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Tech giants and nuclear leaders make news at CERAWeek
Microsoft and Nvidia have formed an “AI for nuclear” partnership intended to streamline the permitting, design, and operations of nuclear power plant facilities, and highlighted the collaboration at CERAWeek 2026 in Houston earlier this week.
Microsoft said in an announcement that the collaboration will build a “connected, AI-powered foundation” of AI tools that energy developers will be able to use to make work “repeatable, traceable, secure, and predictable,” all the while reducing work timelines and maintaining safety.
K. Lisa Reed, Farzad Rahnema
Nuclear Technology | Volume 208 | Number 3 | March 2022 | Pages 562-574
Technical Note | doi.org/10.1080/00295450.2021.1935166
Articles are hosted by Taylor and Francis Online.
Previous work presented a set of stylized three-dimensional benchmark problems based on the Oak Ridge National Laboratory (ORNL) preconceptual design of a fluoride-salt-cooled small modular advanced high-temperature reactor, or SmAHTR, with prismatic assemblies fueled by tri-isotropic (TRISO) particles. That previous work created a detailed description of the benchmark problems by closing several outstanding design gaps from the ORNL preconceptual design report, notably by addressing the lack of active control mechanisms, for which control rod “bundles” were implemented.
In this technical note, the creation of two additional stylized benchmark problem sets based on that past work is detailed, offering two new control rod configurations. The fluoride salt, small size, and highly heterogeneous TRISO-fueled pins make these additional benchmark problem sets useful numerical validation references in benchmarking neutronics tools against continuous-energy stochastic Monte Carlo results. Detailed reference results, including the eigenvalue (keff) and 1/11th assembly-averaged relative fission density distributions, are provided for both control rod configurations in full-core cases with all control rods withdrawn and all control rods fully inserted. A near-critical core benchmark problem and results are provided for one configuration. The provided results are calculated using the continuous-energy Monte Carlo code MCNP.