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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Development and Testing of Nuclear Data Libraries for Improved Energy Deposition Modeling - Paper 144

Accurate calculations of local energy deposition rates require the presence of gamma production data (GPD) in the nuclear data library used by radiation transport codes such as MCNP5, MONACO, or Denovo. The lack of GPD in the underlying nuclear data library can cause significant over-prediction of local heat generation rates with a corresponding under-prediction of heat generation rates away from a region of interest. To address this concern, continuous energy (CE) and multigroup (MG) cross section libraries with additional GPD have been created. Both ACE- and AMPX-formatted CE libraries, as well as ANISN- and AMPX-formatted MG libraries, have been created and are undergoing verification and validation (V and V) testing. A hybrid data set consisting of cross-section evaluations from five different nuclear data repositories has been developed. This hybrid data set, referred to as GPD-Rev0, is intended to support enhanced energy deposition calculations. Code-specific libraries in CE ACE, CE AMPX, MG AMPX, and MG ANISN formats have been created from the GPD-Rev0 data set. V and V testing has demonstrated that these various code-specific libraries are performing well for the intended applications. Localized over- and under-predictions of HFIR heating rates have been addressed without adversely affecting the prediction of other key operating parameters. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Capacity value assessments of wind power

This article describes some of the recent research into the capacity value of wind power. With the worldwide increase in wind power during the past several years, there is increasing interest and significance regarding its capacity value because this has a direct influence on the amount of other (nonwind) capacity that is needed. We build on previous reviews from IEEE and IEA W ind T ask 25 a and examine recent work that evaluates the impact of multiple‐year data sets and the impact of interconnected systems on resource adequacy. We also provide examples that explore the use of alternative reliability metrics for wind capacity value calculations. We show how multiple‐year data sets significantly increase the robustness of results compared to single‐year assessments. Assumptions regarding the transmission interconnections play a significant role. To date, results regarding which reliability metric to use for probabilistic capacity valuation show little sensitivity to the metric. WIREs Energy Environ 2017, 6:e226. doi: 10.1002/wene.226 This article is categorized under: Wind Power > Economics and Policy Wind Power > Systems and Infrastructure Energy Infrastructure > Economics and Policy

Milligan, Michael↗
DOE OSTI2024

Refining Planetary Boundary Layer Height Retrievals From Micropulse‐Lidar at Multiple ARM Sites Around the World

Abstract Knowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Web-based visual data exploration for improved radiological source detection

Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This study describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. Finally, the visualization system is based on a JavaScript front end communicating via REST with a back end web server.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2013

Hierarchical Cluster Analysis of Service Usage and Geographic Variation in Medicare Spending

Per capita Medicare spending varies considerably throughout the Unites States, even after taking into account factors such as cost of living and pre-existing conditions of the Medicare populations. Additionally, per capita spending may vary up to four times as much under Medicare as it does under private insurance. Several studies have explored the Medicare-costs differences between regions with respect to how Medicare services are used. The recent public release of a large, relatively fine-grained set of Medicare data over four annual periods (2007–2010) by the Centers for Medicare and Medicaid Services (CMS), allowed us to perform a broad-scale exploratory analysis of Medicare service-usage patterns. The CMS data set contains data on the Health Care Referral Region level, which subsets the United States into 306 distinct regions. To identify potential trends in service usage over these regions, we used hierarchical agglomerative cluster analysis (HACA) to group regions into clusters based on similarity of service-usage patterns. Much previous work on the subject of cost variation starts with analysis of cost. By beginning with service-usage data, HACA effectively uncoupled patterns of service-usage from overall patterns of cost. We found that while some Medicare services were used in a fairly consistent manner over all clusters, other services varied considerably. Variation in how frequently services are used may provide insight into practices that are particularly expensive or particularly cost effective. It also may be possible to develop recommendations for more cost-efficient high-quality medical care by taking such information into account. In addition, the variance among the frequencies with which some services are used may point to inconsistent standards of medical practice that are not necessarily directly reflected in total cost, or to more deeply underlying geographic or demographic factors that are not necessarily reflected directly in total cost.

Vacha, Linnet↗
DOE OSTI2014

Statistical analysis of content of Cs-137 in soils in Bansko-Razlog region

Statistical analysis of the data set consisting of the activity concentrations of {sup 137}Cs in soils in Bansko–Razlog region is carried out in order to establish the dependence of the deposition and the migration of {sup 137}Cs on the soil type. The descriptive statistics and the test of normality show that the data set have not normal distribution. Positively skewed distribution and possible outlying values of the activity of {sup 137}Cs in soils were observed. After reduction of the effects of outliers, the data set is divided into two parts, depending on the soil type. Test of normality of the two new data sets shows that they have a normal distribution. Ordinary kriging technique is used to characterize the spatial distribution of the activity of {sup 137}Cs over an area covering 40 km{sup 2} (whole Razlog valley). The result (a map of the spatial distribution of the activity concentration of {sup 137}Cs) can be used as a reference point for future studies on the assessment of radiological risk to the population and the erosion of soils in the study area.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗
DOE OSTI2021

Assessment of Local Observation of Atomic Ordering in Alloys via the Radial Distribution Function: A Computational and Experimental Approach

As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni 3 Al. The ability to observe a signal of atomic ordering consistent with the known L1 2 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al 1.3 CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Integrating Predictions for Improving Defect Classification Accuracy in NDT-based Assessment of Concrete - 20229

There is an increasing need to create predictive models for defect classification in concrete using the output of non-destructive testing (NDT) techniques. Recent advancement of machine-learning algorithms has offered several techniques for developing classification models for different types of data sets. However, the performance of these algorithms is very uncertain, mainly when applied to small and noisy datasets. For example, when human access is limited (e.g., nuclear facility), robot-based NDT is preferred. But compared to manual tests with humans present on site, the data sets are small, and more noise can exist. Therefore, it is imperative to develop new approaches to ensure a consistently high classification accuracy for inadequate data sets. This study explores the classification performance on NDT dataset using classifiers from different machine-learning algorithms, namely k-Nearest Neighbor (kNN), Decision Tree, Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). The authors further integrated the predictions from these classifiers using proposed methods. The integration strategy combines the output of the classifiers based on two different measures, accuracy, and performance (ACC and PERF), using equations such as sum, average, and square-root-of-sums-of-squares (SRSS). Our results reveal varying classification accuracies across individual classifiers with different misclassifications across the test data set. The integration strategy provided significant improvement in the classification accuracy compared to the individual classifiers. Furthermore, the results indicate minimal variation across the integration methods as compared to the variation across the individual classifiers. To conclude, prediction integration offers a unique approach for combining the output of multiple classifiers to create redundancies with the potential of achieving high classification performance and improved reliability in predictive models for defect detection in concrete. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

SU-E-T-441: Gamma Passing Rates for IMRT QA and VMAT QA

Purpose: This study compares gamma passing rates for a cohort of similar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) quality assurance (QA) plans to determine the equivalence of the patient specific QA plan delivery. The passing criterion is 90% gamma index with 3% dose difference (DD), 3mm distance-to-agreement (DTA) and a dose threshold of 10%. Methods: Gamma passing results of previously treated IMRT QA plans, delivered to Sun Nuclear MapCheck phantom, and VMAT QA plans, delivered to ScandiDos Delta4 phantom, are organized by anatomical site and treatment machine. Both Varian and Elekta machines are included. Pairs of IMRT and VMAT plans are matched based on site, machine, and PTV volume to ensure similar plan cohorts. A two-tailed t-test analysis of the data with an alpha of 0.05 determines if there exists a statistically significant difference. Power was calculated to detect a difference of 4%; all data sets were strong with above a 0.8 power. Results: The first data set consisting of 20 matched pairs of prostate plans was statistically insignificant (p-value=0.90, Power=0.99). The 14 matched pairs set of head and neck plans has a statistically significant Result (p-value=0.028, Power=0.88). The head and neck IMRT gamma indexes have a mean of 93.1% and range of 82%-100% while the VMAT gamma indexes have a mean of 96.7% and range of 92%-100%. The two combined data sets of matched plans had a statistically insignificant Result (p-value=0.073, Power=0.99). Conclusion: Overall, IMRT and VMAT have equivalent passing rates when comparing the gamma analysis using a passing criterion of 3% DD and 3mm DTA. When separated by site, prostate IMRT and VMAT plans have equivalent passing rates while head and neck plans have a statistically significant variation of passing rates. The passing rates for the two modalities are independent of delivery machine for matched PTV target volumes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Trident: A Universal Tool for Generating Synthetic Absorption Spectra from Astrophysical Simulations

Hydrodynamical simulations are increasingly able to accurately model physical systems on stellar, galactic, and cosmological scales; however, the utility of these simulations is often limited by our ability to directly compare them with the data sets produced by observers: spectra, photometry, etc. To address this problem, we have created trident, a Python-based open-source tool for post-processing hydrodynamical simulations to produce synthetic absorption spectra and related data. trident can (i) create absorption-line spectra for any trajectory through a simulated data set mimicking both background quasar and down-the-barrel configurations; (ii) reproduce the spectral characteristics of common instruments like the Cosmic Origins Spectrograph; (iii) operate across the ultraviolet, optical, and infrared using customizable absorption-line lists; (iv) trace simulated physical structures directly to spectral features; (v) approximate the presence of ion species absent from the simulation outputs; (vi) generate column density maps for any ion; and (vii) provide support for all major astrophysical hydrodynamical codes. trident was originally developed to aid in the interpretation of observations of the circumgalactic medium and intergalactic medium, but it remains a general tool applicable in other contexts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

In-Situ Data for Microphysical Retrievals: TC4, 2007

This data set is derived from measurements collected in situ by the NASA DC8 during the Tropical Cloud Climate Composition Coupling Experiment (TC4) that was conducted during July and August, 2007 (Toon et al., 2010). During this experiment the DC8 was based in San Jose, Costa Rica and sampled clouds in the maritime region of the Eastern Pacific and adjoining continental areas. The primary objective of the DC8 during this deployment was to sample ice clouds associated with convective activity. While the vast majority of the data are from ice-phase clouds that have recent association with convection, other types of clouds such as boundary layer clouds and active convection were also sampled and are represented in this data set. The derived data set, as compiled in this delivery, includes approximately 15,000 5-second averaged measurements collected by the NASA DC8.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Defect Genome of Cubic Perovskites for Fuel Cell Applications

Heterogeneities such as point defects, inherent to material systems, can profoundly influence material functionalities critical for numerous energy applications. This influence in principle can be identified and quantified through development of large defect data sets which we call the defect genome, employing high-throughput ab initio calculations. However, high-throughput screening of material models with point defects dramatically increases the computational complexity and chemical search space, creating major impediments toward developing a defect genome. In this paper, we overcome these impediments by employing computationally tractable ab initio models driven by highly scalable workflows, to study formation and interaction of various point defects (e.g., O vacancies, H interstitials, and Y substitutional dopant), in over 80 cubic perovskites, for potential proton-conducting ceramic fuel cell (PCFC) applications. The resulting defect data sets identify several promising perovskite compounds that can exhibit high proton conductivity. Furthermore, the data sets also enable us to identify and explain, insightful and novel correlations among defect energies, material identities, and defect-induced local structural distortions. Finally, such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2014

LandCast 2050 High-Resolution Population Projection

The LandCast 2050 data set is an empirically-informed spatial distribution of projected population of the contiguous U.S. for 2050 compiled on a 30" x 30" latitude/longitude grid. Population projections of county level numbers were developed using a modified version of the U.S. Census's projection methodology - with the U.S. Census's official projection as the benchmark. Projected census counts were apportioned to each grid cell based on locally adaptive likelihood coefficients, which are based on land cover, slope, road proximity, distances to larger cities, a moving average of current population, and other data sets. The LandCast data set was developed as part of Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk.

Source record↗
DOE OSTI2014

Assessment of interpatient heterogeneity in tumor radiosensitivity for nonsmall cell lung cancer using tumor-volume variation data

Purpose: In our previous work, the authors showed that a distribution of cell surviving fractionsS{sub 2} in a heterogeneous group of patients could be derived from tumor-volume variation curves during radiotherapy for head and neck cancer. In this research study, the authors show that this algorithm can be applied to other tumors, specifically in nonsmall cell lung cancer. This new application includes larger patient volumes and includes comparison of data sets obtained at independent institutions. Methods: Our analysis was based on two data sets of tumor-volume variation curves for heterogeneous groups of 17 patients treated for nonsmall cell lung cancer with conventional dose fractionation. The data sets were obtained previously at two independent institutions by using megavoltage computed tomography. Statistical distributions of cell surviving fractionsS{sub 2} and clearance half-lives of lethally damaged cells T{sub 1/2} have been reconstructed in each patient group by using a version of the two-level cell population model of tumor response and a simulated annealing algorithm. The reconstructed statistical distributions of the cell surviving fractions have been compared to the distributions measured using predictive assays in vitro. Results: Nonsmall cell lung cancer presents certain difficulties for modeling surviving fractions using tumor-volume variation curves because of relatively large fractional hypoxic volume, low gradient of tumor-volume response, and possible uncertainties due to breathing motion. Despite these difficulties, cell surviving fractionsS{sub 2} for nonsmall cell lung cancer derived from tumor-volume variation measured at different institutions have similar probability density functions (PDFs) with mean values of 0.30 and 0.43 and standard deviations of 0.13 and 0.18, respectively. The PDFs for cell surviving fractions S{sub 2} reconstructed from tumor volume variation agree with the PDF measured in vitro. Conclusions: The data obtained in this work, when taken together with the data obtained previously for head and neck cancer, suggests that the cell surviving fractionsS{sub 2} can be reconstructed from the tumor volume variation curves measured during radiotherapy with conventional fractionation. The proposed method can be used for treatment evaluation and adaptation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Rutz, Jonathan J.↗
DOE OSTI2016

Parameterization of photon beam dosimetry for a linear accelerator

Purpose: In radiation therapy, accurate data acquisition of photon beam dosimetric quantities is important for (1) beam modeling data input into a treatment planning system (TPS), (2) comparing measured and TPS modeled data, (3) the quality assurance process of a linear accelerator’s (Linac) beam characteristics, (4) the establishment of a standard data set for comparison with other data, etcetera. Parameterization of the photon beam dosimetry creates a data set that is portable and easy to implement for different applications such as those previously mentioned. The aim of this study is to develop methods to parameterize photon beam dosimetric quantities, including percentage depth doses (PDDs), profiles, and total scatter output factors (S{sub cp}). Methods: S{sub cp}, PDDs, and profiles for different field sizes, depths, and energies were measured for a Linac using a cylindrical 3D water scanning system. All data were smoothed for the analysis and profile data were also centered, symmetrized, and geometrically scaled. The S{sub cp} data were analyzed using an exponential function. The inverse square factor was removed from the PDD data before modeling and the data were subsequently analyzed using exponential functions. For profile modeling, one halfside of the profile was divided into three regions described by exponential, sigmoid, and Gaussian equations. All of the analytical functions are field size, energy, depth, and, in the case of profiles, scan direction specific. The model’s parameters were determined using the minimal amount of measured data necessary. The model’s accuracy was evaluated via the calculation of absolute differences between the measured (processed) and calculated data in low gradient regions and distance-to-agreement analysis in high gradient regions. Finally, the results of dosimetric quantities obtained by the fitted models for a different machine were also assessed. Results: All of the differences in the PDDs’ buildup and the profiles’ penumbra regions were less than 2 and 0.5 mm, respectively. The differences in the low gradient regions were 0.20% ± 0.20% (<1% for all) and 0.50% ± 0.35% (<1% for all) for PDDs and profiles, respectively. For S{sub cp} data, all of the absolute differences were less than 0.5%. Conclusions: This novel analytical model with minimum measurement requirements was proved to accurately calculate PDDs, profiles, and S{sub cp} for different field sizes, depths, and energies.

61 RADIATION PROTECTION AND DOSIMETRY↗