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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Detection of lowland flooding using active microwave systems

The development of radar systems with longer wavelenths (greater than 3 cm) has provided new possibilities regarding the utilization of radar. Thus, it has been found that the interpretation of data from radar images can be a valuable classification aid for applications related to water resources. In the case of an interpreter accustomed to photographic or visible/infrared images, an evaluation of radar images presents some problems, because the radar is sensing a set of surface characteristics which have little influence on visible/infrared systems. Detectable features in radar images caused by differences in dielectric properties are usually associated with the water content of either soils or vegetation. The present paper is concerned with studies which were initiated in 1976. The studies had the objective to define the magnitude of the effects on radar data caused by flood waters under vegetation. The obtained results indicate the feasibility to detect flood conditions beneath a forest canopy, and to obtain an improved definition of the land-water boundary.

Ormsby, J. P.↗
DOE OSTI2023

Unsupervised classification for region of interest in X-ray ptychography

X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Detection of nuclear gamma rays from Centaurus A

Results are reported for an observation of nuclear gamma rays in the energy range between 0.033 and 12.25 MeV from Centaurus A using a balloon-borne actively collimated NaI(Tl) crystal scintillation counter. The observing procedure is outlined, no systematic errors are found in the data, and power-law fits to the source's energy spectrum are attempted. A power law of approximately 0.86E to the -1.9 power photon/sq cm/sec per keV is shown to give an acceptable fit to the continuum, and the detection of two gamma-ray lines at 1.6 and 4.5 MeV, respectively, is discussed. It is found that the low-energy gamma-ray luminosity of Cen A is 9.4 by 10 to the 43rd power erg/sec for a distance of 5 Mpc and that Cen A is apparently variable in low-energy gamma radiation. It is suggested that the broad feature detected at 1.6 MeV may be due to three blended lines (possibly excited Ne-20, Mg-24, and Si-28), the 4.5-MeV line is most likely due to deexcitation of excited C-12, and the nuclear excitation results from either cosmic-ray bombardment of Cen A's interstellar medium or nucleosynthesis within the source.

Hall, R. D.↗
DOE OSTI2022

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗
NASA NTRS1976

Electron-impact excitation of UF6 at an electron energy of 20 eV in the energy-loss range of 0-10 eV

A technique combining electron impact excitation and optical absorption spectroscopy was applied to UF6. The crucial features of the experiment were that: (1) the electron optics was differentially pumped relative to the scattering chamber and (2) the target UF6 beam was condensed on a liquid nitrogen cold trap placed immediately above the scattering center. Energy loss spectra are presented at an incident electron energy of 20 eV and at scattering angles between 20 and 135 degrees. It is shown that no transitions are found below the first-detected feature at 3.0 eV and an optically forbidden excitation is found at 4.2 eV. A fairly strong optical absorption at 4.8 eV is observed to 'fill-in' at a scattering angle of 20 degrees but is practically absent at higher angles.

Chutjian, A.↗
NASA NTRS1981

IUE observations of two late-type stars Bx Mon (M + pec) and TV Gem (M1 Iab)

The IUE observations of two late type stars BX Mon and TV Gem that reveal the emission properties in the ultraviolet of subluminous companions are discussed. Analysis of the continuum emission observed from BX Mon suggests the companion, is a middle A III star. High excitation emission lines observed between 1200 A and 2000 A that generally do not typify emission observed in either late M type variables or A type stars are also detected. It is suggested that these strong high excitation lines arise in a large volume of gas heated by nonradiation processes that could be the result of tidal interaction and mass exchange in the binary system. In contrast to stars such as BX Mon, the luminous M1 supergiant TV Gem shows unexpected intense UV continuum throughout the sensitivity range of IUE. The UV spectrum of TV Gem is characterized by intense continuum with broad absorption features detected in the short wavelength range. The analysis shows that the companion could be a B9 or A1 III-IV star. Alternate suggestions are presented for explaining the UV continuum in terms of an accretion disk in association with TV Gem.

Michalitsianos, A. G.↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Combined Feature Based and Shape Based Visual Tracker for Robot Navigation

We have developed a combined feature based and shape based visual tracking system designed to enable a planetary rover to visually track and servo to specific points chosen by a user with centimeter precision. The feature based tracker uses invariant feature detection and matching across a stereo pair, as well as matching pairs before and after robot movement in order to compute an incremental 6-DOF motion at each tracker update. This tracking method is subject to drift over time, which can be compensated by the shape based method. The shape based tracking method consists of 3D model registration, which recovers 6-DOF motion given sufficient shape and proper initialization. By integrating complementary algorithms, the combined tracker leverages the efficiency and robustness of feature based methods with the precision and accuracy of model registration. In this paper, we present the algorithms and their integration into a combined visual tracking system.

Deans, J.↗
NASA NTRS2022

Velocimeter LIDAR Based Bulk Velocity Estimation for Terrain Relative Navigation Applications

A novel batch state estimation approach to estimate the translational and angular velocity states of a vehicle using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for use in Terrain and Hazard Relative Navigation (TRN/HRN) applications is presented in this paper. The velocimeter LIDAR is capable of measuring three dimensional position and line-of-sight (LOS) velocity associated with every pixel in the field-of-view (FOV). This new batch state estimation methodology is shown to provide accurate and statistically consistent velocity estimates with no a priori information. In contrast to traditional computer vision approaches for TRN, the proposed technique is not dependent on image features. This feature alleviates the need for accurate feature detection and correspondence to a predefined map making it suitable for unknown operating environments. Following a detailed development of the mathematical details associated with the batch state estimation methodology, the efficacy and utility of the proposed algorithms are evaluated through emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory.

Skulsky, Eli↗
NASA NTRS2004

Automated Extraction of Flow Features

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, recirculation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; iso-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for (co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

Dorney, Suzanne↗
NASA NTRS2005

Automated Extraction of Flow Features

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, re-circulation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; isc-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.

Dorney, Suzanne↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

The Observed Evolution of the Stellar Mass–Halo Mass Relation for Brightest Central Galaxies

We quantify evolution in the cluster-scale stellar mass–halo mass (SMHM) relation's parameters using 2323 clusters and brightest central galaxies (BCGs) over the redshift range 0.03 ≤ z ≤ 0.60. The precision on the inferred SMHM parameters is improved by including the magnitude gap (m gap ) between the BCG and fourth-brightest cluster member (M14) as a third parameter in the SMHM relation. At fixed halo mass, accounting for m gap , through a stretch parameter, reduces the SMHM relation's intrinsic scatter. To explore this redshift range, we use clusters, BCGs, and cluster members identified using the Sloan Digital Sky Survey C4 and redMaPPer cluster catalogs and the Dark Energy Survey redMaPPer catalog. Through this joint analysis, we detect no systematic differences in BCG stellar mass, m gap , and cluster mass (inferred from richness) between the data sets. We utilize the Pareto function to quantify each parameter's evolution. We confirm prior findings of negative evolution in the SMHM relation's slope (3.5σ), and detect negative evolution in the stretch parameter (4.0σ) and positive evolution in the offset parameter (5.8σ). This observed evolution, combined with the absence of BCG growth, when stellar mass is measured within 50 kpc, suggests that this evolution results from changes in the cluster's m gap . For this to occur, late-term growth must be in the intracluster light surrounding the BCG. We also compare the observed results to IllustrisTNG 300-1 cosmological hydrodynamic simulations and find modest qualitative agreement. However, the simulations lack the evolutionary features detected in the real data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
DOE OSTI2022

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results—especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes’ rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated ‘engine’ capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Science & Technology - Other Topics↗
NASA NTRS1994

Al III, Si IV, and C IV absorption toward zeta Ophiuchi: Evidence for photionized and collisionally ionized gas

We present Goddard High-Resolution Spectrograph observations at 3.5 km/s resolution and signal-to-noise ratios of 30 to 60 for the Al III, Si IV, and N V absorption lines in the far-ultraviolet spectrum of the O9.5 V star zeat Ophiuchi. The measurement reveal three types of highly ionized gas along the 140 pc line of sight. (1) Narrow components of Al III (b = 4.3 km/s, the mean value of (v(helio)) = -7.8 km/s; b = 3.2 km/s, the mean value of (v(sub helio)) = -14.4 km/s) and Si IV (b = 5.3 km/s, the mean value of (v(sub helio)) = -15.0 km/s) trace photionized gas in the expanding H II region surrounding zeta Oph. The observed magnitude and direction of the velocity offset between the Al III and Si IV profiles can be explained by models of H II regions that incorporate expansion. Narrow C IV absorption associated with the H II region is not detected. Predictions of the expected amounts of Si IV and C IV overestimate the column densities of these ions by factors of 30 and more than 10, respectively. The discrepancy may be due to the effects of elemental depletions in the gas and/or to the interaction of the stellar wind with surrounding matter. (2) Broad (b = 15 to 18 km/s) and weak Si IV and C IV absorption components are detected near the mean value of (v(sub helio)) = -26 km/s. The high-ionization species associated with these absorption components are probably produced by electron collisional ionization in a heated gas. This absorption may be physically related to the zeta Oph bow shock ot to a cloud complex situated within the local interstellar medium at d less than 60 pc. The C IV to Si IV column density ratio in this gas is 8, a factor of 6 less than conductive interface models predict, but this discrepancy may be removed by considering the effects of self-photoionization within the cooling gas in the model calculations. (3) A broad (b = 13 km/s) and weak C IV absorption feature detected at the mean value of (v(sub helio)) = -61 km/s is not seen in other species. We tentatively ascribe this absorption to gas in a postshock ragion of an optically thin shock in the zeta Oph stellar wind.

Sembach, Kenneth R.↗
NASA NTRSDate not supplied

Optical and Near-Infrared Coronagraphy of the Quasar 3C 273

We present preliminary results of our observations of the ultraluminous quasar 3C 273 using the HST/STIS coronagraph and the Keck/NIRC2 vortex coronagraph to provide high-contrast imaging of the inner regions of the host galaxy in the optical and near-infrared. We use reference star differential imaging (RDI) techniques developed for high-contrast imaging of exoplanets and circumstellar disks to observe the morphology of features down to the inner few tenths of arcseconds (inner few 100s of pc) around the quasar point source. Broadband optical (50CORON) coronagraphy with STIS reveals the inner structure of jets and shocks, while Ks-band coronagraphy with NIRC2 suggests the presence of a candidate kpc-scale circumnuclear disk. If confirmed, this disk plays a potentially important role in driving the extreme feedback state of 3C 273 by up-regulating accretion, and its presence is consistent with recent observations of atomic and molecular gas in this system at larger spatial scales. We conclude by discussing potential applications of the observing techniques we applied to 3C 273 to other PSF-dominated systems at moderate redshifts to detect features such as circumnuclear disks and multiple nuclei with direct imaging.

Kevin Fogarty↗
DOE OSTI2021

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

36 MATERIALS SCIENCE↗