A primary goal of this research was to develop a technically defensible approach for modeling the receptor dose due to smaller "hot spots" of residual radioactivity. Nearly 700 combinations of environmental pathways, radionuclides and hot spot sizes were evaluated in this work. The hot spot sizes studied ranged from 0.01 m2 to 10 m2, and included both building and land area exposure pathways. Dose modeling codes RESRAD, RESRAD-BUILD, and MicroShield were used to assess hot spot doses and develop pathway-specific area factors for eleven radionuclides. These area factors are proposed for use within the existing Multiagency Radiation Survey and Site Investigation Manual (MARSSIM) context of final status survey design and implementation. The research identified pathways that are particularly "hot spot sensitive"—i.e., particularly sensitive to changes in the areal size of the contaminated area. The external radiation pathway was the most hot spot sensitive for eight of the eleven radionuclides studied. These area factors were evaluated both when the receptor was located directly on the soil hot spot and ranged from 6.6 to 11.4 for 1 m2 hot spot; and ranged from 650 to 785 when the receptor was located 6 m from the 1 m2 hot spot. The external radiation pathway was also the most sensitive of the building occupancy pathways. For the smallest building hot spot studied (100 cm2), the area factors were approximately 1100 for each of the radionuclides. A Bayesian statistical approach for assessing the acceptability of hot spots is proposed. A posterior distribution is generated based on the final status survey data that provides an estimate of the 99th percentile of the contaminant distribution. Hot spot compliance is demonstrated by comparing the upper tolerance limit——defined as the 95% upper confidence level on the 99th percentile of the contaminant distribution in the survey unit—with the DCGL99th value. The DCGL99th is the hot spot dose limit developed using the dose modeling research to establish area factors mentioned above. The proposed approach provides a hot spot assessment approach that considers hot spots that may be present, but not found. Examples are provided to illustrate this approach.

Radiation Effects on Metastable States of Superheated Water covers theory, application, and experimentation into the behavior of water at temperatures above the boiling point. The backgrounds of Positron Emission Tomography target design, bubble chambers, and superheat measurements are presented. The quantitative theory of metastable liquids and their characteristic waiting time is discussed. Energetics of bubble formation from two different perspectives are included. Finally, the design of an apparatus for measuring liquid superheats in the presence of radiation is covered in some detail, including several design iterations, first measurements made on the apparatus, and techniques for data reduction.

An analytical knockout-ablation-coalescence model capable of making quantitative predictions of the neutron and light-ion spectra from high-energy nucleon-nucleus and nucleus-nucleus collisions is being developed for use in space radiation protection studies. The FORTRAN computer code that implements this model is called UBERNSPEC. The knockout or abrasion stage of the model is based on Glauber multiple scattering theory. The ablation part of the model uses the classical evaporation model of Weisskopf-Ewing. In earlier work, the knockout-ablation model was extended to incorporate important coalescence effects into the formalism. Recently, the coalescence model was reformulated in UBERNSPEC and alpha coalescence incorporated. In addition, the ability to predict light ion spectra with the coalescence model was added. Earlier versions of UBERNSPEC were limited to nuclei with mass numbers less than 68. In this work, the UBERNSPEC code has been extended to include heavy charged particles with mass numbers as large as 238. Representative predictions from the code are compared with published measurements of neutron energy and angular production spectra and light ion energy spectra for a variety of collision pairs.

A "Clinical Decision Support System" (CDSS) is a concept which has advanced rapidly in health care over the last few decades, and it is defined as "an interactive computer program that is designed to assist physicians and other health professionals with decision-making tasks." Radiation therapy oncologists are required to make decisions that do not involve making a disease diagnosis. Therefore this work focuses on developing a modified CDSS which can be used to aid oncology staff in identifying cancer patients who will require adaptive radiation therapy (ART). An image-guided radiation therapy (IGRT) tool was developed that consists of both diagnostic and prognostic processes. Patients who will require ART are those whose crosssectional neck measurements change by more than half of a centimeter over the entire course of treatment. First, the tool allows one to “diagnose” or identify which patients would benefit from adaptive therapy and then make a “prognosis,” or identify when ART is required. Thirty head and neck (H&N) patients were used in this study, and 15 required ART. Each diagnosis was made by predicting if the threshold of 0.5 cm would be crossed for each of the four cross-sectional measurements, and each prediction was made by determining when the threshold would cross. The diagnosis results show that half (61/120) of the measurements predicted that patients would need ART given the first 15 observations and 28 of 120 predicted needing ART within 20 observations. Therefore, 74% of patients' measurements accurately diagnosed that ART would be required given just the first 20 observations. The prediction results indicate that an average of 11 observations is needed to make adequate time predictions with a v reliability of at least 0.5. However, more accurate time predictions with higher reliability values (0.6 and 0.7) could be made given an average of 16 and 18 observations, respectively. These predictions, while requiring more observations, provided additional lead time in knowing when ART is required.

The use of Bonner Sphere Spectrometer (BSS) is a well-established method of measuring the energy distribution of neutron emission sources. The purpose of this research is to apply the Generalized Regression Neural Network (GRNN), a kind of Artificial Neural Network (ANN), to predict the neutron spectrum using the count rate data from a BSS. The BSS system was simulated with the MCNP5 Monte-Carlo code to calculate the response to neutrons of different energies for each combination of thermal neutron detector and polyethylene sphere. One hundred and sixty-three different types of neutron spectra were then investigated. GRNN Training and testing was carried out in the MATLAB environment. In the GRNN testing, eight-one predicted spectra were obtained as outputs of the GRNN. Comparison with standard spectra shows that 97.5% of the prediction errors were controlled below 1%, indicating ANN could be used as an alternative with high accuracy in neutron spectrum unfolding methodologies. Advantages and further improvements of this technique are also discussed.

ANSI/ANS 8.3 “Criticality Accident Alarm System”, Appendix B states “Determining the adequacy of criticality alarm detector placement is far from an exact process”. With this statement in mind a novel method for establishing areas of acceptable Criticality Accident Alarm System (CAAS) coverage was developed and demonstrated utilizing Los Alamos’ Monte Carlo N-Particle Code 5 (MCNP5) in multigroup Adjoint. Validation of the methodology was shown in the comparison of benchmark calculations with empirical results of Sandia testing and with hand calculations utilizing ANS 8.3. Demonstration involved the determination of zones of CAAS coverage from detector sensitivity maps generated by MCNP5 for a conceptual geometry of a new storage facility involving homogenized concrete and BoroBond® slabs. Multiple detector coverage was further demonstrated through the superimposing of several maps corresponding to differing detector locations.

This paper assesses the uncertainty associated with the utilization and implementation of advanced fuel cycles to control plutonium inventories. The specific fuel cycles investigated are a partially closed cycle utilizing MOX reactors and completely closed one-tier fuel cycles utilizing fast reactors. Multiple methods for assessing these uncertainties were utilized. A scenario approach that varied the time and number of the implementation of the advanced reactors was used. It was found that the implementation of 3 FR/yr with a CR of 0.5 could reduce the amount of Pu by over 36% in reference to building 3 LWR/yr. In addition to reducing the inventory with respect to the reference LWR case, the growth rate can be reduced from an initial 22 tons Pu/ year growth to 5 tons Pu / year growth with the 2030 actual initial Pu inventory implementation cases. The MOX cases keep the Pu/ TWhe inventory slightly above 1 ton Pu/TWhe and the extremely low CR FR cases even lower than that value. Thus from this work the extremely low CR FR scenarios show the greatest ability to control the growing Pu inventory. In addition to the scenario approach a Monte Carlo uncertainty model was developed and analyzed. The uncertainty analysis showed the high burn up cases are comparable with the of the low CR FR cases in there ability to control the Pu inventory with the Pu inventories ranging from 2500 tons of Pu to 7500 tons of Pu. However, for the high burn up cases the majority of the Pu is Out-Of-Pile as opposed to the FR cases where a considerable amount of the Pu is In-Pile. From a proliferation stand point, the low CR FR case is better at the controlling the Pu inventory because the total inventories are relatively the same for the majority of the runs, and the FR cases keep most of the Pu In-Pile rather than the high burn up cases which keep most of it Out-Of-Pile. Lastly, a brief economic uncertainty model was developed. The economic results show that the once-through cycle is the cheapest with over 50% of the test cases coming in cheaper than all of the FR and MOX cases. The FR cases come out to be the next cheapest with the MOX cases being the most expensive.

The hydrocyclone is commonly used to separate oil from water, and particulates from fluid streams in various process industries. Two-fluid hydrodynamic theory is used here to develop a model for hydrocyclone performance in the application of separation of helium bubbles larger than 30 micron in diameter from liquid mercury at system pressure near 1 bar. The application is related to high power liquid metal target development for proton beams used in spallation neutron sources.

Cavitation is a significant concern for the reliable operation of a centrifugal pump. Liquid metal flow loops are used in nuclear, chemical, metal forming, and liquid metal dynamo applications. Understanding of the cavitation characteristics of liquid metals is increasingly important to the design and operation of these facilities. One recent field of cavitation research has developed for mercury flow in spallation targets used in neutron sources. To further the understanding of mercury cavitation, a review of the existing literature on water cavitation, liquid metal cavitation, and mercury cavitation is performed. The mechanics of cavitation and the analytical methods applied to cavitation problems are discussed and analyzed. Acoustic data from the centrifugal pump for the mercury flow loop at the Spallation Neutron Source in Oak Ridge National Laboratory are examined.

Spallation Neutron Source at Oak Ridge National Laboratory is a neutron source operating with a liquid mercury target. Pulsed energy deposition in the target from the proton beam causes pressure waves that limit operation due to cavitation damage on the target container. Damage mitigation is proposed through the introduction of a 0.5 per cent gas volume fraction of small diameter bubbles to create compressibility in mercury. Desired bubble diameter is 30 microns, and two ultrasonic methods are studied for detection and characterization of such bubbles. These methods are tested first in water, and then in mercury. Ultrasound Doppler velocity profiler directly measures bubble rise velocity, which is then used to determine bubble diameter. Ultrasonic imaging allows direct observation of the bubbles both in water and in mercury. However, challenges were encountered in medical ultrasound image optimization and interpretation for this engineering application. This research explores techniques for implementing ultrasound in opaque fluids for bubble rise velocity and diameter characterization