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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures

To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials.

Arzola-Villegas, Xavier (ORCID:0000000305369766)↗
DOE OSTI2024

Addressing Low-Cost Methane Sensor Calibration Shortcomings with Machine Learning

Quantifying methane emissions is essential for meeting near-term climate goals and is typically carried out using methane concentrations measured downwind of the source. One major source of methane that is important to observe and promptly remediate is fugitive emissions from oil and gas production sites but installing methane sensors at the thousands of sites within a production basin is expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using machine learning (ML) to generate a model that converts metal oxide sensor output to methane mixing ratios. To generate test data, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. Over the duration of the measurements, the trace gas analyzer’s average methane mixing ratio was 2.40 ppm with a maximum of 147.6 ppm. The average calculated methane mixing ratios for the TGS2600 and TGS2611 using the ML algorithm were 2.42 ppm and 2.40 ppm, with maximum values of 117.5 ppm and 106.3 ppm, respectively. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios shows overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was a good agreement between the ML-derived metal oxide sensors’ mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with precision and accuracy comparable to higher priced trace methane analyzers. This would improve confidence in low-cost sensors’ response, reduce the cost of sensor deployment, and allow for timely and accurate tracking of methane emissions.

03 NATURAL GAS↗
DOE OSTI2022

A Census of the 32 Ori Association with Gaia*

I have used high-precision photometry and astrometry from the third data release of Gaia (DR3) to identify candidate members of the 32 Ori association. Spectral types and radial velocities have been measured for subsets of the candidates using new and archival spectra. For the candidates that have radial velocity measurements, I have used UVW velocities to further constrain their membership, arriving at a final catalog of 169 candidates. I estimate that the completeness of the survey is ~90% for spectral types of ≲M7 (≳0.06 M ⊙ ). The histogram of spectral types for the 32 Ori candidates exhibits a maximum at M5 (~0.15 M ⊙ ), resembling the distributions measured for other young clusters and associations in the solar neighborhood. The available UVW velocities indicate that the association is expanding, but they do not produce a well-defined kinematic age. Based on their sequences of low-mass stars in color–magnitude diagrams, the 32 Ori association and Upper Centaurus-Lupus/Lower Centaurus-Crux (UCL/LCC) are coeval to within ±1.2 Myr, and they are younger than the β Pic moving group by ~3 Myr, which agrees with results from previous analysis based on the second data release of Gaia. Finally, I have used mid-IR photometry from the Wide-field Infrared Survey Explorer to check for excess emission from circumstellar disks among the 32 Ori candidates. Disks are detected for 18 candidates, half of which are reported for the first time in this work. The fraction of candidates at ≤M6 that have full, transitional, or evolved disks is 10/149 = 0.07$^{+0.03}_{-0.02'}$ which is consistent with the value for UCL/LCC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Census of the Taurus Star-forming Region and Neighboring Associations with Gaia*

I have used high-precision photometry and astrometry from the third data release of Gaia to perform a survey for members of the Taurus star-forming region and young associations in its vicinity. This work has produced a new catalog of 532 adopted members of Taurus, which has only minor changes relative to the previous catalog from Esplin & Luhman. I have used the Gaia astrometry to divide the Taurus members into 13 groups that have distinct kinematics. Meanwhile, I have identified 1378 candidate members of seven associations near Taurus. All of these associations have histograms of spectral types that peak near M5 (~0.15 M ⊙ ), resembling other young populations in the solar neighborhood. For the Taurus groups and neighboring associations, I have estimated ages from their sequences of low-mass stars in Hertzsprung–Russell diagrams. Most of the Taurus groups have median ages of ~1–3 Myr, while the associations have ages ranging from 13 to 56 Myr. I have used mid-infrared photometry from the Wide-field Infrared Survey Explorer to search for excess emission from circumstellar disks among the candidate members of the associations. Disks are detected for 51 stars, 20 of which are reported for the first time in this work. Some recent studies have proposed that samples of older stars (≳10 Myr) found in the vicinity of Taurus represent a distributed population that is associated with the Taurus cloud complex. However, I find that most of those stars have kinematics that are inconsistent with any relationship with Taurus.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Boundary of the Distribution of Solar Wind Proton Beta versus Temperature Anisotropy

Abstract The frequency distribution of solar wind protons, measured in the vicinity of Earth’s orbit, is customarily plotted in ( β ∥ , T ⊥ / T ∥ ) phase space. Here, T ⊥ / T ∥ is the ratio of perpendicular and parallel temperatures, and β ∥ = 8 π nT ∥ / B 2 is the ratio of parallel thermal energy to background magnetic field energy, the so-called “parallel beta,” with ⊥ and ∥ denoting directions with respect to the ambient magnetic field. Such a frequency distribution, plotted as a two-dimensional histogram, forms a peculiar rhombic shape defined with an outer boundary in the said phase space. Past studies reveal that the threshold conditions for temperature anisotropy–driven plasma instability partially account for the boundary on the high- β ∥ side. The low- β ∥ side remains largely unexplained despite some efforts. Work by Vafin et al. recently showed that certain contours of collisional relaxation frequency, ν pp , when parameterized by T ⊥ / T ∥ and β ∥ , could match the overall shape of the left-hand boundary, thus suggesting that the collisional relaxation process might be closely related to the formation of the left-hand boundary. The present paper extends the analysis by Vafin et al. and carries out the dynamical computation of the collisional relaxation process for an ensemble of initial proton states with varying degrees of anisotropic temperatures. The final states of the relaxed protons are shown to closely match the observed boundary to the left of the ( β ∥ , T ⊥ / T ∥ ) phase space. When coupled with a similar set of calculations for the ensemble in the collective instability regime, it is found that the combined collisional/collective effects provide the baseline explanation for the observation.

Astronomy & Astrophysics↗
DOE OSTI2022

Thermodynamics of the Inner Heliosheath

We derive annual sky maps of the proton temperature in the inner heliosheath (IHS), and track their temporal evolution over the years 2009–2016 of Interstellar Boundary Explorer observations. Other associated thermodynamic parameters also determined are the density, kappa (the parameter that characterizes kappa distributions), temperature rate, polytropic index, and entropy. We exploit the theory of kappa distributions and their connection with polytropes, to (i) express a new polytropic quantity Π that remains invariant along streamlines where temperature and density may vary, (ii) parameterize the proton flux in terms of the Π invariant and kappa, and (iii) derive the temperature and density, respectively, from the slope and intercept of the linear relationship between kappa and logarithm of Π. We find the following thermodynamic characteristics: (1) temperature sky maps and histograms shifted to their lowest values in 2012 and their highest in 2015; (2) temperature negatively correlated with density, reflecting the subisothermal polytropic behavior; (3) temperature positively correlated with kappa, revealing characteristics of the mechanism responsible for generating kappa distributions; (4) processes in IHS are subisothermal tending toward isobaric, consistent with previously published results; (5) linear relationship between kappa and polytropic indices, revealing characteristics of the particle potential energy; and (6) entropy positively correlated with polytropic index, aligned with the underlying theory that entropy increases toward the isothermal state where the kappa distribution reduces to the Maxwell–Boltzmann description.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm

In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential for specialized applications in segmented inverse beta decay (IBD) neutrino detectors, astronomy, machine learning, and more. Although this method may easily extend to 3D scalar fields, our focus here is on 2D real-valued fields as it directly applies to directionality. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2025

Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels

Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

58 GEOSCIENCES↗
DOE OSTI2025

$\bar{\nu}_\mu$ charged-current $\pi^0$ data release

Data release for the NOvA muon antineutrino charged-current (CC) pi^0 cross section presented in arXiv:2511.05807. The signal for this analysis is defined as muon antineutrino CC interactions in the fiducial volume of the NOvA near detector (a 2.7 m × 2.7 m × 9.0 m region) that produce at least one pi^0 in the final state emerging from the nucleus, within the phase space of muon momentum [0.5, 2.5) GeV/c and muon angle [0, 60) degree, as described in arXiv:2511.05807. The released zip file contains two files: NOvA_NumubarCCPi0_DataRelease.root README.txt The ROOT file includes the cross-section results as well as the statistical and systematic covariance matrices for each variable used in this analysis. The README provides a detailed description of the contents of the data release. Official Flux: The flux used in this analysis is available from the NOvA Public Docs: https://publicdocs.fnal.gov/cgi-bin/ShowDocument?docid=8. File structure --- The ROOT file contains the following TDirectories: pi0p - pi^0 momentum distributions pi0dir - pi^0 angular distributions muonp - muon momentum distributions muondir - muon angular distributions Q2 - reconstructed Q^2 distributions Wmass - reconstructed W_mass distributions Each directory contains three histograms: xsec (TH1D): Cross section result cov_stat (TH2D): Statistical covariance matrix cov_syst (TH2D): Systematic covariance matrix Usage notes: - xsec gives the measured differential cross section w.r.t. the corresponding variable. - cov_stat and cov_syst provide the full covariance matrices. - The bin definitions and kinematic phase spaces follow those used in arXiv:2511.05807. Citation --- If you use these data, please cite: NOvA Collaboration, arXiv:2511.05807.

Wu, Wanwei [Pittsburgh U.] (ORCID:0000000326327215↗
DOE OSTI2024

Maps of ice wedge thermokarst pool expansion from twenty-seven circumpolar survey areas

This repository includes data and code to accompany the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. The data include satellite imagery and derived maps of thermokarst pools from twenty-seven survey areas in North America and Siberia. The code, written in MATLAB (R2021a), contains demonstrations of the workflow for generating the maps. The demonstrations include training a generalized UNet for mapping thermokarst pools using data from three survey areas, 'fine tuning' the UNet for use at a specific survey area using transfer learning, applying a trained UNet to infer thermokarst pool extent within satellite imagery, and performing histogram matching as a pre-processing step to improve satellite imagery contrast. Contains MATLAB script files and M files, TIF files, shape files, XML, Excel, TXT, and CSV files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Accelerating fissile material detection with a neutron source

A system discriminating fissile material from nonfissile material wherein a digital data acquisition unit collects data at high rate, and processes large volumes of data directly to count neutrons from the unknown source and detect excess grouped neutrons to identify fission. The system includes a Poisson neutron generator for in-beam interrogation of a possible fissile neutron source and inducing neutron emission therefrom, and a DC power supply that exhibits electrical ripple of less than one part per million. A neutron count histogram and Poisson count distribution are overlaid to provide a visual indication of the difference in correlation of natural and induced emitted neutrons from the radiation source to characterize the neutron source as fissile material or non-fissile material.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Accelerating fissile material detection with a neutron source

A method of discriminating fissile material from non-fissile material with a digital data acquisition system that collects data at high rate, and processes large volumes of data directly to count neutrons from the unknown source and detect excess grouped neutrons to identify fission. The system includes a Poisson neutron generator for in-beam interrogation of a possible fissile neutron source and inducing neutron emission therefrom, and a DC power supply that exhibits electrical ripple of less than one part per million. A neutron count histogram and Poisson count distribution are overlaid to provide a visual indication of the difference in correlation of natural and induced emitted neutrons from the radiation source to characterize the neutron source as fissile material or non-fissile material.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

An Interactive Visualization Tool for Large-Scale Building Stock Modeling: Preprint

Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.

big data↗
DOE OSTI2023

Reduced Order Model to Predict Dispersion of Flammable Refrigerant into a Space

As the HVAC&R industry mobilizes to deploy more low-GWP refrigerants, relevant standards are being continually reviewed and updated. Those include the general safety standards ISO 5149 and ASHRAE 15, and the equipment standards IEC and UL. The standards systematically set the allowable maximum amount of refrigerant that should be used in different equipment types and different applications. To do so, they rely on predictions of how a leaked refrigerant mass will disperse into a space. Dispersion characteristics, such as total flammable volume and its residence time, determine the risk associated with the presence of the flammable refrigerant. The standards have included provisions for the use of flammable refrigerants for approximately two decades. They relied on limited analytical analyses and test cases in their development. Dispersion of a refrigerant into a space is complex. Computational fluid dynamics (CFD) are the most accurate in predicting a given problem. However, CFD is computationally expensive and requires specialized expertise and resources and is not suitable for use by standards development working group as prediction tool. This paper presents the development of a reduced order model (ROM) that predicts the key dispersion characteristics relevant to the dispersion of a leaked refrigerant into a space for any combination of input variables. The inputs are the refrigerant release height, the total released refrigerant mass and its release flow rate, the refrigerant molecular weight, the ventilation flow rate, the floor area and height of the space, recirculation air flow rate, and the tightness of the space. The outputs are histograms of volume fraction of the room in prescribed concentration bins and the total mass of the refrigerant in each bin normalized by the total refrigerant charge at 13 prescribed simulation time stamps between 1 and 900 seconds. The ROM is constructed from a set of CFD simulations with carefully chosen combinations of input parameters. The selection if done using a multidimensional sparse grid which is a generalization of the classical tensor approach but offers additional flexibility and thus can be more carefully tuned towards a specific model. The tuning is done to improve the accuracy, measured in the difference between the output values of the ROM and the CFD model, while minimizing the computational cost, measured in number of CFD simulations which is orders of magnitude more expensive than the processing the training data.

Edwards, Dean↗
DOE OSTI2023

Analysis of the MUSIC 3 He Multiplicity Data

A measurement campaign called the Measurement of Uranium Subcritical and Critical (MUSiC) was performed on a range of configurations of highly-enriched uranium (HEU) from December 2020 through April of 2021. While part of the focus was to measure reactor kinetics parameters on delayed supercritical systems, an additional focus was performing neutron noise measurements on subcritical configurations from deeply subcritical to nearly delayed critical. Multiple detector systems were used to perform these measurements, such as a 3 He multiplicity detector called the Neutron Multiplicity Array Detector (NoMAD) and a liquid scintillator system called the Rossi-α Measurement Rapid Organic Discriminating Detector (RAM-RODD). Also included were a scintillator system from the University of Michigan and a set of small 3He tubes that have previously been used to measure Rossi-α values on near-critical systems. The focus of this paper will be a comparison of prospective analysis methods for the NoMAD measurements. Previous subcritical measurements at the National Criticality Experiments Research Center (NCERC) submitted to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) used the Hage-Cifarelli formalism of the Feynman Variance-to-Mean method. This relies on the time correlations of neutron detections to infer the spontaneous fission rate and neutron multiplication of a system through binning the time tagged detections and analyzing resulting histograms of the numbers of counts. However, there are other neutron noise methods that rely on similar processes, such as the Hansen-Dowdy formalism which uses a slightly different methodology to extract multiplication from the neutron multiplicity counting moments. Comparisons can be made between these experimental results and those obtained through simulations to validate or identify deficiencies in analysis, detection methods, or the underlying nuclear data. Different time gating strategies and their effects on count rate uncertainties are also investigated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Investigation of Local Parameters of PERC Solar Cells Metallized with Screen Printed Cu-Paste

For 40 TW of PV required to transition our planet to 100% renewables, the silver (Ag) should disappear from PV production. Advantages of copper (Cu) over silver (Ag) include: 1) bulk Cu has a similar conductivity to Ag (1.7 one millionth O-cm for Cu, 1.6 one millionth O-cm for Ag, and 2) Cu is -100 times cheaper than Ag, making it an excellent potential replacement. Problems associated with copper contacts include: 1) easy oxidation, and 2) diffusion into the Si cell and recombination activity. To summarize: 1) Rs map shows some regions with very high Rs, indicating no contact in those areas; 2) the histogram shows the peak Rs -5 ohm.cm2, which explains the high FF loss due to Rs; and 3) DLIT indicates non-uniformity in J01 and J02.

copper↗
DOE OSTI2020

Determining Cs-137 Background body burdens for Wild Pigs At Savannah River Site

Savannah River Site (SRS) is a unique U.S. Department of Energy facility in that most of the 310-square miles of land has not be industrialized since operations began in 1953. This has allowed abundant, undisturbed vegetation structure to exists as well as an increase in herd sizes among wildlife populations onsite. Cs-137 is widespread across SRS due to the production of nuclear materials, but is mostly from global atmospheric fallout from nuclear weapons testing. Due to most of SRS being undisturbed land, the concentrations of Cs-137 are higher on site than off site. The SRS began conducting annual deer harvests that were open to the public beginning in 1965. All animals harvested during these hunts are monitored for Cs-137, which is measured using a Sodium Iodide detector. There has been an effective method for calculating the background Cs-137 body burden for the deer population at the SRS, developed by Gaines and Novak using data collected during hunts and gamma overflight data to determine the areas contaminated by site activities. For animals that have a Cs-137 concentration above the established background level, a dose is assigned to the hunter. By applying the same method for wild pigs using data collected from hunts, gamma overflight, and developing a site-specific home range of wild pigs, we were able to calculate the Cs-137 background body burden concentrations of wild pigs on site. Objectives: Develop site-specific home range for wild pigs at SRS; Determine the average concentrations of Cs-137 in wild pigs from background; Use Gaines and Novak method for determining background Cs-137 body burdens wild pigs on SRS. Methods and Materials: Development of site-specific home range of wild pigs was determined to be 2.5 miles using: Four studies conducted between 1969 and 1992 for the SRS wild pig population. (Mayer and Brisbin) The background compartments were determined using: Gamma overflight data showing contaminated areas; Site-specific home range of the wild pigs. Results and Conclusion: All data were generated through the JMP software to provide a histogram and summary statistics. The estimate of an upper tolerance limit for the background contamination with 95% confidence is 1.965 pCi/g for the wild pigs. This means that for wild pigs on SRS from the background compartments are expected to have Cs-137 concentrations of less than 1.965 pCi/g. The current background for the deer is 2.59 pCi/g. For the background compartments Cs-137 concentrations from 2012-2018: Mean: 0.815 pCi/g, Maximum: 2.341 pCi/g, Minimum: -0.704 pCi/g.

07 ISOTOPE AND RADIATION SOURCES↗