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 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
Latest Magazine Issues
Jun 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
July 2026
Nuclear Technology
June 2026
Fusion Science and Technology
May 2026
Latest News
North American construction is back—smaller and faster—at OPG’s Darlington
“The nuclear renaissance is real here,” said Ontario Power Generation’s Subo Sinnathamby on May 8, one year to the day after OPG secured a final investment decision to build the first of four planned BWRX-300 reactors at its Darlington nuclear power plant, and shortly after the new reactor’s foundation was lifted into place. “We got our license to construct in April and our [final investment decision] in May, and we’ve been off to the races since.”
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.