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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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
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.
C. J. Solomon, A. Sood, T. E. Booth, J. K. Shultis
Nuclear Science and Engineering | Volume 176 | Number 1 | January 2014 | Pages 1-36
Technical Paper | doi.org/10.13182/NSE12-81
Articles are hosted by Taylor and Francis Online.
A method for deterministically minimizing the cost of a single Monte Carlo tally employing weight-dependent weight-window variance reduction has been developed. This method relies on deterministic calculations of the tally's variance and average computational time per history, the product of which is the cost (inverse figure of merit) of the tally calculation. The tally's variance is deterministically computed by solving the history-score moment equations that describe the moments of the tally's score distribution, and the average time per history is computed by solving the future time equation that describes the expected amount of computational time a particle and its progeny require to process to termination. Both equations are solved by the Sn method. Results are presented for one- and two-dimensional problems that demonstrate increased calculation efficiency, by factors of 1.1 to 2, of the optimized problems over standard adjoint (importance) biasing.
