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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Reflectance spectra of Fe(2+)-Mg(2+) disordered pyroxenes: Implications to remote-sensed spectra of planetary surfaces

The reflectance spectra of Fe(2+)-Mg(2+) disordered orthopyroxenes are relevant to surfaces of terrestrial planets onto which basaltic magma has been extruded. If cooling rates of basalt lava flows were fast, equilibrium iron intersite partitioning may not have been achieved so that abnormal enrichments of Fe(2+) ions in M1 sites would occur. The two intense pyroxene Fe(2+) site CF bands in the 1 micron and 2 micron regions would continue to dominate the the reflectance spectra so that the pyroxene composition and structure type would be readily identified in telescopic spectral profiles. However, abnormal intensification of the Fe(2+)/M1 site CF band at 1.20 microns could lead to the false identification of olivine in remote sensed spectra because in pyroxene-olivine mixtures the inflection around 1.20 microns is the only spectral feature for detecting the presence of olivine. The identification of iron-bearing plagioclase feldspars, too, would be obscured by the pyroxene Fe(2+)/M1 site CF band at 1.20 microns. Such interference would be a major problem if in situ reflectance spectra could be measured on the surface of Venus where ambient temperatures are as high as 475 C. Disordering of Fe(2+) and Mg(2+) ions comparable to that in the orthopyroxenes used in this spectral chemical study might be expected in low Ca pyroxenes occurring on the Venusian surface. Researchers conclude that Fe(2+)/M1 site spectral features need to be carefully assessed in remote-sensed spectra before deductions are made about the presence of olivine on planetary surfaces.

Burns, Roger G.↗
DOE OSTI2023

Classification Analytics of Pu-239 and U-235 Source Signatures Using Gamma Spectral Regions

Machine learning detection methods using gamma signatures from spectral measurements of low-intensity Pu-239 and U-235 sources are studied. NaI detectors located at different distances fromthe source have been used to collect the training and independent testing data sets. The source is introduced via a shielded conduit into the facility where it is surrounded by 21 NaI detectors deployed over 6 x 6 meters area in the formation of two concentric circles and a spiral. The counts in gamma spectral regions associated with these two sources are estimated at 1 second intervals for each NaI detector, and are used as classifier features for detecting the source presence. Eight different classifiers with five basic properties — namely, smooth, non-smooth, statistical, structural, and hyper-parameter tuning — are trained and tested using the background and source measurements collected over multiple experimental runs. While the overall classifier performance improved as detectors closer to the source are used, some identically produced detectors under-performed but differently between two sources. Some classifiers achieved lower training error but their testing error based on independent measurements is higher for both sources. Overall, these results indicate significant over-fitting by these methods, and illustrate the complexity of training and selecting the machine learning methods to solve these detection problems.

Rao, Nageswara↗
NASA NTRS1994

Future enhancements to ground-based microburst detection

This set of viewgraphs presents the results of the Cockpit Weather Information (CWI) program at M.I.T. Lincoln Laboratory. The CWI program has been funded through NaSA Langley Research Center by the joint NASA/FAA Integrated Airborne Wind Shear Program for the past four years. During this time, over 120 microburst penetrations by research aircraft have been conducted under Terminal Doppler Weather Radar (TDWR) testbed radar surveillance at Orlando, FL. The results of these in-situ measurements have been compared with ground-based detection methods. Several valuable insights were gained from this research activity. First, it was found that the current TDWR microburst shapes do not permit accurate characterization of microburst hazard in terms of the F factor hazard index, because they are based on loss value rather than shear. Second, it was found that the horizontal component of the F factor can be accurately estimated from shear, provided compensation is made for the dependence of outflow strength on altitude. Third, it was found that a simple continuity assumption for estimating the vertical component of the F factor yielded poor results. However, further research has shown that downdraft strength is correlated with features aloft detected by the TDWR radar scan strategy. The outcome of the CWI program is to move from the loss-based wind shear detection algorithm used in the TDWR to a shear-based detection scheme as proposed in the Integrated Terminal Weather System (ITWS).

Campbell, Steven D.↗
DOE OSTI2021

Hybrid feature-driven learning system for abnormality detection and localization

A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time representing current operation of the system. A data-driven features extraction computer platform may receive the series of current monitoring node values and generate current data-driven feature vectors based on the series of current monitoring node values. A residual features extraction computer platform may receive the series of current monitoring node values, execute a system model and utilize a stochastic filter to determine current residual values, and generate current residual-driven feature vectors based on the current residual values. An abnormal detection platform may then receive the current data-driven and residual-driven feature vectors and compare the current data-driven and residual-driven feature vectors with at least one decision boundary associated with an abnormal detection model. An abnormal alert signal may then be transmitted when appropriate based on a result of said comparison.

Abbaszadeh, Masoud↗
NASA NTRS1994

The 3.2-3.6 micron spectra of monoceros R2/IRS-3 and Elias 16

We have obtained 3.2-3.6 micron spectra, with a resolution lambda/delta-lambda approximately 750, of the protostar Mon R2/IRS-3 and of Elias 16, a background K giant behind the Taurus molecular cloud. A feature at 3,482 microns (2872/cm), with a full width at half-maximum of 0.09 microns (76/cm), is clearly seen in Mon R2/IRS-3. This feature is not detected in Elias 16. The 3.482 micron feature in Mon R2/IRS-3 is similar to a feature at 3.466-3.478 microns (2875-2885/cm) detected by Allamandola et al. in four protostars and attributed by these authors to a CH stretch in hydrocarbons dominated by sp3-bonded carbon. Neither Mon R2/IRS-3 nor Elias 16 shows absorption at 3.540 microns (2825/cm), which has been detected in two of the four protostars observed by Allamandola et al. and attributed by them to CH3OH ice. Our limit on CH3OH ice toward Elias 16 is compared to models of gas-grain chemistry in dark clouds. Our results confirm those of Allamandola et al. that at this resolution the 3.4 micron absorption due to dust in molecular clouds has very different spectral structure than that due to dust in the diffuse interstellar medium.

Sellgren, K.↗
NASA NTRS2021

Active Wide-Field Sky Simulator Design for the JPL Astrometry Testbed

Exoplanet detection through astrometry will provide mass measurements that are key to our understanding of exoplanet demographics. Astrometric surveys are also critical pre-cursor observations for identifying targets for future direct imaging missions, such as the HabEx and LUVOIR mission concepts. However, Earth-mass planets orbiting Sun-like stars within 10 pc only cause a 0.3 uas astrometric signal, which is about two orders of magnitude smaller than the current state-of-the-art. In order to advance the technologies needed for future astrometry instruments, a testbed capable of simulating and measuring equivalent Earth-like exoplanet astrometric signals was created. The testbed has a stable light source that simulates a field of stars and can introduce a sub-uas astrometric signal onto a central artificial target star. This is done by illuminating an array of pinholes lithographically printed onto a glass substrate and translating, with picometer resolution, a target-star pinhole located on a centrally cut flexure feature. To detect this motion, a diffractive pupil is used to calibrate the limiting optical distortions of an Offner relay. Presented in this paper are the requirements, design, and implementation of the wide-field astrometry testbed’s light source.

Bendek, Eduardo↗
DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Exceptional stratospheric contribution to human fingerprints on atmospheric temperature

In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO 2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S 25 − 50 ). To date, however, S 25 − 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S 25 − 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S 25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S 25 − 50 signal and noise patterns are accompanied by large cooling of S 25 − 50 (1 to 2 ° C over 1986 to 2022) and low S 25 − 50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere.

climate modeling↗
NASA NTRS2006

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

classification↗
DOE OSTI2025

Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities

Identifying low-dimensional structure in high-dimensional probability measures is an essential pre-processing step for efficient sampling. To identify this structure, we approximate the target measure as a perturbation of an arbitrary reference measure along a few directions in $\mathbb{R}^{d}$. These directions are determined by minimizing an upper bound on the Kullback–Leibler (KL) divergence between the target and its approximation. Our contribution improves upon previous works by leveraging dimensional logarithmic Sobolev inequalities to refine the bound on the KL divergence. These inequalities lead to a uniformly tighter bound on the KL divergence, thereby enhancing the identification of the most significant perturbation directions. In particular, when the target and reference are both Gaussian, minimizing the resulting bound is equivalent to minimizing the KL divergence. We further demonstrate the applicability of this analysis to the squared Hellinger distance, where analogous reasoning shows that the dimensional Poincaré inequality offers improved bounds.

Bayesian inference↗
DOE OSTI2022

Characterization of nonlinear ultrasonic waves behavior while interacting with poor interlayer bonds in large-scale additive manufactured materials

Over the past decades, researchers have developed several nonlinear ultrasonic techniques for quality control of materials commonly used in different applications. Owing to the superior sensitivity of nonlinear ultrasound waves to small defects such as micro-cracks, their applicability in different nondestructive testing (NDT) problems has been investigated in numerous studies. These studies utilize frequency domain analysis to detect the generation of higher harmonics because of the formation of defects in the inspected medium. Frequency domain analysis based on the Fourier transform is a significant approach used in linear systems; however, it may not perform adequately on nonlinear systems. Hence, studies on nonlinear dynamics and physics consider analyzing systems' behavior in the phase-space domain. In contrast to the frequency domain analysis, which can result in information loss, analysis in the phase-space domain retains all the information regarding a system's states. Here, we investigate the nonlinearities induced by poor interlayer bonding in polymer-based additive manufactured parts in the phase-space domain. It is convenient to characterize the nonlinearity in the phase-space domain because it provides a geometrical representation of a system's states. Two types of low quality interlayer bond are considered. The first type is simulated artificially while the second type is manufactured by reducing the bond quality during the printing process. The analysis verified that the received ultrasonic signals exhibit classical nonlinear behavior in the phase-space domain while interacting with simulated poor interlayer bonds. In addition, the results showed that the behavior of ultrasonic waves is amplitude-dependent and evolves into models that have not been previously reported. Furthermore, Largest Lyapunov Exponent (LLE) is used to quantify the behavior of nonlinear ultrasonic waves while interacting with poor interlayer bonds. Using LLE, it was observed that the divergence rate of the phase-space trajectories depends on the amplitude of the excitation. This observation quantitatively proves that nonlinear behavior of ultrasound while interacting with poor interlayer bonds can be amplitude-dependent. The results of both simulated and inherent poor interlayer bond cases showed that LLE can be used as a reliable quantitative damage-sensitive feature to detect and potentially characterize weak bonds, which are difficult to detect using conventional approaches. Additionally, the reported results in the phase-space domain provide a basis for proposing a new mathematical model for ultrasonic waves interacting with poor interlayer bonds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Controllable oxygen vacancy defect engineering of BiVO 4 porous structures for room temperature NH 3 detection

Controlling structural features of sensing material while judiciously introducing vacancy defect states for revamping the electronic properties of the sample to obtain its superior gas sensing performance, is quite rare. Herein, we report for the first time, the room temperature (RT) ammonia (NH 3 ) detection of peanut-like porous bismuth vanadate (BiVO 4 ) with a stable monoclinic phase. The developed BiVO 4 possess abundant oxygen vacancies and porosity by virtue of calcinations (400–800 ℃). BiVO 4 calcined at 400 ℃ exhibits high selectivity towards NH 3 with a maximum response of 1421 @ 270 ppm at RT, which is 2.5 fold enhanced compared to without calcined BiVO 4 (response of 547 @ 270 ppm NH 3 ). The oxygen defective BiVO 4 appeared highly durable and stable even under high humid conditions (∼60 %). Besides, the porosity of BiVO 4 not only enhances the specific surface area (19.8 m 2 /g) but also results in fast diffusion of NH 3 molecules, leading to a reduction in the decay time (34 s for 90 ppm NH 3 ). The density functional theory (DFT) uncovers that the oxygen vacancy formation in BiVO 4 augments the NH 3 sensing capabilities by enhancing the adsorption energy of NH 3 . This work provides insight into the sensing mechanism of increased response caused by defect engineering and porosity, which will be favourable for fabricating high-performance NH 3 sensors at RT.

DFT analysis↗
DOE OSTI2024

Tethered balloon system and High-Resolution Mass Spectrometry Reveal Increased Organonitrates Aloft Compared to the Ground Level

Atmospheric particles play critical roles in climate. However, significant knowledge gaps remain regarding the vertically resolved organic molecular-level composition of atmospheric particles due to aloft sampling challenges. To address this, we use a tethered balloon system at the Southern Great Plains Observatory and high-resolution mass spectrometry to, respectively, collect and characterize organic molecular formulas (MF) in the ground level and aloft (up to 750 m) samples. We show that organic MF uniquely detected aloft were dominated by organonitrates (139 MF; 54% of all uniquely detected aloft MF). Organonitrates that were uniquely detected aloft featured elevated O/C ratios (0.73 ± 0.23) compared to aloft organonitrates that were commonly observed at the ground level (0.63 ± 0.22). Unique aloft organic molecular composition was positively associated with increased cloud coverage, increased aloft relative humidity (~40% increase compared to ground level), and decreased vertical wind variance. Furthermore, 29% of extremely low volatility organic compounds in the aloft sample were truly unique to the aloft sample compared to the ground level, emphasizing potential oligomer formation at higher altitudes. Overall, this study highlights the importance of considering vertically resolved organic molecular composition (particularly for organonitrates) and hypothesizes that aqueous phase transformations and vertical wind variance may be key variables affecting the molecular composition of aloft organic aerosol.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A high internal heat flux and large core in a warm Neptune exoplanet

Interactions between exoplanetary atmospheres and internal properties have long been proposed to be drivers of the inflation mechanisms of gaseous planets and apparent atmospheric chemical disequilibrium conditions. However, transmission spectra of exoplanets have been limited in their ability to observationally confirm these theories owing to the limited wavelength coverage of the Hubble Space Telescope (HST) and inferences of single molecules, mostly H 2 O. Here, in this work, we present the panchromatic transmission spectrum of the approximately 750 K, low-density, Neptune-sized exoplanet WASP-107b using a combination of HST Wide Field Camera 3 (WFC3) and JWST Near-Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI). From this spectrum, we detect spectroscopic features resulting from H 2 O (21σ), CH 4 (5σ), CO (7σ), CO 2 (29σ), SO 2 (9σ) and NH 3 (6σ). The presence of these molecules enables constraints on the atmospheric metal enrichment (M/H is 10–18× solar), vertical mixing strength (log 10 K zz = 8.4–9.0 cm 2 s -1 ) and internal temperature (>345 K). The high internal temperature is suggestive of tidally driven inflation4 acting on a Neptune-like internal structure, which can naturally explain the large radius and low density of the planet. These findings suggest that eccentricity-driven tidal heating is a critical process governing atmospheric chemistry and interior-structure inferences for most of the cool (<1,000 K) super-Earth-to-Saturn-mass exoplanet population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The mass and galaxy distribution around SZ-selected clusters

ABSTRACT We present measurements of the radial profiles of the mass and galaxy number density around Sunyaev–Zel’dovich (SZ)-selected clusters using both weak lensing and galaxy counts. The clusters are selected from the Atacama Cosmology Telescope Data Release 5 and the galaxies from the Dark Energy Survey Year 3 data set. With signal-to-noise ratio of 62 (45) for galaxy (weak lensing) profiles over scales of about 0.2–20 h−1 Mpc, these are the highest precision measurements for SZ-selected clusters to date. Because SZ selection closely approximates mass selection, these measurements enable several tests of theoretical models of the mass and light distribution around clusters. Our main findings are: (1) The splashback feature is detected at a consistent location in both the mass and galaxy profiles and its location is consistent with predictions of cold dark matter N-body simulations. (2) The full mass profile is also consistent with the simulations. (3) The shapes of the galaxy and lensing profiles are remarkably similar for our sample over the entire range of scales, from well inside the cluster halo to the quasilinear regime. We measure the dependence of the profile shapes on the galaxy sample, redshift, and cluster mass. We extend the Diemer & Kravtsov model for the cluster profiles to the linear regime using perturbation theory and show that it provides a good match to the measured profiles. We also compare the measured profiles to predictions of the standard halo model and simulations that include hydrodynamics. Applications of these results to cluster mass estimation, cosmology, and astrophysics are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Optical spectral observations of three binary millisecond pulsars

We present the results of optical spectroscopy of stellar companions to three binary millisecond pulsars, PSRs J0621+2514, J2317+1439, and J2302+4442, obtained with the Gran Telescopio Canarias. The spectrum of the J0621+2514 companion shows a blue continuum and prominent Balmer absorption lines. The latter are also resolved in the spectrum of the J2317+1439 companion, showing that both are DA-type white dwarfs. No spectral features are detected for the J2302+4442 companion; however, its broad-band magnitudes and the spectral shape of the continuum emission imply that this is also a DA-type white dwarf. Based on the spectral analyses, we conclude that the companions of J0621+2514 and J2317+1439 are relatively hot, with effective temperatures T eff = 8600 ± 200 and 9600 ± 2000 K, respectively, while the J2302+4442 companion is significantly cooler, T eff <6000 K. We also estimated the distance to J0621+2514 of 1.1 ± 0.3 kpc and argue that its companion and the companion of J2317+1439 are He-core white dwarfs providing constraints on their cooling ages of $\lesssim$2 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses

Computer vision algorithms are increasingly leveraged to accelerate geospatial analysis for disaster response and recovery. As the diversity of remote sensing imagery grows with optical, SAR, and other modalities, a perquisite for analytics is cross-modal image registration. There is a high potential to harness computer vision for this pre-processing requirement toward enabling downstream analytics such as heterogeneous change detection, automated feature extraction, and data fusion. Advancement in these areas has the potential to simplify data wrangling tasks and further accelerate disaster response timelines. The SpaceNet 9 challenge (launching in mid-2024) focuses on addressing the cross-modal image registration problem and demonstrating the utility of such modules on earthquake impacted scenarios. This paper describes the motivation for the SpaceNet 9 and provides a first overview of the dataset, the baseline algorithm, and implications for seeking cross-modal image registration in Earth observation. Code is available at https://github.com/SpaceNetChallenge/SpaceNet9.

Hansch, Ronny↗