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The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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NRC begins special inspection at Hope Creek
The Nuclear Regulatory Commission is conducting a special inspection at Hope Creek nuclear plant in New Jersey to investigate the cause of repeated inoperability of one of the plant’s emergency diesel generators, the agency announced in a February 25 news release.
M. W. Mickael
Nuclear Science and Engineering | Volume 119 | Number 1 | January 1995 | Pages 34-43
Technical Paper | doi.org/10.13182/NSE95-A24069
Articles are hosted by Taylor and Francis Online.
A fast automated method is developed to estimate particle importance in the Los Alamos Monte Carlo code MCNP. It provides an automated and efficient way of predicting and setting up an important map for the weight windows technique. A short analog simulation is first performed to obtain effective group parameters based on the input description of the problem. A solution of the multigroup time-dependent adjoint diffusion equation is then used to estimate particle importance. At any point in space, time, and energy, the particle importance is determined, based on the calculated parameters, and used as the lower limit of the weight window. The method has been tested for neutron, photon, and coupled neutron-photon problems. Significant improvement in the simulation efficiency is obtained using this technique at no additional computer time and with no prior knowledge of the nature of the problem. Moreover, time and angular importance that are not available yet in MCNP are easily implemented in this method.