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Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
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.