On the advantages of radial variation in assembly design for a nuclear reactor core

The geometric flexibility of additively manufactured metals and ceramics generates a vast, open design space requiring advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to inform the design space, to enhance evaluation of potential designs, and to generate optimized results more efficiently. This report documents efforts of the Transformational Challenge Reactor (TCR) program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, thus driving the evolution of the cooling channel shapes to minimize temperature peaking and material stress. The flexibility of additive manufacturing allows for the design of unique assemblies for each radial ring of the reactor core, which drives an increase in the selected performance metrics. One of the unique contributions made by this work is the capability to computationally demonstrate a 26.5% improvement in the selected reactor core performance metric by exploiting the freedom of axially variable assembly design for a nuclear reactor core. At the same pressure, the drop in the core decreased by a factor of 2.6, and the maximum temperature dropped by 10.7%. Additionally, the radially optimized design uses less materials, with a 15% decrease in total fuel and a 17.5% decrease in total moderator volume. These are the first quantitative results to allow for variation in assembly design in the core's different radial rings.