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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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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Grant awarded for advanced reactor workforce needs in southeast U.S.
North Carolina State University and the Electric Power Research Institute have been awarded a $500,000 grant by the NC Collaboratory for “An Assessment to Define Advanced Reactor Workforce Needs,” a project that aims to investigate job needs to help enable new nuclear development and deployment in North Carolina and surrounding areas.
M. M. R. Williams, Edward W. Larsen
Nuclear Science and Engineering | Volume 139 | Number 1 | September 2001 | Pages 66-77
Technical Paper | doi.org/10.13182/NSE01-A2222
Articles are hosted by Taylor and Francis Online.
The majority of earlier work on neutron transport in spatially random media has relied on special models of the random process, closure techniques or perturbation theory. The purpose of the present paper is to further develop a technique, which employs the source-sink method and simulation, and which in principle leads to exact probability distributions, to assess the accuracy of such approximate methods. To this end, we also use perturbation theory, and extend it to eigenvalue problems thereby enabling random fluctuations in reactivity to be studied and some measures of their statistical properties to be calculated. We have found, by comparing results for the variance in the reactivity fluctuations with essentially exact values, that the perturbation method is an accurate way to deal with stochastic equations and is far more efficient numerically than the more exact simulation method.