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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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NASA NTRS2019

2018 Mars Insight Trajectory Reconstruction and Performance from Launch Through Landing

The InSight mission successfully launched to Mars on an Atlas V 401 launch vehicle from the Western Test Range (WTR) at Vandenberg Air Force Base (VAFB) at 04:05:00 PDT on May 5th, 2018 and landed in the Elysium Planitia Region on November 26th, 2018. Data confirming nominal touchdown was received at 11:52:59 AM PST. This paper summarizes in detail the actual vs. predicted performance of the InSight spacecraft and all associated assets in terms of launch vehicle events, injection performance, DSN performance, cruise performance, and Entry, Descent, and Landing events.

Abilleira, Fernando↗
NASA NTRS2019

The Demonstration of a Light Extinction Tomography System at the NASA Glenn Research Center's Icing Research Tunnel

A prototype light extinction tomography system has been developed for acquiring real-time in-situ icing cloud uniformity and density measurements in the NASA Glenn Research Center's Icing Research Tunnel (IRT). These measurements are currently obtained through periodic manual calibrations of the IRT. These calibrations are time consuming and assume that cloud uniformity and density does not greatly vary between the periodic calibrations. It is envisioned that the new light extinction tomography system will provide the means to make these measurements in-situ in real-time and minimize the need for these manual calibrations. This new system uses the principle of light extinction tomography to measure the spray density and distribution in the test section. The prototype system was installed and successfully demonstrated in the Icing Research Tunnel in early 2018. Data sets were acquired for several standard spray and simulated fault conditions to assess system capability and sensitivity. This paper will describe the prototype light extinction system, the theory behind it, and the results of the demonstration test that was conducted in the IRT.

Test Facility↗
NASA NTRS2019

The Demonstration of a Light Extinction Tomography System at the NASA Glenn Research Center's Icing Research Tunnel

A prototype light extinction tomography system has been developed for acquiring real-time in-situ icing cloud uniformity and density measurements in the NASA Glenn Research Center's Icing Research Tunnel (IRT). These measurements are currently obtained through periodic manual calibrations of the IRT. These calibrations are time consuming and assume that cloud uniformity and density does not greatly vary between the periodic calibrations. It is envisioned that the new light extinction tomography system will provide the means to make these measurements in-situ in real-time and minimize the need for these manual calibrations. This new system uses the principle of light extinction tomography to measure the spray density and distribution in the test section. The prototype system was installed and successfully demonstrated in the Icing Research Tunnel in early 2018. Data sets were acquired for several standard spray and simulated fault conditions to assess system capability and sensitivity. This paper will describe the prototype light extinction system, the theory behind it, and the results of the demonstration test that was conducted in the IRT.

Tomography↗
DOE OSTI2025

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Teller 47 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory’s (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 47 along the Teller Road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDAR System was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, and lidar data and imagery can be found in NGA281 (https://doi.org/10.5440/1671794).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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LiDAR Point Cloud Data from the 2018 NGEE Arctic UAS Campaign at the Kougarok 64 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 64 along the Kougarok road (Nome-Taylor Highway) between Nome, Alaska and Taylor, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, raw LiDAR data, and RGB imagery can be found in NGA298.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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

River and hyporheic zone water geochemical data from OsmoSamplers, East River Pumphouse, Colorado, Nov 2017-Sept 2018

This data package contains geochemical and river water temperature data from OsmoSamplers deployed at East River, Colorado pump house. There are three data files. The first one contains sulfate (SO4) and chloride (Cl) concentrations from riverwater and hyporheic zone (sediment water interface (SWI), 4 cm below seafloor (cmbsf), and 12 (cmbsf)). The second file contains methane concentrations, methane stable carbon isotope ratios and water isotopes from hyporheic zone porewaters. And the third is from a temperature logger during the deployment. Files are provided as .xls or .csv. This research was performed as part of the SBR funded project “Quantifying Subsurface Biogeochemical Variability in a High Altitude Watershed During Winter Isolation” DE-SC0018328. The novel part of this dataset is that we quantify these geochemical parameters throughout the winter isolation period, which is difficult to measure, as well as within the hyporheic zone. The data files were updated 3/1/2021 because the column label for the water isotopes was incorrect. Also, the csv files were also updated since they were not formatted correctly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

L0 Data from the 2018 NGEE Arctic LiDAR and Imagery Unoccupied Aerial System Campaign at the Teller 47 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from EES-14 (Earth System Observations group) at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 47 along the Teller road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data, and DJI Phantom 4 Advanced was used to collect optical red/green/blue (RGB) imagery at regular intervals along 11 flight paths. This data package contains unprocessed data products (processing level 0) including flight paths, raw photos, and raw lidar data files (*.kml, *.jpg, and *.lpd formats). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included (see Supplemental Files, *.rinex, and *.rtcm3 files). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), 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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

L0 Data from the 2018 NGEE Arctic LiDAR and Imagery Unoccupied Aerial System Campaign at the Teller 27 Field Site, Seward Peninsula, Alaska

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 27 along the Teller road between Nome, Alaska and Teller, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data along 12 flight paths, and DJI Phantom 4 Advanced was used to collect optical red/green/blue (RGB) imagery at regular intervals along 5 flight paths. This data package contains unprocessed data products (processing level 0) including flight paths, raw photos, and raw lidar data files (*.kml, *.jpg, and *.lpd formats). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included (see Supplemental Files, *.rinex, and *.rtcm3 files). 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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Gas Hydrate Saturation Estimation Form Acoustic Log Data in the 2018 Alaska North Slope Hydrate-01 Stratigraphic Test

Completed in December 2018, the Alaska North Slope Hydrate 01 stratigraphic test well provides a wealth of logging-while-drilling (LWD) data for strata to below the base of gas hydrate stability (BGHS). This well is intended to be the first of three wells drilled for a comprehensive long-term gas hydrate production test conducted by the National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey (USGS). The Hydrate 01 stratigraphic test well confirmed the presence of gas hydrate in two sand reservoirs within the hydrate stability zone, indicating the suitability of this location for a long-term gas hydrate production test.<p>The USGS, using an effective-medium-theory rock-physics approach, has estimated gas hydrate saturations from compressional (P) and shear (S) wave log data acquired in the Hydrate 01 well. We assume that gas hydrate occurs as pore-filling load-bearing material (i.e., part of the grain matrix). For Unit D, approximately 500 feet above the BGHS, both P-wave and S-wave acoustic logs indicate moderate gas hydrate saturations with S-wave results slightly lower than those for P-waves. For the Unit B, located just above the BGHS, we obtain moderate to high gas hydrate saturation estimates from both sonic logs. Our P-wave saturation estimates agree well with results from electrical-resistivity-based estimates, whereas estimates from nuclear magnetic resonance LWD data generally suggest 5 to 10 percent higher saturations; our S-wave results suggest lower saturations. These differences likely indicate complexities in the form of gas hydrate occurrence within the sediment pore space, potentially including differences between hydrate occurrence in Units D and B.</p>

Haines, Seth↗
DOE OSTI2020

Time–series Analysis of Volume Change at Brady Hot Springs, Nevada, USA using Geodetic Data from 2003 – 2018

Brady Hot Springs geothermal field has exhibited subsidence, as measured by interferometric synthetic aperture radar (InSAR). Previous studies have examined both the temporal evolution of the deformation from 2004 through 2016 and the spatial extent of the deformation, directly relating the observed subsidence to volumetric changes below the surface. We extend the modeling at Brady to analyze a data set of interferometric pairs spanning from the end of 2003 through 2018. We examine spatial and temporal trends in the observed deformation by time–series analysis of each of the 1656 cubic voxels in a parameterized elastic dislocation model to identify areas where the subsurface volume changes as a function of time. Joint time–series analysis of Global Positioning System and InSAR pairs confirm significant changes in rates of volume change during time intervals when well operations were varied. Here, the rate of subsidence increases with increased injection, consistent with the identification of thermal contraction of the rock matrix as the dominant driving mechanism. Conversely, the modeled volume increases when pumping ceases, suggesting thermal expansion of the rock matrix.

58 GEOSCIENCES↗
DOE OSTI2026

Multi-Doppler radar analysis from CSAPR, CHIVO, COW, and RMA-1 radars during the CACTI/RELAMPAGO experiments in Argentina in 2018

This data set contains multi-Doppler radar analysis from CSAPR-2, CSU-CHIVO, COW, and RMA-1 radars. These radars were collecting dual-polarization data during the CACTI/RELAMPAGO experiments in Argentina in 2018. Doppler analysis is systematically conducted for 31 days with convection. Three-dimensional wind fields are retrieved using the PyDDA algorithm. Dual-polarization information is also included in the data set.

3D cartesian gridded corrected mean Doppler veloci↗
DOE OSTI2023

Riverbank temperatures on the Selawik River, Alaska 2010-2012, and Koyukuk River, Alaska June to July 2018.

The data package includes temperature measurements from riverbanks along the Selawik and Koyukuk Rivers in northwest Alaska. The Selawik River data also includes meteorological data collected locally and the Koyukuk River includes bulk density and ice content data from the riverbanks. The Selawik River data was collected between 2010 and 2013 with meteorological data collected for the entire time period and riverbank temperatures collected in shorter time intervals between 2010 to 2012. All of the Selawik River data were collected a single river bend. The Koyukuk River temperature data was collected from late June to early July 2018 at a total of five locations on three riverbanks. Ice content measurements on the Koyukuk were made at five riverbank locations. The data collection on both rivers was done in support of studies on the influence of permafrost on riverbank erosion. The temperature data was collected to monitor the thermal conditions of the river banks both seasonally and during periods of erosion. The ice content of the banks was measured to better understand how bank erosion rates are influenced by ice in the frozen riverbanks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sharing the Sun: Understanding Community Solar Deployment and Subscriptions

This presentation reviews trends in the national community solar market, with project data through 2019 and subscriber data through 2018. It summarizes data on community solar deployment over time, by state, and by project characteristics. It also examines how market factors have shaped community solar deployment and explores data on the community solar value proposition to subscribers.

14 SOLAR ENERGY↗
DOE OSTI2022

Measurement of the anomalous precession frequency in the Muon $g-2$ experiment at Fermilab

The muon anomalous magnetic moment, $a_\mu=\frac{g-2}{2}$, is a low energy observable that can be both measured and computed with high precision, therefore it can provide an important test of the Standard Model and it is a sensitive probe for new physics. In particular, any discrepancy between the experimental value and the theoretical prediction can be due to Beyond Standard Model physics contribution. The E989 Muon $g-2$ Experiment at Fermilab aims to measure $a_\mu$ with a precision of 140 parts per billion, four time more precisely than the previous experiment at Brookhaven National Laboratory (BNL). E989 seeks to either resolve or confirm the observed discrepancy of $3.7\sigma$ between the Standard Model value and the experimental one. The $a_\mu$ measurement requires a precise determination of both the muon spin anomalous precession frequency and the average magnetic field seen by the muons as they circulate in a storage ring. The anomalous precession frequency measurement is b ased on the time distribution of high-energy decay positrons observed by 24 electromagnetic calorimeters placed around the inner circumference of the ring, while the magnetic field is constantly monitored by NMR probes. Last spring, E989 has published a new measurement of $a_\mu$ from the 2018 data-taking campaign, confirming the previous BNL value with comparable precision, bringing the discrepancy with the theoretical value to $4.2\sigma$. The candidate worked on the measurement of the anomalous precession frequency and on the estimate of the related systematic uncertainties of the 2018 data, giving an important contribution to the data analysis. The result presented in chapters 4 and 5 of this dissertation was included, among three other analyses, to determine the estimate of the anomalous magnetic moment by the Muon g-2 experiment. This work will present the anomalous precession frequency analysis technique and its result, the related systematics and finally the latest $a_\mu$ result.

Sorbara, Matteo↗
DOE OSTI2021

Surface Meteorology at Teller Mile 47 Watershed, Seward Peninsula, Alaska, Ongoing from 2018

Meteorological data are currently being collected at one location at the Teller Mile 47 (TL_MM47) Research Basin Site, Seward Peninsula (N64 58' 36.918", W166 12' 32.67", 67 meters above sea level). The site was installed and initial measurements started in September 2018 and it has operated continuously since then. The meteorological station is co-located with a continuous snow depth sensor and two soil pits for subsurface temperature and moisture measurements. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements also at this location. The data could also be used as supporting measurements for other research and modeling activities. There are 35 comma separated value format (*.csv) files provided, where each file contains the full data for an individual parameter (e.g. air temperature at 1.5 meters above the ground surface (teller_m47_air_temperature_150cm_ags_Avg.csv) or soil temperature 20 centimeters below ground surface (teller_m47_dry_soil_pit_temperature_20cmbgs_Avg.csv)) plus the time in Universal Coordinated Time (UTC) and Alaska Standard Time (UTC time minus nine hours). The site was installed and initial measurements started in September 2018. It has been operated continuously since. Primary data gaps are due to battery failure or sensor failure. These data are being collected to better understand the surface energy dynamics above the active layer and permafrost. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) 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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Beaver Creek Soil Incubation Experiment July 2018

The data accompanies the manuscript Antecedent conditions determine the biogeochemical response of coastal soils to seawater exposure" that evaluates impact of periodic seawater inundations of soils from in a first-order coastal watershed in the Olympic Peninsula of Washington state, USA. Samples were collected in July 2018 and subjected to laboratory incubations. The files contain a read me file, data files including soil chemical characteristics, FTICR-MS molecular and data file, FTICR-MS derived processed data from FREDA, reference file for transformation analysis, LC-MS derived molecular feature abundance data, gas flux data for multiple regression and R codes. The files are related to the publication and have been used in the results and analyses section.

54 ENVIRONMENTAL SCIENCES↗