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New laws offer nuclear industry incentives for existing power plant uprates
This year, the U.S. nuclear industry received a much-needed economic boost that could help preserve operating nuclear power plants and incentivize upgrades that extend their lifespan and power output.
Signed into law in 2022, the Inflation Reduction Act offers production tax credits (PTCs) for existing nuclear power plants and either PTCs or investment tax credits (ITCs) for new carbon-free generation. These credits could make power uprates—increasing the maximum power level at which a commercial plant may operate—a much more appealing option for utilities.
D. F. Hollenbach, L. M. Petri, H. L. Dodds
Nuclear Science and Engineering | Volume 116 | Number 3 | March 1994 | Pages 147-164
Technical Paper | doi.org/10.13182/NSE94-A19810
Articles are hosted by Taylor and Francis Online.
The object of this research project is to develop a vectorized version of the KENO-V.a criticality safety code, benchmark it against the original version of the code, and determine its speedup factor for various classes of problems. The current generation of supercomputers is equipped with vector processors that allow the same operation to be simultaneously performed on a string of data. Unfortunately, the Monte Carlo algorithm used in KENO-V.a, which tracks particles individually, cannot utilize these vector processors. A new Monte Carlo algorithm that would efficiently utilize the vector processors currently used in computers is needed. The algorithm developed for the vectorized version of KENO-V.a is an event-based, stack-driven, all-zone, implicit-stack Monte Carlo algorithm. This algorithm divides the particles into one of four main stacks: free flight, inward crossing, outward crossing, or collision. A fifth stack, kill, contains all particles that have either leaked from the system or have been terminated by Russian roulette. The main stack, containing the largest number of particles, is the next stack processed. All the particles in the longest stack are processed simultaneously. The generation is complete when the four main stacks are empty. Only the particle number is transferred between stacks; the particle data remain in permanent vector locations and are updated as the particles traverse through the system. This approach minimizes data transfer between stacks and optimizes the vector length, thus maximizing the speedup. For the 25 benchmark problems, speedup factors ranging from 1.8 to 5.7 relative to the optimized scalar version of KENO-V.a were obtained. Problem geometry, material composition, and the number of histories per generation—all have significant effects on the speedup factor of a problem.