Trafficking of radioactive material, particularly special nuclear material (SNM), has long been a worldwide concern. To interdict this material the US government has installed radiation portal monitors (RPMs) around the globe. Building materials surrounding an RPM can greatly effect the detector’s background radiation levels due to Naturally Occurring Radioactive Material (NORM). In some cases this effect is so great that the initial RPM setup had to be rebuilt. This thesis develops a methodology for quick and efficient determination of the specific activity and composition of building materials surrounding a RPM to predict background levels, therefore determining the minimum detectable quantity (MDQ) of material. This methodology builds on previous work by Ryan et al by generating material and source cards for a detailed Monte Carlo N-Particle (MCNP) deck, based on an experimental RPM setup to predict the overall gamma background at a site. Gamma spectra were acquired from samples of building materials and analyzed to determine the specific activity of the samples. A code was developed to estimate the elemental composition of building materials using the gamma transmission of the samples. These results were compared to previous Neutron Activation Analysis (NAA) on the same samples. It was determined that densitometry provided an elemental approximation within 5% of that found through NAA. Using the specific activity and material composition, an MCNP deck was used to predict the gamma background levels in the detectors of a typical RPM. These results were compared against actual measurements at the RPM site, and shown to be within 10% of each other.

The purpose of this project is to establish a correlation between precipitation and background gamma radiation levels at radiation portal monitors (RPM) deployed at various ports worldwide, and to devise a mechanism by which the effects of these precipitation-induced background fluctuations could be mitigated. The task of detecting special nuclear materials (SNM) by passive gamma spectroscopy is very difficult due to the low signal-to-noise ratio observed in an uncontrolled environment. Due to their low activities and the low energies of their characteristic gamma rays, the signals from many types of SNM can easily be obscured by background radiation. While this can be somewhat mitigated by taking regular background radiation measurements, even this cannot resolve the issue if background levels change suddenly and dramatically. Furthermore, any increase in background count rate will increase the statistical uncertainty of the count rate measurement, and thus decrease the minimum quantity of SNM that can be reliably detected. Existing research suggests that the advent of precipitation is the culprit behind many such large and sudden increases in background radiation. The correlation between precipitation and background levels was explored by in-situ testing on a full-scale portal monitor at Oak Ridge National Laboratory, and by comparing previously recorded background radiation and weather data from portal monitors located at ports worldwide. The first was utilized to determine the frequency and magnitude at which precipitation introduces background activity, and the second was used to quantify the effects of various quantities and types of precipitation in various parts of the world. Once this analysis was complete, various methods of mitigating these changes in background radiation were developed based on the collected data. Precipitation was found to be the most common culprit for rapid increases in background count rate, and was attributable to 85.6% of all such events. Based on extensive simulation via the Origen-ARP and MCNP software, a response function for the portal monitor was developed, and an algorithm designed to predict the contribution of the precipitation to the background count rate was developed. This algorithm was able to attenuate the contribution of precipitation to the background count rate by an average of 45% with very minimal over-correction. Such an algorithm could be utilized to adjust the alarm levels of the RPM to better allow it to compensate for the rise and fall in background count rate due to precipitation. Additionally, the relative contribution of precipitation which landed at various distances from the portal monitor to the increase in background count rate was measured via simulation. This simulation demonstrated that 37.2% of all background counts were due to the radon daughters which landed within a 2.76 m radius from the center of the portal monitor. This radius encompasses the area between the two portals. Based on this, several designs for shielding were simulated, the most successful of which was a concrete structure that was able to attenuate 71.3% of the background radiation caused by a given precipitation event at a materials cost of approximately $6,000 per RPM. This method is recommended as the primary means of mitigating this issue.

A hypothetical smuggling of material suitable for a nuclear weapon is known as a threat scenario. There is a considerable effort by the U.S. government to reduce this threat by placing radiation detectors at key interdiction points around the world. These detectors provide deterrence and defense against smuggling attempts by scanning vehicles, ships, and pedestrians for threat objects. Formulating deployment strategies for these detectors within the global transportation network requires an understanding of the complex interactions between the attributes of a smuggler and the detection systems. These strategies are rooted in the continued development of novel detection systems and alarm algorithms. Radiation transport simulation provides a means for characterizing detection system response to threat scenarios. However, this task is computationally expensive with existing radiation transport codes. Furthermore, the degrees of freedom in smuggler and threat scenario attributes create a large, constantly evolving problem space. Previous research has demonstrated that decomposing the scenario into independently simulated components using Green's functions can simulate photon detector signals with coarse energy resolution. This dissertation presents a general form of this approach, applicable to a wide range of threat scenarios through physics enhancements and numerical treatments for high energy resolution photon transport, neutron transport, and time dependent transport. While each Green's function implicitly captures the full transport phase-space within each component, these new methods ensure that this information is preserved between components. As a result, detector signals produced from full forward transport simulations can be replicated within 20% while requiring multiple orders of magnitude less computation time. This capability is presented as a general threat scenario simulation platform which can efficiently model a large problem space while preserving the full radiation transport phase-space. / text