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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
Nano Nuclear wins Air Force contract for Kronos MMR
New York City–based advanced nuclear technology developer Nano Nuclear Energy has been awarded a Direct-to-Phase II Small Business Innovation Research contract for its Kronos micro modular reactor (MMR) by AFWERX, the innovation and venture arm of the U.S. Air Force. The contract calls for AFWERX, with the 11th Civil Engineering Squadron, to explore the feasibility of deploying the Kronos MMR Energy System at Joint Base Anacostia-Bolling (JBAB) in Washington, D.C.
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 | 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.