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Division Spotlight
Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
Meeting Spotlight
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
Oklo to collaborate with Atomic Alchemy on isotope production
Fast reactor developer Oklo, which recently went public on the New York Stock Exchange, announced on May 13 that it has signed a memorandum of understanding with Atomic Alchemy to cooperate on the production of radioisotopes for medical, energy, industry, and science applications.
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.