Title

A Source Biasing and Variance Reduction Technique for Monte Carlo Radiation Transport Modeling of Emission Tomography Problems

Abstract

A numerical radiation transport methodology for predicting gamma emission tomographs was developed utilizing the deterministic fuel burn-up software, ORIGEN, in the SCALE code package as a source definition input for Monte Carlo N Particle Transport ver. 6.1 to simulate gamma emission spectra from irradiated nuclear fuel and measured by an inorganic scintillator detector. Variance reduction utilized analytical expressions for the solid angle and field of view between source, collimator and detector to normalize the gamma energy spectrum from a non-analog monodirectionally biased beam source problem to approximate the equivalent analog problem of an isotropic source. One normalization scheme, which assumes that the source is distributed in a thin cylindrical volume, can achieve lower than 6% error and an order of 107 reduction in the computational cost. A different normalization scheme involving a truncated cone source distribution overestimated the count rate by approximately 45% but had similar computational savings. In both approaches, the accuracy and computational savings of the method improves with increasing collimator aspect ratio. This method is therefore useful for problems with high aspect ratio collimators.

Department(s)

Mining and Nuclear Engineering

Comments

This material is based upon work supported by the U.S. Department of Energy, Nuclear Energy University Programs, Project 17-13011, and by the U.S. Nuclear Regulatory Commission, Nuclear Education Program under Award NRC-HQ-13-G-38-0026.

Keywords and Phrases

Monte-Carlo N Particle Transport (MCNP); Oak Ridge Isotope Generation (ORIGEN); Variance Reduction

International Standard Serial Number (ISSN)

0236-5731; 1588-2780

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Akadémiai Kiadó, All rights reserved.

Publication Date

01 Apr 2019

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