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Conference Spotlight
2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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
Nuclear News 40 Under 40: The wait is over
Following the enthusiastic response from the nuclear community in 2024 for the inaugural NN 40 Under 40, the Nuclear News team knew we had to take up the difficult task in 2025 of turning it into an annual event—though there was plenty of uncertainty as to how the community would receive a second iteration this year. That uncertainty was unfounded, clearly, as the tight-knit nuclear community embraced the chance to celebrate its up-and-coming generation of scientists, engineers, and policy makers who are working to grow the influence of this oft-misunderstood technology.
Dumitru Serghiuta, John Tholammakkil
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1513-1528
Critical Review | doi.org/10.1080/00295450.2019.1570751
Articles are hosted by Taylor and Francis Online.
This paper reviews the attributes and challenges of applying the functional failure concept and the use of Best-Estimate Plus Uncertainty methods in evaluating protective systems in the risk space. As an illustrative example, the paper uses the case of the effectiveness of CANada Deuterium Uranium (CANDU) reactor shutdown systems. A risk-informed formulation is first introduced for estimation of a reasonable limit for functional failure probability using the Swiss Cheese model. In the real application, there are several challenges in realistically estimating probabilities of exceeding a prescribed design or regulatory limit. Key challenges discussed in this critical review include the use of complex, computationally intensive predictive models; modeling completeness; assumptions about input distributions; validation; separation of uncertainties; and selection of statistical model and algorithms. The use of hybrid deterministic-probabilistic methods may address these challenges to a certain extent.