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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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April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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ARG-US Remote Monitoring Systems: Use Cases and Applications in Nuclear Facilities and During Transportation
As highlighted in the Spring 2024 issue of Radwaste Solutions, researchers at the Department of Energy’s Argonne National Laboratory are developing and deploying ARG-US—meaning “Watchful Guardian”—remote monitoring systems technologies to enhance the safety, security, and safeguards (3S) of packages of nuclear and other radioactive material during storage, transportation, and disposal.
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.