Problem Statement Risk-informed licensing methods being developed by the United States Nuclear Regulatory Commission allow for uncertainty-informed analysis, but techniques to apply this analysis for reactor design are unexplored. State-of-the-Art multiphysics modeling techniques currently applied to reactor concepts allow for unprecedented fidelity in performance and safety analyses but require vast computing resources to perform even a single regulatory study. Risk-informed analysis and the iterative design process require much faster techniques to compute sufficiently accurate results. Solution to be Explored A method for reduced-order modeling will be developed to map high-order reactor physics models to low-order models that are computationally more efficient. Linking the models while retaining both allows for risk-informed analysis with many low-order simulations while maintaining the more accurate results resolution of the high-order models. Phase I Phase I of this effort will demonstrate the feasibility of a risk-informed analysis for a complex modeling challenge that is crucial to demonstrating the safety of the reactor that will be computed from a reduced-order model faster than real-time simulation. The scope is limited to advanced particle fuel (TRISO) performance to demonstrate the full sequence of high-order modeling, reduction of order, and simulation for regulatory safety cases. Phase I culminates in simulating fuel temperature and fission product release throughout a microreactor core during normal operation, loss of cooling, and reactivity insertion accident scenarios. Commercial Applications and Other Benefits The capability demonstrated for particle fuel may be applied to all uncertainties in reactor design, such as irradiated and unirradiated material properties and geometry of various components in the system. When combined with comprehensive failure modes and effects analysis, the risk-informed approach developed here can be used to accelerate licensing for both microreactors and Small Modular Reactors. The risk-informed analysis also allows for cost optimization of production reactors;. This cost can then be returned to the end consumer, allowing for a risk-informed reduction of energy costs. In follow on work, the approaches developed here will be used to perform analysis for licensing and can be a regulatory test case for a risk-informed approach.
