Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data Characterization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Summary of Savannah River Site FY21 Salt Waste Qualification Data and Characterization Data from Salt Waste Batches Processed Since 2008

The Savannah River National Laboratory analyzed samples from Savannah River Site Waste Tank 41H and 21H to support qualification of Salt Waste Processing Facility Waste Batches 3 and 4 (the FY21 Salt Batch Qualification samples) for processing. Neither of the samples displayed any unusual or unexpected characteristics such as large amounts of solids, floating solids, or unusual color. Characterization of these samples confirmed similar chemical composition and characteristics to previous salt waste batches. These results were initially provided to Savannah River Remediation (Liquid Waste Operations Sub-contractor) as External Sample Results LIMS Reports. The analytical results (both expedited and routine) are now summarized and discussed in this technical report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now—with the rise of multimodal acquisition systems and the associated processing capability—the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. This framework delivers authenticated, “push-button” execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory's (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM's design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Snake River Plain FORGE: Site Characterization Data

The site characterization data used to develop the conceptual geologic model for the Snake River Plain site in Idaho, as part of phase 1 of the Frontier Observatory for Research in Geothermal Energy (FORGE) initiative. This collection includes data on seismic events, groundwater, geomechanical models, gravity surveys, magnetics, resistivity, magnetotellurics (MT), rock physics, stress, the geologic setting, and supporting documentation, including several papers. Also included are 3D models (Petrel and Jewelsuite) of the proposed site. Data for wells INEL-1, WO-2, and USGS-142 have been included as links to separate data collections. These data have been assembled by the Snake River Geothermal Consortium (SRGC), a team of collaborators that includes members from national laboratories, universities, industry, and federal agencies, lead by the Idaho National Laboratory (INL). Other contributors include the National Renewable Energy Laboratory (NREL), Lawrence Livermore National Laboratory (LLNL), the Center for Advanced Energy Studies (CEAS), the University of Idaho, Idaho State University, Boise State University, University of Wyoming, University of Oklahoma, Energy and Geoscience Institute-University of Utah, US Geothermal, Baker Hughes Campbell Scientific Inc., Chena Power, US Geological Survey (USGS), Idaho Department of Water Resources, Idaho Geological Survey, and Mink GeoHydro.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Internet of Things Data Characterization Process: Pattern of Life Behavioral Data Study

The HoneyBee™ TARDIS LDRD team completed a data scoping study that identified the initial processes and procedures to baseline the normal and expected behaviors during operability and interoperability of Internet of Things (IoT) device networks. This research is the initial step in developing a process (or methodology) to inform a much broader information framework incorporating machine learning to determine device pattern-of-life which enables the detection of abnormal IoT behaviors on an individual device, as well as in the context of a larger network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization

Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.

3D seismic↗
DOE OSTI2013

A Method for Optimizing Waste Management and Disposal Practices Using a Group-Based Uncertainty Model for the Analysis of Characterization Data - 13191

It is a universal requirement for characterization of radioactive waste, that the consignor shall calculate and report a Total Measurement Uncertainty (TMU) value associated with each of the measured quantities such as nuclide activity. For Non-destructive Assay systems, the TMU analysis is typically performed on an individual container basis. However, in many cases, the waste consignor treats, transports, stores and disposes of containers in groups for example by over-packing smaller containers into a larger container or emplacing containers into groups for final disposal. The current standard practice for container-group data analysis is usually to treat each container as independent and uncorrelated and use a simple summation / averaging method (or in some cases summation of TMU in quadrature) to define the overall characteristics and associated uncertainty in the container group. In reality, many groups of containers are assayed on the same system, so there will be a large degree of co-dependence in the individual uncertainty elements. Many uncertainty terms may be significantly reduced when addressing issues such as the source position and variability in matrix contents over large populations. The systematic terms encompass both inherently 'two-directional' random effects (e.g. variation of source position) and other terms that are 'one-directional' i.e. designed to account for potential sources of bias. An analysis has been performed with population groups of a variety of non-destructive assay platforms in order to define a quantitative mechanism for waste consignors to determine overall TMU for batches of containers that have been assayed on the same system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

PE1 Site Characterization: Data Documentation on Geologic and Hydrologic Lab Testing

This data documentation report describes geologic and hydrologic laboratory analysis and data collected in support of site characterization of the Physical Experiment 1 (PE1) testbed, Aqueduct Mesa, Nevada. The documentation includes a summary of laboratory tests performed, discussion of sample selection for assessing heterogeneity of various testbed properties, methods, and results per data type.

58 GEOSCIENCES↗
DOE OSTI2021

Characterization data of an (AlFeNiTiVZr) 1-x Cr x multi-principal element alloy continuous composition spread library

The data provided in this article is related to the research article entitled “Phase stabilization and oxidation of a continuous composition spread multi-principal element (AlFeNiTiVZr) 1-x Cr x alloy”. This data article describes the high-throughput synthesis and characterization processes of an (AlFeNiTiVZr) 1-x Cr x alloy system. Continuous composition spread (CCS) thin-film libraries were synthesized by co-depositing an AlFeNiTiVZr metal alloy target and Cr target via magnetron sputtering. Post-processing was performed on the sample libraries with a vacuum anneal at 873 K and an air anneal at 873 K. Compositional data was determined via WDS in order to verify parameters provided by an in-house sputter model. Crystallographic data was captured via synchrotron diffraction and diffractograms were compared as a function of the change in Cr concentration. These measurements were taken in order to observe phase behavior after oxidation throughout the composition library. Furthermore, vibrational spectrographic data is provided of the oxidized library to show surface speciation along the composition gradient of the alloy system. The structural and oxidative behavior of the (AlFeNiTiVZr) 1-x Cr x alloy can be analysed using the data provided in this article. Additionally, this characterization dataset can be utilized in machine learning algorithms for determining important features and parameters for future hypothesis generation of functional multi-principal element alloys (MPEAs).

36 MATERIALS SCIENCE↗
DOE OSTI2013

Characterization Data Package for Containerized Sludge Samples Collected from Engineered Container SCS-CON-210

This data package contains the K Basin sludge characterization results obtained by Pacific Northwest National Laboratory during processing and analysis of four sludge core samples collected from Engineered Container SCS-CON-210 in 2010 as requested by CH2M Hill Plateau Remediation Company. Sample processing requirements, analytes of interest, detection limits, and quality control sample requirements are defined in the KBC-33786, Rev. 2. The core processing scope included reconstitution of a sludge core sample distributed among four to six 4-L polypropylene bottles into a single container. The reconstituted core sample was then mixed and subsampled to support a variety of characterization activities. Additional core sludge subsamples were combined to prepare a container composite. The container composite was fractionated by wet sieving through a 2,000 micron mesh and a 500-micron mesh sieve. Each sieve fraction was sampled to support a suite of analyses. The core composite analysis scope included density determination, radioisotope analysis, and metals analysis, including the Waste Isolation Pilot Plant Hazardous Waste Facility Permit metals (with the exception of mercury). The container composite analysis included most of the core composite analysis scope plus particle size distribution, particle density, rheology, and crystalline phase identification. A summary of the received samples, core sample reconstitution and subsampling activities, container composite preparation and subsampling activities, physical properties, and analytical results are presented. Supporting data and documentation are provided in the appendices. There were no cases of sample or data loss and all of the available samples and data are reported as required by the Quality Assurance Project Plan/Sampling and Analysis Plan.

core sampling↗
DOE OSTI2023

Surface Water Disinfection Byproducts and Organic Matter Characterization Data Associated with: “Disinfection byproducts formed during drinking water treatment reveal an export control point for dissolved organic matter in a subalpine headwater stream”

This dataset is associated with the publication “Disinfection byproducts formed during drinking water treatment reveal an export control point for dissolved organic matter in a subalpine headwater stream” published in Water Research X (Leonard et al. 2022; https://doi.org/10.1016/j.wroa.2022.100144). The associated study analyzed temporal trends from the Town of Crested Butte water treatment facility and synoptic sampling at Coal Creek in Crested Butte, Colorado, US. This work demonstrates how drinking water quality archives combined with synoptic sampling and targeted analyses can be used to identify and understand export control points for dissolved organic matter. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) metadata and international geo-sample number (IGSN) mapping file; (4) dissolved organic carbon (DOC), ultraviolet absorbance at 254 nanometers (UV254), total nitrogen (TN), and specific ultraviolet absorbance (SUVA) data; (5) disinfection bioproduct formation potential (DBP-FP) data; (6) readme; (7) methods codes; (8) water collection protocol; (9) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory); and (10) folder of excitation emissions matrix (EEM) spectra. The FTICR folder contains a file of DOC (measured as non-purgeable organic carbon; NPOC) used for FTICR sample preparation. The FTICR folder also contains three subfolders: one subfolder containing the raw .xml data files, one containing the processed data, and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .dat, .R, .ref, or .xml

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

WHONDRS Surface Water Geochemistry and Organic Matter Characterization Data from Streams Distributed across Latin America

This dataset supports a broader study examining global transferability of stream biogeochemistry and was generated in collaboration with the MicroSudAqua (µSudAqua) network (https://microsudaqua.netlify.app/en/). The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen, cations) and organic matter characterization (FTICR-MS) from streams in Argentina, Brazil, Chile, and Colombia. Samples were collected across stream orders (1st to 6th order) within five basins. Related data were collected and will be published separately in collaboration with the µSudAqua network. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to this readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data, (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) file-level metadata; (5) data dictionary; (6) field metadata; (7) readme; (8) international generic sample number (IGSN) mapping file; and (9) field protocol. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) anions and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.

Anions↗
DOE OSTI2020

Verification of Reactor Activation Modelling with Physical Characterization Data and the Impact on Long Term Assessments - 20303

Whiteshell Laboratories, located in Manitoba, Canada, provided research facilities for the Canadian nuclear industry since the early 1960's. The facility was centered on Whiteshell Reactor 1 (WR-1), an organically cooled, heavy water moderated nuclear reactor. WR-1 was safely shut down in 1985, defueled and drained, and has been in a safe Storage with Surveillance (Deferred Decommissioning) state until present day. In support of an Environmental Assessment for proposed in-situ disposal of WR-1, detailed characterization information regarding the reactor core components (calandria, calandria tubes, and pressure tubes) was necessary to provide confidence that the long-term impacts of the facility can be assessed adequately. A simplified estimate of the total activation of core components was completed in 1992 using conservative assumptions, expected reactor configuration and computer models built with WIMS-CRNL, ONEDANT and ORIGEN-S. Due to the simplification, and age of the work, there was considerable doubt that the modelled inventory estimate of the core components was sufficient to support long-term performance modeling of the planned in-situ decommissioning. Additional data would be required to provide this confidence. Several options were considered for characterizing the WR-1 core components, but it was decided to collect only a limited number of samples in an effort to validate the 1992 simplified estimate as bounding. The limited number of samples reduced risks to workers, both radiological and non-radiological, and significantly reduced the costs required to confidently define and bound the radiological inventory of activation products in the core. The results of the sampling were compared to the simplified activation estimation. There was good agreement between the modelled and sampled results, with all major contributors to total activity showing similar relative quantities. The samples were generally lower than predicted by the models, with few exceptions. Notable exceptions included Nb-94, which were higher in the sample results than the model. This was determined to be caused by the use of Niobium containing steel alloys in the stainless steel pressure tube that were not accounted for in the models. The comparison of results provided confidence that the original inventory estimates produced through the simplified model are conservative and bounding, and therefore suitable for use in the long-term assessment of in situ disposal of the WR-1 reactor. This paper provides details of the simplified computer model results, their comparison to results of the sampling, and the changes adopted in the assessment of the in-situ decommissioning of WR-1 as a result. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

A comprehensive collection of systems biology data characterizing the host response to viral infection

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). As a result, by comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Wave Tank Characterization Data

This data set was collected from a 16k gallon single paddle type wave tank after adjustments were made to the transfer function in the control software. The file names describe the commanded wave height and period which can then be compared with the actual measured height and period of the generated waves.

16 TIDAL AND WAVE POWER↗