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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 1,603 records · Page 89

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NASA NTRS1991

Kinematic and precipitation structure of a small, long-lived mesoscale convective system

A comprehensive analysis using conventional single Doppler radar analyses, analysis of multiparameter (CP-2) data, network sounding and surface data, and GOES satellite data is presented to characterize the internal kinematic and precipitation structure of a small, long-lived mesoscale convective system. The data pertain to the Cooperative Huntsville Meteorological experiment conducted over northern Alabama and central Tennessee during June and July of 1986. The mesoscale convective system pulsations visible in the IR patterns were associated with the development of closely interacting aggregates of convective elements. The aggregates were about 20 km in diameter, attained echo tops of about 13 km, and were dominated by a single relatively intense convective element. The measured difference in radial velocity across the upper portion of these elements was about 20 m/s over a distance of about 10 km.

Knupp, Kevin R.↗

NASA NTRS1972

Bayesian recursive image estimation.

The enhancement of images that are characterized only by statistical data where the picture contains additive noise is considered. The random process representing the output of the scanner is characterized by the output of a dynamic system with white noise input. The dynamic system describes a first-order vector-Markov process. The procedure of Kalman filtering is then utilized to recursively determine the minimum mean-square error estimate of the image. The result is then extended to obtain the smoothing of the data.

Nahi, N. E.↗

DOE OSTI2025

Geoanalytical Evaluation of Saline Storage (GEESS) Geodatabase v2.0

The Geoanalytical Economic Evaluation of Saline Storage (GEESS) geodatabase was developed to support the United States Department of Energy (DOE) and National Energy Technology Laboratory (NETL) in their geologic carbon storage efforts by characterizing saline geologic formations present in the FECM/NETL CO2 Saline Storage Cost Model (CO2_S_COM) [1]. Using publicly available literature and data, the GEESS geodatabase characterizes 57 geologic formations across the lower-48 U.S. states in what are called Fully Integrated Geodatabases (FIGs). The FIG is a vector polygon feature containing thousands or tens of thousands of individual polygons, which each contain discrete geologic parameter values. A list of the critical geologic parameters that are characterized in the GEESS geodatabase are described in the “Processing Steps and Workflow” part of the ReadMe file, as well as the Data Catalog accompanying the GEESS geodatabase. The FIG is the basis of the GEESS geodatabase and is the direct representation of the collected geologic data. In addition to the FIG, the GEESS system contains grid files. Due to the complexity of the FIGs, grids are used to sample the geologic data so they can be exercised within CO2_S_COM. The grid files contain the geologic data sampled from the FIG, as well as estimates of “Plume Uncertainty Diameter” and “First-year Break-even Price of CO2” derived from CO2_S_COM based on the GEESS grid data.

carbon↗

DOE OSTI2019

Geochemical characterization of floodplain sediments from Meander C and O in the East River Watershed, CO, USA

This data package is part of the Watershed Function SFA data collection and contains geochemical characterization of sediments as a function of depth in the pumphouse vicinity, including Meander C and Meander O (oxbow). This data was used in the manuscript "Fox, P.M., Bill, M., Heckman, K., Conrad, M., Anderson, C., Keiluweit, M., and Nico, P.S.Shale as a Source of Organic Carbon in Floodplain Sediments of a Mountainous Watershed" published in Journal of Geophysical Research: Biogeosciences. Data includes sieving, total organic carbon (TOC), total inorganic carbon (TIC), total nitrogen (TN), carbon isotopes (14C and 13C) of whole sediment. Sediment density fractionation data is also included, with measurements of TOC, TN, 14C and 13C on density fractions. Metal and carbon concentrations from various sediment extractions (HCl, dithionite, oxalate, water, and pyrophosphate) are included. Characterization of extracted carbon by fourier transform infrared spectroscopy (FTIR) and UV-Visible light spectroscopy. Characterization of density fractionated (light fractions) organic carbon by FTIR. Carbon X-ray spectroscopy (C near edge X-ray absorption fine structure spectroscopy [NEXAFS] and scanning transmission X-ray microscopy [STXM]) is also included for shale samples and sediment samples.Additional acknowledgements:1. M.K. and C.A. were supported by the Department of Energy, Office of Biological and Environmental Research, Subsurface Biosphere Research program (award no. DE-SC0016544).2. STXM and NEXAFS data was collected at the Canadian Light Source, which is supported by the Canada Foundation for Innovation, Natural Sciences and Engineering Research Council of Canada, the University of Saskatchewan, the Government of Saskatchewan, Western Economic Diversification Canada, the National Research Council Canada, and the Canadian Institutes of Health Research.3. Radiocarbon analysis was supported in part by the Radiocarbon Collaborative, which is jointly sponsored by the USDA Forest Service Northern Research Station, the KCCAMS Facility at UC Irvine, and Michigan Technological University.Update 11/06/2019: An .kml file was added with the GPS locations for all the sampling sites.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Materials Characterization: A Primer for Solid Phase Processing Applications

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development (LDRD) Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). As a two-year LDRD investment requiring focused research, the MCPC project applied only a subset of the wide range of available destructive and nondestructive characterization methods to provide data to the predictive modeling and data analytics tasks. The purpose of this report is to review a wide range of destructive and nondestructive characterization methods that are relevant in solid-phase processing (SPP) applications, but not necessarily applied in the MCPC Project as a guide to the planning of characterization activities in future research. Particular attention is given to measured characteristics that can correlate to other material characteristics, with a particular interest in nondestructive evaluation (NDE) that can be applied to samples obtained in the MCPC Project. Destructive examinations include tensile tests, optical and electron microscopy, micro-hardness, and residual stress tests. NDE tests include surface visual inspection, eddy current examination for cracks, 4-point potential drop, ultrasound, x-ray, and computed tomography.

36 MATERIALS SCIENCE↗

NASA NTRS1987

Real-time hierarchically distributed processing network interaction simulation

The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

Zimmerman, W. F.↗

NASA NTRS2000

Airborne Sunphotometry of African Dust and Marine Boundary Layer Aerosols in PRIDE

The Puerto Rico Dust Experiment (PRIDE) was conducted during summer 2000 to study the radiative, microphysical and transport properties of Saharan dust in the Caribbean region. During PRIDE, NASA Ames Research Center's six-channel airborne autotracking sunphotometer (AATS-6) was operated aboard a Piper Navajo airplane based at Roosevelt Roads Naval Station on the northeast coast of Puerto Rico. AATS-6 measurements were taken during 21 science flights off the coast of Puerto Rico in the western Caribbean. Data were acquired within and above the Marine Boundary Layer (MBL) and the Saharan Aerosol Layer (SAL) up to 5.5 km altitude tinder a wide range of dust loadings. Aerosol optical depth (AOD) spectra and columnar water vapor (CWV) values have been calculated from the AATS-6 measurements by using sunphotometer calibration data obtained at Mauna Loa Observatory (3A kin ASL) before (May) and after (October) PRIDE. Mid-visible AOD values measured near the surface during PRIDE ranged from 0.07 on the cleanest day to 0.55 on the most turbid day. Values measured above the MBL were as high as 0.35; values above the SAL were as low as 0.01. The fraction of total column AOD due to Saharan dust cannot be determined precisely from AATS-6 AOD data alone due to the uncertainty in the extent of vertical mixing of the dust down through the MBL. However, analyses of ground-based and airborne in-situ aerosol sampling measurements and ground-based aerosol lidar backscatter data should yield accurate characterization of the vertical mixing that will enable calculation of the Saharan dust AOD component from the sunphotometer data. Examples will be presented showing measured AATS-6 AOD spectra, calculated aerosol extinction and water vapor density vertical profiles, and aerosol size distributions retrieved by inversion of the AOD spectra. Near sea-surface AOD spectra acquired by AATS-6 during horizontal flight legs at 30 m ASL are available for validation of AOD derived from coincident satellite sensor (TOMS, MODIS, MISR) measurements. AATS-6 AOD data acquired during numerous aircraft ascents and descents through the MBL and the SAL should permit atmospheric column closure analyses with respect to aerosol optical depth, extinction, and size distribution by comparison with coincident aircraft-based in-situ particle size distribution measurements and ground-based (Cabras Island, Puerto Rico) micropulse lidar aerosol backscatter measurements. The aerosol information derived from the column closure analyses can be used subsequently to calculate radiative flux changes, which can then be compared with coincident spectral flux measurements taken from aboard the aircraft with a solar spectral flux radiometer.

Livingston, John M.↗

NASA NTRS2010

ISS Plasma Environment: Status of CCMC Products for ISS Mission Ops

ISS Program currently using FPMU Ne, Te in-situ measurements to support operations and anomaly investigations. Working to acquire alternative data sources if FPMU is not available. Work is progressing on CCMC tools for low Earth orbit ionosphere characterization. Validation against FPMU data required before model output can be used for ISS operational support. MSFC plans to continue comparing CTIP output during FPMU campaigns. Results to date have been useful in identifying ionospheric origins of high latitude charging environments.

Minow, Joseph↗

NASA NTRS2016

A New Look at Data Usage by Using Metadata Attributes as Indicators of Data Quality

This study reviews the key metrics (users, distributed volume, and files) in multiple ways to gain an understanding of the significance of the metadata. Characterizing the usability of data by key metadata elements, such as discipline and study area, will assist in understanding how the user needs have evolved over time. The data usage pattern based on product level provides insight into the level of data quality. In addition, the data metrics by various services, such as the Open-source Project for a Network Data Access Protocol (OPeNDAP) and subsets, address how these services have extended the usage of data. Over-all, this study presents the usage of data and metadata by metrics analyses, which may assist data centers in better supporting the needs of the users.

metadata↗

DOE OSTI2020

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: Joint Study (FY2020 Final Report)

Identification and characterization of underground events from surface or remote data requires a thorough understanding of the rock material properties. However, material properties usually come from borehole data, which is expensive and not always available. A potential alternative is to use topographic characteristics to approximate the strength, but this has never been done before quantitatively. Here we present the results from the first steps towards this goal. We have found that there are strong correlations between compressive and tensile strengths and slopes, but these correlations vary depending on data analysis details. Rugosity may be better correlated to strength than slope values. More comprehensive analyses are needed to fully understand the best method of predicting strength from topography for this area. We also found that misalignment of multiple GIS datasets can have a large influence on the ability to make interpretations. Lastly, these results will require further study in a variety of climatic conditions before being applicable to other sites.

58 GEOSCIENCES↗

DOE OSTI2023

Cost and Performance Baseline for Fossil Energy Plants, Volume 5: Natural Gas Electricity Generating Units for Flexible Operation

To address the data needs of energy system designers and to serve as a baseline for research and development, NETL has carried out a study to characterize the flexibility attributes - both performance and cost - of nine common commercial natural gas-fueled electricity generating units. The intermittent output of low-carbon, renewable power generation sources such as wind and solar create challenges to grid stability and reliability. Fossil-fueled power generation technologies are currently used to provide reliable, on-demand power during periods of reduced renewable output. Dispatchable generators must be able to accommodate increasing renewable generation as the nation pursues the Administration’s target of a decarbonized energy sector by 2035. As energy system experts seek to identify least-cost approaches to decarbonization, accurate cost and performance data characterizing dispatchable fossil generators that operate flexibly, at capacity factors that have been declining over time, and are needed to inform models for capacity expansion. Furthermore, these technologies continue to be a significant source of carbon dioxide emissions, providing the impetus for research and development, including the advancement and potential incorporation of carbon capture technologies. This study characterizes the cost and performance of select state-of-the-art natural gas-fueled power generation technologies: reciprocating internal combustion engines (RICE), simple cycle combustion turbines, and natural gas combined cycles (NGCC). An emphasis is placed on flexibility characteristics, such as part-load heat rate, ramp rates, start up times, and start up costs.

03 NATURAL GAS↗

DOE OSTI2020

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Panorama 360 (Final Report)

This is the final technical report for the DOE-funded Panorama 360 project. Panorama 360 provided a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities. The work focused on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: 1. A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); 2. A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; 3. A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and 4. Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Panorama 360 (Final Report)

This final technical report from the lead institution, USC grant #DE-SC0012636, serves as the final technical report for collaborative institution UNC-CH grant #DE-SC0012390. The goal was to develop a repository and associated capabilities for data collection, ingestion, and analysis for a broad class of DOE applications that span experimental and simulation science workflows. In particular, this work focuses on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: (1) A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); (2) A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; (3) A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and (4) Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 1

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems in national security and advanced energy applications (Smith 2021). The MCPC Project executed research across three scientific vertices—material characterization, predictive modeling, and data analytics—with extensive support by a data curation and management team. The central technical objective in the MCPC Project was to improve the prediction and characterization of the process-structure-property relationships within the microstructurally refined region of stainless-steel samples prepared utilizing friction stir processing (FSP). Application of the FSP technique is well established at PNNL within the Solid Phase Processing capability through many years of investment across a range of materials and applications (PNNL 2024). Three distinct rounds of FSP experiments were performed by the experimental team, producing replicate samples utilizing across different nominal processing conditions (Condition IDs) listed in Table 1. The starting material on which FSP was applied was commercially available unprocessed stainless-steel type 316L material. Chosen processing conditions were very diverse, and some were intentionally chosen to produce defects. Several samples experienced tool breakage during experimentation, so a full set of three replicates was not produced for every nominal processing condition.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 2

36 MATERIALS SCIENCE↗

DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Summary Report on Material Characterization, Part 3

36 MATERIALS SCIENCE↗