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Division Spotlight
Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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
NRC engineers share their expertise at the University of Puerto Rico
Robert Roche-Rivera and Marcos Rolón-Acevedo are licensed professional engineers who work at the U.S. Nuclear Regulatory Commission. They are also alumni of the University of Puerto Rico–Mayagüez (UPRM) and have been sharing their knowledge and experience with students at their alma mater since last year, serving as adjunct professors in the university’s Department of Mechanical Engineering. During the 2023–2024 school year, they each taught two courses: Fundamentals of Nuclear Science and Engineering, and Nuclear Power Plant Engineering.
Travis A. Cunning, Paul Chan, Mahesh D. Pandey, Aniket Pant
Nuclear Technology | Volume 187 | Number 3 | September 2014 | Pages 270-281
Technical Paper | Fuel Cycle and Management | doi.org/10.13182/NT13-126
Articles are hosted by Taylor and Francis Online.
This study employs a novel approach to the prediction of CANDU [Canada deuterium uranium (reactor)] fuel reliability. Probability distributions are fitted to actual fuel manufacturing data sets provided by Cameco Fuel Manufacturing. They are used to form input for two industry-standard fuel performance codes: ELESTRES for the steady-state case and ELOCA for the transient case—a hypothesized 80% reactor outlet header break loss-of-coolant accident. Using a Monte Carlo technique for input generation, 105 independent trials are conducted, and probability distributions are fitted to key model output quantities. Comparing model output against recognized industrial acceptance criteria, no fuel failures are predicted for either case. Output distributions are well removed from failure limit values, implying that margin exists in current fuel manufacturing and design. To validate the results and attempt to reduce the simulation burden of the methodology, two dimensional reduction methods are assessed. Using just 36 trials, both methods are able to produce output distributions that agree strongly with those obtained via the brute-force Monte Carlo method, often to a relative discrepancy of <0.3% when predicting the first statistical moment and to a relative discrepancy of <5% when predicting the second statistical moment. In terms of global sensitivity, pellet density proves to have the greatest impact on fuel performance, with an average sensitivity index of 48.93% on key output quantities. Pellet grain size and dish depth are also significant contributors, at 31.53% and 13.46%, respectively. A traditional “limit of operating envelope” case is also evaluated. This case produces output values that exceed the maximum values observed during the 105 Monte Carlo trials for all output quantities of interest. In many cases the difference between the predictions of the statistical methods and the limit method is very prominent, and the highly conservative nature of the deterministic approach is demonstrated.