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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Deep Space: The new frontier of radiation controls
In commercial nuclear power, there has always been a deliberate tension between the regulator and the utility owner. The regulator fundamentally exists to protect the worker, and the utility, to make a profit. It is a win-win balance.
From the U.S. nuclear industry has emerged a brilliantly successful occupational nuclear safety record—largely the result of an ALARA (as low as reasonably achievable) process that has driven exposure rates down to what only a decade ago would have been considered unthinkable. In the U.S. nuclear industry, the system has accomplished an excellent, nearly seamless process that succeeds to the benefit of both employee and utility owner.
Victor Ontiveros, Adrien Cartillier, Mohammad Modarres
Nuclear Science and Engineering | Volume 166 | Number 3 | November 2010 | Pages 179-201
Technical Paper | doi.org/10.13182/NSE10-05
Articles are hosted by Taylor and Francis Online.
Fire simulation codes are powerful tools for use in risk-informed and performance-based approaches for risk assessment. Following initial work performed in a joint effort between the U.S. Nuclear Regulatory Commission and the Electric Power Research Institute of a verification and validation of five popular fire simulation codes and research performed at the University of Maryland to quantify total code output uncertainty following a “black-box” approach, this research presents a “white-box” methodology with the goal of also accounting for uncertainties within a simulation code prediction. In this paper the white-box probabilistic approach is discussed to assess uncertainties associated with fire simulation codes. Uncertainties associated with the input variables to the codes as well as the uncertainties associated with the submodels and correlations used inside the code are accounted for. To validate code output calculations, experimental tests may also be available to compare against code calculations. These experimental results may also be used in the assessment of the code uncertainties. Building upon earlier research on model uncertainty performed at the University of Maryland, the methodology employed to estimate the uncertainties is based on a Bayesian estimation approach. This Bayesian estimation approach integrates all evidence available to arrive at an estimate of the uncertainties associated with a reality of interest being estimated by the simulation code. Examples of applications with results of the associated uncertainties are discussed in this paper.