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Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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Latest News
Matthew Marzano confirmed as newest NRC commissioner
A nuclear engineer, former reactor operator, and nuclear navy educator earned U.S. Senate approval today to take a seat on the Nuclear Regulatory Commission.
Matthew Marzano was confirmed in a 50–45 vote in the Senate and steps into an existing five-year term that will expire June 30, 2028. He joins the five-member commission, which has been without a tiebreaker vote since June 2023, when Jeff Baran’s term expired.
Marzano brings more than a decade of industry experience both working in nuclear plants and advising energy policy on Capitol Hill.
Paul Sermer, Fred M. Hoppe, Dan Pun-Quach, Keith R. Weaver, Charles Olive, Ismail Cheng
Nuclear Science and Engineering | Volume 178 | Number 2 | October 2014 | Pages 119-155
Technical Paper | doi.org/10.13182/NSE13-75
Articles are hosted by Taylor and Francis Online.
The response of a complex physical system is often evaluated by a tolerance interval for a percentile of the distribution of a variable of interest that is estimated by best-estimate codes. This tolerance interval is used as a test statistic to make decisions about the behavior of the system in the context of the specific safety issue. The most common methods to determine such tolerance intervals are order statistics methods leading to the so-called “95/95” level of safety standard. The application of these methods is predicated on the assumption that the simulated responses are identical to those of an actual reactor under the postulated conditions. We present here a novel statistical framework [referred to as EVS (extreme value statistics) methodology], not relying on the above assumption, for deriving tolerance limits involving data selected from a population that is different from the population of interest. Such a situation arises when the unobservable population is being estimated by an imperfect code and imperfect input. This leads us to distinguishing between “true” system stochastic (aleatory) variables and those resulting from the safety codes (subject to epistemic uncertainty). Methods using Monte Carlo sampling or sensitivity analysis, order statistics, and EVS methods produce different solutions as a consequence of a difference in how the true values and data are distinguished. This difference ultimately leads to different test statistics that are used to solve a decision-making problem. Closed-form expressions for the EVS-based tolerance limits are derived for a large class of models representing complex systems. Problems, both analytical and using actual reactor operating data, are presented and solved. EVS results demonstrate substantial improvements in operational and safety margins when compared to results obtained from existing methods used in the nuclear industry.