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2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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DOE, NNSA select partners for AI supercomputers
The Department of Energy, Argonne National Laboratory, NVIDIA, and Oracle have agreed to a public-private partnership to deliver the DOE’s largest AI supercomputers, named Solstice and Equinox.
Zack Taylor, Benjamin S. Collins, G. Ivan Maldonado
Nuclear Science and Engineering | Volume 196 | Number 5 | May 2022 | Pages 497-525
Technical Paper | doi.org/10.1080/00295639.2021.1996197
Articles are hosted by Taylor and Francis Online.
A numerical framework for modeling depletion and mass transport in liquid-fueled molten salt reactions is presented based on exponential time differencing. The solution method involves using the finite volume method to transform the system of partial differential equations (PDEs) into a much larger system of ordinary differential equations. The key part of this method involves solving for the exponential of a matrix. We explore six different algorithms to compute the exponential in a series of progression problems that explore physical transport phenomena in molten salt reactors. This framework shows good results for solving linear parabolic PDEs with each of the six matrix exponential algorithms. For large problems, the series solvers such as Padé and Taylor have large run times, which can be mitigated by using the Krylov subspace.