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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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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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Deep Isolation asks states to include waste disposal in their nuclear strategy
Nuclear waste disposal technology company Deep Isolation is asking that the National Association of State Energy Officials (NASEO) consider how spent nuclear fuel and radioactive waste will be managed under its strategy for developing advanced nuclear power projects in participating states.
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.