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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning-based approach to plasticity in athermal sheared amorphous packings: Improving softness

The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between the plastic dynamics and the structure of a particle’s local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements called “softness.” Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data are limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here, we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle’s observed mobility, allowing for the use of statistical models that are both simpler and provide greater accuracy in limited datasets. Second, we employ persistent homology as a systematic means of identifying simple, topologically informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information that predicts small variations in the response is also predictive of where plastic events will localize. We also find that an excellent accuracy is achieved in athermal sheared packings using simply a particle’s species and the number of nearest neighbor contacts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Detecting hidden layers from spreading dynamics on complex networks

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. Furthermore, we conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Crabbing system for an electron-ion collider

As high energy and nuclear physicists continue to push further the boundaries of knowledge using colliders, there is an imperative need, not only to increase the colliding beams' energies, but also to improve the accuracy of the experiments, and to collect a large quantity of events with good statistical sensitivity. To achieve the latter, it is necessary to collect more data by increasing the rate at which these processes are being produced and detected in the machine. This rate of events depends directly on the machine's luminosity. The luminosity itself is proportional to the frequency at which the beams are being delivered, the number of particles in each beam, and inversely proportional to the cross-sectional size of the colliding beams. There are several approaches that can be considered to increase the events statistics in a collider other than increasing the luminosity, such as running the experiments for a longer time. However, this also elevates the operation expenses, while increasing the frequency at which the beams are delivered implies strong physical changes along the accelerator and the detectors. Therefore, it is preferred to increase the beam intensities and reduce the beams cross-sectional areas to achieve these higher luminosities. In the case where the goal is to push the limits, sometimes even beyond the machines design parameters, one must develop a detailed High Luminosity Scheme. Any high luminosity scheme on a modern collider considers|in one of their versions|the use of crab cavities to correct the geometrical reduction of the luminosity due to the beams crossing angle. In this dissertation, we present the design and testing of a proof-of-principle compact superconducting crab cavity, at 750 MHz, for the future electron-ion collider, currently under design at Jefferson Lab. In addition to the design and validation of the cavity prototype, we present the analysis of the first order beam dynamics and the integration of the crabbing systems to the interaction region. Following this, we propose the concept of twin crabs to allow machines with variable beam transverse coupling in the interaction region to have full crabbing in only the desired plane. Finally, we present recommendations to extend this work to other frequencies.

43 PARTICLE ACCELERATORS↗
NASA NTRS2013

Combined Experimental and Numerical Simulations of Thermal Barrier Coated Turbine Blades Erosion

A combined experimental and computational study was conducted to investigate the erosion of thermal barrier coated (TBC) blade surfaces by alumina particles ingestion in a single stage turbine. In the experimental investigation, tests of particle surface interactions were performed in specially designed tunnels to determine the erosion rates and particle restitution characteristics under different impact conditions. The experimental results show that the erosion rates increase with increased impingement angle, impact velocity and temperature. In the computational simulations, an Euler-Lagrangian two stage approach is used in obtaining numerical solutions to the three-dimensional compressible Reynolds Averaged Navier-Stokes equations and the particles equations of motion in each blade passage reference frame. User defined functions (UDF) were developed to represent experimentally-based correlations for particle surface interaction models which were employed in the three-dimensional particle trajectory simulations to determine the particle rebound characteristics after each surface impact. The experimentally based erosion UDF model was used to predict the TBC erosion rates on the turbine blade surfaces based on the computed statistical data of the particles impact locations, velocities and angles relative to the blade surface. Computational results are presented for the predicted TBC blade erosion in a single stage commercial APU turbine, for a NASA designed automotive turbine, and for the NASA turbine scaled for modern rotorcraft operating conditions. The erosion patterns in the turbines are discussed for uniform particle ingestion and for particle ingestion concentrated in the inner and outer 5 percent of the stator blade span representing the flow cooling the combustor liner.

Hamed, Awate↗
NASA NTRS2017

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Ecosystems

The combined effects of repeated fires, climate, and landscape features (e.g., edges) need greater focus in fire ecology studies, which usually emphasize characteristics of the most recent fire and not fire history. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells that represented potential territories because frequent fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities between states varied annually as functions of environmental covariates. Covariates included vegetative type, edges, precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presenceabsence of fire covariate, but also fire history covariates: time since the previous fire, the maximum fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Measuring territory quality states and environmental covariates each year combined with multistate modeling provided a useful empirical approach to quantify the effects of repeated fire in combinations with environmental variables on transition probabilities that drive management strategies and ecosystem change.

Breininger, David R.↗
DOE OSTI2025

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗
NASA NTRS2004

Uncertainty-Based Approach for Dynamic Aerodynamic Data Acquisition and Analysis

Development of improved modeling methods to provide increased fidelity of flight predictions for aircraft motions during flight in flow regimes with large nonlinearities requires improvements in test techniques for measuring and characterizing wind tunnel data. This paper presents a method for providing a measure of data integrity for static and forced oscillation test techniques. Data integrity is particularly important when attempting to accurately model and predict flight of today s high performance aircraft which are operating in expanded flight envelopes, often maneuvering at high angular rates at high angles-of-attack, even above maximum lift. Current aerodynamic models are inadequate in predicting flight characteristics in the expanded envelope, such as rapid aircraft departures and other unusual motions. Present wind tunnel test methods do not factor changes of flow physics into data acquisition schemes, so in many cases data are obtained over more iterations than required, or insufficient data may be obtained to determine a valid estimate with statistical significance. Additionally, forced oscillation test techniques, one of the primary tools used to develop dynamic models, do not currently provide estimates of the uncertainty of the results during an oscillation cycle. A method to optimize the required number of forced oscillation cycles based on decay of uncertainty gradients and balance tolerances is also presented.

Heim, Eugene H. D.↗
DOE OSTI2023

A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films

Abstract Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Studying language evolution in the age of big data

The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated, data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight declaring worthy prior commitments and interesting hypotheses in need of comparison.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1995

A water blown urethane insulation for use in cryogenic environments

Thermal Protection Systems (TPS) of NASA's Space Shuttle External Tank include polyurethane and polyisocyanurate modified polyurethane foam insulations. These insulations, currently foamed with CFC 11 blowing agent, serve to maintain cryogenic propellant quality, maintain the external tank structural temperature limits, and minimize the formation of ice and frost that could potentially damage the ceramic insulation on the space shuttle orbiter. During flight the external tank insulations are exposed to mechanical, thermal and acoustical stresses. TPS must pass cryogenic flexure and substrate adhesion tests at -253 C, aerothermal and radiant heating tests at fluxes up to approximately 14 kilowatts per square meter, and thermal conductivity tests at cryogenic and elevated temperatures. Due to environmental concerns, the polyurethane insulation industry and the External Tank Project are tasked with replacing CFC 11. The flight qualification of foam insulations employing HCFC 141b as a foaming agent is currently in progress; HCFC 141b blown insulations are scheduled for production implementation in 1995. Realizing that the second generation HCFC blowing agents are an interim solution, the evaluation of third generation blowing agents with zero ozone depletion potential is underway. NASA's TPS Materials Research Laboratory is evaluating third generation blowing agents in cryogenic insulations for the External Tank; one option being investigated is the use of water as a foaming agent. A dimensionally stable insulation with low friability, good adhesion to cryogenic substrates, and acceptable thermal conductivity has been developed with low viscosity materials that are easily processed in molding applications. The development criteria, statistical experimental approach, and resulting foam properties will be presented.

Blevins, Elana↗
NASA NTRS2018

Dynamic Modeling using Output-Error Parameter Estimation based on Frequency Responses Estimated with Multisine Inputs

A method is developed for estimating model parameters, such as nondimensional stability and control derivatives, by fitting transfer function or state-space models to empirical frequency response data using the output-error approach. The frequency response data were computed using Fourier transforms of measured input and output data. The control surfaces were excited with periodic multisine inputs which facilitated time-ecient estimation of multiple-input multiple-output frequency responses. The method was applied to lateral data from a nonlinear flight dynamics simulation of the F-16 aircraft, and to longitudinal data from multiple repeated flight test maneuvers of the NASA T-2 subscale aircraft. Results using simulation data showed the frequency response method compared well to other standard methods for parameter estimation. In addition to including all the available inputs, outputs, and harmonic frequencies in the estimation, relatively small subsets of the measured data could also be used to focus on identifying specific parts of the model. Results from flight test data showed that parameter estimates and uncertainties determined from repeated maneuvers were accurate and in statistical agreement with each other.

Grauer, Jared A.↗
NASA NTRS2024

Wind Tunnel Test of A Scale Model of A Venus Probe to Determine Aeroacoustics Environment

The Zephyr probe for NASA’s DAVINCI project is being designed to fly to Venus and collect measurements to characterize the Venusian atmosphere while it descends towards the surface. Despite a slow descent speed as the probe approaches the surface, the very dense atmosphere means the vibro-acoustic environment that the probe must withstand could be very harsh. A wind tunnel test campaign was conducted to characterize the surface-pressure fluctuations (acoustics) on the external surface of the probe during the descent. The goal was to provide forcing functions for vibro-acoustic analysis of all instruments inside the probe. A 25% scale-model of the Zephyr probe was manufactured, instrumented with 18 microphones, and tested in two wind tunnels located in the Fluid Mechanics Lab at NASA Ames Research Center at velocities ranging from 13 m/s to 48 m/s. Trip strips were added to the model to ensure a turbulent boundary layer on the model. Microphones were located appropriately to provide spectra of pressure fluctuations in regions with different flow characteristics as well as for calculating various two-point statistics. The acoustic spectra measured in the wind tunnel tests were scaled up to flight-scale using the estimated flight trajectory information. Besides fluctuating surface-pressure measurements, hot-film velocity measurements as well as smoke-laser flow visualization were conducted to better understand the flow around the probe. Lastly, several different geometry configurations were tested to see whether any reduction in acoustic levels could be achieved, but none of the configurations that were tested offered significant improvements. The flow visualization showed 3 primary flow regimes on the model: an attached turbulent boundary layer on the front of the model, a region of separated flow upstream of the flared lip, and a large, separated wake at the rear of the model. The acoustic spectra generally show three different shapes corresponding to these flow regions, with the highest levels seen at the rear of the model, and directly in front of the drag plate. The scaled overall levels of pressure fluctuations were found to be high: in the range of 130dB to 155dB.

planetary probe↗
NASA NTRS2006

Automated Classification of Thermal Infrared Spectra Using Self-organizing Maps

Existing and planned space missions to a variety of planetary and satellite surfaces produce an ever increasing volume of spectral data. Understanding the scientific informational content in this large data volume is a daunting task. Fortunately various statistical approaches are available to assess such data sets. Here we discuss an automated classification scheme based on Kohonen Self-organizing maps (SOM) we have developed. The SUM process produces an output layer were spectra having similar properties lie in close proximity to each other. One major effort is partitioning this output layer into appropriate regions. This is prefonned by defining dosed regions based upon the strength of the boundaries between adjacent cells in the SOM output layer. We use the Davies-Bouldin index as a measure of the inter-class similarities and intra-class dissimilarities that determines the optimum partition of the output layer, and hence number of SOM clusters. This allows us to identify the natural number of clusters formed from the spectral data. Mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) are used to test and evaluate the classification scheme. We label the library sample spectra in a hierarchical scheme with class, subclass, and mineral group names. We use a portion of the spectra to train the SOM, i.e. produce the output layer, while the remaining spectra are used to test the SOM. The test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. We then evaluate these assignments to assess the scientific meaning and accuracy of the derived SOM classes as they relate to the labels. We demonstrate that unsupervised classification by SOMs can be a useful component in autonomous systems designed to identify mineral species from reflectance and emissivity spectra in the therrnal IR.

Roush, Ted L.↗
DOE OSTI2021

Hurricanes alter 10 Be concentrations in tropical river sediment but do not change regional erosion rate estimates

Tropical islands, including many in island arcs, are subjected to recurring disturbances from extreme storms such as tropical cyclones. To test whether such storms influence cosmogenic nuclide concentrations such that they do not reflect long-term rates of erosion, we measured meteoric and in situ 10Be in river sediment samples from Dominica, an andesitic island in the Caribbean, before and after category five Hurricane Maria (2017). Populations of before- and after-storm concentrations are statistically indistinguishable (n=7 pairs for in situ 10 Be, n=11 pairs for meteoric 10 Be). 10 Be concentrations vary from -138% to +73% within before-after sample pairs relative to the mean of the pair. These new data suggest that the effects of extreme storms on the depth and amount of near-surface erosion on Dominica vary spatially. Our data support calculations of Niemi, et al. (2005) and Yanites, et al. (2009) suggesting that basin-by-basin comparisons of erosion rates based on cosmogenic nuclides should be approached with caution in small (<~100 km 2 ) watersheds affected by mass movements and extreme storms. Erosion rates determined from in situ 10Be in Dominica (geometric mean=0.102 mm y-1, n=12) are low compared to similarly steep and wet areas globally and correlate positively with the spatial density of mass movements.

58 GEOSCIENCES↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1985

Statistical Signal Analysis for Systems with Interferenced Inputs

A new approach is introduced, based on statistical signal analysis, which overcomes the error due to input signal interference. The model analyzed is given. The input signals u sub 1 (t) and u sub 2 (t) are assumed to be unknown. The measurable signals x sub 1 (t) and x sub 2 (t) are interferened according to the frequency response functions, H sub 12 (f) and H sub 21 (f). The goal of the analysis was to evaluate the power output due to each input, u sub 1 (t) and u sub 2 (t), for the case where both are applied to the same time. In addition, all frequency response functions are calculated. The interferenced system is described by a set of five equations with six unknown functions. An IBM XT Personal Computer, which was interfaced with the FFT, was used to solve the set of equations. The software was tested on an electrical two-input, one-output system. The results were excellent. The research presented includes the analysis of the acoustic radiation from a rectangular plate with two force inputs and the sound pressure as an output signal.

Bai, R. M.↗
DOE OSTI2025

Evaluating User Errors and Temporal Trends in Marine Fish Communities Using 360-Degree Underwater Photography

The use of environmental DNA (eDNA) sampling has been proposed as a complementary method to monitor fish species in marine environments, offering a non-invasive and potentially more efficient approach to marine species observations. eDNA monitoring could be especially useful in and around sites targeted for marine energy generation as these regions need regular monitoring that would be impractical with traditional techniques. Before we can fully rely upon eDNA, we must first verify its accuracy against other proven methods, such as the use of underwater photography. In this study, I deployed a 360-degree camera in the tidal channel of Sequim Bay once a month during several hours overlapping slack tide. I investigated how having multiple people identify and count fish on underwater images could affect the overall results. Using chi square tests in R, I compared my fish identifications and counts to those made by another intern on the same images recorded in August. I found significant differences in the number of species identified and the total individual counts between the two different datasets. I also tested the statistical differences in both Shannon diversity and Pielou evenness indices between the August, September, and November camera deployments using a Hutcheson t-test. Only one significant difference was found in the Shannon index comparisons, and none were found between the Pielou evenness comparisons. These findings show that if multiple identifiers are used to process underwater images, quality control checks must be made to reduce the potential for error. This also points toward the possibility to leverage more advanced image analysis processes, such as automated image analysis software. The findings from this study also show that the dynamics of marine fish communities can vary over a few months; however, further analysis is needed to determine the extent of the seasonal changes in Sequim Bay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Distribution Development for Residual Inventory at the New York West Valley Site - 20400

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351 ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Operations led to contamination in a number of facilities and locations. Some of that contamination has migrated from waste disposal zones to other layers, formations, and features on and off the WNYNSC. Phase I decommissioning activities are ongoing and involve the removal of a number of areas and structures that have been associated with contamination. The purpose of this work is to outline the approach for characterizing contamination not associated with disposed wastes, contaminated structures, or specific releases. In this work, the term, residual radiological activity, is used to describe environmental contamination that exists subsequent to the completion of Phase I decommissioning activities, that is not associated with disposed wastes, contaminated structures, or specific releases. Contamination from the Site was quantified relative to data that characterize the concentrations of radionuclides that exist in background. Background concentrations are those present in the area but having no influence from Site related activities. The existence of residual radiological activity that is elevated relative to background has the potential to contribute to future risks to human health and the environment. As a consequence, the residual inventory information is used to inform the West Valley Probabilistic Performance Assessment (PPA) model to characterize potential future risks to human health and the environment. The centralized West Valley Data Management System (DMS) was the source of information for the data assembled in this analysis. The DMS is a fairly large compilation consisting of thousands of records from investigation studies, with sample dates ranging from 1990 to present. Samples from monitoring wells, boreholes, geoprobe studies, surface water, surface soils, storm water outfalls, ventilation stack filters, plant and animal tissues, and more are included in the DMS. Results are typically reported in units of activity per unit volume. For the purpose of the analyses presented here, all results were converted into consistent units of pCi per unit volume. Since 1990, data have been collected from various locations across the WNYNSC at different times with varying frequency over the course of several decades. As a consequence, a number of potential issues can arise with respect to the assembly of a dataset that is deemed adequate for the characterization of residual radiological activity. These issues were assessed and resolved to the extent possible through careful consideration of the properties of the distributions. The intent was to use data which characterize the current state of the Site. Radionuclides can be designated to one of several groups depending on their origin. In this work the groups considered were 1) Naturally Occurring Radioactive Material (NORM), 2) fallout, and 3) Other (including power plant, medical research, etc). This grouping is a useful construct with respect to the interpretation of fixed laboratory results. For example, NORM radionuclides that exist within a decay chain should have approximately equivalent distributions of concentrations if they are representative of background conditions. Insights such as these can be used as a check to identify sample results that need to be further investigated or omitted due to issues associated with reported results from fixed laboratory analyses. This type of analysis provided a foundation for the assessment of the adequacy of sample results for use in subsequent components of an assessment. The general process for the assessment of residual radiological contamination at the Site consists of a sequence of several steps. First, for each analyte, several statistical tests were performed to assess the weight of evidence against the null hypothesis that the mean of the distribution of concentrations was equal to zero. If the mean of the distribution of concentrations for a given radionuclide was not found to be greater than zero, then it was removed from consideration as a component of the residual radiological contamination. If there was significant evidence to reject the hypothesis of the mean being equal to zero, the second step was to compare the distribution of the data from the Site to that of the corresponding background. A suite of tests was used to compare the distributions of the site and background data. The results of these tests were collectively used to determine if site data are elevated relative to background. The third step was to develop distributions using a Bayesian framework to characterize the distribution of mean of the increment present above background for each of the radionuclides. The Bayesian model implemented allowed for the comparison of site-specific records to background concentrations to better approximate contamination attributed to the Site. A final screening step was employed for radionuclides that exceed background. This screening step compared 95% upper confidence limits (UCLs) from the increment distribution developed in the previous step to the risk screening levels. This approach yields a list of analytes that were determined to be elevated relative to background.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗