Erin A. Miller

Coupling Deterministic and Monte Carlo Transport Methods for the Simulation of Gamma-Ray Spectroscopy Scenarios

Simulation is often used to predict the response of gamma-ray spectrometers in technology viability and comparative studies for homeland and national security scenarios. Candidate radiation transport methods generally fall into one of two broad categories: stochastic (Monte Carlo) and deterministic. Monte Carlo methods are the most heavily used in the detection community and are particularly effective for calculating pulse-height spectra in instruments. However, computational times for scattering- and attenuation-dominated problems can be extremely long - many hours or more on a typical desktop computer. Deterministic codes that discretize the transport in space, angle, and energy offer potential advantages in computational efficiency for these same kinds of problems, but pulse-height calculations are not readily accessible. This paper investigates a method for coupling angular flux data produced by a three-dimensional deterministic code to a Monte Carlo model of a gamma-ray spectrometer. Techniques used to mitigate ray effects, a potential source of inaccuracy in deterministic field calculations, are described. Strengths and limitations of the coupled methods, as compared to purely Monte Carlo simulations, are highlighted using example gamma-ray detection problems and two metrics: (1) accuracy when compared to empirical data and (2) computational time on a typical desktop computer.

Vulnerability of larval and juvenile white sturgeon to barotrauma: can they handle the pressure?

Techniques were developed to determine when fish are vulnerable to barotrauma when rapidly decompressed during hydroturbine passage. Sturgeons were decompressed in early life-stages and X-ray radiographs were taken to determine when gas was present in the swim bladder. Barotrauma was observed on day 9 and greater than 75 days after hatching.

Scatter in cargo radiography

As a complement to passive detection systems, radiographic inspection of cargo is an increasingly important tool for homeland security because it has the potential to detect highly attenuating objects associated with special nuclear material or surrounding shielding, in addition to screening for items such as drugs or contraband. Radiographic detection of such threat objects relies on high image contrast between regions of different density and atomic number (Z). Threat detection is affected by scatter of the interrogating beam in the cargo, the radiographic system itself, and the surrounding environment, which degrades image contrast. Here, we estimate the extent to which scatter plays a role in radiographic imaging of cargo containers. Stochastic transport simulations were performed to determine the details of the radiography equipment and surrounding environment, which are important in reproducing measured data and to investigate scatter magnitudes for typical cargo. We find that scatter plays a stronger role in cargo radiography than in typical medical imaging scenarios, even for low-density cargo, with scatter-to-primary ratios ranging from 0.14 for very low density cargo, to between 0.20 and 0.40 for typical cargo, and higher yet for dense cargo.

Deterministic Transport Methods for the Simulation of Gamma-Ray Spectroscopy Scenarios

Radiation transport modeling methods used in the radiation detection community fall into one of two broad categories: stochastic (Monte Carlo) and deterministic. Monte Carlo methods are typically the tool of choice for simulating gamma-ray spectrometers operating in homeland and national security settings (e.g. portal monitoring of vehicles or isotope identification using handheld devices), but deterministic codes that discretize the linear Boltzmann transport equation in space, angle, and energy offer potential advantages in computational efficiency for many complex radiation detection problems. This paper describes the development of deterministic algorithms for simulating gamma-ray spectroscopy scenarios. Key challenges include: formulating methods to automatically define an energy group structure that can support modeling of gamma-ray spectrometers ranging from low to high resolution; combining deterministic transport algorithms (e.g. ray-tracing and discrete ordinates) to mitigate ray effects for a wide range of problem types; and developing efficient and accurate methods to calculate gamma-ray spectrometer response functions from the deterministic angular flux solutions. In this paper, the software framework aimed at addressing these challenges is described and results from test problems that compare deterministic and Monte Carlo approaches are provided.

THE COUPLING OF A DETERMINISTIC TRANSPORT FIELD SOLUTION TO A MONTE CARLO BOUNDARY CONDITION FOR THE SIMULATION OF LARGE GAMMA-RAY SPECTROMETERS

Abstract Monte Carlo methods are typically used for simulating radiation fields around gamma-ray spectrometers and pulse-height tallies within those spectrometers. Deterministic codes that discretize the linear Boltzmann transport equation can offer significant advantages in computational efficiency for calculating radiation fields, but stochastic codes remain the most dependable tools for calculating the response within spectrometers. For a deterministic field solution to become useful to radiation detection analysts, it must be coupled to a method for calculating spectrometer response functions. This coupling is done in the RADSAT toolbox. Previous work has been successful using a Monte Carlo boundary sphere around a handheld detector. It is desirable to extend this coupling to larger detector systems such as the portal monitors now being used to screen vehicles crossing borders. Challenges to providing an accurate Monte Carlo boundary condition from the deterministic field solution include the greater possibility of large radiation gradients along the detector and the detector itself perturbing the field solution, unlike smaller detector systems. The method of coupling the deterministic results to a stochastic code for large detector systems can be described as spatially defined rectangular patches that minimize gradients. The coupled method was compared to purely stochastic simulation data of identical problems, showing the methods produce consistent detector responses while the purely stochastic run times are substantially longer in some cases, such as highly shielded geometries. For certain cases, this method has the ability to faithfully emulate large sensors in a more reasonable amount of time than other methods.

Bayesian Radiation Source Localization

Abstract Locating illicit radiological sources using gamma-ray or neutron detection is a key challenge for both homeland security and nuclear nonproliferation. Localization methods using an array of detectors or a sequence of observations in time and space must provide rapid results while accounting for a dynamic attenuating environment. In the presence of significant attenuation and scatter, more extensive numerical transport calculations in place of the standard analytical approximations may be required to achieve accurate results. Numerical adjoints based on deterministic transport codes provide relatively efficient detector response calculations needed to determine the most likely location of a true source given a set of observed count rates. Probabilistic representations account for uncertainty in the source location resulting from uncertainties in detector responses and the potential for nonunique solutions. A Bayesian approach improves on previous likelihood methods for source localization by allowing the incorporation of all available information to help constrain solutions. We present an approach to localizing radiological sources that uses numerical adjoints and a Bayesian formulation and demonstrate the approach on two simple example scenarios. Results indicate accurate estimates of source locations. We briefly study the effect of neglecting the contribution of all scattered radiation in the adjoints, as analytical transport approximations do, for a case with moderately attenuating material between detectors and sources. The source location accuracy of the uncollided-only solutions appears to be significantly worse at the source strength considered here, suggesting that the higher physical fidelity that is provided by full numerical adjoint-based solutions may provide an advantage in operational settings.

Coupling among Microbial Communities, Biogeochemistry, and Mineralogy across Biogeochemical Facies.

Physical properties of sediments are commonly used to define subsurface lithofacies and these same physical properties influence subsurface microbial communities. This suggests an (unexploited) opportunity to use the spatial distribution of facies to predict spatial variation in biogeochemically relevant microbial attributes. Here, we characterize three biogeochemical facies—oxidized, reduced, and transition—within one lithofacies and elucidate relationships among facies features and microbial community biomass, richness, and composition. Consistent with previous observations of biogeochemical hotspots at environmental transition zones, we find elevated biomass within a biogeochemical facies that occurred at the transition between oxidized and reduced biogeochemical facies. Microbial richness—the number of microbial taxa—was lower within the reduced facies and was well-explained by a combination of pH and mineralogy. Null modeling revealed that microbial community composition was influenced by ecological selection imposed by redox state and mineralogy, possibly due to effects on nutrient availability or transport. As an illustrative case, we predict microbial biomass concentration across a three-dimensional spatial domain by coupling the spatial distribution of subsurface biogeochemical facies with biomass-facies relationships revealed here. We expect that merging such an approach with hydro-biogeochemical models will provide important constraints on simulated dynamics, thereby reducing uncertainty in model predictions.

3D imaging of microbial biofilms: integration of synchrotron imaging and an interactive visualization interface.

Understanding the structure of microbial biofilms and other complex microbial communities is now possible through x-ray microtomography imaging. Feature detection and image processing for this type of data focuses on efficiently identifying and segmenting biofilm biomass in the datasets. These datasets are very large and segmentation often requires manual interventions due to low contrast between objects and high noise levels. New software is required for the effectual interpretation and analysis of such data. This work specifies the evolution and ability to analyze and visualize high resolution x-ray microtomography datasets. Major functionalities include read/write with multiple popular file formats, down-sampling large datasets to generate quick-views on low-power computers, image processing, and generating high quality output images and videos. These capabilities have been wrapped into a new interactive software toolkit, BiofilmViewer. A major focus of our work is to facilitate data transfer and to utilize the capabilities of existing powerful visualization and analytical tools including MATLAB, ImageJ, Paraview, Chimera, Vaa3D, Cell Profiler, Icy, BioImageXD, and Drishti.

Investigating biofilm structure using x-ray microtomography and gratings-based phase contrast

Direct examination of natural and engineered environments has revealed that the majority of microorganisms in these systems live in structured communities termed biofilms. To gain a better understanding for how biofilms function and interact with their local environment, fundamental capabilities for enhanced visualization, compositional analysis, and functional characterization of biofilms are needed. For pore-scale and community-scale analysis (100’s of nm to 10’s of microns), a variety of surface tools are available. However, understanding biofilm structure in complex three-dimensional (3-D) environments is considerably more difficult. X-ray microtomography can reveal a biofilm’s internal structure, but obtaining sufficient contrast to image low atomic number (Z) biological material against a higher-Z substrate makes detecting biofilms difficult. Here we present results imaging Shewanella oneidensis biofilms on a Hollow-fiber Membrane Biofilm Reactor (HfMBR), using the x-ray microtomography system at sector 2-BM of the Advanced Photon Source (APS), at energies ranging from 12.9-15.4 keV and pixel sizes of 0.7 and 1.3 μm/pixel. We examine the use of osmium (Os) as a contrast agent to enhance biofilm visibility and demonstrate that staining improves imaging of hydrated biofilms. We also present results using a Talbot interferometer to provide phase and scatter contrast information in addition to absorption. Talbot interferometry allows imaging of unstained hydrated biofilms with phase contrast, while absorption contrast primarily highlights edges and scatter contrast provides little information. However, the gratings used here limit the spatial resolution to no finer than 2 μm, which hinders the ability to detect small features. Future studies at higher resolution or higher Talbot order for greater sensitivity to density variations may improve imaging.

Transport Test Problems for Hybrid Methods Development

This report presents 9 test problems to guide testing and development of hybrid calculations for the ADVANTG code at ORNL. These test cases can be used for comparing different types of radiation transport calculations, as well as for guiding the development of variance reduction methods. Cases are drawn primarily from existing or previous calculations with a preference for cases which include experimental data, or otherwise have results with a high level of confidence, are non-sensitive, and represent problem sets of interest to NA-22.