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Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
Meeting Spotlight
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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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Christmas Night
Twas the night before Christmas when all through the houseNo electrons were flowing through even my mouse.
All devices were plugged in by the chimney with careWith the hope that St. Nikola Tesla would share.
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.