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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
X-energy receives federal tax credit for TRISO fuel facility
Advanced reactor company X-energy has been awarded $148.5 million in tax credits under the Inflation Reduction Act for construction of its TRISO-X fuel fabrication facility in Oak Ridge, Tenn.
Balazs Molnar, Gabor Tolnai, David Legrady
Nuclear Science and Engineering | Volume 190 | Number 1 | April 2018 | Pages 56-72
Technical Paper | doi.org/10.1080/00295639.2017.1413876
Articles are hosted by Taylor and Francis Online.
A novel particle tracking framework is introduced in this paper that utilizes null-collisions to sample distance to collision in Monte Carlo particle transport problems. The sampling process is described in the most general form as it covers all of the main developments concerning the Woodcock method (delta tracking). We show that none of the previously suggested modifications are optimal in terms of either variance or efficiency. Variance analysis is provided for a general transport problem along with the estimation of computational cost. Simplified models with analytic solutions are further investigated and propositions for optimal settings are discussed based on the derived equations. A well-known variance reduction technique, exponential transform, is found to be a limiting case of the biased Woodcock tracking method and comparison shows the proposed framework may outperform the exponential transform in real-case scenarios.