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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enriching the physics program of the CMS experiment via data scouting and data parking

Specialized data-taking and data-processing techniques were introduced by the CMS experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new physics and the precision of standard model measurements. These techniques, termed data scouting and data parking, extend the data-taking capabilities of CMS beyond the original design specifications. The novel data-scouting strategy trades complete event information for higher event rates, while keeping the data bandwidth within limits. Data parking involves storing a large amount of raw detector data collected by algorithms with low trigger thresholds to be processed when sufficient computational power is available to handle such data. The research program of the CMS Collaboration is greatly expanded with these techniques. The implementation, performance, and physics results obtained with data scouting and data parking in CMS over the last decade are discussed in this Report, along with new developments aimed at further improving low-mass physics sensitivity over the next years of data taking.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1994

Applications drivers for data parking on the Information Superhighway

As the cost of data storage continues to decline (currently about one-millionth of its cost four decades ago) entirely new applications areas become economically feasible. Many of these new areas involved the extraordinarily high data rates and universal connectivity soon to be provided by the National Information Infrastructure (NII). The commonly held belief is that the main driver for the NII will be entertainment applications. We believe that entertainment applications as currently touted (multi-media, 500 video channels, video-on-demand, etc.) will play an important but far from dominant role in the development of the NII and its data storage components. The most pervasively effective drivers will be medical applications such as telemedicine and remote diagnosis, education and environmental monitoring. These applications have a significant funding base and offer a clearly perceived opportunity to improve the nation's standard of living. The NII's wideband connectivity both nationwide and worldwide requires a broad spectrum of data storage devices with a wide-range of performance capabilities. These storage centers will be dispersed throughout the system. Magnetic recording devices will fill the majority of these new data storage requirements for at least the rest of this century. The storage needs of various application areas and their respective market sizes will be explored. The comparative performance of various magnetic technologies and competitive alternative storage systems will be discussed.

Johnson, Clark E., Jr.↗
DOE OSTI2021

Sodar - PNNL Scintec MFAS, Oregon Raceway Park - Reviewed Data

These data provide measurements of wind speed and direction up to 400 m above ground level (AGL) (max). The data are stored in two forms: ASCII and raw (binary). ASCII files contain averaged data (currently: 15 min time step and 10 m range gate). Raw files can be reprocessed with sodar software (APRun by Scintec) to produce ASCII files with different time and/or height averaging settings (highest resolution is approximately 90 sec and 10 m). NOTE: Wind direction is reported with respect to magnetic North.

17 WIND ENERGY↗
DOE OSTI2022

Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps. All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions. Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive. To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to: 1. Reduce parking-seeking behavior in the study area by 20%; 2. Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%; 3. Increase curb space, alley space and private loading bay occupancy rates in the study area. The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighbourhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analysed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Assessing Tree Health Conditions in New York City’s Central Park with Earth Observation Data

The Central Park Conservancy stewards New York City’s iconic Central Park with a mission to preserve the park for all. This mission is complicated by the spread of Dutch elm disease (DED) which has threatened the culturally and ecologically significant American elm tree (Ulmus americana). Central Park is home to one of the largest and last remaining urban concentrations of American elm and the Conservancy currently protects them through integrated pest management. This project is an interdisciplinary feasibility study that assessed the application of NASA Earth observations from 2014 to 2023 to detect changes in forest phenology possibly related to DED. Landsat 8 and 9 imagery was used to calculate a multiyear time series of the Normalized Difference Vegetation Index (NDVI) and quantify changes in land surface phenology. A pixel-based logistic regression analysis was performed using changes in NDVI, tree site locations, and recorded occurrences of trees infected with DED as inputs. The results of this analysis show that changes in NDVI derived from Landsat data are capable of detecting unhealthy tree canopies with 71% precision and healthy tree canopies with 41% precision. The study had uncertainties and limitations due to the spatial and temporal resolutions of Landsat, the natural variability in land surface phenology and NDVI, and the attempt to detect disease impacts while disease prevention and mitigation are occurring. As is, the findings of this study and its methods provide managers with an approach for integrating Earth observations to make more informed decisions in the application and timing of urban forest management activities.

John Hocknell↗
NASA NTRS1974

Mapping a recent forest fire with ERTS-1 MSS data

Accurate fire boundary delineation provides essential information to forest managers in allocating suppression costs and planning regeneration efforts. The objective of this study was to test the capability of computer-aided analysis of ERTS-1 MSS data to accurately define the boundary of a recent forest fire and to discriminate spectral classes within the perimeter. Two frames of ERTS-1 MSS data were selected for analysis of the Moccasin Mesa Fire in Mesa Verde National Park. Data sets were collected one-half growing season and one full growing season after the fire. Results indicate that computer-aided analysis of ERTS-1 MSS data has the capability for accurately delineating fire boundaries and determining acreage of the burned area. Distinct spectral classes may also be defined within the fire perimeter.

Hitchcock, H. C.↗
NASA NTRS2024

Central Park Ecological Conservation: Assessing Tree Health Conditions in New York City’s Central Park with NASA Earth Observation Data

The Central Park Conservancy stewards New York City’s iconic Central Park with a mission to preserve the park for all. This mission is complicated by the spread of Dutch elm disease (DED) which has threatened the culturally and ecologically significant American elm tree (Ulmus americana). Central Park is home to one of the largest and last remaining urban concentrations of American elm and the Conservancy currently protects them through integrated pest management. This paper discusses an interdisciplinary feasibility study that assessed the application of NASA Earth observations from 2014 to 2023 to detect changes in forest phenology possibly related to DED. Landsat 8 and 9 imagery was used to calculate multiyear time series of the Normalized Difference Vegetation Index (NDVI) and quantify changes in land surface phenology for a given year. A pixel-based logistic regression analysis was performed using changes in NDVI, tree site locations, and recorded occurrences of trees infected with DED as inputs. The results of this analysis show that changes in NDVI derived from Landsat data are capable of detecting unhealthy tree canopies with 71% precision and healthy tree canopies with 41% precision. The study had uncertainties and limitations due to the spatial and temporal resolutions of Landsat, the natural variability in land surface phenology and NDVI, and the attempt to detect disease impacts while disease prevention and mitigation is occurring. As is, the findings of this study and its methods provide managers with an approach for integrating Earth observations to make more informed decisions in the application and timing of urban forest management activities.

Central Park↗
NASA NTRS2000

Prince Albert National Park Forest Cover Data in Vector Format

This data set provides detailed canopy, understory, and ground cover height, density, and condition information for PANP in the western portion of the BOReal Ecosystem-Atmosphere Study (BOREAS) Southern Study Area (SSA) in vector form. The original biophysical resource data set was produced in 1978 based on aerial photographs taken in 1968 and field work conducted in the mid-1970s, and PANP's update/revision of the data set was completed in 1994. The data are stored in an ARC/INFO export file.

Fitzsimmons, Michael↗
DOE OSTI2022

A framework to collect human reliability analysis data for nuclear power plants using a simplified simulator and student operators

Data scarcity in human reliability analysis (HRA) has been a major challenge in the quantification process. Many institutes have collected HRA data through experiments using full-scope simulators with actual operators. Nevertheless, there are still some limitations to relying solely on full-scope studies. This paper aims to propose how full-scope data collection studies can be supported through the Simplified Human Error Experimental Program (SHEEP). The SHEEP framework was developed by Idaho National Laboratory (INL) to collect HRA data through a simplified simulator and student operators. This paper introduces the major tasks in the SHEEP framework, with a particular focus on differences that arise due to participant type (i.e., student vs. actual operator), based on experiments using a simplified simulator (i.e., the Rancor Microworld). This paper also describes whether the data collected via this approach could support a representative full-scope data collection study (i.e., the HuREX study) based on the experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Approach for Inferring Full-Scope Human Reliability Data Based on Simplified Simulator Data

This paper proposes a method for inferring full-scope human reliability data based on the Simplified Human Error Experimental Program (SHEEP) data. It mainly focuses on the human errors observed when using simulators with different complexity levels. In the proposed method, the manner in which human error probabilities (HEPs) change as a result of increasing simulator complexity and how simulator complexity levels are quantified represent key information for inferring full-scope data. In the present study, SHEEP error data pertaining to actual professional operators using Rancor Microworld (Rancor) (i.e., a more simplified simulator) and Compact Nuclear Simulator (CNS) (i.e., a less simplified simulator) were compared with the HuREX error data. An approach to quantifying simulator complexity levels was then proposed based on information theory and acquired eye-tracker data.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2001

Mapping Ungulate Habitats in Yellowstone National Park with Airborne Hyperspectral Data

Mapping vegetation habitats of ungulates (e.g., bison, elk, and deer) is critical to the development of efficient wildlife management and monitoring practices in Yellowstone National Park. Image endmembers were chosen using the ENVI minimum noise fraction, pixel purity index, N-dimensional visualizer approach. The spectral angle mapper algorithm was used to classify the image. This process was applied to low altitude AVIRIS and Probe-1 hyperspectral imagery of the Lamar River/Soda Butte Creek confluence to map several ungulate habitats (e.g., grasses, sedge, sage, aspen, willow, and cottonwood. The results are being compared to field measurements and large-scale color infrared aerial photography to assess mapping accuracy. The use of AVIRIS and Probe-1 data enabled the examination of hyperspectral data collected at different spatial and spectral resolutions.

Terrie, Gregory↗
DOE OSTI2026

A Feasibility Study on the Integration of Human Performance Data From Diverse Sources Based on the Complexity of a Proceduralized Task

Securing the safety of socio-technical systems including nuclear facilities is the upmost goal to ensure their sustainability because historical records demonstrate that the performance degradation of human operators (e.g., human errors) is one of the crucial contributors to the occurrence of unexpected events resulting in extensive casualties and financial losses. This implies that the collection of human performance data in diverse conditions with which they could be faced during the operation of nuclear facilities. As this collection requires significant resources, it is necessary to resolve how to accomplish it with limited resources. To address this challenge, as suggested in the SHEEP framework, it is indispensable to extract valuable insights after integrating various kinds of human performance data obtained from different sources. However, a practical method to soundly integrate them seems to be still incomplete. Accordingly, the applicability of TACOM (Task Complexity) measure is investigated as a tool to identify useful information based on the integration of human performance data observed from different simulation conditions. As a result, it is expected that the TACOM measure would play an important role in addressing the technical challenge in securing human performance data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Using SPT polarization, Planck 2015, and non-CMB data to constrain tilted spatially-flat and untilted nonflat ΛCDM, XCDM, and ΦCDM dark energy inflation cosmologies

We use six tilted spatially-flat and untilted non-flat dark energy cosmological models in analyses of South Pole Telescope polarization (SPTpol) cosmic microwave background (CMB) data, alone and in combination with Planck 2015 CMB data and non-CMB data, namely, the Pantheon Type Ia supernovae apparent magnitudes, a collection of baryon acoustic oscillation data points, Hubble parameter measurements, and growth rates. Although the cosmological models that best-fit the Planck CMB and non-CMB data do not provide good fits to the SPTpol data, with the χ2's exceeding the expected value, given the uncertainties, in each model the cosmological parameter constraints from the SPTpol data and from the Planck CMB and non-CMB data are largely mutually consistent. Furthermore, when the smaller angular scale SPTpol data are used jointly with either the Planck data alone or with the Planck CMB and the non-CMB data to constrain untilted non-flat models, spatially-closed models remain favored over their corresponding flat limits. When used in conjunction with Planck data, non-CMB data (baryon acoustic oscillation measurements in particular, from six experiments) have significantly more constraining power than the SPTpol data.

79 ASTRONOMY AND ASTROPHYSICS↗