Uncertainty Quantification in Emission Quantitative Imaging

Imaging detectors have potential to improve the reliability of plutonium holdup measurements. Holdup measurement is a significant challenge for nuclear safeguards and criticality safety. To infer holdup mass today, inspectors must combine data from counting (non-imaging) detectors with spatial measurements, process knowledge, and survey estimates. This process results in limited certainty about the holdup mass. Imaging detectors provide more information about the spatial distribution of the source, increasing certainty. In this dissertation we focus on the emission quantitative imaging problem using a fast-neutron coded aperture detector. We seek a reliable way to infer the total intensity of a neutron source with an unknown spatial distribution. The source intensity can be combined with other measurements to infer the holdup mass. To do this we first create and validate a model of the imager. This model solves the forward problem of estimating data given a known source distribution. We use cross-validation to show that the model reliably predicts new measurements (with predictable residuals). We then demonstrate a non-Bayesian approach to process new imager data. The approach solves the inverse problem of inferring source intensity, given various sources of information (imager data, physical constraints) and uncertainty (measurement noise, modeling error, absence of information, etc). Bayesian approaches are also considered, but preliminary findings indicate the need for advanced Markov chain algorithms beyond the scope of this dissertation. The non-Bayesian results reliably provide confidence intervals for medium-scale problems, as demonstrated using a blind-inspector measurement. However, the confidence interval is quite large, due chiefly to modeling error.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136929/1/ambevill_1.pd

Performance of hybrid methods for representative nonproliferationproblems

Adjoint-derived weight windowing is a hybrid deterministic/Monte Carlo method to simulate radiation transport. In adjoint-derived weight windowing, a deterministic adjoint solution is used to create weight windows for a Monte Carlo simulation. The intent of this work is to identify factors that reduce the Figure of Merit (FOM) of Monte Carlo simulations using adjoint derived weight windowing. The method used in this study pairs Transpire's deterministic code Attila™ and MCNP5. Two computationally difficult source/detector problems of interest to nuclear nonproliferation are used as case studies to determine the factors that affect the FOM. Test Case I is an active interrogation problem similar to many radiography problems. The model is used in two sets of trials: in the first, the quality of the deterministic adjoint solution is varied to observe the effect of adjoint solution quality on the FOM. In the second, the shielding density is varied to determine the effect of increased shielding on the FOM. Results from Test Case I suggest that weight windows that decrease monotonically along relevant paths from the source to the detector maximize the FOM. The results also suggest that weight windowing is susceptible to false convergence that could be avoided using a different hybrid method, such as the Local Importance Function Transform (LIFT). A more sophisticated method for generating weight windows relevant to the forward Monte Carlo simulation is described for future work. Test Case II is a detailed model of a detector array passively interrogating a uranium hexafluoride cylinder. Test Case II is used to test the effect of appropriate source biasing on the FOM. Results from Test Case II confirm prior work, that source biasing is important for problems in which the adjoint function varies widely in the source domain. Since spectral information from the detector is very useful for nonproliferation purposes, a new use of the forward weighted consistent adjoint driven importance sampling (FW-CADIS) method is described to model the energy-dependent flux in a region of interest. Properly modeling Test Case II also requires the use of rejection sampling of the source position paired with source biasing, which currently cannot be used together in MCNP5. The new use for the FW-CADIS method and a method to allow the use of rejection sampling with source biasing are described for future work

Random Inspection Planning for Misuse Detection in Safeguards

The IAEA uses random inspections (RIs) to, inter alia, provide credible assurance that declared nuclear facilities are not used for undeclared purposes. These inspections are random in the sense that they are scheduled randomly in date and time, with short notice given to the inspected site. The IAEA has interest in employing statistical models for RI planning that take advantage of any potential efficiency gains while maintaining a high level of effectiveness.This paper first introduces the model parameters that are necessary for a quantitative analysis of RI models for misuse inspections (subsequently referred to as RI models) and discusses their importance. Then, using the model parameters, the set of all RI models is introduced, and three example RI models are presented. Next, for any RI model the probability is derived that any facility is selected at least once per year for an RI, and – regarding the objective of an RI – the probability that a misuse is detected within days after its start, where the parameter is the duration of misuse signatures at the facility. Next, the question is addressed which RI model should be chosen for RI planning: If no further constraints from the IAEA are imposed on the RI models (e.g., maximum unpredictability of the number of RIs in each year, resource constraints leading to an upper number of RIs, etc.), then the RI model that maximizes the achieved detection probability for a given set of input parameters should be selected. This maximization problem, however, is by no means trivial, because the maximization is performed over a set of RI models and not over a subset of real numbers.Finally, the functionality and features of the software prototype TRIPS (Tool for Random Inspection Planning in Safeguards) are demonstrated, and future work topics are highlighted