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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
NASA NTRS2001

Cloud Overlapping Detection Algorithm Using Solar and IR Wavelengths with GOES Data Over ARM/SGP Site

One of the most perplexing problems in satellite cloud remote sensing is the overlapping of cloud layers. Although most techniques assume a one layer cloud system in a given retrieval of cloud properties, many observations are affected by radiation from more than one cloud layer. As such, cloud overlap can cause errors in the retrieval of many properties including cloud height, optical depth, phase, and particle size. A variety of methods have been developed to identify overlapped clouds in a given satellite imager pixel. Baum et al used CO2 slicing and a spatial coherence method to demonstrate a possible analysis method for nighttime detection of multilayered clouds. Jin and Rossow also used a multispectral CO2 slicing technique for a global analysis of overlapped cloud amount. Lin et al. used a combination infrared (IR), visible (VIS), and microwave data to detect overlapped clouds over water. Recently, Baum and Spinhirne proposed a 1.6 and 11 micron bispectral threshold method. While all of these methods have made progress in solving this stubborn problem none have yet proven satisfactory for continuous and consistent monitoring of multilayer cloud systems. It is clear that detection of overlapping clouds from passive instruments such as satellite radiometers is in an immature stage of development and requires additional research. Overlapped cloud systems also affect the retrievals of cloud properties over the Atmospheric Radiation Measurement (ARM) domains and hence should be identified as accurately as possible. To reach this goal, it is necessary to determine which information can be exploited for detecting multilayered clouds from operational meteorological satellite data used by ARM. This paper examines the potential information available in spectral data available on the Geostationary Operational Environmental Satellite (GOES) imager and the National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) used over the ARM Program's Southern Great Plains (SGP), and North Slope of Alaska (NSA) sites to study the capability of detecting overlapping clouds.

Kawamoto, K.↗
NASA NTRS1997

The Voyager Interstellar Mission

The Voyager Interstellar Mission began on January 1, 1990, with the primary objective being to characterize the interplanetary medium beyond Neptune and to search for the transition region between the interplanetary medium and the interstellar medium. At the start of this mission, the two Voyager spacecraft had already been in flight for over twelve years, having successfully returned a wealth of scientific information about the planetary systems of Jupiter, Saturn, Uranus, and Neptune, and the interplanetary medium between Earth and Neptune. The two spacecraft have the potential to continue returning science data until around the year 2020. With this extended operating lifetime, there is a high likelihood of one of the two spacecraft penetrating the termination shock and possibly the heliopause boundary, and entering interstellar space before that time. This paper describes the Voyager Interstellar Mission--the mission objectives, the spacecraft and science payload, the mission operations system used to support operations, and the mission operations strategy being used to maximize science data return even in the event of certain potential spacecraft subsystem failures. The implementation of automated analysis tools to offset and enable reduced flight team staffing levels is also discussed.

Flight Experiment↗
NASA NTRS2015

Simultaneous Radar and Satellite Data Storm-Scale Assimilation Using an Ensemble Kalman Filter Approach for 24 May 2011

Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

Jones, Thomas A.↗
DOE OSTI2014

A dark matter search using the final CDMS-II data and 100 mm SuperCDMS germanium detector ionization test

Astrophysical observations indicate that approximately 85% of the matter in the universe is nonluminous, nonbaryonic, and nonrelativistic (cold) dark matter. Weakly Interacting Massive Particles (WIMPs) are a particularly well motivated dark matter particle candidate. They would be thermally produced in the early universe and their relics account for the current dark matter abundance. WIMP candidate particles are naturally provided by extensions to the Standard Model of particle physics, such as supersymmetry. The Cryogenic Dark Matter Search (CDMS) experiment operates cryogenic germanium and silicon particle detectors in the low-background environment of the Soudan Underground Laboratory in northern Minnesota to search for WIMP-nucleus scatters while rejecting electron-recoil background. The detectors simultaneously measure the ionization and phonon energies of each scattering event. The difference in ionization yield (ratio of ionization energy to recoil energy) discriminates nuclear recoils from the electron-recoil background efficiently.More sensitive detectors are required to probe the WIMP parameter space with lower WIMP-nucleon scattering cross sections. To support the R&D effort especially the detector R&D and characterization of the SuperCDMS experiment, a new CDMS test facility has been developed on the University of Minnesota campus. This thesis documents the test facility and the work involved in its development. In the test facility, we performed the first ionization collection efficiency measurements of the ionization test devices. The test devices are fabricated with detector-grade germanium crystals that are 100 mm in diameter, which is the largest available, and 33 mm in thickness. The measured efficiencies are consistent with the earlier measurements conducted with smaller Ge crystals, demonstrating that these 100 mm crystals can be used for development of the next generation dark matter detectors.Improvements of data analysis methods can also potentially improve the sensitivity of an experiment. The data taken during the last four runs of CDMS II with total raw exposure 612 kg-day were reprocessed with improved ionization pulse reconstruction algorithm. We present the classic timing analysis with the reprocessed data in this thesis. For the four runs combined, this analysis resulted in a new WIMP-nucleon cross section 4.4×10 -44 cm 2 for a WIMP mass of 70 GeV, which is a factor of 1.6 improvement compared to the original c58 classic timing analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Stellar LOCI. III. Photometric metallicities for half million FGK stars of Stripe 82

We develop a method to estimate photometric metallicities by simultaneously fitting the dereddened colors u−g, g−r, r−i, and i−z from the SDSS with those predicted by the metallicity-dependent stellar loci. The method is tested with a spectroscopic sample of main-sequence stars in Stripe 82 selected from the SDSS DR9 and three open clusters. With 1% photometry, the method is capable of delivering photometric metallicities precise to about 0.05, 0.12, and 0.18 dex at metallicities of 0.0, −1.0, and −2.0, respectively, comparable to the precision achievable with low-resolution spectroscopy at a signal-to-noise ratio of 10. We apply this method to the re-calibrated Stripe 82 catalog and derive metallicities for about 0.5 million stars of colors 0.3<g−i<1.6 mag and distances between 0.3 and 18 kpc. Potential systematics in the metallicities thus derived, due to the contamination of giants and binaries, are investigated. Photometric distances are also calculated. About 91%, 72%, and 53% of the sample stars are brighter than r = 20.5, 19.5, and 18.5 mag, respectively. The median metallicity errors are around 0.19, 0.16, 0.11, and 0.085 dex for the whole sample, and for stars brighter than r = 20.5, 19.5, and 18.5 mag, respectively. The median distance errors are 8.8%, 8.4%, 7.7%, and 7.3% for the aforementioned four groups of stars, respectively. The data are publicly available. Potential applications of the data in studies of the distribution, (sub) structure, and chemistry of the Galactic stellar populations, are briefly discussed. The results will be presented in future papers.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2011

NASA Human Research Wiki - An Online Collaboration Tool

In preparation for exploration-class missions, the Exploration Medical Capability (ExMC) element of NASA's Human Research Program (HRP) has compiled a large evidence base, which previously was available only to persons within the NASA community. The evidence base is comprised of several types of data, for example: information on more than 80 medical conditions which could occur during space flight, derived from several sources (including data on incidence and potential outcomes of these medical conditions, as captured in the Integrated Medical Model's Clinical Finding Forms). In addition, approximately 35 gap reports are included in the evidence base, identifying current understanding of the medical challenges for exploration, as well as any gaps in knowledge and/or technology that would need to be addressed in order to provide adequate medical support for these novel missions. In an effort to make the ExMC information available to the general public and increase collaboration with subject matter experts within and outside of NASA, ExMC has developed an online collaboration tool, very similar to a wiki, titled the NASA Human Research Wiki. The platform chosen for this data sharing, and the potential collaboration it could generate, is a MediaWiki-based application that would house the evidence, allow "read only" access to all visitors to the website, and editorial access to credentialed subject matter experts who have been approved by the Wiki's editorial board. Although traditional wikis allow users to edit information in real time, the NASA Human Research Wiki includes a peer review process to ensure quality and validity of information. The wiki is also intended to be a pathfinder project for other HRP elements that may want to use this type of web-based tool. The wiki website will be released with a subset of the data described and will continue to be populated throughout the year.

Barr, Y. R.↗
DOE OSTI2020

Substation Secondary Asset Health Monitoring and Management System (SSHM)

Electric Power Group, LLC (EPG) was awarded DOE OE0000850 to design, develop, and demonstrate a real-time software application for substation secondary equipment health monitoring and management at host utility American Electric Power (AEP), a cost share partner. This project addresses the need for monitoring substation equipment health and providing operators with tools to identify and take pre-emptive action to avoid catastrophic equipment failure. By monitoring synchrophasor data in real time, data anomalies that indicate potential asset failure can be detected and alerts can be sent to operators in time to take corrective actions. EPG developed two data driven algorithms to identify abnormal equipment signature patterns as well as a method using substation linear state estimation (SLSE). The software has been deployed on hardened PC’s and tested and validated for cost share partner AEP’s substations - 138kV and 765 kV. The SSHM software has been accepted by AEP and final demonstration of DOE - OE0000850 for AEP and DOE was successfully completed on March 17th, 2020. EPG developed the Substation Secondary Asset Health Monitoring (SSHM) Platform to analyze equipment failure signatures in PMU data and alert substation personnel when pre-emptive inspection, repairs and other actions are warranted to prevent potential catastrophic failures. This is the first system that automatically monitors the health of substation secondary assets and alerts users to emerging failures using synchrophasor measurements. SSHM uses high resolution PMU data from PTs, CTs, and CCVTs to analyze equipment signatures and identify anomalies that are precursor indicators of potential equipment failure. When an anomaly is detected, there is a likelihood of potential failure of monitored equipment, the system alerts the user with visual alarms including alarm trend charts and indicator lights on a oneline diagram of the substation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Molecular dynamics simulation of effects of solutes on dislocation propagation in Ni-based superalloys

Ni-based superalloys are used in the hot sections of jet turbine engines because of their high strength, stability and resistance to oxidation. The properties of these alloys can be further optimized by adding alloying elements. Therefore, a fundamental knowledge on the effect of different elements on properties of Ni-based superalloys is required. Molecular dynamics simulation could shed light here but its application is hindered by the absence of reliable and computationally cheap semi-empirical potential of the interatomic interaction for 4 and more element alloys. We will present a new Ni-Al-Cr-Nb Finnis-Sinclair (FS) potential specially designed to simulate the dislocation propagation from to  phase. In order to construct this potential, we designed a special algorithm to incorporate the data on element partitioning in the potential development procedure. For example, it is known from experiment, that Cr is mostly present in the gamma phase. Figure 1 shows a snapshot obtained after equilibration of the model of the Ni68Al17Cr15 alloy at T=1000 K using the hybrid Monte-Carlo (MC)/molecular dynamics (MD) simulation with the developed semi-empirical potential. One can clearly see that the Cr partitioning is in agreement with the experimental data. We will discuss the developed algorithm to incorporate the solute partition data in details. Using the developed semi-empirical potential, we first investigated the effect of anti-site defects in the  phase on the single dislocation propagation. It was found that the dislocation velocity increases with the increasing of the anti-site defect concentration. This effect was attributed to smaller number of Al-Al pairs forming during the dislocation migration in the presence of the anti-site defects. Next, we investigated the effect of Nb on the dislocation pair propagation in the Kolbe mechanism. It was found that the addition of Nb leads to considerable decrease in the dislocation propagation rate. This is in agreement with the experimental data on the effect of Nb on the creep resistance of the Ni-based superalloys. We will discuss the origin of this effect.

Mikhail I. Mendelev↗
NASA NTRS1988

Assessing forest damage in high-elevation coniferous forests in Vermont and New Hampshire using Thematic Mapper data

This study evaluates the potential of measuring/mapping forest damage in spruce-fir forests in the Green Mountains of Vermont and White Mountains of New Hampshire using Landsat Thematic Mapper (TM) data. The TM 1.65/0.83-micron (TM5/4) and 2.22/0.83-micron (TM7/4) band ratios were found to correlate well with ground-based measurements of forest damage (a measure of percentage foliar loss) at 11 spruce-fir stands located on Camels Hump, a mountain in northern Vermont. Images using 0.56 and 1.65-micron bands with 1.65/0.83-micron band ratios indicated locations of heavy conifer forest damage. Both 1.65/0.83 and 2.22/0.83-micron band ratios were used to quantify levels of conifer forest damage among individual mountains throughout many of the Green and White Mountains. Damage was found to be consistently higher for the Green than the White Mountains.

Vogelmann, James E.↗
NASA NTRS1996

Venusian k(sub 2) Tidal Love Number from Magellan and PVO Tracking Data

The k(sub 2) potential ove number which scales the tidal deformation of Venus by the Sun has been estimated from Doppler tracking of Magellan and Pioneer Venus Orbiter (PVO) spacecraft data. The nominal range for k(sub 2) from theoretical models is 0.23(less than or equal to)k(sub 2)(less than or equal to)0.29 for a liquid iron core and about 0.17 if the iron core has solidified. Our best estimate of this parameter is k(sub 2) = 0.295 +/- 0.662 (2X formal {delta}) and supports the hypothesis that Venus core is solid.

orbiter pvo↗
DOE OSTI2016

Full empirical potential curves for the X{sup 1}Σ{sup +} and A{sup 1}Π states of CH{sup +} from a direct-potential-fit analysis

All available “conventional” absorption/emission spectroscopic data have been combined with photodissociation data and translational spectroscopy data in a global analysis that yields analytic potential energy and Born-Oppenheimer breakdown functions for the X{sup 1}Σ{sup +} and A{sup 1}Π states of CH{sup +} and its isotopologues that reproduce all of the data (on average) within their assigned uncertainties. For the ground X{sup 1}Σ{sup +} state, this fully quantum mechanical “Direct-Potential-Fit” analysis yielded an improved empirical well depth of D{sub e} = 34 362.8(3) cm{sup −1} and equilibrium bond length of r{sub e} = 1.128 462 5 (58) Å. For the A{sup 1}Π state, the resulting well depth and equilibrium bond length are D{sub e} = 10 303.7(3) cm{sup −1} and r{sub e} = 1.235 896 (14) Å, while the electronic isotope shift from the hydride to the deuteride is ΔT{sub e} = − 5.99(±0.08) cm{sup −1}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
DOE OSTI2016

A global reaction route mapping-based kinetic Monte Carlo algorithm

We propose a new on-the-fly kinetic Monte Carlo (KMC) method that is based on exhaustive potential energy surface searching carried out with the global reaction route mapping (GRRM) algorithm. Starting from any given equilibrium state, this GRRM-KMC algorithm performs a one-step GRRM search to identify all surrounding transition states. Intrinsic reaction coordinate pathways are then calculated to identify potential subsequent equilibrium states. Harmonic transition state theory is used to calculate rate constants for all potential pathways, before a standard KMC accept/reject selection is performed. The selected pathway is then used to propagate the system forward in time, which is calculated on the basis of 1st order kinetics. The GRRM-KMC algorithm is validated here in two challenging contexts: intramolecular proton transfer in malonaldehyde and surface carbon diffusion on an iron nanoparticle. We demonstrate that in both cases the GRRM-KMC method is capable of reproducing the 1st order kinetics observed during independent quantum chemical molecular dynamics simulations using the density-functional tight-binding potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2023

Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acquired data over the area and select modeling efforts. This encompasses a 3D geological model (inclusive of full Leapfrog files, Leapfrog viewer files, and XYZ data for faults and stratigraphy) with embedded geophysical modeling, controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data packages, aqueous spring geochemistry data, seismic reflection interpretations, and a gravity data package. The stratigraphic reservoir in Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, added data which further highlighted potential resource targets in the basin. Geophysical surveys, complimented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a power-capable reservoir is economically accessible in the Paleozoic carbonates of the deep/central basin. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the Steptoe resource. The geophysical tools, interpretations, lessons learned, and publicly available data generated by this study establish an exploration methodology to inform decisions for successful development of stratigraphic reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Statistical Analysis of Demographic and Temporal Differences in LANL's 2014 Voluntary Protection Program Survey

Voluntary Protection Program (VPP) surveys were conducted in 2013 and 2014 to assess the degree to which workers at Los Alamos National Laboratory feel that their safety is valued by their management and peers. The goal of this analysis is to determine whether the difference between the VPP survey scores in 2013 and 2014 is significant, and to present the data in a way such that it can help identify either positive changes or potential opportunities for improvement. Data for several questions intended to identify the demographic groups of the respondent are included in both the 2013 and 2014 VPP survey results. These can be used to identify any significant differences among groups of employees as well as to identify any temporal trends in these cohorts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

​Understanding a Stratigraphic Hydrothermal Resource – Geophysical Imaging at Steptoe Valley, Nevada

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The stratigraphic reservoir in northern Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, included data which further highlighted potential resource targets in the basin. Geophysical surveys, complemented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a >100MWe power-capable reservoir is likely economically accessible using conventional well placement and stimulation techniques in the Paleozoic carbonates of the deep/central basin of northern Steptoe Valley. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the northern Steptoe resource. The geophysical tools, interpretations, lessons learned, and public data generated by this study establish an exploration methodology to inform decisions for characterization and development of northern Steptoe Valley and other stratigraphic geothermal reservoirs in the western U.S.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset

Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. Additionally, we tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data.

60 APPLIED LIFE SCIENCES↗