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NRC proposes changes to its rules on nuclear materials
In response to Executive Order 14300, “Ordering the Reform of the Nuclear Regulatory Commission,” the NRC is proposing sweeping changes to its rules governing the use of nuclear materials that are widely used in industry, medicine, and research. The changes would amend NRC regulations for the licensing of nuclear byproduct material, some source material, and some special nuclear material.
As published in the May 18 Federal Register, the NRC is seeking public comment on this proposed rule and draft interim guidance until July 2.
Matthew J. Marcath, Shaun D. Clarke, Brian M. Wieger, Enrico Padovani, Edward W. Larsen, Sara A. Pozzi
Nuclear Science and Engineering | Volume 181 | Number 1 | September 2015 | Pages 72-81
Technical Paper | doi.org/10.13182/NSE14-89
Articles are hosted by Taylor and Francis Online.
Monte Carlo particle transport codes used to model detector responses are traditionally run in analog mode. However, analog simulations of cross-correlation measurements are extremely time-consuming because the probability of coincident detection is small, approximately equal to the product of the probabilities of a single detection in each detector. The new implicit correlation method described here increases the number of correlated event scores, thereby reducing variance and required computation times. The cost of the implicit correlation method is comparable to the cost of simulating single-event detection for the lowest absolute detector efficiency in the problem. The new method is especially useful in the nuclear nonproliferation and safeguards fields for simulating correlation measurements of shielded special nuclear material.
The new method was implemented in MCNPX-PoliMi for neutron-neutron cross-correlations with a 252Cf spontaneous fission source measured by 14 detectors at various angles. The method demonstrated good agreement with analog simulation and reference measurement results. Small differences between nonanalog and analog cross-correlation distributions are attributed to discretization errors that are often not present in practical applications. Improvement in the figure of merit was greater than a factor of 100 in all tested cases.