Master of Science / Department of Mechanical and Nuclear Engineering / William L. Dunn / This report investigates the feasibility of a standoff interrogation method to identify nitrogen-rich explosive samples shielded by other materials (“clutter”) using neutron beams from Cf-252 and deuterium-tritium (D-T) generator sources. Neutrons from the beams interact with materials in the target to produce inelastic-scatter gamma rays, and, after slowing down to thermal energies, prompt-capture gamma rays. By detection of these gamma rays, a response vector is formed that is used to calculate a figure-of-merit, whose value is dependent upon the contents of the target. Various target configurations, which include an inert-material shield and a sample that may or may not be explosive, were simulated using the MCNP5 code. Both shielding and collimation of 14.1-MeV neutron beams were simulated to produce effective neutron beams for target interrogation purposes and to minimize dose levels. Templates corresponding to particular target scenarios were generated, and their effectiveness at nitrogen-rich explosive identification was explored. Furthermore, methods were proposed yielding more effective templates including grouping target responses by density and composition. The results indicate that neutron-based interrogation has potential to detect shielded nitrogen-rich explosives. The research found that using a tiered filter approach, in which a sample must satisfy several template requirements, achieved the best results for identifying the explosive cyclonite (RDX). A study in which a 14.1-MeV neutron beam irradiated a target containing a shielded sample, which could either be explosive (RDX) or inert, yielded no false negatives and only 2 false positives over a large parameter space of clutter-sample combination.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/2166 |
| Date | January 1900 |
| Creators | Johll, Mark |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.002 seconds