Advances in the MCNPX-­--PoliMi Code for Nuclear Safeguards Applications

Year
2014
Author(s)
P. Peerani - European Commission
J.L. Dolan - Department of Nuclear Engineering & Radiological Sciences
S. A. Pozzi - Department of Nuclear Engineering & Radiological Sciences
S.D. Clarke - Department of Nuclear Engineering & Radiological Sciences
E. W. Larsen - Department of Nuclear Engineering and Radiological Sciences
M. Flaska - Department of Nuclear Engineering & Radiological Sciences
M. L. Ruch - Department of Nuclear Engineering & Radiological Sciences
M. J. Marcath - Department of Nuclear Engineering & Radiological Sciences
T. H. Shin - Department of Nuclear Engineering & Radiological Sciences
E. Padovani - Polytechnic of Milan, Milan, Italy
Abstract
The -­--PoliMi extension to the MCNP code has been used for the past decade to simulate the response of organic scintillation detectors, particularly for correlated-­--particle measurement. In 2011, this extension has been made available through the Radiation Safety Information Computational Center RSICC as a patch to the MCNPX code, version 2.7.0, along with the associated detection post-­--processor, MPPost. MCNP-­--PoliMi and MCNPX-­--PoliMi have been used for simulations across a spectrum of applications including nuclear nonproliferation and international safeguards. MCNPX-­--PoliMi has been used to design and optimize several systems based on correlated particle detection such as a liquid scintillator multiplicity counter and a neutron/gamma-­--ray imaging system. The design of these systems, and other safeguards-­--relevant systems, relies on accurate simulation of event-­--by-­--event physics, a capability not present in the standard Monte Carlo codes. In addition, many safeguards systems rely on the detection of nuclear fission to verify declared materials, one key signature of which is correlated particle emissions. The full paper will present the current status of MCNPX-­--PoliMi features including models for nuclear fission, the detector response module, and advanced tallies for correlated detection. Each of these features will be demonstrated and supported with measured data.