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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the National Park Service: Golden Gate National Recreation Area

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy's Idaho National Laboratory, is the lead laboratory for U.S. Department of Energy Advanced Vehicle Testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (ITSNA) to collect data on federal fleet operations as part of the Advanced Vehicle Testing Activity's Federal Fleet Vehicle Data Logging and Characterization study. The Advanced Vehicle Testing Activity study seeks to collect data to validate the utilization of advanced electric drive vehicle transportation. This report focuses on the Golden Gate National Recreation Area (GGNRA) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (PEVs) into the agencies' fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (PHEV) (collectively PEVs) can fulfill the mission requirements. GGNRA identified 182 vehicles in its fleet, which are under the management of the U.S. General Services Administration. Fleet vehicle mission categories are defined in Section 4, and while the GGNRA vehicles conduct many different missions, only two (i.e., support and law enforcement missions) were selected by agency management to be part of this fleet evaluation. The selected vehicles included sedans, trucks, and sport-utility vehicles. This report will show that battery electric vehicles and/or PHEVs are capable of performing the required missions and providing an alternative vehicle for support vehicles and PHEVs provide the same for law enforcement, because each has a sufficient range for individual trips and time is available each day for charging to accommodate multiple trips per day. These charging events could occur at the vehicle home base, high-use work areas, or intermediately along routes that the vehicles frequently travel. Replacement of vehicles in the current fleet would result in significant reductions in the emission of greenhouse gases and petroleum use, while also reducing fuel costs. The San Francisco Bay Area is a leader in the adoption of PEVs in the United States. PEV charging stations, or more appropriately identified as electric vehicle supply equipment, located on the GGNRA facility would be a benefit for both GGNRA fleets and general public use. Fleet drivers and park visitors operating privately owned PEVs benefit by using the charging infrastructure. ITSNA recommends location analysis of the GGNRA site to identify the optimal placement of the electric vehicle supply equipment station. ITSNA recognizes the support of Idaho National Laboratory and ICF International for their efforts to initiate communication with the National Parks Service and GGNRA for participation in the study. ITSNA is pleased to provide this report and is encouraged by the high interest and support from the National Park Service and GGNRA personnel.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the National Park Service: Fort Vancouver National Historic Site

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for the U.S. Department of Energy’s Advanced Vehicle Testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (ITSNA) to collect data on federal fleet operations as part of the Advanced Vehicle Testing Activity’s Federal Fleet Vehicle Data Logging and Characterization study. The Advanced Vehicle Testing Activity study seeks to collect data to validate the use of advanced electric drive vehicle transportation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the United States Forest Service: Caribou-Targhee National Forest

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for U.S. Department of Energy Advanced Vehicle Testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (ITSNA) to collect and evaluate data on federal fleet operations as part of the Advanced Vehicle Testing Activity’s Federal Fleet Vehicle Data Logging and Characterization study. The Advanced Vehicle Testing Activity study seeks to collect and evaluate data to validate the utilization of advanced electric drive vehicle transportation. This report focuses on the Caribou-Targhee National Forest (CTNF) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (PEVs) into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements. ITSNA acknowledges the support of Idaho National Laboratory and CTNF for participation in the study. ITSNA is pleased to provide this report and is encouraged by enthusiasm and support from the Forest Service and CTNF personnel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Tensor Analysis Methods for Activity Characterization in Spatiotemporal Data

Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Characterization of Venera 15/16 geologic units from Pioneer Venus reflectivity and roughness data

The characterization of geologic units defined for the Venus surface on the basis of Venera 15/16 image data is presently conducted in light of Pioneer Venus reflectivity and rms slope data. Four geologic-unit groups are distinguished: smooth rocky units, rough ones, rough high-dielectric units, and diffusely-scattering ones. Models have been tested for the surface-property origins of several units, and it is determined that plains and tectonic units may be contrasted in terms not only of surface roughness but of surface-roughening tectonic deformation that appears to dominate the erosional regime of Venus.

Bindschadler, D. L.↗
DOE OSTI2024

Public Data Set: Initial Characterization of Electron Temperature and Density Profiles in PEGASUS Spherical Tokamak Discharges Driven Solely by Local Helicity Injection

This public data set contains openly-documented, machine readable digital research data corresponding to figures published in G.M. Bodner et al., ‘Initial Characterization of Electron Temperature and Density Profiles in PEGASUS Spherical Tokamak Discharges Driven Solely by Local Helicity Injection,’ Physics of Plasmas 28, 102504 (2021) and its erratum in Physics of Plasmas 31, 129904 (2024).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1978

A statistical package for computing time and frequency domain analysis

The spectrum analysis (SPA) program is a general purpose digital computer program designed to aid in data analysis. The program does time and frequency domain statistical analyses as well as some preanalysis data preparation. The capabilities of the SPA program include linear trend removal and/or digital filtering of data, plotting and/or listing of both filtered and unfiltered data, time domain statistical characterization of data, and frequency domain statistical characterization of data.

Brownlow, J.↗
NASA NTRS1974

Evaluation of satellite remote sensing and automatic data techniques for characterization of wetlands and coastal marshlands

The author has identified the following significant results. ERTS digital data has been used to detect and measure accretion in the large estuarial system of the Atchafalaya River in southern Louisiana. Comparisons of aerial photography taken in October 1970 with a computer printout of ERTS digital data collected on October 1, 1972, show that in a delineated area of 1400 hectares (3460 acres) an accretion of land area in the amount of 112 hectares (277 acres) had occurred. Analysis of band 3 of the ERTS MSS was used to make a land-water map of the area. The accretion test area was marked on this map and the percentage of elements indicated as land was calculated. This percentage was then multiplied by the total test area to obtain the area of land on the date of the ERTS observation for comparison with the aerial photography. Significant improvement in classification accuracy of ERTS MSS data has been achieved by use of a priori probabilities of occurrence in pattern recognition programs. Two classifications were made with accuracies of 76.0% and 81.5%, respectively.

Cartmill, R. H.↗
DOE OSTI2019

Electricity consumption patterns within cities: application of a data-driven settlement characterization method

Urban areas presently consume around 75% of global primary energy supply, which is expected to significantly increase in the future due to urban growth. Having sustainable, universal energy access is a pressing challenge for most parts of the globe. Understanding urban energy consumption patterns may help to address the challenges to urban sustainability and energy security. However, urban energy analyses are severely limited by the lack of urban energy data. Such datasets are virtually non-existent for the developing countries. As per current projections, most of the new urban growth is bound to occur in these data-starved regions. Hence, there is an urgent need of research methods for monitoring and quantifying urban energy utilization patterns. Here, we apply a data-driven approach to characterize urban settlements based on their formality, which is then used to assess intra-urban urban energy consumption in Johannesburg, South Africa; Sana’a, Yemen; and Ndola, Zambia. Electricity is the fastest growing energy fuel. By analyzing the relationship between the settlement types and the corresponding nighttime light emission, a proxy of electricity consumption, we assess the differential electricity consumption patterns. Our study presents a simple and scalable solution to fill the present data void to understand intra-city electricity consumption patterns.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Bayesian Inversion of Seismic and Electromagnetic Data for Marine Gas Reservoir Characterization Using Multi-chain Markov Chain Monte Carlo Sampling

In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic amplitude versus angle (AVA) and controlled source electromagnetic (CSEM) data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo (MCMC) sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis (DREAM) and Adaptive Metropolis (AM) samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and CSEM data. The multi-chain MCMC is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic AVA and CSEM joint inversion provides better estimation of reservoir saturations than the seismic AVA-only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated – reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.

Ren, Huiying↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for NASA Glenn Research Center

The Advanced Vehicle Testing Activity’s study seeks to collect and evaluate data to validate the utilization of advanced plug-in electric vehicle (PEV) transportation. This report focuses on the NASA Glenn Research Center (GRC) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2012

The Characterization of Virgo Data and Its Impact on Gravitational-Wave Searches

Between 2007 and 2010 Virgo collected data in coincidence with the LIGO and GEO gravitational-wave (GW) detectors. These data have been searched for GWs emitted by cataclysmic phenomena in the universe, by non-axisymmetric rotating neutron stars or from a stochastic background in the frequency band of the detectors. The sensitivity of GW searches is limited by noise produced by the detector or its environment. It is therefore crucial to characterize the various noise sources in a GW detector. This paper reviews the Virgo detector noise sources, noise propagation, and conversion mechanisms which were identified in the three first Virgo observing runs. In many cases, these investigations allowed us to mitigate noise sources in the detector, or to selectively flag noise events and discard them from the data. We present examples from the joint LIGO-GEO-Virgo GW searches to show how well noise transients and narrow spectral lines have been identified and excluded from the Virgo data. We also discuss how detector characterization can improve the astrophysical reach of gravitational wave searches.

Characterization↗
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

Construction and Resource Utilization Explorer (CRUX): Implementing Instrument Suite Data Fusion to Characterize Regolith Hydrogen Resources

CRUX is a modular suite of geophysical and borehole instruments combined with display and decision support system (MapperDSS) tools to characterize regolith resources, surface conditions, and geotechnical properties. CRUX is a NASA-funded Technology Maturation Program effort to provide enabling technology for Lunar and Planetary Surface Operations (LPSO). The MapperDSS uses data fusion methods with CRUX instruments, and other available data and models, to provide regolith properties information needed for LPSO that cannot be determined otherwise. We demonstrate the data fusion method by showing how it might be applied to characterize the distribution and form of hydrogen using a selection of CRUX instruments: Borehole Neutron Probe and Thermal Evolved Gas Analyzer data as a function of depth help interpret Surface Neutron Probe data to generate 3D information. Secondary information from other instruments along with physical models improves the hydrogen distribution characterization, enabling information products for operational decision-making.

in situ resource utilization (ISRU)↗
DOE OSTI2022

Data-driven Resilience Characterization of Control Dynamical Systems

In this paper, we define and quantify resiliency of a power network and propose data-driven algorithms for computing the same for the power grid. To do this, we use the Koopman operator framework to lift the controlled dynamical system to an abstract (possibly higher) dimensional space, where the evolution is linear. The linear system representation allows us to relate small time local controllability and observability of a general nonlinear control system to the controllability and observability of the lifted linear system. Finally, we define the resiliency of the underlying power grid in terms of the controllability and observability gramians of the lifted linear system. We illustrate the proposed approach to compute the resiliency metrics on time-series data obtained from a microgrid.

koopman operator, resilience, control↗