional design spaces and optimize reactor performance parameters such as power distribution and fuel cycle length. These advanced computational methods enable efficient exploration of complex reactor designs and accelerate the development of next-generation reactors, including microreactors and small modular reactors, with improved operational characteristics. The resulting improvements in design and control algorithms have significant implications for both reactor safety and economic competitiveness.