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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 OSTI2023

Fast Semi-automated Filtration Method for Non-targeted LC-QTOF Data of Aged Nitroplasticizer Samples

A full dataset of aged nitroplasticizer (NP) is composed of more than 2000 unique mass-to-charges (m/z) when combining the non-targeted data obtained from both positive and negative electrospray ionization modes in time-of-flight mass spectrometry. Therefore, manual processing of these data often takes days, weeks, or even months to scrutinize for mechanistic insights. To effectively extract meaningful signals that represent vital degradation intermediates in the early NP degradation mechanism, a semi-automated postprocessing workflow for data filtering, tailored to the aging experiment of NP, has been developed. The automated portion of this workflow is written in a Python code (using pandas, numpy, and matplotlib libraries), which removes more than 65% of potential false signals within seconds via four threshold-based adjustable filters: signal sensitivity, coefficient of variation, number of measurements, and retention time variability. As for the manual portion, a pattern-based inspection method is employed to reduce another 23% or more false positives, which greatly simplifies data visualization and results in less than 3% of potential candidate m/z needing in-depth data interpretation. As a positive control, known compounds are verified. Using this semi-automated data reduction method, the amount of time required is reduced to a matter of hours for data filtering in the non-targeted datasets of aged NP, which saves more time and effort for compound identification.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Subsurface mechanical damage correlations after grinding of various optical materials

Loose abrasive grinding was performed on a wide range of optical workpiece materials [single crystals of Al 2 O 3 (sapphire), SiC, Y 3 Al 5 O 12 (YAG), CaF 2 , and LiB 3 O 5 (LBO); a SiO 2 -Al 2 O 3 -P 2 O 5 -Li 2 O glass-ceramic (Zerodur); and glasses of SiO 2 : TiO 2 (ULE), SiO 2 (fused silica), and P 2 O 5 -Al 2 O 3 -K 2 O-BaO (phosphate)]. Using the magneto rheological finishing (MRF) taper wedge technique (where a wedge was polished on each of the ground workpieces and the resulting samples were appropriately chemically etched), the subsurface mechanical damage (SSD) characteristics were measured. The SSD depth for most of the workpiece materials was found to scale as E1 1/2 / H 1 , where E 1 is the elastic modulus and H 1 is the hardness of the workpiece. This material scaling is the same as that for the growth of lateral cracks, suggesting that lateral cracks are a dominant source for SSD rather than radial/median cracks, as previously proposed. Utilizing the SSD depth data from both this study and others, semiempirical relationships have been formulated, which allows for estimating the SSD depth as a function of workpiece material and important grinding parameters (such as abrasive size and applied pressure).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids

This dataset contains all data used in the paper "Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids". The dataset includes genotypes and phenotypic data from two autotetraploid species Miscanthus sacchariflorus and Vaccinium corymbosum that was used used for genome wide association studies and genomic prediction and the scripts used in the analysis. In this V2, 2 files have the raw data are added: "Miscanthus_sacchariflorus_RADSeq.vcf" is the VCF file with the raw SNP calls of the Miscanthus sacchariflorus data used for genotype calling using the 6 genotype calling methods. "Blueberry_data_read_depths.RData" is the a RData file with the read depth data that was used for genotype calling in the Blueberry dataset.

allelic dosage↗
DOE OSTI2020

Evaluating proxy influence in assimilated paleoclimate reconstructions – Testing the exchangeability of two ensembles of spatial processes

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. In this study, we apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Importance of Quantifying the Full-depth Carbon Reservoir of Jamaica Bay Salt Marshes, New York

Constraining uncertainty in the global carbon cycle requires valid assessment of both surface and stored carbon in marine and coastal ecosystems (Blue Carbon) as well as terrestrial carbon (forests, peatlands, and soils) [Pendleton et al., 2012]. Quantifying the global carbon stock of coastal salt marshes, potentially the most efficient carbon-burying ecosystems in the world per area, is a key area of further research in both of these fields Pendleton et al., 2012. One of the largest challenges is that despite the fact that salt marshes often sequester carbon several meters deep, nearly all estimates of salt marsh carbon stocks consider only the upper 1 m of sediment (Windham-Myers et al., 2015) [54]. This is particularly concerning because coastal wetlands are increasingly at risk due to climate change, sea level rise, and anthropogenic disturbance and destruction (Deegan et al., 2012) [15]. Using full-depth measurements from marsh cores, we estimate the carbon stock of five salt marshes in the highly urbanized estuary of Jamaica Bay, New York and argue that partial-depth measurements can underestimate carbon stocks. These estimates use calculated carbon content and probe depth data of these marshes collected between 2000 and 2019, applying this data across the full area of the marsh obtained from satellite imagery. Carbon density measurements are then multiplied by the full-depth volume of the marshes to create an estimate of total carbon stock. In addition to calculating present-day estimates, we compare our carbon stock estimates to historical Jamaica Bay imagery to calculate historical carbon stocks and carbon loss. The carbon stock estimates presented here show a 95% carbon stock loss between 1885 and 2019 in Jamaica Bay and highlight the severe underestimation of carbon stocks without full-depth calculations. These findings have important implications for disappearing salt marshes with regard to the global carbon cycle and the incorporation of belowground carbon into global climate models. The findings are increasingly relevant for advocacy efforts aiming to conserve these marshes with sea level rise.

Tidal wetlands↗
NASA NTRS1980

Stability of sulfur slopes on Io

The mechanical properties of elemental sulfur are such that the upper crust of Io cannot be primarily sulfur. For heat flows in the range 100-1000 ergs/sq cm sec sulfur becomes ductile within several hundred meters of the surface and would prevent the formation of calderas with depths greater than this. However, the one caldera for which precise depth data are available is 2 km deep, and this value may be typical. A study of the mechanical equilibrium of simple slopes shows that the depth to the zone of rapid ductile flow strongly controls the maximum heights for sulfur slopes. Sulfur scarps with heights greater than 1 km will fail for all heat flows greater than 180 ergs/sq cm sec and slope angles greater than 22.5 deg. The observed relief on Io is inconsistent with that anticipated for a predominantly sulfur crust. However, a silicate crust with several percent sulfur included satisfies both the mechanical constraints and the observed presence of sulfur on Io.

Clow, G. D.↗
DOE OSTI2017

3-D advanced gas-cooled nuclear reactor fuel channel reconstruction using structure-from-motion - 110

During planned, periodic outages, a selection of fuel channels within the UK fleet of Advanced Gas-cooled Reactor (AGR) cores are inspected using specialist tools which record video footage and other sensory data for each channel which undergoes inspection. Current visualization techniques comprise of manually produced montages by inspection engineers of points of interest (i.e. structural defects) and 2-D panoramic images of the fuel channels automatically produced using bespoke image stitching software. Both techniques however provide limited structural information due to the loss of depth data as a result of the image formation process. By recovering the depth information from the footage, a 3D model could be constructed and subsequently, allow for more accurate profiling of specific defects observed during inspection in addition to obtaining the fuel channels structure using existing footage. This work explores the preliminary application of a 3-D visualization technique known as Structure-from-Motion (SfM) which aims to obtain 3-D information by exploiting image correspondences across multiple viewpoints of the same scene in the RVI footage. This paper investigates the difficulties of applying state-of-the-art SfM to RVI footage and we present new techniques to improve feature correspondence searching in repetitive, nondescript environments. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Reference Site Condition Datasets for Floating Wind Arrays in the United States

Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.

16 TIDAL AND WAVE POWER↗
NASA NTRS2020

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

Arctic Ocean↗
DOE OSTI2018

Scaling laws for the additive manufacturing

The evaluation of simple thermal model of selective laser melting (SLM) process shows that the temperature distribution in the sample is characterized by two dimensionless parameters: normalized enthalpy and the ratio of dwell time to the thermal diffusion time. Here we demonstrated that the melt depth data taken for different machines for different materials collapsed in one curve, making possible to rescale the optimal processing parameters between the different materials and machines. The melt pool depth, width and the length are the universal function of these two parameters. Within the operational range of parameters for SLM these functions can be interpolated by the simple algebraic expressions given the possibility to reduce the calculation of the melt pool characteristic to spreadsheet model.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

Leptoukh, Gregory↗
DOE OSTI2018

ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs

Detecting objects that are carried when someone enters or exits a room is very useful for a wide range of smart building applications including safety, security, and energy efficiency. While there has been a significant amount of work on object recognition using large-scale RGB image datasets, RGB cameras are too privacy invasive in many smart building applications and they work poorly in the dark. Additionally, deep object detection networks require powerful and expensive GPUs. We propose a novel system that we call ODDS (Object Detector using a Depth Sensor) that can detect objects in real-time using only raw depth data on an embedded GPU, e.g., NVIDIA Jetson TX1. Hence, our solution is significantly less privacy invasive (even if the sensor is compromised) and less expensive, while maintaining a comparable accuracy with state of the art solutions. Specifically, we resort to training a deep convolutional neural network using raw depth images, with curriculum based learning to improve accuracy by considering the complexity and imbalance in object classes and developing a sparse coding based technique that speeds up the system ~2× with minimal loss of accuracy. Based on a complete implementation and real-world evaluation, we see ODDS achieve 80.14% mean average precision in object detection in real-time (5-6 FPS) on a Jetson TX1.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Utah FORGE: 16B(78)-32 RFS DSS Strain Change Rate vs. Depth During 16A(78)-32 Stimulation

This dataset contains strain change rate versus depth data acquired using a Rayleigh frequency shift (RFS) distributed strain sensing (DSS) system during hydraulic stimulation of well 16A(78)-32 at the Utah FORGE site in April 2024. The data were collected from an optical fiber installed in the annulus of production well 16B(78)-32, approximately 300 feet from the injection well. The dataset includes tabulated strain data and an explanation of the methodology used to generate the frac log, which integrates strain change rate signals over selected time windows to identify fracture events.

15 GEOTHERMAL ENERGY↗
NASA NTRS1973

Exploitation of ERTS-1 imagery utilizing snow enhancement techniques

Photogeological analysis of ERTS-simulation and ERTS-1 imagery of snowcovered terrain within the ERAP Feather River site and within the New England (ERTS) test area provided new fracture detail which does not appear on available geological maps. Comparative analysis of snowfree ERTS-1 images has demonstrated that MSS Bands 5 and 7 supply the greatest amount of geological fracture detail. Interpretation of the first snow-covered ERTS-1 images in correlation with ground snow depth data indicates that a heavy blanket of snow (more than 9 inches) accentuates major structural features while a light "dusting", (less than 1 inch) accentuates more subtle topographic expressions. An effective mail-based method for acquiring timely ground-truth (snowdepth) information was established and provides a ready correlation of fracture detail with snow depth so as to establish the working limits of the technique. The method is both efficient and inexpensive compared with the cost of similarly scaled direct field observations.

Wobber, F. J.↗
NASA NTRS2023

A Cloud Detection Neural Network for Above-Aircraft Clouds Using Airborne Cameras

We introduce a method convolutional neural networks to detect the presence of clouds in airborne camera images. We quantify the performance of this Cloud Detection Neural Network (CDNN) using human-labeled validation data where we report a 96% accuracy in detecting clouds in testing datasets for both Zenith- viewing and Forward-viewing models. We assess our performance by comparing the flight-averaged cloud fraction of zenith and forward CDNN retrievals, with that of the prototype hyperspectral total-diffuse Sunshine Pyranometer (SPN-S) instrument’s cloud optical depth data. Comparison of the CDNN with the SPN-S on time specific intervals resulted in 93% accuracy for the zenith- viewing CDNN and 84% for the forward- viewing CDNN. The comparison of the CDNNs with the SPN-S on flight-averaged cloud fraction resulted in an agreement of 0.15 for the Forward CDNN and 0.07 for the Zenith CDNN. We then quantify the ability of the CDNN to identify the presence of clouds above the aircraft using a forward- looking camera mounted inside the aircraft cockpit compared to the use of an All- Sky upward-looking camera that is mounted outside the fuselage on top of the aircraft. We present results from the CDNN based on airborne imagery from the NASA Aerosol Cloud Meteorology Interactions Over the Western Atlantic Experiment (ACTIVATE) and the Clouds, Aerosol and Monsoon Processes- Philippines Experiment (CAMP2Ex). For CAMP2Ex 53% of flight dates had above- aircraft cloud fraction above 50%, while for ACTIVATE 52% and 54% of flight dates observed above-aircraft cloud fraction above 50% for 2020 and 2021, respectively.

Joseph D Nied↗
NASA NTRS1970

Solid State Studies of the Radiation Histosy of Lunar Samples

Particle track densities up to greater than 3 x 10(exp 9) per square centimeter have been measured in different samples. Rocks 17, 47, 57, and 58 have VH (Z greater than 22) galactic cosmic ray ages of 11, 14, 28, and 13 x 10(exp 6) years, respectively. Rock 57 has a calculated erosion rate of approximately less than 10(exp -7) centimeter per year. Near-surface track versus depth data in rock 17 can be fit with solar flare particles that have a differential energy spectrum alpha E(exp -s); lunar samples can be used to study the history of solar activity. The uranium in the crystalline rocks occurs principally in small regions less than 10 to approximately equal to 100 micrometers in size. The (low) thermoluminescence of the fines increases with depth in core 10004. With one possible exception, x-ray studies have not shown pronounced radiation damage effects. The total energy release upon heating is small up to 900 C and occurs in three broad regions.

Crozaz, G.↗
NASA NTRS1994

Dynamics of the transition zone in coastal zone color scanner-sensed ocean color in the North Pacific during oceanographic spring

A transition zone in phytoplankton concentration running across the North Pacific basin at 30 deg to 40 deg north latitude corresponds to a basin-wide front in surface chlorophyll observed in a composite of coastal zone color scanner (CZCS) images for May, June, and July 1979-1986. This transition zone with low chlorophyll to the south and higher chlorophyll to the north can be simulated by a simple model of the concentration of phytoplankton, zooplankton, and dissolved nutrient (nitrate) in the surface mixed layer of the ocean applied to the North Pacific basin for the climatological conditions during oceanographic springtime (May, June, and July). The model is initialized with a 1 deg x 1 deg gridded estimate of wintertime (February, March, and April) mixed layer nitrate concentrations calculated from an extensive nutrient database and a similarly gridded mixed layer depth data set. Comparison of model predictions with CZCS data provides a means of evaluating the dynamics of the transition zone. We conclude that in the North Pacific, away from major boundary currents and coastal upwelling zones, wintertime vertical mixing determines the total nutrient available to the plankton ecosystem in the spring. The transition zone seen in basin-scale CZCS images is a reflection of the geographic variation in the wintertime mixed layer depth and the nitracline, leading to a latitudinal gradient in phytoplankton chlorophyll.

Glover, David M.↗
DOE OSTI2025

Quarterly Soil Core and Root Analyses from the Missouri Ozarks AmeriFlux (MOFLUX) Site, Ashland, Missouri, 2017-2023

This dataset contains quarterly soil core measurements from the Missouri Ozarks AmeriFlux (MOFLUX) site located at the University of Missouri’s Thomas H. Baskett Wildlife Research and Education Area near Ashland, Missouri. These data will be used to parameterize an ensemble of MOFLUX-optimized soil carbon-nitrogen models, used to simulate carbon (C) and nitrogen (N) cycling responses to future hydroclimatic scenarios and the trajectory of soil C stocks with concomitant forest decline. Beginning in 2017, eight soil cores were collected approximately quarterly near plot 1 of the southeast transect, near the automated soil respiration flux chambers, from 0–15 cm depth. Data are currently available through 2023 (2017-06-14 to 2023-11-13); additional observations will be appended to this dataset as they become available. Cores were analyzed for gravimetric moisture content, pH, total carbon and nitrogen, texture, microbial biomass carbon and nitrogen, and extractable dissolved organic carbon and nitrogen. This dataset contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗