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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

G-LiHT Campaign Leaf Sample details & photos, March 2017: Puerto Rico

This data package includes details of leaves sampled for leaf spectra and chemistry from 5 sites in Puerto Rico, in March of 2017. Sunlit canopy and shaded leaves of 66 species were collected. Data for each sample includes species, leaf age, type of analysis (spectroscopy, gas exchange, chemistry), sample number and sample photographs. The data package includes a spreadsheet with sample information and a zip file of photographs (1.6 GB). This data was collected as part of the 2017 BNL–G-LiHT leaf spectra campaign. See related datasets for leaf spectral reflectance and transmittance, leaf mass area (LMA), and leaf chemistry. Note that leaf sample details are also included in related datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Using Geophysical Information to Investigate Subsurface Structure within the High-Hydraulic Conductivity Analysis Zone

Within the 200 East Area of the Central Plateau and southeastward toward the Columbia River, a high-hydraulic conductivity zone (HCZ) has been interpreted to extend through the 200-PO-1 and 200-BP-5 operable units on the Hanford Site. The HCZ is a controlling hydraulic feature that impacts groundwater flow out of the 200 East Area and the fate of eastwardly migrating plumes from the 200 West Area. The lateral extent of the HCZ is highly uncertain, and despite strong evidence for the existence of the HCZ based on water-level data and contaminant plume tracking, there is still a limited understanding of how to define its boundaries. To provide additional information on the nature and extent of the HCZ, three surface geophysical methods – electrical resistivity tomography (ERT), time-domain electromagnetics (TEM), and seismic methods – were used to collect data south of 200 East. In addition, existing data from 200 East, consisting of surface seismic data, a borehole check shot survey in 699-37-47A, and borehole stratigraphic interpretations, were used to aid interpretations of newly collected seismic data south of 200 East. This work presumed that the contrast in subsurface geophysical properties would be a first-order aid identifying a transmissive zone(s) within the HCZ analysis area by imaging contrasts and/or anomalies in geophysical properties. While seismic, ERT, and TEM methods have sensitivity to overlapping physical properties (porosity, moisture content, lithology), the resolution and physics used to acquire each of these datasets are different, and therefore the information can also be different. Figure S.1 shows the locations of the geophysical data considered in this report.

58 GEOSCIENCES↗
DOE OSTI2024

Search for a heavy neutral lepton that mixes predominantly with the tau neutrino

We report a search for a heavy neutral lepton (HNL) that mixes predominantly with ν τ . The search utilizes data collected with the Belle detector at the KEKB asymmetric energy e + e − collider. The data sample was collected at and just below the center-of-mass energies of the ϒ ( 4 S ) and ϒ ( 5 S ) resonances and has an integrated luminosity of 915 fb − 1 , corresponding to ( 836 ± 12 ) × 10 6 e + e − → τ + τ − events. We search for production of the HNL (denoted N ) in the decay τ − → π − N followed by its decay via N → μ + μ − ν τ . The search focuses on the parameter-space region in which the HNL is long-lived, so that the μ + μ − originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the τ neutrino as a function of the HNL mass. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2026

FleetREDI Insight: Intrastate Coach Bus Dataset

Capturing real-world data is critical to improving efficiency and supporting technology advancements in commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores coach buses operating in Colorado. Coach buses are a primary mover for intrastate transit and are primarily used for longer trips with more comfortable seats and a restroom. All Aboard America! Holdings Inc. offers various fixed-service and charter routes across Colorado on its Bustang fleet out of its depot in Golden, Colorado. NLR installed logging devices and collected operational data on nine 40-foot Bustang motorcoaches operating on fixed routes from May through August 2022. Using NLR’s FleetREDI data platform, this dataset provides a summary of daily operation to help understand duty cycle characteristics. This includes daily distance, fuel use, and estimated engine-produced energy consumption for nine motorcoaches that operated more than 33,000 miles. These vehicles primarily operated on Interstate 25 and Interstate 70. ![FleetREDI interstate bus](FleetREDI-interstate-bus.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

TOI-2076 and TOI-1807: Two Young, Comoving Planetary Systems within 50 pc Identified by TESS that are Ideal Candidates for Further Follow Up

We report the discovery of two planetary systems around comoving stars: TOI-2076 (TIC 27491137) and TOI-1807 (TIC 180695581). TOI-2076 is a nearby (41.9 pc) multiplanetary system orbiting a young (204 ± 50 Myr), bright (K = 7.115 in TIC v8.1) start. TOI-1807 hosts a single transiting planet and is similarly nearby (42.58 pc), similarly young (180 ± 40 Myr ), and bright. Both targets exhibit significant, periodic variability due to starspots, characteristic of their young ages. Using photometric data collected by TESS we identify three transiting planets around TOI-2076 with radii of R {sub b} = 3.3 ± 0.04 R {sub ⊕}, R {sub c} = 4.4 ± 0.05 R {sub ⊕}, and R {sub d} = 4.1 ± 0.07 R {sub ⊕}. Planet TOI-2076b has a period of P {sub b} = 10.356 days. For both TOI-2076c and d, TESS observed only two transits, separated by a 2 yr interval in which no data were collected, preventing a unique period determination. A range of long periods (<17 days) are consistent with the data. We identify a short-period planet around TOI-1807 with a radius of R {sub b} = 1.8 ± 0.04 R {sub ⊕} and a period of P {sub b} = 0.549 days. Their close proximity, and bright, cool host stars, and young ages make these planets excellent candidates for follow up. TOI-1807b is one of the best-known small (R < 2 R{sub ⊕}) planets for characterization via eclipse spectroscopy and phase curves with JWST. TOI-1807b is the youngest ultra-short-period planet discovered to date, providing valuable constraints on formation timescales of short-period planets. Given the rarity of young planets, particularly in multiple-planet systems, these planets present an unprecedented opportunity to study and compare exoplanet formation, and young planet atmospheres, at a crucial transition age for formation theory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Practices for Demonstrating Energy Savings from Commercial PACE Projects

Nearly three-fourths of U.S. states have authorized local governments to use voluntary special assessments on commercial properties to finance energy improvements that boost economic development, create jobs, increase property values and advance energy goals. Commercial Property Assessed Clean Energy (C-PACE) financing allows building owners to repay the borrowed capital — from private or public sources — over time using their property as security. Berkeley Lab is supporting the Department of Energy’s Commercial PACE Working Group by developing a series of C-PACE issue briefs. The second brief in this series, Practices for Demonstrating Energy Savings from Commercial PACE Projects, looks at common practices for demonstrating energy savings to support state and local governments that sponsor C-PACE programs and want to track their energy impacts. This brief reviews: -The value proposition and trade-offs of conducting energy impact assessments for C-PACE programs; -Methods to quantify energy savings impacts from energy efficiency building improvements; and -Available resources and tools to support energy impact assessments. C-PACE programs may benefit from energy impact assessments for many reasons, including: -Validating the public benefits of the programs -Demonstrating that C-PACE can deliver participant benefits -Illustrating program impacts on public policy goals -Generating data to help improve program performance Many C-PACE programs are collecting data on project energy savings impacts, and these data can be leveraged to further support decision making and program implementation. Potential drawbacks to energy impact assessments may include added cost and burdens on property owners (e.g., the need to collect building energy consumption data). Where these burdens are considerable, they might slow program uptake. State and local governments can balance the benefits of energy impact assessments with the range of costs and accuracy inherent to available assessment methodologies. Additionally, depending on the policy context in the state or local government, a C-PACE program may be able to leverage existing efforts (e.g., building energy benchmarking programs) to reduce impact assessment costs, align with building owner practices and expectations, and efficiently assess program impact.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Integrating Multi-Source Data for Bi-Level Traffic Simulator Calibration: A Literature Review and Highway Case Study

Traffic simulation serves as a powerful tool for pre-evaluating policies and technologies. In this context, simulation-based Dynamic traffic assignment (DTA) models are capable of capturing traffic dynamics. They are well-known as critical tools in controlling and predicting traffic situations. The reliability of simulation results heavily depends on the calibration process. Most studies in the literature formulate and calibrate simulators based on a single source of collected data or multiple data sets with the same spatiotemporal characteristics. However, in practice, traffic data is collected by various tools with usually different spatial and temporal resolutions. This study introduces a novel approach to taking into account diverse input data from a variety of sources. An iterative bi-level solution is proposed. to equally treat traffic flow and speed data. The upper level solves flow calibration with the exact solution method, and the lower level calibrates the speed with the simultaneous perturbation stochastic approximation (SPSA) algorithm. Subsequently, the effectiveness of the proposed model is investigated using data from a six-mile section of Nashville's I-24 highway in Tennessee. The results demonstrate that our proposed model creates an effective feedback loop between the optimizer and the simulator for calibrating flow and speed to reduce the error between simulated and real data.

42 ENGINEERING↗
DOE OSTI2020

FAST: Continuing the Focus on Data Quality

This presentation provides an overview of fiscal year 2019 federal motor vehicle fleet data, collected at the individual vehicle level during the fall of 2019, how the the collecting project has reviewed that information for potential quality issues, how the quality of this year's data submission compare to the prior year, and recommendations for federal agencies in their efforts to continue to improve the quality of their submissions. This presentation will be given at the January 2020 FedFleet training event, hosted by the US General Services Administration in Washington, DC. The information is collected through the Federal Automotive Statistical Tool (FAST) project. FAST is a Web-based information system managed by the US Department of Energy, the US General Services Administration, and the Energy Information Administration. FAST is used to collect information about the fleet of motor vehicles used and managed by the Federal government. FAST is developed and maintained by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Plant Characteristics, Porewater, Gas Flux, and Soil Biogeochemistry at Council Road Site Mile Marker 71, Seward Peninsula, Alaska, 2023

Data collected at Council, AK (64°51’35.0”N 163°41’59.1”W) during a summer campaign in 2023. Water data consists of soil porewater collected by centrifuging soil cores and also by field collection with porewater samplers (rhizons). Gas data consists of CO2, CH4 and N2O surface soil fluxes measured with a portable FTIR analyzer. Plant and root data consists of biomass, root length, diameter and mass. Soil data consists of total C and N. Air, water and soil samples span two main locations: a thermokarst wetland and an upland tussock. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).This dataset was generated to broadly address the following research question: how will climate change (i.e., thawing permafrost, landscape change) alter the ecosystem flux (sink versus source) of important greenhouse gases such as CO2, CH4 and N2O?Description of the contents of this data package: This dataset contains 5 different individual .csv files containing plant, soil, water and gas data. No software is needed to utilize them. PFTCover: Plant functional type ground cover in 1x1 meter plots. SoilCores: Solidphase and porewater phase soil biogeochemical variablesPlantData: Above and belowground plant traits. GasFlux: Surface plant-soil gas measurementsFieldPorewater: Field collected porewater biogeochemical variables

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Impala's horn applied to posterior samples of Ti-6Al-4V strength model parameters

We have generated strength model parameters for the Preston-Tonks-Wallace (PTW) model of plastic deformation based on a collection of Ti-6Al-4V alloy data and a Bayesian framework. This data collected from the literature includes different chemistries and processes. The experiments include quasistatic compression, Split Hopkinson Pressure Bar (SHPB) compression, and Taylor cylinders. Here we use posterior parameter distributions applied to a hypothetical impact referred to as the Impala's horn. The Impala's horn is like a Taylor cylinder, but modified to have a conical pro le that is truncated before coming to a point. The smaller diameter at the impact end results in higher calculated strain rates and strains than the same velocity impact with the traditional Taylor profile. The modified geometry exercises the behavior of the strength model outside the range of conditions found in the calibration data. Here we sort posterior strength models based on their flow stress at characteristic values for this hypothetical deformation. Then we compare the performance of these posterior models based on two simple observable final state properties of the Impala's horn.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ganged-PV System Evaluation

The following report contains data and data summaries collected for the SkySun LLC elevated Ganged PV arrays. These arrays were fabricated as a series of PV panels in various orientations, suspended by cables, at the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories (SNL). Starting in February of 2021, Sandia personnel have collected power and accelerometer data for these arrays to assess design and operational efficacy of varying ganged- PV configurations. The purpose of this power data collection was to see how the various array orientations compare in power collection capability depending on the time of day, year, and the specific daily solar direct normal irradiance (DNI). The power data was collected as a measurement of the power output from the various series strings. The project team measured direct current (DC) voltage and current from the respective arrays. The accelerometer data was collected with the purpose of demonstrating potential destructive mode shapes that could take place with each of the arrays when exposed to high winds. This allowed the team to evaluate whether impacts with respect to specific array orientations using suspended cables is a safe design. All data collection was performed during calendar year 2021.

14 SOLAR ENERGY↗
DOE OSTI2025

EPCAPE Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility recently deployed its First ARM Mobile Facility (AMF1) to La Jolla, California as part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) campaign. Some of the goals behind EPCAPE were to characterize the diurnal and seasonal cycles of stratocumulus clouds and to investigate the cloud-aerosol-radiation interactions and feedbacks in the area. The deployment of the AMF1 for a full year from 15 February 2023 to 14 February 2024 aided in addressing these scientific questions. While AMF1 collected data year-round, enhanced measurements were taken during two intensive operational periods (IOPs). The first IOP occurred from April to June and focused on the chemistry of low clouds (EPCAPE_Chem), while the second IOP occurred from July to September and was focused on the radiation of high clouds (EPCAPE_Radiation). Several cloud radars were deployed with AMF1 to collect valuable data on cloud properties that will help users address key science objectives. As in past ARM campaigns, a1-level radar data is extensively analyzed and calibration techniques are performed to generate b1-level data (Matthews et al. 2023, Feng et al. 2024). Radar data at the b1-level are of the highest quality and thus can be used to examine scientific questions. The status of the a1-level data and the a1-to-b1 process for the EPCAPE radars is subsequently detailed in this document.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Turbidity and suspended sediment data for Gwynns Falls, Baisman Run, and Pond Branch, Baltimore County and Baltimore City, MD, USA

This resource includes turbidity and suspended sediment data collected at two sampling stations located on Gwynns Falls in Baltimore County, MD, USA. In addition, two forested reference sites, Baisman Run and Pond Branch at Oregon Ridge, and two urban sites, Dead Run and Maiden's Choice Run (tributaries to Gwynns Falls), were sampled in Baltimore County and Baltimore City, MD, USA. Turbidity sensor data were collected at a 5-minute frequency using YSI EXO2 sondes. Suspended sediment was collected using ISCO samplers for the purpose of establishing correlations between turbidity and suspended sediment concentration. The six sites are co-located with USGS stream gages. This resource is part of the Baltimore Social-Environmental Collaborative Urban Integrated Field Laboratory supported by Department of Energy as well as the Critical Zone Collaborative Network supported by National Science Foundation. This resource includes a technical report summarizing the findings.

58 GEOSCIENCES↗
DOE OSTI2025

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗
DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

The high level trigger and express data production at STAR

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Current loss analysis in photoelectrochemical devices

Ongoing efforts to stabilize the operation of photoelectrochemical (PEC) devices remain critical for achieving economically viable solar fuel production, as devices with lifetimes on the order of 10 to 30 years are projected requirements for utility-scale, PEC device implementation. However, insight into the causes of device degradation and activity losses is generally provided by monitoring the device current, a quantity which masks the relative contributions of photovoltaic component degradation and electrocatalyst activity drops to overall performance losses. In this study, an approach for deconvoluting the various contributors to PEC device losses is described. In particular, the causes for observed fluctuations in device performance are determined through the collection of real-time, current–voltage data, paired with an analytical method that enables the decomposition of drops in device current into its constituent photovoltaic- and catalyst-driven performance losses. We test the validity of this approach by applying it to the data collected for a PEC hydrogen evolution test-bed.

14 SOLAR ENERGY↗