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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 NTRS1974

Research and investigation of geology, mineral, and water resources of Maryland

The authors have identified the following significant results. Field work in Baltimore County revealed that the signature returns of serpentinitic and nonserpentinitic rocks correlates with the vegetation cover and land use pattern. In Maryland Piedmont, bedrock lithology and structure are enhanced only to the extent that land use is geologically dictated. Two prominent sets of linear features are detected on ERTS-1 imagery at N 45 deg E and N 20 deg E. Beaches of Chesapeake Bay are classified as broad and narrow beaches based on the width of the backshore zone. It is shown by comparing historical shorelines of Ocean City, from the inlet to the Maryland-Delaware line that reversal zones of erosion and accretion occur at different locations for different periods. High reflectance levels (high marsh-high topographic areas) for the lower Eastern Shore are found to be distributed as two distinct trending linear ridge systems. Observations of MSS band 5 dated 9 April 1974 exhibited an unique sedimentation pattern for Chesapeake Bay. Following a 1.5 inch rainfall, heavy concentration of suspended sediments is observed on the imagery, particularly in the area of the turbidity maximum.

Weaver, K. N.↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

The utility of ERTS-1 data for applications in agriculture and forestry

A comprehensive study has been undertaken to determine the extent to which ERTS-1 data could be used to detect, identify (classify), locate and measure features of applications interest in the disciplines of Agriculture and Forestry. The study areas included: six counties in five states in which were located examples of the most important crops and practices of American agriculture; and a portion of the Sam Houston National Forest, a typical Gulf coastal plain pine forest. The investigation utilized conventional image interpretation and computer-aided (spectral pattern recognition) analysis using both image products and computer compatible tapes. The emphasis was generally upon the computer-aided techniques. It was concluded that ERTS-1 data can be used to detect, identify, locate and measure a wide array of features of interest in agriculture and forestry.

Erb, R. B.↗
DOE OSTI2017

Metabolic and spatio-taxonomic response of uncultivated seafloor bacteria following the Deepwater Horizon oil spill

The release of 700 million liters of oil into the Gulf of Mexico over a few months in 2010 produced dramatic changes in the microbial ecology of the water and sediment. Here in this work, we reconstructed the genomes of 57 widespread uncultivated bacteria from post-spill deep-sea sediments, and recovered their gene expression pattern across the seafloor. These genomes comprised a common collection of bacteria that were enriched in heavily affected sediments around the wellhead. Although rare in distal sediments, some members were still detectable at sites up to 60 km away. Many of these genomes exhibited phylogenetic clustering indicative of common trait selection by the environment, and within half we identified 264 genes associated with hydrocarbon degradation. Alkane degradation ability was near ubiquitous among candidate hydrocarbon degraders, whereas just three harbored elaborate gene inventories for the degradation of alkanes and aromatic and polycyclic aromatic hydrocarbons (PAHs). Differential gene expression profiles revealed a spill-promoted microbial sulfur cycle alongside gene upregulation associated with PAH degradation. Gene expression associated with alkane degradation was widespread, although active alkane degrader identities changed along the pollution gradient. Finally, analyses suggest that a broad metabolic capacity to respond to oil inputs exists across a large array of usually rare indigenous deep-sea bacteria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Pattern recognition with magnonic holographic memory device

In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the periphery of the matrix to excite and detect spin waves. The principle of operation is based on the effect of spin wave interference, which is similar to the operation of optical holographic devices. Input information is encoded in the phases of the spin waves generated on the edges of the magnonic matrix, while the output corresponds to the amplitude of the inductive voltage produced by the interfering spin waves on the other side of the matrix. The level of the output voltage depends on the combination of the input phases as well as on the internal structure of the magnonic matrix. Experimental data collected for several magnonic matrixes show the unique output signatures in which maxima and minima correspond to specific input phase patterns. Potentially, magnonic holographic devices may provide a higher storage density compare to optical counterparts due to a shorter wavelength and compatibility with conventional electronic devices. The challenges and shortcoming of the magnonic holographic devices are also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

An Engineered Minimal-Set Stimulus for Periodic Information Leakage Fault Detection on a RISC-V Microprocessor

Recent evaluations of counter-based periodic testing strategies for fault detection in Microprocessor (μP) have shown that only a small set of counters is needed to provide complete coverage of severe faults. Severe faults are defined as faults that leak sensitive information, e.g., an encryption key on the output of a serial port. Alternatively, fault detection can be accomplished by executing instructions that periodically test the control and functional units of the μP. In this paper, we propose a fault detection method that utilizes an ’engineered’ executable program combined with a small set of strategically placed counters in pursuit of a hardware Periodic Built-In-Self-Test (PBIST). We analyze two distinct methods for generating such a binary; the first uses an Automatic Test Generation Pattern (ATPG)-based methodology, and the second uses a process whereby existing counter-based node-monitoring infrastructure is utilized. We show that complete fault coverage of all leakage faults is possible using relatively small binaries with low latency to fault detection and by utilizing only a few strategically placed counters in the μP.

42 ENGINEERING↗
DOE OSTI2018

Degree of pectin methyl esterification in endosperm cell walls is involved in embryo bending in Arabidopsis thaliana

Highlights: • Pectin methyl esterification of cell wall endosperm is involved in seed development. • Seed and embryo morphology are altered in bdx-1 mutant. • Low-esterified pectins are related to embryo bending in WT and bdx-1 seeds. The endosperm is a transitory structure involved in proper embryo elongation. The cell walls of mature seed endosperm are generally composed of a uniform distribution of cellulose, unesterified homogalacturonans, and arabinans. Recent studies suggest that changes in cell wall properties during endosperm development could be related to embryo growth. The degree of methyl esterification of homogalacturonans may be involved in this endosperm tissue remodelling. The relevance of the degree of homogalacturonan methyl esterification during seed development was determined by immunohistochemical analyses using a panel of probes with specificity for homogalaturonans with different degrees of methyl esterification. Low-esterified and un-esterified homogalacturonans were abundant in endosperm cells during embryo bending and were also detected in mature embryos. BIDXII (BDX) could be involved in seed development, because bdx-1 mutants had misshapen embryos. The methyl esterification pattern described for WT seeds was different during bdx-1 seed development; un-esterified homogalacturonans were scarcely present in the cell walls of endosperm in bending embryos and mature seeds. Our results suggested that the degree of methyl esterification of homogalacturonans in the endosperm cell wall may be involved in proper embryo development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Skyrmion based microwave detectors and harvesting

Magnetic skyrmions are topologically protected states that are very promising for the design of the next generation of ultra-low-power electronic devices. In this letter, we propose a magnetic tunnel junction based spin-transfer torque diode with a magnetic skyrmion as ground state and a perpendicular polarizer patterned as nano-contact for a local injection of the current. The key result is the possibility to achieve sensitivities (i.e., detection voltage over input microwave power) larger than 2000 V/W for optimized contact diameters. We also pointed out that large enough voltage controlled magnetocrystalline anisotropy could significantly improve the sensitivity. Our results can be very useful for the identification of a class of spin-torque diodes with a non-uniform ground state and to understand the fundamental physics of the skyrmion dynamical properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Non-destructive evaluation of impact damage on carbon fiber laminates: Comparison between ESPI and Shearography

The aim of this paper is to investigate the ability of two different interferometric NDT techniques to detect and evaluate barely visible impact damage on composite laminates. The interferometric techniques allow to investigate large and complex structures. Electronic Speckle Pattern Interferometry (ESPI) works through real-time surface illumination by visible laser (i.e. 532 nm) and the range and the accuracy are related to the wavelength. While the ESPI works with the “classic” holographic configuration, that is reference beam and object beam, the Shearography uses the object image itself as reference: two object images are overlapped creating a shear image. This makes the method much less sensitive to external vibrations and noise but with one difference, it measures the first derivative of the displacement. In this work, different specimens at different impact energies have been investigated by means of both methods. The delaminated areas have been estimated and compared.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Local residual stress monitoring of aluminum nitride MEMS using UV micro-Raman spectroscopy

Localized stress variation in aluminum nitride (AlN) sputtered on patterned metallization has been monitored through the use of UV micro-Raman spectroscopy. This technique utilizing 325 nm laser excitation allows detection of the AlN E2(high) phonon mode in the presence of metal electrodes beneath the AlN layer with a high spatial resolution of less than 400 nm. The AlN film stress shifted 400 MPa from regions where AlN was deposited over a bottom metal electrode versus silicon dioxide. Thus, across wafer stress variations were also investigated showing that wafer level stress metrology, for example using wafer curvature measurements, introduces large uncertainties for predicting the impact of AlN residual stress on the device performance.

42 ENGINEERING↗
DOE OSTI2026

Simultaneous global and local clustering in multiplex networks with covariate information

Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.

community detection↗
DOE OSTI2020

Improving qubit readout with hidden Markov models

We demonstrate the application of pattern recognition algorithms via hidden Markov models (HMM) for qubit readout. This scheme provides a state-path trajectory approach capable of detecting qubit-state transitions and makes for a robust classification scheme with higher starting-state assignment fidelity than when compared to a multivariate Gaussian or a support vector machine scheme. Therefore, the method also eliminates the qubit-dependent readout time optimization requirement in current schemes. Using a HMM state discriminator we estimate fidelities reaching the ideal limit. Unsupervised learning gives access to transition matrix, priors, and IQ distributions, providing a toolbox for studying qubit-state dynamics during strong projective readout.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Advancing the central role of non-model biorepositories in predictive modeling of emerging pathogens

The COVID-19 pandemic demonstrated the insufficiency of a reactive approach to emerging zoonotic pathogens. With spillover increasing in frequency as environments change and the human footprint continues to grow, pandemic prevention will require predictive models that can identify (i) potential zoonoses with a high likelihood of emergence and (ii) environmental or other features that may trigger a shift in host, vector, or pathogen baselines associated with emergence and/or spillover. Artificial intelligence (AI), and particularly its machine learning and deep learning branches, holds enormous potential for detecting shifts in large-scale biodiversity and disease datasets (genomic, ecological, geospatial, etc.). Such algorithms can be trained to identify subtle patterns in large volumes of data to yield insights into complex phenomena for which we have limited knowledge of the true cause(s) or predictor(s), as is the case for emerging infectious diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

EXTERNAL COMPTON SCATTERING IN BLAZAR JETS AND THE LOCATION OF THE GAMMA-RAY EMITTING REGION

I study the location of the γ -ray emission in blazar jets by creating a Compton-scattering approximation that is valid for all anisotropic radiation fields in the Thomson through Klein–Nishina regimes, is highly accurate, and can speed up numerical calculations by up to a factor of ∼10. I apply this approximation to synchrotron self-Compton, external Compton scattering of photons from the accretion disk, broad line region (BLR), and dust torus. I use a stratified BLR model and include detailed Compton-scattering calculations of a spherical and flattened BLR. I create two dust torus models, one where the torus is an annulus and one where it is an extended disk. I present detailed calculations of the photoabsorption optical depth using my detailed BLR and dust torus models, including the full angle dependence. I apply these calculations to the emission from a relativistically moving blob traveling through these radiation fields. The ratio of γ -ray to optical flux produces a predictable pattern that could help locate the γ -ray emission region. I show that the bright flare from 3C 454.3 in 2010 November detected by the Fermi Large Area Telescope is unlikely to originate from a single blob inside the BLR. This is because it moves outside the BLR in a time shorter than the flare duration, although emission by multiple blobs inside the BLR is possible. Also, γ -rays are unlikely to originate from outside of the BLR, due to the scattering of photons from an extended dust torus, since the cooling timescale would be too long to explain the observed short variability.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Mariner 10 interplanetary magnetic field results

Interplanetary magnetic field data obtained by Mariner 10 between November 1973 and March 1974 are examined. Though large variations in the field due to the effects of high speed streams and stream-stream interactions were observed, on average the large scale interplanetary magnetic field radial component was found to have a distance dependence of r to the minus 2.0 plus or minus 0.3, while the azimuthal field component was found to vary as r to the minus 1.3 plus or minus 0.4, and a dependence of r to the minus 1.4 plus or minus 0.6 was found for the component normal to the solar equatorial plane. A close correspondence was found between the magnetic field stream signatures and persistent but evolving coronal hole regions on the sun, and a clear pattern of large southward fields in stream interaction regions and high levels of field fluctuations within the high-speed streams was detected.

Behannon, K. W.↗
NASA NTRS1986

Plasma fluctuations and large-scale mixing near Comet Giacobini-Zinner

The relationship among plasma densities, flow speeds and directions, and temperatures, at distances within about 100,000 km of Comet Giacobini-Zinner's nucleus, are presently examined in view of lowpass-filtered data from the International Cometary Explorer's electron spectrometer. While the largest amplitude density spikes often have more significant flow changes associated with them, a consistent pattern is not found. Power spectral analyses in and near the sheath/transition regions show that density fluctuation levels are enhanced at all detectable frequencies, consistent with strong density fluctuations on all time scales. Such mechanisms as the amplification of convected ion pickup waves and cometary rays for producing the large plasma variations are discussed. It is suggested that the Rayleigh-Taylor effect-driven mixing mechanism at a mass-loading boundary abut 100,000 km from the nucleus may be operative.

Baker, D. N.↗
NASA NTRS1986

Optical image subtraction techniques, 1975-1985

Real- and nonreal-time optical image subtraction (OIS) techniques are reviewed. Real-time OIS techniques include source encoding, polarization modulation, pseudocolor image difference detection, the holographic shear lens technique, and nonlinear optics. Included in the nonreal-time category are speckle diffuser encoding, speckle-pattern encoding, halftone screen encoding, and polarization-shifted encoding. It is concluded that the most useful techniques are the real-time operations. It is noted that some nonreal-time optical techniques can be applied directly while others may be converted into real-time ones through the use of advance real-time spatial light modulators or electrooptic devices.

Liu, Hua-Kuang↗