ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Mar 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
March 2026
Nuclear Technology
February 2026
Fusion Science and Technology
April 2026
Latest News
Kentucky disburses $10M in nuclear grants
The Kentucky Nuclear Energy Development Authority (KNEDA) recently distributed its first awards through the new Nuclear Energy Development Grant Program, which was established last year. In total, KNEDA disbursed $10 million to a variety of companies that will use the funding to support siting studies, enrichment supply-chain planning, workforce training, and curriculum development.
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.