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Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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Excelsior University student section awarded community education grant
The American Nuclear Society Student Section at Excelsior University in Albany, N.Y., was awarded a $5,000 grant from the ANS Student Section Strategic Fund initiative for its program, Empowering Tomorrow’s Nuclear Innovators: A Collaborative Approach to Nuclear Technology Education and Awareness.
Ross Pivovar, Ole Wieckhorst
Nuclear Technology | Volume 205 | Number 7 | July 2019 | Pages 945-950
Regular Technical Paper | doi.org/10.1080/00295450.2018.1548220
Articles are hosted by Taylor and Francis Online.
All licensable critical heat flux (CHF) correlations/regressions models must determine and demonstrate a “design limit” that bounds the CHF correlation predicted/measured residuals via a 95/95 tolerance limit. This is a quick and straightforward calculation when the residuals are well behaved, exhibiting no trends and no heteroscedasticity. However, as models become increasingly complex and as required parameter ranges become more extended, the likelihood of nonconservative subregions increases. A suggested solution from the open literature is the overly conservative approach of basing the design limit on the subregion with the largest variance. This approach unavoidably overly constrains the overall regression model and often is too conservative for subregions due to a loss in degrees of freedom. Quantile regressions alleviate these issues by smoothly varying the design limit based on covariates and adapting to each subregion. Thus, a quantile regression achieves the objective of appropriately bounding all subregions without overly biasing the overall regression model.