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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗
DOE OSTI2024

Mauka Energy FEVER Tool DOE SBIR Phase 1 Final Scientific/Technical Report

This report is on the Forestry Electric Vehicle Energy Routing (FEVER) Tool, a novel software system developed to support heavy-duty electric vehicle (EV) operations in remote, forested, and mountainous regions. The Phase I project aimed to demonstrate the feasibility of modeling EV energy consumption using terrain elevation, road conditions, and route features specific to forestry logistics. The tool combines geographic information systems (GIS), electric motor physics, and vehicle-specific data to calculate feasible, energy-efficient routes. Collaborations with Oregon State University’s Research Forests and Titan Freight Systems enabled collection and validation of GPS and elevation-based trip data. The FEVER Tool offers substantial opportunities for the efficient management of medium- and heavy-duty electric vehicles in sectors like forestry, agriculture, mining, defense and waste management—areas which are beginning to adopt HDEVs. The project demonstrated technical feasibility and lays the groundwork for commercial development and deployment in other industries and environmental conditions in Phase II.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Activity coefficient acquisition with thermodynamics–informed active learning for phase diagram construction

This work explores the use of thermodynamics-informed Gaussian processes (GPs) and active learning (AL) to model activity coefficients and construct phase diagrams. Relying on synthetic data generated from an excess Gibbs energy model, GPs were found to accurately describe the activity coefficients of several binary mixtures across large composition and temperature ranges. Moreover, GPs could estimate their own uncertainty and identify composition/temperature regions where activity coefficient data provide the most information to the models. This was leveraged to build AL algorithms targeted at modeling phase equilibria. In many cases, a single active-learning-acquired data point was sufficient to describe the phase diagrams studied. Lastly, the ability of AL to greatly reduce the amount of data needed to obtain accurate models was further verified on experimental case studies, namely individual ion activity coefficients, the solid–liquid and vapor–liquid equilibrium of deep eutectic solvents, and phase equilibria in ternary mixtures.

25 ENERGY STORAGE↗
DOE OSTI2021

Mapping Stellar Surfaces. II. An Interpretable Gaussian Process Model for Light Curves

The use of Gaussian processes (GPs) as models for astronomical time series data sets has recently become almost ubiquitous, given their ease of use and flexibility. In particular, GPs excel at marginalization over the stellar signal when the variability due to starspots is treated as a nuisance, as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest, since it is not obvious how the parameters of the GP relate to physical parameters like the spot size, contrast, and latitudinal distribution. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is ill-posed and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many stars. Here we derive a closed-form expression for a GP that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more light curves, including the typical spot radii and latitudes. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from light curves and radial velocity measurements, as well as for modeling correlated noise in exoplanet searches. Our implementation is efficient, user-friendly, and open-source, available in the package starry-process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2023

Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels

Abstract A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP’s analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of $$O(N^3)$$ O ( N 3 ) in computation and $$O(N^2)$$ O ( N 2 ) in storage. All existing methods addressing this issue utilize some form of approximation—usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user’s flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover—instead of induce—sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Utah FORGE: Phase 3 Microgravity Data

A 4D microgravity survey began in early 2019 in order to benchmark surface and subsurface elevations prior to Phase 3 drilling and stimulation campaigns. Microgravity stations are collocated with GPS deformation benchmarks and use the same location survey data. Surveys are undertaken on a quarterly basis. The focus of current activities is comparative analysis of GPS survey results and InSAR analysis (see GDR Submission 1251) to check seasonal variance and to evaluate possible changes in the groundwater elevation. The attached zip file contains a text file and an Excel spreadsheet containing Utah FORGE Phase 3 processed "final" microgravity data through time. The data are identical in both files. The spreadsheet is only supplied for convenience. There is also a ReadMe.txt file that describes the field headings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Groundwater table elevation and temperature from 2015 to 2024 at the Lower Montane site in the East River Watershed, Colorado.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells with Location ID's ER-DOW (alias DO1West), ER-DOE (alias DO2East), ER-MBA1 (alias M1Bend1), ER-MBA2 (alias M1Bend2), ER-UPW (alias UP1West), ER-UPM (alias UP2), ER-UPE (alias UP3East). Another dataset contains the data from wells with Location ID's ER-CPA1 to ER-CPA6. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 8 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Groundwater table elevation and temperature from 2015 to 2024 across Meander C at the Lower Montane site in the East River Watershed, Colorado.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells ER-CPA1 to ER-CPA6. Another dataset contains the data from wells at nearby Locations. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 7 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wave Measurements taken NW of Culebra Is., PR, 2023

Wave and sea surface temperature measurements collected by a Sofar Spotter buoy in 2023. The buoy was deployed on July 27, 2023 at 11:30 UTC northwest of Culebra Island, Puerto Rico, (18.3878 N, 65.3899 W) and recovered on Nov 5, 2023 at 12:45 UTC. Data are saved here in netCDF format, organized by month, and include directional wave statistics, GPS, and SST measurements at 30-minute intervals. Figures produced from these data are provided here as well. They include timeseries of wave height/period/direction and SST, GPS location, wave roses, and directional spectra. Additionally, raw CSV files from the Spotter's memory card can also be found below. NetCDF files can be read in python using the netCDF4 or Xarray packages, or through MATLAB using the "ncread()" command.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Tesla Model 3 Autopilot On-road Data

This dataset encompasses about 60 individual drives of a 2020 Tesla Model 3 with Autopilot in its relevant operational domain covering more than 1,000 miles. The majority of the data was collected during highway and suburban driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![tesla m3 image](tesla-m3.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Cadillac CT6 Super Cruise On-road Data

This dataset encompasses about 60 individual drives of a 2019 Cadillac CT6 with Super Cruise in its relevant operational domain covering more than 1,000 miles. Adhering to the limited operational design domain of the investigated version of the Supercruise system, the majority of the data was collected during highway driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![cadillac-ct6 image](ct6.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

New Lightning-Derived Vertical Total Electron Content Data Provide Unique Global Ionospheric Measurements

A newly released, novel ionospheric data set of global gridded vertical total electron content (VTEC) is introduced in this paper. This VTEC data set, provided by Los Alamos National Laboratory (LANL), is derived from very high frequency (VHF; defined as 30–300 MHz) broadband radio-frequency (RF) measurements of lightning made by U.S. Department of Defense sensing systems on board Global Positioning System (GPS) satellites. This paper presents the new data set (LANL VTEC), discusses the errors inherent in VHF TEC estimation due to ionospheric dispersion, and compares the LANL VTEC to two community standard VTEC gridded products: Jet Propulsion Laboratory's Global Ionospheric Model (JPL GIM) and the CEDAR community's Open Madrigal VTEC gridded measurements of L-band GNSS (global navigation satellite systems) TEC. We find that the LANL VTEC data have an offset of 3 TECU from CEDAR Madrigal GNSS VTEC and a full-width-half-maximum (FWHM) of 6 TECU. In comparison, the offset between LANL VTEC and the JPL GIM model is –3 TECU, but with a FWHM of 5 TECU. We also compare to Jason-3 VTEC measurements over the ocean, finding an offset of less than 0.5 TECU and an FWHM of <5 TECU. Because this technique uses a completely different methodology to determine TEC, the sources of errors are distinct from the typical ground-based GNSS L-band (GHz) TEC measurements. Also, because it is derived from RF lightning signals, this data set provides measurements in regions that are not well covered by ground-based GPS measurements, such as over oceans and over central Africa.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Hybrid System for High-Accuracy Timekeeping Using Millisecond Pulsars

Millisecond pulsars exhibit extremely stable rotational periods with negligible long-term variation, comparable to atomic clocks. Leveraging pulsar signal stability, this paper presents a hybrid Pulse Per Second (PPS) generation system for high-accuracy and GPS-independent timing. The system combines pulsar and GPS signals through a high-speed data acquisition platform featuring FPGA-driven processing and techniques. This design provides a reliable and precise timekeeping solution for energy systems, ensuring functionality when GPS signals are unavailable.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗
DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

2004 Kansas City Regional Household Travel Survey

The 2004 Regional Household Travel Survey documented the travel behavior characteristics of Kansas City residents to update the area's transportation model. The Mid-America Regional Council and the Kansas and Missouri Departments of Transportation sponsored the survey, which was administered by NuStats. Activity and travel information was collected for all household members, regardless of age, during a specific 24-hour period. It relied on the willingness of households to provide demographic information about its members and vehicles and to have all household members record all travel and activity for a specific 24-hour period. The study also included a subsample of household vehicles with global positioning system (GPS) devices. The objectives of the GPS component were twofold: (1) to provide an independent data stream of vehicular travel in order to measure the accuracy of the travel data reported over the telephone, and (2) to obtain details about those trips that were captured by GPS but not reported over the telephone, in order to derive a trip-correction factor.

1Hz data↗
DOE OSTI2026

Truck Platooning Performance with ADAS and Onboard Camera Data Describing Traffic Interactions

This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

2001-2002 Southern California Regional Travel Survey

The 2001-2002 Southern California Regional Travel Survey collected data on household characteristics and travel behavior to update regional travel demand models. It covered six counties including Imperial, Los Angeles, Orange, Riverside, San Bernardino, and Ventura. The Southern California Association of Governments contracted with NuStats Partners to conduct the survey following the 2000 decennial census. Survey data collection occurred in 2001 and 2002 using computer-assisted telephone interviewing and travel diaries. Roughly 17,000 households completed the survey. An additional global positioning system (GPS) sample was taken for the purpose of auditing the self-reported diaries. Battelle provided data collection support for the GPS sample.

1Hz data↗