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The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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Nuclear Science and Engineering
Fusion Science and Technology
The consequences of closure: The local cost of shutting down a nuclear power plant
When on May 7, 2013, the Kewaunee nuclear power plant in rural Wisconsin was shut down, it took with it more than 600 full-time jobs and more than $70 million in lost wages, not including temporary employment from refueling and maintenance outages. Taking into account indirect business-to-business activity, the total economic impact of the closure of the single-unit pressurized water reactor was estimated to be more than $630 million to the surrounding three-county area.
Sterling M. Harper, Paul K. Romano, Benoit Forget, Kord S. Smith
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 1009-1015
Technical Paper | dx.doi.org/10.1080/00295639.2020.1719765
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) transport codes offer high-fidelity modeling of particle transport physics, but their high computational cost makes them impractical for many applications. For some applications such as multiphysics and depletion that use finely discretized geometries, a large portion of this computational cost is attributable to ray tracing. Neighbor lists are a well-known method for accelerating ray-tracing calculations in a MC code, but despite their prevalence, little work has been published on the details of their implementation. The fine details can have a significant impact on performance, particularly when using shared-memory parallelism. This paper addresses these details of implementation with a discussion of different neighbor list schemes and their impact on software runtime.
Performance tests were run by using OpenMC on a pin-cell problem discretized with up to 200 axial regions. The results demonstrate that switching from surface-based to cell-based neighbor lists leads to a 10 faster calculation rate for the most fine discretization. Furthermore, using a threadsafe shared-memory data structure results in a 20% faster calculation rate versus simple threadprivate neighbor lists. Results here show that a data structure that is contiguous in memory improves performance by only 1% to 2% over noncontiguous linked lists.