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Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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Nuclear News 40 Under 40 discuss the future of nuclear
Seven members of the inaugural Nuclear News 40 Under 40 came together on March 4 to discuss the current state of nuclear energy and what the future might hold for science, industry, and the public in terms of nuclear development.
To hear more insights from this talented group of young professionals, watch the “40 Under 40 Roundtable: Perspectives from Nuclear’s Rising Stars” on the ANS website.
Ronald E. Pevey
Nuclear Science and Engineering | Volume 152 | Number 1 | January 2006 | Pages 56-64
Technical Paper | doi.org/10.13182/NSE06-A2563
Articles are hosted by Taylor and Francis Online.
Most criticality safety calculations are performed using Monte Carlo techniques because of Monte Carlo's ability to handle complex three-dimensional geometries. For Monte Carlo calculations, the more histories sampled, the lower the standard deviation of the resulting estimates. Therefore, the common intuition is that the more histories the better; as a result, analysts tend to run Monte Carlo analyses as long as possible (or at least to a minimum acceptable uncertainty). For Monte Carlo criticality safety analyses, however, the optimization situation is complicated by the fact that procedures usually require that an extra margin of safety be added because of the statistical uncertainty of the Monte Carlo calculations. This additional safety margin affects the impact of the choice of the calculational standard deviation, both on production and on safety. This paper shows that, under the assumptions of normally distributed benchmarking calculational errors and exact compliance with the upper subcritical limit (USL), the standard deviation that optimizes production is zero, but there is a nonzero value of the calculational standard deviation that minimizes the risk of inadvertently labeling a supercritical configuration as subcritical. Furthermore, this value is shown to be a simple function of the typical benchmarking step outcomes - the bias, the standard deviation of the bias, the USL, and the number of standard deviations added to calculated k-effectives before comparison to the USL.