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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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ANS Student Conference 2025
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.
M. M. R. Williams
Nuclear Science and Engineering | Volume 193 | Number 4 | April 2019 | Pages 327-345
Technical Paper | doi.org/10.1080/00295639.2018.1531620
Articles are hosted by Taylor and Francis Online.
A number of approximate probability distribution functions (pdf’s) for the neutron density are examined with reference to low source startup. The most accurate method for determining the safe source strength, to reduce the likelihood of a rogue transient during startup, is that arising from the Pál-Bell equations. When these equations are extended to include space and energy dependence the numerical work becomes extensive. A pdf is developed which gives results that compare favorably with those from the exact solution but requires very much less numerical work. The method is applicable to space- and energy-dependent problems. Extensive numerical examples are given of the new method and of others which have been proposed over the years. In addition, we explore other approximations, unrelated to the generating function, that can lead to substantial computational savings. We have additionally described the principles behind, and provided a simple review of, the low source algorithm from which anyone unfamiliar with low source concepts can benefit.