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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep generative models for vehicle speed trajectories

Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self-driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed-forward layers and are trained using adversarial techniques. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Influence of biotic and abiotic factors on home range size and shape of invasive wild pigs ( Sus scrofa )

Determining factors influencing animal movements at a temporal scale that is similar to that at which management actions are conducted (e.g. weekly) is crucial for identifying efficient methods of wildlife conservation and management. Using global positioning system (GPS) data from 49 wild pigs in the southeast United States, we constructed weekly 50% and 95% utilization distributions to quantify the effects of biotic and abiotic factors on weekly core area and home range size, as well as home range shape. Here we found vegetative composition (i.e. proportion of bottomland hardwoods), season (based on forage availability), meteorological conditions (i.e. temperature and pressure), and sex influenced wild pig weekly home range and core area size, while vegetative composition (i.e. proportion of upland pines) and landscape features (i.e. distance to streams) also were important factors influencing home range shape. At close distances to streams, wild pigs had more elongate home ranges when their home ranges comprised less upland pine habitat; however, farther from streams, there was no change in home range shape across fluctuating proportions of upland pines. These results demonstrate that fine-scale wild pig home ranges and movements are pliable from week to week and influenced by several habitat, landscape, and meteorological attributes that can easily be quantified from available land use and meteorological databases. These findings are important for designing monitoring studies, identifying high risk zones for disease transmission, planning response to disease emergence events, and allowing more effective and efficient short-term management planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Geodetic Evidence for Distributed Shear Below the Brittle Crust of the Walker Lane, Western United States

Abstract Models of active deformation of the Earth's crust are predominantly represented with dislocations having a downdip continuation into the lower crust, where the fault slips continuously. This model predicts surface strain accumulation concentrated near the fault during the interseismic period. In an alternative model, faults do not extend beneath the elastic portion of the crust and are accompanied by a wide zone of distributed shear underneath, predicting a more constant strain rate lacking concentrations at the faults. We use high‐precision GPS data collected across the northern and central Walker Lane, USA— a region of complex faulting near the western edge of the Basin and Range Province to evaluate which model is appropriate. Despite the existence of dense continuous and semi‐continuous geodetic networks that have been surveyed for ∼20 years, the horizontal velocities reveal no evidence of localized strain accumulation across the fault surface expressions. Instead, deformation within the Walker Lane is uniformly linear, suggesting that the surface deformation reflects distributed shear within the ductile crust rather than focused deformation at faults. This suggests no downdip extension of the faults below the seismogenic layer. The shear zone is 172 ± 6 km wide in the northernmost Walker Lane narrowing to 116 ± 4 km in the central Walker Lane. The total velocity budget across the shear zone is 7.2 ± 0.1 mm/yr in the north, increasing to 10.1 ± 0.1 mm/yr in the central Walker Lane. We conclude that assuming the presence of lower crustal dislocations when estimating geodetic faults slip rates may be inappropriate.

Geochemistry & Geophysics↗
DOE OSTI2021

Behavioral state resource selection in invasive wild pigs in the Southeastern United States

Elucidating correlations between wild pig ( Sus scrofa ) behavior and landscape attributes can aid in the advancement of management strategies for controlling populations. Using GPS data from 49 wild pigs in the southeastern U.S., we used hidden Markov models to define movement path characteristics and assign behaviors (e.g., resting, foraging, travelling). We then explored the connection between these behaviors and resource selection for both sexes between two distinct seasons based on forage availability (i.e., low forage, high forage). Females demonstrated a crepuscular activity pattern in the high-forage season and a variable pattern in the low-forage season, while males exhibited nocturnal activity patterns across both seasons. Wild pigs selected for bottomland hardwoods and dense canopy cover in all behavioral states in both seasons. Males selected for diversity in vegetation types while foraging in the low-forage season compared to the high-forage season and demonstrated an increased use of linear anthropogenic features across seasons while traveling. Wild pigs can establish populations and home ranges in an array of landscapes, but our results demonstrate male and female pigs exhibit clear differences in movement behavior and there are key resources associated with common behaviors that can be targeted to improve the efficiency of management programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The nature and origins of decametre-scale porosity in Ordovician carbonate rocks, Halahatang oilfield, Tarim Basin, China

At >7 km depths in the Tarim Basin, hydrocarbon reservoirs in Ordovician rocks of the Yijianfang Formation contain large cavities ( c . 10 m or more), vugs, fractures and porous fault rocks. Although some Yijianfang Formation outcrops contain shallow (formed near surface) palaeokarst features, cores from the Halahatang oilfield lack penetrative palaeokarst evidence. Outcrop palaeokarst cavities and opening-mode fractures are mostly mineral filled but some show evidence of secondary dissolution and fault rocks are locally highly ( c . 30%) porous. Cores contain textural evidence of repeated formation of dissolution cavities and subsequent filling by cement. Calcite isotopic analyses indicate depths between c . 220 and 2000 m. Correlation of core and image logs shows abundant cement-filled vugs associated with decametre-scale fractured zones with open cavities that host hydrocarbons. A Sm–Nd isochron age of 400 ± 37 Ma for fracture-filling fluorite indicates that cavities in core formed and were partially cemented prior to the Carboniferous, predating Permian oil emplacement. Repeated creation and filling of vugs, timing constraints and the association of vugs with large cavities suggest dissolution related to fractures and faults. In the current high-strain-rate regime, corroborated by velocity gradient tensor analysis of global positioning system (GPS) data, rapid horizontal extension could promote connection of porous and/or solution-enlarged fault rock, fractures and cavities. Supplementary material: Stable isotopic analyses and the velocity gradient tensor and principal direction and magnitude calculation are available at https://doi.org/10.6084/m9.figshare.c.4946046 Thematic collection: This article is part of the The Geology of Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/the-geology-of-fractured-reservoirs

Geology↗
DOE OSTI2025

LLUDA (Low-cost Logging for Ubiquitous Decarbonization and Analysis) [SWR-24-131]

This repository features an Arduino-based GPS logger, called LLUDA, which uses the Adafruit Ultimate GPS FeatherWing with the Adafruit Feather M0 Adalogger, designed to capture and log GPS data in CSV format onto an SD card (embedded inside the Feather M0 Adalogger). The FeatherWing is powered through the vehicle's auxiliary power (from cigarette lighter) socket via a micro USB cable. The data it collects can be used to analyze vehicle operations and support vehicle modeling applications, helping to assess the potential for electrification.

Fakhimi, Setayesh [National Renewable Energy Labor↗
DOE OSTI2022

Improving SGP4 Orbit Determination with New State Estimation Algorithm

The Simplified General Perturbations 4 Model (SGP4) is a well-known tool for performing satellite orbit determination. However, uncertainties and inaccuracies in the initial state inputs (required by SGP4) degrade the performance of the propagator. We present a new state estimation algorithm that allows for independent computation of these initial inputs using Unscented Kalman Filtering and GPS data from a satellite. The algorithm is tested on real flight data and demonstrates a notable performance improvement over the standard method of orbit determination using SGP4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thermal Tracker / Raw Data

This dataset contains ThermalTracker-3D flying fauna trajectories saved in json files, along with camera orientation and GPS location data. The 3D trajectories are in x,y,z camera space coordinates. They can be converted to geo-referenced coordinates using the orientation and GPS data.

17 WIND ENERGY↗
DOE OSTI2024

Thermal Tracker / Raw Data

This dataset contains ThermalTracker-3D flying fauna trajectories saved in json files, along with camera orientation and GPS location data. The 3D trajectories are in x,y,z camera space coordinates. They can be converted to geo-referenced coordinates using the orientation and GPS data.

17 WIND ENERGY↗
DOE OSTI2020

GradeIT (Road Grade Inference Tool)

The National Renewable Energy Laboratory (NREL) has developed the Road Grade Inference Tool, "GradeIT". This tool is a modular Python package the leverages the United States Geological Survey’s (USGS) 1/3 arc-second Digital Elevation Model (DEM), an open and free public dataset. Roadway gradient (slope) has a strong influence on vehicle energy consumption behavior and longitudinal dynamics. Therefore, any tools, models, or analyses that consider vehicle energy consumption or driving profiles is incomplete without accurate elevation and grade information. Despite the acute need for this data, it is rarely collected alongside GPS (latitude and longitude) data, and tools to post-process GPS data to append elevation and grade are difficult to come by. GradeIT satisfies this significant need in the research community. Utilization of the USGS DEM makes the underlying data available to any potential user and ensures continuous data anywhere in the continental United States. The USGS DEM is a great resource; however, in order to be useful for automotive and traffic engineering applications, significant interpolation and filtering is required to achieve an accurate representation of gradient on the roadway. GradeIT employs a two-dimensional interpolation to approximate elevation at a point on the road from the nearest nodes in the DEM. Then, GradeIT applies multiple filtering techniques to smooth the elevation profile to a realistic road topology. Finally, road grade is derived from the elevation profile and returned as the primary output.

Holden, Jacob↗
DOE OSTI2021

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Geomagnetic Storm Effects on Ionospheric Irregularities: Insights from SuperDARN, Madrigal GPS TEC, and ISR Data [Slides]

Geomagnetic disturbances trigger an influx of electrons from the magnetosphere, leading to an increase electron density (even in lower altitudes), amplified ionospheric currents, and subsequently, increased conductance. A surge in the AE index, indicating heightened auroral currents, correlates with a rise in precipitated charged particles, including electrons, into the polar ionosphere. This correlation is detectable through increased parameter values measured by SuperDARN. The downward flow of electron density towards lower altitudes, as observed in PFISR data, potentially obstructs the transmission frequency from radar stations passing through the PFISR location (just like we saw in the KOD station). During the geomagnetic storm, there is increased backscatter power (increased amount of ionospheric irregularities), and the plasma flow draws closer to the radar, suggesting a possible southward shift in the auroral oval as a result of the heightened auroral activity. Increased geomagnetic activity correlates with an elevation in average TEC and induces the occurrence of SED (Storm Enhanced Density).

58 GEOSCIENCES↗
DOE OSTI2026

2014-2015 Puget Sound Regional Travel Study

The 2014-2015 Puget Sound Regional Travel Study collected information about household and individual travel patterns for residents throughout a four-county region in Washington State. Study results were used to update the region's travel and land-use models and to calibrate local traffic and travel models. The study also helped the Puget Sound Regional Council (PSRC) and its regional partners develop plans that accommodate the diverse travel needs and preferences of residents. The Resource Systems Group administered the study on behalf of PSRC. Global positioning system (GPS)-equipped smartphones were used to provide data pertaining to the daily travel of 547 individual participants. Because the region's university students may have been underrepresented in the initial 2014 household travel study, the PSRC added a college-population travel survey in fall 2014. In spring 2015, a second household data collection effort was conducted to increase the frequency of data collection and to collect GPS data as well as a sample of longitudinal data from households that completed the 2014 survey.

1Hz data↗
DOE OSTI2024

Learning From User Behavior: A Survey-Assist Algorithm for Longitudinal Mobility Data Collection

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data. We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%. While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ~ 92k trips from 235 users over a period of roughly one and a half years. With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.

clustering↗
DOE OSTI2024

cheny-lanl/PreMevE-MEO-2024

This package contains R&D python codes that can nowcast >2 MeV electron distributions in the Earth's outer radiation belt, using LANL's GPS electron data (publicly available) as the primary driver. Functions of this PreMevE-MEO model has been described with details in a manuscript titled "PreMevE-MEO: Predicting Ultra-relativistic Electrons Using Observations from GPS Satellites" (LA-UR-24-23843), which has been submitted to Space Weather Journal.

Chen, Yue↗