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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Massive Trajectory Data Based on Patterns of Life

Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.

Amiri, Hossein↗
DOE OSTI2020

Semi-analytic model of magnetized liner inertial fusion

The code that was developed is called SAMM (Semi-Analytic MagLIF Model). In 2015, McBride and Slutz published all of the equations that are solved by the code in the original SAMM paper: R. D. McBride and S. A. Slutz, ?A semi-analytic model of magnetized liner inertial fusion?, Phys. Plasmas 22, 052708 (2015); http://doi.org/10.1063/1.4918953. The SAMM code is now implemented in both the MATLAB and Python programming languages. Students from multiple universities have requested copies of the code so that they can become more familiar with the MagLIF concept. We would like to seek an open-source solution. There is no market value to this code, as there are plenty of more sophisticated simulation codes already available; SAMM is merely a simplified model that is purely for educational purposes. In fact, at least one graduate student (from the University of California, San Diego) has already implemented and published his own modified version of the model: J. Narkis, H. U. Rahman, J. C. Valenzuela, F. Conti, R. D. McBride, D. Venosa, and F. N. Beg, ?A semi-analytic model of gas-puff liner-on-target magneto-inertial fusion?, Phys. Plasmas 26, 032708 (2019); https://doi.org/10.1063/1.5086056. SAND2020-12244 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moore, Thomas↗
DOE OSTI2021

sas-temper

Modeling remains a considerable challenge for practitioners of SAXS and SANS because the materials being studied are not highly ordered, the length scales being studied are large, and the information content of the data is low. Data analysis is both challenging and time consuming. Novices often rely on the assistance of an expert, such as the instrument scientist who supported them at the facility where the experiment was performed, to analyze their data. The high flux provided by modern facilities makes it possible to study dozens of samples or sample conditions during a single trip. Ultimately, the high throughput of modern instruments limits access to instrument scientists. A bottleneck in the research effort results that reduces facility productivity. Sas-temper seeks to address this problem, as well as the intrinsic difficulties of being confident in non-linear least squared fitting of data, by providing tools that automate much of the manual process of initial data fitting and refinement, as well as by providing tools for characterizing the nature of the parameter space that fits the measured data. The tools provided by sas-temper help small-angle scattering practitioners transition data into results.

Heller, William [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2023

Mobiliti v1.0

Mobiliti is a software platform designed to emulate the dynamics of a regional transportation road network. It is built on open-source software that provides parallel discrete-event simulation. The software is transformative in the area of transportation network simulation because of the geospatial scale and fidelity of the network model and the computational time it takes to model a full day of travel demand. For example, it runs a simulation of the entire San Francisco Bay Area, with a network model of ~1M links and a population that completes ~19M trips in ~5 minutes. This scale of simulation has not been attempted with existing simulation models due to the complexity of the model and the computational time it would take to complete. The intent of the software is to create a digital twin capability for cities to evaluate consequences of infrastructure or policy changes on road network dynamics.

Macfarlane, Jane↗
DOE OSTI2021

Coupled Air Quality and Boundary-Layer Meteorology in Western U.S. Basins during Winter: Design and Rationale for a Comprehensive Study

The wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical–meteorological interactions that drive high-pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in western U.S. basins. Approximately 120 people participated, representing 50 institutions and five countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological–chemical linkages outlined here, nor to validate complex processes within coupled atmosphere–chemistry models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K 1 , S 1 , and P 1 ) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S 1 ) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Lobo, Matthew↗
DOE OSTI2021

Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: EnKF System Design and Preliminary Assessment

Abstract Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: Observing System Experiments

Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. We report that observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reliability of Open Public Electric Vehicle Direct Current Fast Chargers

The aim was to systematically evaluate the usability of all public electric vehicles (EV) direct current fast chargers (DCFC) in the San Francisco region. To achieve a rapid transition to EVs, a highly reliable and easy to use charging infrastructure is critical to building confidence among consumers. The functionality and usability of all 182 open, public DCFC charging stations with CCS connectors (combined charging system) in the 9 counties of the Bay Area were tested (655 electric vehicle service equipment (EVSE) ports). An EVSE was classified as functional if it charged an EV for 2 minutes. Overall, 73.3% of the 655 EVSEs were functional. The causes of the nonfunctioning EVSEs (23.5%) were blank or unresponsive screens or error messages; payment system failures; charge initiation failures; network failures; or broken connectors. In addition, the cable was too short to reach the EV inlet for 3.2% of the EVSEs. A random sampling of 10% of the EVSEs, approximately 8 days after the first evaluation, found no overall change in functionality. The level of functionality found with field testing conflicts with the 95–98% uptime reported by the EV service providers (EVSPs) who operate the EV charging stations. There is a need for precise and verifiable definitions of uptime, downtime, and excluded time, as applied to public EV chargers. In conclusion, the level of failure of the existing public EV DCFC charge infrastructure highlights the importance of improving the system design and maintenance to improve adoption of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

The scientific case for concurrent neutron and X-ray scattering and spectroscopy

The interrogation of materials with X-rays or neutrons to determine structure, energetics, and dynamics is fundamental to advancing physical and chemical materials science and enabling innovative material technologies. A persistent challenge in materials development is that progress depends on understanding structure and dynamics across multiple length and time scales in increasingly complex, multicomponent systems featuring interfaces, heterogeneity, and hierarchical organization. Despite rapidly growing demands on materials characterization, current experimental approaches are almost exclusively based on isolated X-ray or neutron scattering and spectroscopy, reflecting a paradigm largely unchanged for decades. To assess the scientific need for a new experimental paradigm, a 3-day workshop sponsored by the U.S. National Science Foundation (NSF) was held at the SpringHill Suites, San Jose, California, from June 2 to 4, 2022. The workshop brought together 70 national and international experts who critically evaluated opportunities enabled by concurrent neutron and X-ray (NeX) scattering, spectroscopy, and imaging experiments. The participants reached a clear consensus that establishing NeX capabilities is crucial for advancing the science of complex materials in the United States. This report illustrates the scientific drivers for NeX experiments through representative examples spanning biomaterials, energy materials, soft matter, nanomaterials, quantum materials, geoscience, and applied materials research. The complementarity of neutrons and X-rays is essential for robust model development and refinement, particularly in multiphase and multicomponent systems. While joint refinement of data from separate experiments is valuable, concurrent measurements uniquely eliminate uncertainties arising from sample evolution, environmental drift, and irreproducibility associated with experiments performed at different locations and times. Realizing NeX capabilities will require the development of new instrumentation, data analysis frameworks, and robust sample environments compatible with both neutron and X-ray probes. Addressing these challenges will enable unambiguous interpretation of complex materials behavior and open new frontiers in materials research.

X-ray↗
DOE OSTI2024

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

58 GEOSCIENCES↗
DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗
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↗
DOE OSTI2026

1981 Bay Area Metropolitan Transportation Commission Household Survey

The San Francisco Bay Area's Metropolitan Transportation Commission (MTC) conducted the study in 1981, collecting household and travel information by telephone from 6,209 households in the MTC's nine-county jurisdiction. The sample gave good geographic coverage and provided a good representation of the population groups in the Bay Area. The survey was primarily intended to measure weekday travel; however, the size of the weekend travel sample was sufficient to enable MTC to make inferences about weekend trip generation. Travel days in the sample were spread out evenly over the three-month interviewing period to eliminate seasonal bias.

1Hz data↗
DOE OSTI2026

2000 Bay Area Travel Survey

In 2000, the San Fransisco Bay Area Metropolitan Transportation Commission launched its fifth travel survey effort in the nine-county Bay Area. More than 15,000 households participated. The survey was an activity-based travel survey that collected information on all in-home and out-of-home activities over a two-day period, including weekday and weekend pursuits. Like the 1990 survey, the 2000 Bay Area Travel Survey also included an additional sample of 3,000 households using Bay Area Rapid Transit. Respondents were asked to report detailed information regarding their travel, including trip locations, mode of transportation, trip start and end times, trip purpose, and trip activities. Demographic variables include gender, age, employment status, occupation, income, education level, ethnicity, as well as whether a respondent held a valid driver's license, was a student, and had a disability.

1Hz data↗
DOE OSTI2025

Levoglucosan data from five coastal streams impacted by the 2020 CZU Lightning Complex Fires, California, United States

This dataset includes levoglucosan data for five coastal California (United States) streams impacted by the 2020 CZU Lightning Complex Fires which burned from August 16th through September 22nd. Levoglucosan is a highly soluble and biolabile fraction of pyrogenic carbon. The five watersheds (San Lorenzo River, Pescadero Creek, Majors Creek, Laguna Creek, and Scott Creek) were impacted by the fires with watersheds experiencing a range of burn severity and extents. Grab samples were collected from each stream between October 2020 and May 2021, targeting both baseflow and event flow hydrologic conditions. Additional biogeochemistry data (i.e., organic and black carbon concentrations) can be found in a separate data package (https://doi.org/10.4211/hs.421c0226bb38460c8393d67fe0c4f802). This data package consists of one main data folder that contains (1) readme; (2) file-level metadata; (3) data dictionary; (4) field metadata with international generic sample numbers (IGSN); (5) methods codes; and (6) levoglucosan data. All files are .csv or .pdf.

2020 CZU Lightning Complex Fires↗
DOE OSTI2025

Metagenome-assembled genomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of LBNL (Lawrence Berkeley National Laboratory) TES (Terrestrial Ecosystem Science) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from soil depth profiles collected from 2014 to 2021 from three paired control and warming plots. We collected soil samples across a range of depth profiles (spanning surface to 90 cm deep) from three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. 101 soil metagenomes were sequenced at JGI (Joint Genome Institute) and UCSF (University of California San Francisco) Center for Advanced Technology and can be found under the JGI (Joint Genome Institute) GOLD (Genomes Online Database) Sequencing project Gs0151586 and NCBI (National Center for Biotechnology Information) Projects PRJNA1225762 and PRJEB39497. Metagenomes were assembled using JGI (Joint Genome Institute) Metagenome Workflow (10.1128/mSystems.00804-20). For each metagenome, the assembled contigs were binned into genomes using 3 binning algorithms (cocacola, metabat, and maxbin) and the resulting bins were consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>50%) and contamination (<25%), and dereplicated at 99% ANI (average nucleotide identity) using dRep (https://github.com/MrOlm/drep).The dataset includes a zip file of 2321 MAG (Metagenome Assembled Genome) fasta files, the accession numbers for the underlying metagenomes, and a csv file with MAG (Metagenome Assembled Genome) quality metrics and taxonomic classification (GTDB -Genome Taxonomy Database-RS220). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. A sample metadata file (samples.csv) that contains site information has also been included.

54 ENVIRONMENTAL SCIENCES↗