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Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
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International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
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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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Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
Christine Latten, Dan G. Cacuci
Nuclear Science and Engineering | Volume 178 | Number 2 | October 2014 | Pages 156-171
Technical Paper | doi.org/10.13182/NSE13-110
Articles are hosted by Taylor and Francis Online.
This work illustrates reactor physics applications of the predictive modeling of coupled multiphysics systems (PMCMPS), formulated by Cacuci (2014), by means of the benchmarks Godiva (a bare uranium sphere) and Jezebel-239 and Jezebel-240 (bare plutonium spheres). The PMCMPS methodology was ab initio developed in the response space, to reduce as much as possible the computational memory requirements for predictive modeling of very large systems involving not only many model parameters but also many model responses. The model parameters considered in this work include individual cross sections for each material, nuclide, reaction type, and energy group, giving the following totals: 2241 parameters for Jezebel-239, 1458 parameters for Jezebel-240, and 2916 parameters for Godiva. Eight responses were considered for Jezebel-239 (the effective multiplication factor; the center core fission rates for 233U, 238U, 237Np, and 239Pu; and the center core radiative capture rates for 55Mn, 93Nb, and 63Cu). Three responses (the effective multiplication factor and the center core fission rates for 233U and 237Np) were selected for Jezebel-240, and eleven responses were selected for Godiva (the reaction rate types listed for Jezebel-239, along with the radiative capture rates for 107Ag, 127I, and 81Br). The PMCMPS methodology ensures that increasing the amount of information yields more accurate predictions, with smaller predicted uncertainties, as long as the considered information is consistent. This fact is amply illustrated in this work, which shows that the interdependence of responses that were measured in more than one benchmark is stronger than for responses that were measured in a single benchmark. More generally, the consideration of the complete information, including couplings, provided jointly by all three benchmarks (as opposed to consideration of the benchmarks as separate systems) leads to more accurate predictions of nominal values for responses and model parameters, yielding larger reductions in the predicted uncertainties that accompany the predicted mean values of responses and model parameters.