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Division Spotlight
Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
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.