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2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
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AI and productivity growth
Craig Piercycpiercy@ans.org
This month’s issue of Nuclear News focuses on supply and demand. The “supply” part of the story highlights nuclear’s continued success in providing electricity to the grid more than 90 percent of the time, while the “demand” part explores the seemingly insatiable appetite of hyperscale data centers for steady, carbon-free energy.
Technically, we are in the second year of our AI epiphany, the collective realization that Big Tech’s energy demands are so large that they cannot be met without a historic build-out of new generation capacity. Yet the enormity of it all still seems hard to grasp.
or the better part of two decades, U.S. electricity demand has been flat. Sure, we’ve seen annual fluctuations that correlate with weather patterns and the overall domestic economic performance, but the gigawatt-hours of electricity America consumed in 2021 are almost identical to our 2007 numbers.
Mustafa K. Jaradat, Namjae Choi, Abdalla Abou-Jaoude
Nuclear Science and Engineering | Volume 198 | Number 12 | December 2024 | Pages 2403-2436
Research Article | doi.org/10.1080/00295639.2024.2306702
Articles are hosted by Taylor and Francis Online.
The molten salt reactor (MSR) flowing-fuel simulation capability of the Griffin-Pronghorn-coupled multiphysics code system developed by Idaho National Laboratory (INL) was verified against the Center National de la Recherche Scientifique (CNRS) MSR benchmark problem. Griffin and Pronghorn, which are INL’s neutronics and thermal-hydraulics codes built upon the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, have been recently extended to handle the flowing fuel of MSRs causing the drift of delayed neutron precursors (DNP). In the Griffin-Pronghorn code system, Griffin provides the fission rate density to Pronghorn, which simulates the generation, decay, and transport of DNPs along with the fluid, and the redistributed DNP densities are fed back to Griffin. The coupling and transfers are largely automatically managed at the framework level by the powerful MultiApp system of MOOSE. The verification results against the CNRS benchmark problem demonstrate that the Griffin-Pronghorn code system can accurately simulate the unique physics phenomena of MSRs in both steady-state and transient conditions.