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Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
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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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Latest News
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.
Aldo Dall'Osso
Nuclear Science and Engineering | Volume 154 | Number 2 | October 2006 | Pages 241-246
Technical Paper | doi.org/10.13182/NSE06-A2630
Articles are hosted by Taylor and Francis Online.
The accuracy of a neutronics model depends not only on the validity of the equations that are solved but also on the quality of the cross-section model. This last is currently constituted by a set of correlations, the parameterized tables, relating the data of the neutronics problem to the local conditions. The more the correlations represent the local conditions, the more the results will be accurate. For a simulation model, this means that the results will be closer to the measurements. The goal of the data identification method presented is to solve a constrained inverse problem and to obtain the parameters of some further correlations that will enhance the accuracy of the results. The constraint imposed minimizes the error committed in solving the diffusion equation, using as reference the results of a more accurate computer code or the measurements performed for in-core flux maps. Some purely numerical examples and an application in conjunction with in-core measurements illustrate the method.