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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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2024 ANS Annual Conference
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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
G7 pledges support for nuclear at Italy meeting
The Group of Seven (G7) recommitted its support for nuclear energy in the countries that opt to use it at a Ministerial Meeting on Climate in Italy last month.
In a statement following the April meeting, the group committed to support multilateral efforts to strengthen the resilience of nuclear supply chains, referencing the goal set by 25 countries during last year’s COP28 climate conference in Dubai to triple global nuclear generating capacity by 2050.
Emily H. Vu, Aaron J. Olson
Nuclear Science and Engineering | Volume 197 | Number 2 | February 2023 | Pages 212-232
Technical Paper | doi.org/10.1080/00295639.2022.2116378
Articles are hosted by Taylor and Francis Online.
Conditional Point Sampling (CoPS) is our newly proposed Monte Carlo method for transport in stochastic media that has been demonstrated to achieve highly accurate mean response results and to compute variance of the mean caused by random spatial mixing. The ability of CoPS to efficiently characterize the effects of random spatial mixing beyond the mean is hindered by the algorithm’s potentially unbounded computer memory footprint. Thus, in previous work, we established two limited-memory techniques for CoPS to improve required computer memory, i.e., recent memory (RM) CoPS and amnesia radius (AR) CoPS, the latter of which enables CoPS to tractably compute probability density functions (PDFs) of response. In this work, we create a limited-memory framework that allows CoPS to combine the advantages of limited-memory techniques and populate the framework with the two inaugural techniques of RM and AR. The proposed framework enables the user to control the computational performance of CoPS by making problem-specific trade-offs between accuracy, computer memory footprint, and characterization of response distributions based on input parameters. We present mean leakage results, material-dependent scalar flux, leakage PDFs, and computer memory footprint computed using this new framework. By selecting different input parameters in our proposed limited-memory framework, CoPS is demonstrated to roughly match the accuracy and computer memory footprint of the established approximate method Chord Length Sampling or to provide response distribution information comparable to the brute-force benchmark approach while improving the computer memory footprint compared to the original CoPS algorithm.