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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Hourly gap-filled meteorological data from PIE LTER measurements (2004-2023) used as drivers to run ELM PFLOTRAN simulations

This dataset contains continuous gap-filled precipitation, solar radiation, photosynthetically active radiation (PAR), air temperature, relative humidity, wind speed, and barometric pressure data recorded primarily at the Marshview Farm weather station within the Plum Island Long Term Ecosystems Research (PIE LTER) in Newbury Massachusetts (MA) from 2004 to 2023. We compiled the data set from published annual data packages in 15min resolution available on DataOne. Gaps were filled using different statistical techniques or available observations from the vicinity, e.g. the US-PLo and the US-PHM Ameriflux sites, also located within the PIE LTER. Flags are included in this dataset to indicate the origin of each data point. Metadata files ELMPFLOTRAN_met_dd.csv and ELMPFLOTRAN_met_flmd.csv contain more information on site locations, gap filling protocols, data variables, flags, and QA/QC methods. The data set was used in the spin up and simulations of a land surface model coupled to a biogeochemical reaction network (ELM PFLOTRAN) assessing impacts of hydrology and salinity input on methane fluxes in 2022 and 2023 (Sulman et al., 2024).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Natural and Hydraulic Fracture Density Prediction and Identification of Controllers

Discrete Fracture Networks (DFN’s) incorporated into hydraulic fracture modeling and reservoir simulation are typically constructed and calibrated to all available high-quality natural fracture data from image logs and core, which generally results in an extremely limited calibration data set. To extrapolate these data over large areas, more broadly sampled data sets, such as discontinuity-related 3-D seismic attributes are often used. Broad spatial trending methodologies can potentially misrepresent natural fracture systems through over-reliance on seismic attributes that are commonly influenced by noise. The Hydraulic Fracture Test Site (HFTS) provides a rare insight of the subsurface natural fracture network and controlling factors on fracture distribution from a mechanical and lithological standpoint. The physical occurrence of hydraulic fractures and their interaction and relationship to preexisting natural fractures can be predicted using analytical models. Such model outputs can be applied to provide higher confidence when developing DFN’s.

Campbell, Whitney↗
DOE OSTI2012

Modeling Sustainable Agricultural Residue Removal at the Subfield Scale

This study developed a computational strategy that utilizes data inputs from multiple spatial scales to investigate how variability within individual fields can impact sustainable residue removal for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in limiting soil erosion and maintaining soil C, health, and productivity. Increased availability of subfield‐scale data sets such as grain yield data, high‐fidelity digital elevation models, and soil characteristic data provides an opportunity to investigate the impacts of subfield‐scale variability on sustainable agricultural residue removal. Using three representative fields in Iowa, this study contrasted the results of current NRCS conservation management planning analysis with subfield‐scale analysis for rake‐and‐bale removal of agricultural residue. The results of the comparison show that the field‐average assumptions used in NRCS conservation management planning may lead to unsustainable residue removal decisions for significant portions of some fields. This highlights the need for additional research on subfield‐scale sustainable agricultural residue removal including the development of real‐time variable removal technologies for agricultural residue.

Muth, Jr., D. J.↗
DOE OSTI2010

OES-IA Annex IV: Environmental Effects of Marine and Hydrokinetic Devices - Report from the Experts’ Workshop September 27th – 28th 2010 Clontarf Castle, Dublin Ireland

An experts' workshop was convened in Dublin Ireland September 27th – 28th 2010 in support of IEA Ocean Energy Systems Implementing Agreement Annex IV. PNNL was responsible for organizing the content of the workshop, overseeing the contractors (Irish Marine Institute) hosting the event, presenting material on Annex IV and materials applicable to the workshop intent. PNNL is also overseeing a contractor (Wave Energy Center/University of Plymouth – WEC/UP) in the collection and analysis of the Annex IV data. Fifty-eight experts from 8 countries attended the workshop by invitation, spending two days discussing the needs of Annex IV. Presentations by DOE (background on Annex IV), PNNL (process for developing Annex IV; presentation of the draft database for PNNL project, plans for incorporating Annex IV data), WEC/UP on the environmental effect matrix, and four MHK developers (two from the UK, one from Ireland and one from Sweden; each discussing their own projects and lessons learned for measuring and mitigating environmental effects, as well as interactions with consenting [permitting] processes) helped provide background. The workshop participants worked part of the time in the large group and most of the time in four smaller breakout groups. Participants engaged in the process and provided a wealth of examples of MHK environmental work, particularly in the European nations. They provided practical and actionable advice on the following: • Developing the Annex IV database, with specific uses and audiences • Strong consensus that we should collect detailed metadata on available data sets, rather than attempting to draw in copious datasets. The participants felt there would then be an opportunity to then ask for specific set of data as needed, with specific uses and ownership of the data specified. This is particularly important as many data collected, particularly in Europe but also in Canada, are proprietary; developers were not comfortable with the idea of handing over all their environmental effects data, but all said they would entertain the request if they specifics were clear. • The recommendation was to collect metadata via an online interactive form, taking no more than one hour to complete. • Although the idea of cases representing the “best practices” was recognized as useful, the participants pointed out that there are currently so few MHK projects in the water, that any and all projects were appropriate to highlight as “cases”. There was also discomfort at the implication that “best practices” implied “lesser practices”; this being unhelpful to a new and emerging industry. • Workshop participants were asked if they were willing to continue to engage in the Annex IV process; all expressed willingness. The workshop was successful in adequately addressing its objectives and through participation and interaction in the breakout sessions around the various topics. As a result of the workshop, many delegates are now better informed and have a greater understanding of the potential environmental effects of MHK devices on the marine environment. There is now a greater sense of understanding of the issues involved and consensus by those regulators, developers and scientists who attended the workshop. A strong network has also been built over the two days between European and US/Canadian technical experts in wave and tidal energy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Benchmark Modeling of the Near-Field and Far-Field Wave Effects of Wave Energy Arrays

This project is an industry-led partnership between Columbia Power Technologies and Oregon State University that will perform benchmark laboratory experiments and numerical modeling of the near-field and far-field impacts of wave scattering from an array of wave energy devices. These benchmark experimental observations will help to fill a gaping hole in our present knowledge of the near-field effects of multiple, floating wave energy converters and are a critical requirement for estimating the potential far-field environmental effects of wave energy arrays. The experiments will be performed at the Hinsdale Wave Research Laboratory (Oregon State University) and will utilize an array of newly developed Buoys' that are realistic, lab-scale floating power converters. The array of Buoys will be subjected to realistic, directional wave forcing (1:33 scale) that will approximate the expected conditions (waves and water depths) to be found off the Central Oregon Coast. Experimental observations will include comprehensive in-situ wave and current measurements as well as a suite of novel optical measurements. These new optical capabilities will include imaging of the 3D wave scattering using a binocular stereo camera system, as well as 3D device motion tracking using a newly acquired LED system. These observing systems will capture the 3D motion history of individual Buoys as well as resolve the 3D scattered wave field; thus resolving the constructive and destructive wave interference patterns produced by the array at high resolution. These data combined with the device motion tracking will provide necessary information for array design in order to balance array performance with the mitigation of far-field impacts. As a benchmark data set, these data will be an important resource for testing of models for wave/buoy interactions, buoy performance, and far-field effects on wave and current patterns due to the presence of arrays. Under the proposed project we will initiate high-resolution (fine scale, very near-field) fluid/structure interaction simulations of buoy motions, as well as array-scale, phase-resolving wave scattering simulations. These modeling efforts will utilize state-of-the-art research quality models, which have not yet been brought to bear on this complex problem of large array wave/structure interaction problem.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Method and application of data integration at a nuclear power plant. Light Water Reactor Sustainability Program report

The data of a nuclear power plant (NPP) is stored in silos for the various plant tools. A previous study conducted by the LWRS program identified the different data sources that can benefit online monitoring in an NPP. These data have different structures and tools and are therefore used independently. Integrating data from various sources can result in multiple benefits to online monitoring by enhancing the ability to compare data, reduce statistical uncertainty due to a larger data set, increase data heterogeneity in space and time to reveal confounded information, develop intelligent methods, and share data. Data integration can result in direct cost saving by reducing the amount of effort needed to find needed information. It can also enable establishing a data warehouse, the backbone repository of data for an NPP, and is necessary to evolve NPPs to a holistic online monitoring approach and automate labor-intensive activities. Whether within an NPP or for a fleet of NPPs, deploying a data warehouse in a plant or data broker for a fleet of plants requires overcoming multiple challenges, including the lack of a standard form of storing and using data and the lack of integration methods to compare and map the different data sources. This effort demonstrates two methods and applications for data integration through an application integration pilot and a data fusion pilot. The application integration is targeted towards the software interfacing of data and data management. This approach is commonly used by data integrators. The data fusion pilot is targeted towards intelligently mapping or coupling data for optimized data use. The pilots were performed in collaboration with two NPPs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Assimilating Scanning Radar Data into High-Resolution Models

This report documents the findings of a project aiming to improve the initial conditions of km-scale simulations of deep convective storms by assimilating cloud-scale weather radar observations. More accurate numerical analyses will increase the effectiveness of research efforts using LES cloud models as tool to better understand land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development. Work reported on herein includes: i) assessment of radar data sets for use in data assimilation (‘DA’) experiments, ii) quality control of the radar data set and format conversion to one acceptable to DA schemes utilized by the Weather Research and Forecasting (WRF) model, and iii) examination of the sensitivity of the numerical representation of cloud-scale wind and microphysical features to a variety of tunable DA parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Open Data for Nuclear Explosion Monitoring (NEM) [Slides]

The data sources tend to have the highest quality data and metadata, particularly for more recent data sets. Early data from sources such as IRIS tend to have some metadata issues.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Strength models for stainless steel 304L alloys

This report provides nominal, weak and strong calibrations of Preston- Tonks-Wallace (PTW) plasticity model [1] parameters for stainless steel 304L alloys. The data sets shown indicate that for these alloys, the material’s prove- nance plays an important role in accurate characterization. First we provide a parameter set fit to compression-tested annealed samples, both quasistatic (QS) and split Hopkinson pressure bar (SHPB), from the MST-8 group at LANL within the last year. Then we compare simulations using the FLAG hydrocode [2], with this and other parameter sets, to data from a Taylor cylinder impact test performed within the same group around 2013.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Ground Vibrational State SiO Emission in the VLA BAaDE Survey

Using a subsample of the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey of stellar SiO masers, we explore the prevalence and characteristics of {sup 28}SiO J = 1 − 0, v = 0 emission. We identify 90 detections of maser, thermal, or composite {sup 28}SiO J = 1 − 0, v = 0 emission out of approximately 13,000 candidate spectra from the National Science Foundation’s Karl G. Jansky Very Large Array (VLA). We find that the detected sources are likely asymptotic giant branch stars belonging to a bright, foreground Milky Way stellar disk population. For the 32 sources showing thermal components, we extract values for outflow velocity by fitting thermal line profiles. We find a range of circumstellar envelope expansion velocities, and compare to previously recorded OH and CO expansion velocities. This preliminary survey is already the largest study of stellar ground vibrational state SiO masers to date, and will be expanded to include the entire VLA BAaDE data set when data reduction for the 18,988 target sources is completed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Magnitude-squared Coherence: A Powerful Tool for Disentangling Doppler Planet Discoveries from Stellar Activity

Abstract If Doppler searches for Earth-mass, habitable planets are to succeed, observers must be able to identify and model out stellar activity signals. Here we demonstrate how to diagnose activity signals by calculating the magnitude-squared coherence C ˆ xy 2 ( f ) between an activity-indicator time series x t and the radial-velocity (RV) time series y t . Since planets only cause modulation in RV, not in activity indicators, a high value of C ˆ xy 2 ( f ) indicates that the signal at frequency f has a stellar origin. We use Welch’s method to measure coherence between activity indicators and RVs in archival observations of GJ 581, α Cen B, and GJ 3998. High RV-H α coherence at the frequency of GJ 3998 b and high RV-S index coherence at the frequency of GJ 3998c, indicate that the planets may actually be stellar signals. We also replicate previous results showing that GJ 581 d and g are rotation harmonics and demonstrate that α Cen B has activity signals that are not associated with rotation. Welch’s power spectrum estimates have cleaner spectral windows than Lomb–Scargle periodograms, improving our ability to estimate rotation periods. We find that the rotation period of GJ 581 is 132 days, with no evidence of differential rotation. Welch’s method may yield unacceptably large bias for data sets with N < 75 observations and works best on data sets with N > 100. Tapering the time-domain data can reduce the bias of the Welch’s power spectrum estimator, but observers should not apply tapers to data sets with extremely uneven observing cadence. A software package for calculating magnitude-squared coherence and Welch’s power spectrum estimates is available on github.

Dodson-Robinson, Sarah E. (ORCID:0000000287964974)↗
DOE OSTI2016

Maps of the Magellanic clouds from combined South Pole Telescope and Planck data

Here, we present maps of the Large and Small Magellanic Clouds from combined South Pole Telescope (SPT) and Planck data. Both instruments are designed to make measurements of the cosmic microwave background but are sensitive to any source of millimeter-wave (mm-wave) emission. The Planck satellite observes in nine mm-wave bands, while the SPT data used in this work were taken with the three-band SPT-SZ camera. The SPT-SZ bands correspond closely to three of the nine Planck bands, namely those centered at 1.4, 2.1, and 3.0 mm. The angular resolution of the Planck data in these bands ranges from 5 to 10 arcmin, while the SPT resolution in these bands ranges from 1.0 to 1.7 arcmin. The combined maps take advantage of the high resolution of the SPT data and the long-timescale stability of the space-based Planck observations to deliver high signal-to-noise and robust brightness measurements on scales from the size of the maps down to ~1 arcmin. In each of the three bands, we first calibrate and color-correct the SPT data to match the Planck data, then we use noise estimates from each instrument and knowledge of each instrument's beam, or point-spread function, to make the inverse-variance-weighted combination of the two instruments' data as a function of angular scale. Furthermore, we create maps assuming a range of underlying emission spectra (for the color correction) and at a range of final resolutions. We perform several consistency tests on the combined maps and estimate the expected noise in measurements of features in the maps. Finally, we compare the maps of the Large Magellanic Cloud (LMC) from this work to maps from the Herschel HERITAGE survey, finding general consistency between the datasets. Furthermore, the broad wavelength coverage provides evidence of different emission mechanisms at work in different environments in the LMC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Machine-learning data and model for autoconversion and accretion rates

This PI product includes two key components of a recently developed machine-learning model for predicting autoconversion and accretion rates. The training data set and the testing data set used to build our machine-learning model, containing sets of four input variables (cloud water content, cloud droplet number concentration, drizzle water content, and drizzle drop number concentration) and two output variables (autoconversion rate and accretion rate). The machine-learning model, containing information on the trained weights and biases, and the coefficients for scaling the inputs and outputs variables. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Urban Parameters Arizona Urban Corridor 100m

132 Urban parameters based on building physical dimensions and location were generated for the cities in six Arizona Counties at 100m resolution using the NATURF model. To use the binary file with WRF, the binary file and the index file must be placed in their own directory in WRF_GEOG and accessed in the same way NUDAPT44 would be accessed.

Dumas, Melissa [ORNL] (ORCID:0000000233190846)↗
DOE OSTI2021

Solar Futures Study Databook

The Solar Futures Study explores pathways for solar energy to drive deep decarbonization of the U.S. electric grid and considers how further electrification could decarbonize the broader energy system. This workbook contains data behind most figures in the U.S. Department of Energy's Solar Futures Study. Data are not provided for figures with data labels and certain figures generated with larger data sets. These data will be made available upon request.

14 SOLAR ENERGY↗
DOE OSTI2012

Gas and liquid measurements in air-water bubbly flows

Local measurements of gas- and liquid-phase flow parameters are conducted in an air-water two-phase flow loop. The test section is a vertical pipe with an inner diameter of 50 mm and a height of 3.2 m. The measurements are performed at z/D = 10. The gas-phase measurements are performed using a four-sensor conductivity probe. The data taken from this probe are processed using a signal processing program to yield radial profiles of the void fraction, bubble velocity, and interfacial area concentration. The velocity measurements of the liquid-phase are performed using a state-of-the-art Particle Image Velocimetry (PIV) system. The raw PIV images are acquired using fluorescent particles and an optical filtration device. Image processing is used to remove noise in the raw PIV images. The statistical cross correlation is introduced to determine the axial velocity field and turbulence intensity of the liquid-phase. Measurements are currently being performed at z/D = 32 to provide a more complete data set. These data can be used for computational fluid dynamic model development and validation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗