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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Extracting the spectrum of a flow by spatial filtering

We show that the spectrum of a flow field can be extracted within a local region by straightforward filtering in physical space. We find that for a flow with a certain level regularity, the filtering kernel must have a sufficient number of vanishing moments in order for the "filtering spectrum" to be meaningful. Our derivation follows a similar analysis by [1] for the wavelet spectrum, where we show that the filtering kernel has to have at least p vanishing moments in order to correctly extract a spectrum k (-alpha) with (alpha) less/equal to p + 2. For example, any ow with a spectrum shallower than k (-3) can be extracted by a straightforward average on grid-cells of a stencil. We construct two new "simple stencil" kernels, MI and MII, with only two and three fixed stencil weight coefficients, respectively, and that have sufficient vanishing moments to allow for extracting spectra steeper than k(-3). We demonstrate our results using synthetic fields, 2D turbulence from a Direct Numerical Simulation, and 3D turbulence from the JHU Database. Our method guarantees energy conservation and can be useful in both simulations and experiments when a straightforward Fourier analysis is not justified, such as within coherent flow structures covering non-rectangular regions or in geophysical flows on Earth's curved surface.

42 ENGINEERING↗
DOE OSTI2025

ToF-SIMS spectral data analysis of Paenibacillus sp. 300A biofilms and planktonic cells

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Paenibacillus sp. 300A (300A) isolated from the Hanford site in Richland, WA. The strain is known to have metal and sulfur reducing properties and can be used for bioremediation, wastewater treatment, bioengineering and technology development. There is a current need to identify small molecules and fragments produced from bacterial biofilms. Static ToF-SIMS spectra of 300A were obtained using an IONTOF TOF-SIMS V instrument equipped with a 25 keV Bi 3 + metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids and fatty acids to flavonoids, quinolones, and other naturally occurring organic compounds. It is anticipated that the spectral identification of key peaks will assist detection of metabolites, extracellular polymeric substance molecules like polysaccharides, and biologically relevant small molecules using ToF-SIMS in future surface and interface research of bacterial biofilms.

Biofilms↗
DOE OSTI2026

FY 2026 Midyear Report: Seismic Monitoring of Underground Vibration Sources Using Distributed Acoustic Sensing and Seismometers

Safeguards-relevant temporal changes in underground facilities can be observed using geophysical monitoring techniques. Seismic waves, in particular, provide valuable insights into subsurface activities and can serve as an important tool for detecting anomalous events that may indicate containment breaches at geological repositories. This midyear report summarizes ongoing efforts to automatically and rapidly detect and locate anomalous vibration signals that could be indicative of potential containment breaches. Previous work during FY25 focused on compiling continuous seismic datasets from two underground sites and developing a database of continuous waveforms and ground-truth event data derived from multiple sensing modalities. Building on this foundation, we are adapting anomaly detection and geolocation algorithms to explore methods for monitoring underground activities using two relatively low-maintenance sensing technologies: a dense surface geophone array deployed at the Pleasant Gap mine in Pennsylvania, and a three-dimensional fiber-optic cable array for distributed acoustic sensing (DAS) installed in the subsurface at the Sanford Underground Research Facility (SURF) in South Dakota. This report summarizes work conducted during the first two quarters of FY26, during which we refined a dynamic power spectral density (PSD)-based detector, applied it independently to each geophone station, and then combined the per‑station detections with density-based spatial clustering of applications with noise (DBSCAN) to cluster events and produce spatial maps over a nine‑day interval. In addition, we outline plans for a field trial at the Waste Isolation Pilot Plant (WIPP) in New Mexico to compare traditional seismic monitoring approaches with DAS techniques and to evaluate the benefits of combined data analysis. Activities during the past two quarters have included the preparation and submission of a Field Test Plan to WIPP for approval, as well as submission to headquarters for review and feedback.

58 GEOSCIENCES↗
DOE OSTI2017

Field evaluation of remote wind sensing technologies: Shore-based and buoy mounted LIDAR systems

In developing a national energy strategy, the United States has a number of objectives, including increasing economic growth, improving environmental quality, and enhancing national energy security. Wind power contributes to these objectives through the deployment of clean, affordable and reliable domestic energy. To achieve U.S. wind generation objectives, the Wind and Water Power Program within the Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) instituted the U.S. Offshore Wind: Removing Market Barriers Program in FY 2011. Accurate and comprehensive information on offshore wind resource characteristics across a range of spatial and temporal scales is one market barrier that needs to be addressed through advanced research in remote sensing technologies. There is a pressing need for reliable offshore wind-speed measurements to assess the availability of the potential wind energy resource in terms of power production and to identify any frequently occurring spatial variability in the offshore wind resource that may impact the operational reliability and lifetime of wind turbines and their components and to provide a verification program to validate the “bankability” of the output of these alternative technologies for use by finance institutions for the financing of offshore wind farm construction. The application of emerging remote sensing technologies is viewed as a means to cost-effectively meet the data needs of the offshore wind industry. In particular, scanning and buoy mounted LIDAR have been proposed as a means to obtain accurate offshore wind data at multiple locations without the high cost and regulatory hurdles associated with the construction of offshore meteorological towers. However; before these remote sensing technologies can be accepted the validity of the measured data must be evaluated to ensure their accuracy. The proposed research will establish a unique coastal ocean test-bed in the Mid-Atlantic for the evaluation of LIDAR-based wind measurement systems to validate the accuracy of remotely measured wind data in marine applications. Specifically, the test-bed will be utilized to systematically evaluate the capability of emerging scanning LIDAR and buoy mounted vertically profiling LIDAR by: (1) Evaluating a fixed scanning LIDAR against land-based 50 and 60 meter high meteorological masts fitted with research quality cup-vane and/or sonic anemometers; (2) Evaluating a buoy mounted vertically profiling LIDAR fixed on land and floating in a sheltered bay against a co-located 60 meter high meteorological mast fitted with a research quality cup-vane and/or sonic anemometers and the fixed scanning LIDAR; and (3) Offshore field evaluation of both LIDAR platforms through a comparison of the fixed scanning LIDAR data and data obtained by the buoy mounted LIDAR located 10 miles offshore. The proposed research will systematically validate Light Detection and Ranging (LIDAR) based wind measurement systems and assess the temporal and spatial variability of the offshore wind resource in the Mid-Atlantic east of New Jersey. The goal of the proposed project is to address the technical and commercial challenges of the offshore wind energy industry by validating and assessing cost-effective, over ocean wind resource characterization technologies. The objective is to systematically evaluate the capability of both scanning and vertically profiling LIDARs to accurately measure 3D wind fields through comparison with fixed met masts and intercomparison among LIDAR platforms. Once validated, data collected by both buoy mounted vertically profiling LIDARs and shore-based, pulsed horizontally scanning LIDARs can be used to accurately assess offshore wind resources and to quantify the spatial and temporal variability in the offshore wind fields. One of the fundamental research questions to be addressed in phase 1 is the assessment of various measurement and data processing schemes to retrieve accurate wind vectors in the marine environment over large sampling ranges (10 to 12 km) and varying atmospheric aerosol levels. Atmospheric conditions and aerosol content within the coastal ocean region of the Mid-Atlantic seaboard of the US can vary significantly over short time periods in response to frontal passages and extratropical and tropical low pressure system passage offshore of the coast. Since aerosols provide the scattering medium for the determination of LIDAR Doppler shifts in the atmosphere the accuracy and range of LIDAR derived velocity measurements as a function of variation in aerosol content in the marine environment is a key research question to be addressed. In phase 1, it is desired to capture as much variation in atmospheric conditions and aerosol content as possible. To this end, collocated measurements of LIDAR and standard anemometer wind fields will be captured by the project PIs over all four seasons and during specific events (e.g., coastal low pressure system passage) in year 1. Additionally, since the meteorological masts are permanent structures, additional events can be captured over the three year duration of the field research project. All research instruments are owned by Fishermen’s Energy and made available to the PIs though a lease agreement as part of the DOE grant. Energy Fishermen’s Energy will be responsible for the operation and maintenance of the scanning LIDAR and met mast anemometers. On a daily basis, environmental data and systems performance indicators will be transmitted from each measurement station to the Fishermen’s project team consisting of both in-house personnel and equipment manufacturer engineers. Data sets include compiled LIDAR files as well as data sets from ancillary sensors. Diagnostic parameters to be monitored include standard deviations of measured values, battery levels and charging systems output, and the operational status. Once data have been confirmed as complete and reliable, files will be transferred to the Garrad Hassan (a subcontractor to Fishermen’s Energy) for incorporation in to the validation database, which is accessible to other scientific team members. Data collection times and durations will be determined by the PI and Co-PIs in consultation with instrument engineers to ensure the capture of data representative of the expected range of mid-Atlantic atmospheric conditions (e.g., temperature, moisture, coastal low pressure systems, tropical systems, rain, snow, fog). The collection and processing of the data is a function of site specific measurement requirements (Kelley et.al. 2007; Hannon et.al. 2008). To determine the optimal profiles of wind speed and direction from the LIDAR radial velocities as a function of azimuth angle, rigorous estimates of the bias and random error of each radial velocity estimate are required. Lockheed Martin Coherent Technologies, Inc., under contract with Fishermen’s Energy, will provide analyses of raw and processed data using various scan patterns to determine optimal performance settings for the pulsed scanning LIDAR. Once optimized, appropriate processing and analyses techniques will be evaluated by Garrad Hassan for use in validating the accuracy of the LIDAR wind field measurements against the standard anemometer measurements from the meteorological masts. The most attractive capability of the scanning LIDAR is the ability to provide high spatial resolution observations in a three-dimensional volume which provides superior statistical accuracy due to the large number of samples obtained. Each radial scan provides measurements in 100 range gates over a distance of 10 to 12 km at an update rate of 5 to 10 Hz and rotation of 2.5° per second. Each rotation at a fixed azimuth requires 2.4 minutes. Depending on the number of azimuths desired a complete scan can take up to 10 minutes or longer to complete. Once collected the radial velocities are processed to produce vector wind velocity estimates based on a set of data distributed in angle and range around points of interest, typically a standard grid within the radial wind map. To calculate wind vectors over a limited spatial area of interest for the comparison of data with other measurement platforms a localized least-squares approach has been applied by Hannon et.al. (2008) and a Variation Assimilation (VAR) processing technique has been applied by Chan and al Assimilation (VAR) processing technique has been applied by Chan and Shao (2006). Additionally, Kelley et.al. (2007) applied a “stare” technique that fixed a scanning LIDAR in both azimuth and elevation angles to measure over collocated sampling volumes of the LIDAR and a 3D sonic anemometer mounted to a fixed mast. Although the stare technique is limited to wind directions aligned with the sampling radial of the LIDAR, it does provide a direct comparison of sampling volumes. Each of the processing techniques described above (and possibly others) will be evaluated to determine the validity of the LIDAR derived wind fields in the marine environment. Numerical methods such as linear regression and comparison of probability density functions of wind fields measured by each instrument platform will be used to assess the processing techniques. Linear regression has the advantage of directly evaluating corresponding pairs of wind data measured by each instrument and can lend insight into deviations and bias between instruments as a function of wind speed. Assessment of the coherency between the probability density function of the wind measured by each instrument provides insight into processes that may not be accurately resolved by each instrument at specific frequencies. Once processed and assessed the most appropriate technique will be utilized to provide valid wind measurements from the pulsed scanning LIDAR. A detailed analysis of the measurement data from the LIDAR and the three meteorological towers and comparison of the coastal wind characteristics from the different systems will be performed by the CO_PI at NREL. This analysis will evaluate how the WT LIDAR performance and measurement of the wind characteristics vary with distance from the LIDAR and by atmospheric conditions; using the tower measurements at different distances (4.8, 9.6, and 19 km) from the LIDAR as a reference. The comparative analysis will include, to the extent possible, evaluation of parameters such as wind speed and direction distributions, wind shear, turbulence intensity and their variations by atmospheric conditions, month or season, and time of day.

17 WIND ENERGY↗
DOE OSTI2023

Density limits as disruption forecasters for spherical tokamaks

Fusion power output from spherical tokamaks would benefit from increased confined plasma density, but there exists a limit on the density before confinement is lost and the plasma current is disrupted. This density limit has long been characterized by a simple, global Greenwald limit proportional to the plasma current and inversely proportional to the cross sectional area of the plasma. It is shown that in the database of discharges from the National Spherical Tokamak Experiment (NSTX) and Mega Ampere Spherical Tokamak (MAST) spherical tokamaks, the likelihood of disruption does increase above the Greenwald limit, and especially in the plasma current rampdown phase. The physics of the density limit has been recently theoretically explored through local criteria. Several of these are tested using the disruption event characterization and forecasting (DECAF TM ) code for their potential effectiveness as disruption warning signals. For a limited set of NSTX discharges, a local island power balance criteria was found to be less reliable, presently, than the Greenwald limit. For this work, an empirical critical edge line density and a boundary turbulent transport limit were both tested for MAST-U, which has an electron density profile measurement with high spatial resolution in the outer part of the plasma. Both were found to have similar dependencies on key plasma parameters. In a limited set of MAST-U discharges that appear to disrupt due to rising density at values under the Greenwald limit, crossing of the boundary turbulent transport limit occurred close to the time of disruption. Finally, these limits were evaluated for their potential use in real-time, and it was found that with the necessary real-time inputs and with refinement through further testing, these limits could be implemented in a real-time disruption forecasting system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Pennsylvania Department of Environmental Protection (PA DEP) 26r Detailed Produced Water Compositions (version 2.0)

A database of geochemical compositions of aqueous species in produced water reported to the PA Department of Environmental Protection (PA DEP). Samples were collected between late-2010 to late-2024. Data from publicly available PA DEP 26r reports were scraped from pdf files and cumulated into tabular spreadsheet format for >3,000 produced water streams from Marcellus Shale wells in Pennsylvania. In addition to providing the original values, the NETL NEWTS team has reformatted the dataset to allow sample streams to be easily copied into OLI Studio and Geochemist WorkBench (GWB) software for modeling the geochemistry and the recovery of critical minerals, such as lithium, from these produced water streams. ***This dataset is an updated version of the PA DEP 26r Detailed Produced Water Compositions (version 1.0) dataset, providing expanded spatial and temporal coverage.***

Aqueous Chemistry↗
DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2020

HarDWR - Water Management Area (WMA) Shapefiles

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Economics↗
DOE OSTI2024

ToF-SIMS spectral analysis of Shewanella oneidensis MR-1 biofilms

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many powerful features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution, mass resolution, and mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Shewanella oneidensis MR-1 isolated from freshwater lake sediment in New York state. The MR-1 strain is known to have metal and sulfur reducing properties and it can be used for bioremediation and wastewater treatment. There is a current need to identify small molecules and fragments produced from bacterial biofilms, especially those from extracellular polymeric substance (EPS). Static ToF-SIMS spectra of MR-1 were obtained using an IONTOF TOF.SIMS V instrument equipped with a 25 keV Bi$^+_3$ metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids, fatty acids, flavonoids, and quinolones to other naturally occurring organic compounds. It is anticipated that the mass spectral identification of key peaks will assist detection of metabolites, EPS molecules like polysaccharides, and biologically relevant small organic molecules using ToF-SIMS in future surface and interface research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Impact of Wildfires on Solar Resource Availability in California in a Changing Climate

Wildfires can emit large amounts of atmospheric particulate matters and influence not only air quality but also availability of photovoltaic (PV) generation due to scattering and absorption of solar radiation. Under anthropogenic changing climate, wildfire activity is projected to increase over western North America due to drier and warmer climate, implying increasing impact on solar resource and larger uncertainty in solar generation especially in regions with faster PV penetration. This study focuses on quantifying the impact of wildfires on aerosol optical depth (AOD) and thus solar resource over California using National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL). This assessment includes historical analysis and estimation of solar resource under wildfire scenarios (2020 wildfire and an enhanced wildfire scenario based on 2020 wildfires). Historical analysis for the period of July-October 2019/2020 (low/high fire activity period) shows that the averaged global horizontal irradiance (GHI) and direct normal irradiance (DNI) are reduced by about 30 and 90 W m-2 (5.6 and 15.3%), respectively, during high fire activity period. To create AOD dataset for enhanced fire scenario, 165% increase in burn area (in around 2050) is selected based on comprehensive literature review, which is further applied to the Fire INventory from NCAR (FINN) fire emission. WRF-Chem model with the enhanced FINN emissions is used to simulate and represent a preserving spatial distribution of burned areas and wildfire-emitted aerosols. Our initial analysis suggests that the"enhanced 2020 wildfire" AOD can increase by a factor of 1.3 - 2, which can significantly reduce solar irradiance and increase uncertainty in generation and reliability of power system in extreme wildfire events under a high solar penetration scenario. Overall, this study provides an estimate of the impacts of wildfires on solar resource to make informed decisions on reserve planning, generation scheduling, and reliability investments.

aerosol optical depth↗
DOE OSTI2015

Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation

Development of reliable source emission inventories is needed to advance the understanding of the origin of Arctic haze using chemical transport modeling. This paper develops a regional anthropogenic black carbon (BC) emission inventory for the Russian Federation, the largest country by land area in the Arctic Council. Activity data from combination of local Russia information and international resources, emission factors based on either Russian documents or adjusted values for local conditions, and other emission source data are used to approximate the BC emissions. Emissions are gridded at a resolution of 0.1° × 0.1° and developed into a monthly temporal profile. Total anthropogenic BC emission of Russia in 2010 is estimated to be around 224 Gg. Gas flaring, a commonly ignored black carbon source, contributes a significant fraction of 36.2% to Russia's total anthropogenic BC emissions. Other sectors, i.e., residential, transportation, industry, and power plants, contribute 25.0%, 20.3%, 13.1%, and 5.4%, respectively. Three major BC hot spot regions are identified: the European part of Russia, the southern central part of Russia where human population densities are relatively high, and the Urals Federal District where Russia's major oil and gas fields are located but with sparse human population. BC simulations are conducted using the hemispheric version of Community Multi-scale Air Quality Model with emission inputs from a global emission database EDGAR (Emissions Database for Global Atmospheric Research)-HTAPv2 (Hemispheric Transport of Air Pollution) and EDGAR-HTAPv2 with its Russian part replaced by the newly developed Russian BC emissions, respectively. The simulation using the new Russian BC emission inventory could improve 30–65% of absorption aerosol optical depth measured at the AERONET sites in Russia throughout the whole year as compared to that using the default HTAPv2 emissions. At the four ground monitoring sites (Zeppelin, Barrow, Alert, and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

THE NARROW X-RAY TAIL AND DOUBLE Hα TAILS OF ESO 137-002 IN A3627

We present the analysis of a deep Chandra observation of a ∼2 L{sub *} late-type galaxy, ESO 137-002, in the closest rich cluster A3627. The Chandra data reveal a long (∼>40 kpc) and narrow tail with a nearly constant width (∼3 kpc) to the southeast of the galaxy, and a leading edge ∼1.5 kpc from the galaxy center on the upstream side of the tail. The tail is most likely caused by the nearly edge-on stripping of ESO 137-002's interstellar medium (ISM) by ram pressure, compared to the nearly face-on stripping of ESO 137-001 discussed in our previous work. Spectral analysis of individual regions along the tail shows that the gas throughout it has a rather constant temperature, ∼1 keV, very close to the temperature of the tails of ESO 137-001, if the same atomic database is used. The derived gas abundance is low (∼0.2 solar with the single-kT model), an indication of the multiphase nature of the gas in the tail. The mass of the X-ray tail is only a small fraction (<5%) of the initial ISM mass of the galaxy, suggesting that the stripping is most likely at an early stage. However, with any of the single-kT, double-kT, and multi-kT models we tried, the tail is always 'over-pressured' relative to the surrounding intracluster medium (ICM), which could be due to the uncertainties in the abundance, thermal versus non-thermal X-ray emission, or magnetic support in the ICM. The Hα data from the Southern Observatory for Astrophysical Research show a ∼21 kpc tail spatially coincident with the X-ray tail, as well as a secondary tail (∼12 kpc long) to the east of the main tail diverging at an angle of ∼23° and starting at a distance of ∼7.5 kpc from the nucleus. At the position of the secondary Hα tail, the X-ray emission is also enhanced at the ∼2σ level. We compare the tails of ESO 137-001 and ESO 137-002, and also compare the tails to simulations. Both the similarities and differences of the tails pose challenges to the simulations. Several implications are briefly discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Flexible classification with spatial quantile comparison and novel statistical features applied to spent nuclear fuel analysis

Multivariate classification algorithms are a common tool in contemporary data science that have begun to be applied to nuclear forensic analyses. Using a multivariate generalization of quantile–quantile plots for comparing unknown statistical distributions, a new model-free multivariate classifier called the Quantile–Quantile Comparator has been developed and tested on the analysis of simulated irradiated nuclear fuel. A method to estimate the sample-specific probability of having made an incorrect classification decision by analyzing the distribution of quantile comparisons has been developed, and methods have been developed to analyze the partition of the known data library used to analyze unknown data. By analyzing this partition, the analyst may identify classes that are likely to be confused, identify test data that are inconsistent with the data library and return a “None-of-the-Above” classification decision, and optimize the class boundaries. In this paper, the development of these capabilities is described, and a case study using real experimental assay data collected from nuclear fuel extracted from the newly-released In conclusion, Spent Fuel Isotopic Composition Database to demonstrate the efficacy of these capabilities is presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

The FLOod Probability Interpolation Tool (FLOPIT): A Simple Tool to Improve Spatial Flood Probability Quantification and Communication

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Environmental controls on the light use efficiency of terrestrial gross primary production

Abstract Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C 3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗
DOE OSTI2022

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks

Performing first-principles calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use predefined features: based only on chemical compositions. Here, we propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed methods, which combine both atomic composition and atomic coordinates in 3D-space, improve the accuracy in voltage prediction significantly when compared to composition-based ML models. The first model directly learns the chemical reaction of electrodes and metal ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (E form ) to compute their average voltage. Our E form -based model demonstrates improved accuracy in transferability from our subset of learned Li ions to Na ions. Moreover, we predicted the theoretical voltage of 10 Na x MPO 4 F (M = Ti, Cr, Fe, Cu, Mn, Co, and Ni) fluorophosphate battery frameworks, which are unavailable in the Material Project database. It could be shown that we can expect average voltages higher than 3.1 V from those Na battery frameworks except from the NaTiPO 4 F and TiPO 4 F pair of electrodes, which offer an average voltage of 1.32 V.

25 ENERGY STORAGE↗