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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Effect of a semi-annular thermal acoustic shield on jet exhaust noise

The effect of a semi-annular acoustic shield on jet exhaust noise is investigated with the rationale that such a configuration would reduce or eliminate the multiple reflection mechanism. A limited range of flow conditions for one nozzle/shield configuration were studied at model scale. Noise measurements for a 10 cm conical nozzle with a semi-annular acoustical shield are presented in terms of lossless free field data at various angular locations with respect to the nozzle. Measurements were made on both the shielded and unshielded sides of the nozzle. Model scale overall sound pressure level directivity patterns and comparisons of model scale spectral data are provided. The results show that a semi-annular thermal acoustic shield consisting of a low velocity, high temperature gas stream partially surrounding a central jet exhibits lower noise levels than when the central jet is operated alone. The results are presented parametrically, showing the effects of various shield and central system velocities and temperatures.

Goodykoontz, J.↗
NASA NTRS1990

Heterodyne spectrophotometry of ozone in the 9.6-micron band using a tunable diode laser

Tunable diode laser heterodyne spectrophotometry (TDLHS) has been used to make extremely high resolution (0.0003/cm) solar spectra in the 9.6-micron ozone band. Observations have shown that a signal-to-noise ratio of 120:1 (about 30 percent of theoretical) for an integration time of 1/8 s can be achieved at a resolution of 0.0013 wave numbers. The spectral data have been inverted to yield a total column amount of ozone, in good agreement with that measured at the nearby NOAA ozone monitoring facility in Boulder, Colorado. Line positions for several ozone lines in the spectral region 996-997/cm are reported. Recent improvements have produced a signal-to-noise ratio of 95:1 (about 40 percent of theoretical) at 0.0003/cm and extended the range of wavelengths which can be observed.

Mcelroy, C. T.↗
NASA NTRS1995

Analysis of conifer forest regeneration using Landsat Thematic Mapper data

Landsat Thematic Mapper (TM) data were used to evaluate young conifer stands in the western Cascade Mountains of Oregon. Regression and correlation analyses were used to describe the relationships between TM band values and age of young Douglas-fir stands (2 to 35 years old). Spectral data from well regenerated Douglas-fir stands were compared to those of poorly regenerated conifer stands. TM bands 1, 2, 3, 5, 6, and 7 were inversely correlated with the age (r greater than or equal to -0.80) of well regenerated Douglas-fir stands. Overall, the 'structural index' (TM 4/5 ratio) had the highest correlation to age of Douglas-fir stands (r = 0.96). Poorly regenerated stands were spectrally distinct from well regenerated Douglas-fir stands after the stands reached an age of approximately 15 years.

Fiorella, Maria↗
DOE OSTI2020

Introductory guide to the application of XPS to epitaxial films and heterostructures

XPS is an important characterization method for epitaxial films and heterostructures. Although standard approaches to XPS data collection and analysis provide useful information such as average composition and the presence of contaminants, more in-depth analysis provides information about film structure, surface termination, built-in electric potentials and band offsets. The high degree of structural order in these materials enables such information to be extracted from spectral data but also adds complications to the analysis. This guide highlights three topics of importance in this field: i) the impacts of crystallinity on XPS signals and quantification, ii) the unexpected spectral line shapes that can occur in unusual or novel materials, and, iii) the ability of XPS to yield information about built-in potentials and band offsets. Concepts are demonstrated using complex oxide heterostructures. Although the topics are highly relevant to epitaxial films and heterostructures, they also apply to signal crystals of complex materials

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Compressive Classification for TEM-EELS

Acquiring atomic-resolution images of beam sensitive materials or under dynamic conditions (e.g. operando STEM) is very difficult and requires precise dose fractioning to minimize the dose delivered to the sample and acquire the data more rapidly than dynamics present. Typical methods of low-dose STEM imaging include adjusting the beam current, dwell-time, and probe Scanning transmission electron microscopy (STEM) is used routinely to obtain atomic-resolution images of a small selection of radiation tolerant materials under stable conditions.size. All three of these determine the dose over a spatial area in STEM. Recently, compressive sensing [1] has been applied to electron microscopy. The computational imaging method of inpainting [2] has been proposed as a method to reduce dose and increase acquisition speed for STEM. Inpainting is a technique to recover missing pixels in an image. By skipping pixels (randomly) during acquisition, a fourth dose-control mechanism is realized [3]. A few implementations of randomly-subsampled STEM exist (e.g., [4]). To make the concepts of compressive sensing accessible to the microscopy community we begin with an example using atomic resolution images of a GaAs crystal (see Figure 1). Because the crystal is periodic it has a sparse representation in reciprocal space. In fact, a sparse representation is a key component of compressive sensing. If an image is sub-sampled randomly we find that the Fourier transform remains sparse, but becomes increasing noisy as less and less data is acquired. Yet, by finding the peaks in the reciprocal image and removing the noise it is possible to recover atomic resolution images with only 1% of the pixels. Figure 1 shows the reconstruction process for GaAs sub-sampled at 50%, 10%, 5%, and 1%. Periodic images form a special class for which the optimal sampling approach (according to CS theory) is a random selection of pixels. For non-periodic images, such as those arising in in-situ STEM, methods such as dictionary learning are necessary [2,5]. The data can be compressed in time for TEM [5] or pixels can be skipped randomly for STEM. There are several issues that arise in developing a random scan for STEM [4] and a coded aperture for TEM. The remainder of the talk will address these issues. References: [1] E Candes and M Wakin. Signal Processing Magazine, IEEE. 2(25) (2008), pp. 21. [2] A Stevens, Pu Y, Sun Y, et al. Artificial Intelligence & Statistics (AISTATS), 2017. [3] A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [4] L Kovarik, A Stevens, A Liyu et al. Appl. Phys. Lett. 109, 164102 (2016). [5] A Stevens, L Kovarik, P Abellan et al. Adv. Structural and Chemical Imaging 1(10), (2015), pp. 1. [6] Supported by the Chemical Imaging, Signature Discovery, and Analytics in Motion Initiatives at PNNL. PNNL is operated by Battelle Memorial Inst. for the US DOE; contract DE-AC05-76RL01830.

Hao, Weituo↗
DOE OSTI2023

Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry

Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Method to Extract Spatially Resolved Polycyclic Aromatic Hydrocarbon Emission from Spitzer Spectra: Application to M51

The mid-infrared spectrum contains rich diagnostics to probe the physical properties of galaxies, among which the pervasive emission features from polycyclic aromatic hydrocarbons (PAHs) offer promising means of estimating the star formation rate (SFR) relatively immune from dust extinction. This paper investigates the effectiveness of PAH emission as a SFR indicator on subkiloparsec scales by studying the Spitzer/IRS mapping-mode observations of the nearby grand-design spiral galaxy M51. We present a new approach of analyzing the spatial elements of the spectral data cube that simultaneously maximizes spatial resolution and spatial coverage, while yielding reliable measurements of the total, integrated 5–20 μm PAH emission. We devise a strategy of extracting robust PAH emission using spectra with only partial spectral coverage, complementing missing spectral regions with properly combined mid-infrared photometry. We find that in M51 the PAH emission correlates tightly with the extinction-corrected far-ultraviolet, near-ultraviolet, and Hα emission, from scales of ∼0.4 kpc close to the nucleus to 6 kpc out in the disk of the galaxy, indicating that PAH serves as an excellent tracer of SFR over a wide range of galactic environments. But regional differences exist. Close to the active nucleus of M51 the 6.2 μm feature is weaker, and the overall level of PAH emission is suppressed. The spiral arms and the central star-forming region of the galaxy emit stronger 7.7 and 8.6 μm PAH features than the inter-arm regions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Relationship of crop radiance to alfalfa agronomic values

Red and photographic infrared spectral data of alfalfa were collected at the time of the third and fourth cuttings using a hand-held radiometer for the earlier alfalfa cutting. Significant linear and non-linear correlation coefficients were found between the spectral variables and plant height, biomass, forage water content, and estimated canopy cover. For the alfalfa of the later cutting, which had experienced a period of severe drought stress which limited growth, the spectral variables were found to be highly correlated with the estimated drought scores.

Tucker, C. J.↗
NASA NTRS1991

The Steward Observatory asteroid relational database

The Steward Observatory Asteroid Relational Database (SOARD) was created as a flexible tool for undertaking studies of asteroid populations and sub-populations, to probe the biases intrinsic to asteroid databases, to ascertain the completeness of data pertaining to specific problems, to aid in the development of observational programs, and to develop pedagogical materials. To date, SOARD has compiled an extensive list of data available on asteroids and made it accessible through a single menu-driven database program. Users may obtain tailored lists of asteroid properties for any subset of asteroids or output files which are suitable for plotting spectral data on individual asteroids. The program has online help as well as user and programmer documentation manuals. The SOARD already has provided data to fulfill requests by members of the astronomical community. The SOARD continues to grow as data is added to the database and new features are added to the program.

Sykes, Mark V.↗
NASA NTRS1992

The Steward Observatory asteroid relational database

The Steward Observatory Asteroid Relational Database (SOARD) was created as a flexible tool for undertaking studies of asteroid populations and sub-populations, to probe the biases intrinsic to asteroid databases, to ascertain the completeness of data pertaining to specific problems, to aid in the development of observational programs, and to develop pedagogical materials. To date SOARD has compiled an extensive list of data available on asteroids and made it accessible through a single menu-driven database program. Users may obtain tailored lists of asteroid properties for any subset of asteroids or output files which are suitable for plotting spectral data on individual asteroids. A browse capability allows the user to explore the contents of any data file. SOARD offers, also, an asteroid bibliography containing about 13,000 references. The program has online help as well as user and programmer documentation manuals. SOARD continues to provide data to fulfill requests by members of the astronomical community and will continue to grow as data is added to the database and new features are added to the program.

Sykes, Mark V.↗
NASA NTRS1994

Development of an Aerosol Opacity Retrieval Algorithm for Use with Multi-Angle Land Surface Images

In 1998, the Multi-angle Imaging SpectroRadiometer (MISR) will fly aboard the EOS-AM1 spacecraft. MISR will enable unique methods for retrieving the properties of atmospheric aerosols, by providing global imagery of the Earth at nine viewing angles in four visible and near-IR spectral bands. As part of the MISR algorithm development, theoretical methods of analyzing multi-angle, multi-spectral data are being tested using images acquired by the airborne Advanced Solid-State Array Spectroradiometer (ASAS). In this paper we derive a method to be used over land surfaces for retrieving the change in opacity between spectral bands, which can then be used in conjunction with an aerosol model to derive a bound on absolute opacity.

Advanced Solid-State Array Spectroradiometer ASAS ↗
NASA NTRS1994

Tomographic separation of composite spectra. 2: The components of 29 UW Canis Majoris

We have analyzed the UV photospheric lines of 29 CMa, a 4.39 day period, double-lined O-type spectroscopic binary. Archival data from International Ultraviolet Explorer (IUE)(28 spectra well distributed in oribital phase) were analyzed with several techniques. We find that the mass ratio is q = 1.20 +/- 0.16 (secondary more massive) based on three independent arguments. A tomography algorithm was used to produce the separate spectra of the two stars in six UV spectral regions. The MK spectral classifications of the primary and secondary, O7.5-8 Iab and O9.7 Ib, respectively, were estimated through a comparison of UV line ratios with those in spectral standard stars. The flux ratio of the stars in the UV is 0.36 +/- 0.07 (primary brighter). The primary has a strong P Cygni NIV wavelength 1718 feature, indicating a strong stellar wind. We also present tomographic reconstructions of visual spectral data in the range 4300-4950 A, based on seven observations of differing orbital phases, which confirm the UV classifications, and show that the primary is an Of star. From the spectral classifications, we estimate the temperatures of the stars to be 33,750 K and 29,000 K for primary and secondary, respectively. We then fit visual and UV light curves and show that reasonably good fits can be obtained with these temperatures, a semicontact configuration, an inclination of 74 deg. +/- 2 deg., and an intensity ratio r is less than 0.5.

Bagnuolo, William G., Jr.↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

User oriented ERTS-1 images

Photographic reproduction of ERTS-1 images are capable of displaying only a portion of the total information available from the multispectral scanner. Methods are being developed to generate ERTS-1 images oriented towards special users such as agriculturists, foresters, and hydrologists by applying image enhancement techniques and interactive statistical classification schemes. Spatial boundaries and linear features can be emphasized and delineated using simple filters. Linear and nonlinear transformations can be applied to the spectral data to emphasize certain ground information. An automatic classification scheme was developed to identify particular ground cover classes such as fallow, grain, rape seed or various vegetation covers. The scheme applies the maximum likelihood decision rule to the spectral information and classifies the ERTS-1 image on a pixel by pixel basis. Preliminary results indicate that the classifier has limited success in distinguishing crops, but is well adapted for identifying different types of vegetation.

Shlien, S.↗
NASA NTRS2006

Assessing Hurricane Katrina Vegetation Damage at Stennis Space Center using IKONOS Image Classification Techniques

Hurricane Katrina inflicted widespread damage to vegetation in southwestern coastal Mississippi upon landfall on August 29, 2005. Storm damage to surface vegetation types at the NASA John C. Stennis Space Center (SSC) was mapped and quantified using IKONOS data originally acquired on September 2, 2005, and later obtained via a Department of Defense ClearView contract. NASA SSC management required an assessment of the hurricane s impact to the 125,000-acre buffer zone used to mitigate rocket engine testing noise and vibration impacts and to manage forestry and fire risk. This study employed ERDAS IMAGINE software to apply traditional classification techniques to the IKONOS data. Spectral signatures were collected from multiple ISODATA classifications of subset areas across the entire region and then appended to a master file representative of major targeted cover type conditions. The master file was subsequently used with the IKONOS data and with a maximum likelihood algorithm to produce a supervised classification later refined using GIS-based editing. The final results enabled mapped, quantitative areal estimates of hurricane-induced damage according to general surface cover type. The IKONOS classification accuracy was assessed using higher resolution aerial imagery and field survey data. In-situ data and GIS analysis indicate that the results compare well to FEMA maps of flooding extent. The IKONOS classification also mapped open areas with woody storm debris. The detection of such storm damage categories is potentially useful for government officials responsible for hurricane disaster mitigation.

Spruce, Joseph P.↗
NASA NTRS1984

Spectral response curve models applied to forest cover-type discrimination

The potential of remote sensing systems to provide a cost-effective inventory tool in the case of forest resources is currently of interest to a variety of natural resources management agencies. A number of studies have been performed regarding the use of Landsat data for mapping forest resources in Michigan. The present paper is concerned with current research, which has been directed toward the development and evaluation of computer-implemented classifications for the identification and characterization of coniferous forest types in Michigan's northern Lower Peninsula. Attention is given to the characteristic response curves from Landsat MSS data, spectral response curve models, and forest cover-type discrimination. It is found that spectral response curve models can be used to evaluate and explain the characteristic spectral responses of coniferous forest types on a snow-covered, winter Landsat scene.

Hudson, W. D.↗
DOE OSTI2013

RESOLVED NEAR-INFRARED SPECTROSCOPY OF WISE J104915.57-531906.1AB: A FLUX-REVERSAL BINARY AT THE L DWARF/T DWARF TRANSITION

We report resolved near-infrared spectroscopy and photometry of the recently identified brown dwarf binary WISE J104915.57-531906.1AB, located 2.02 {+-} 0.15 pc from the Sun. Low-resolution spectral data from Magellan/FIRE and IRTF/SpeX reveal strong H{sub 2}O and CO absorption features in the spectra of both components, while the secondary also exhibits weak CH{sub 4} absorption at 1.6 {mu}m and 2.2 {mu}m. Spectral indices and comparison to low-resolution spectral standards indicate component types of L7.5 and T0.5 {+-} 1, the former consistent with the optical classification of the primary. Both sources also have unusually red spectral energy distributions for their spectral types, which we attribute to enhanced condensate opacity (thick clouds). Relative photometry reveals a flux reversal between the J and K bands, with the T dwarf component being brighter in the 0.95-1.3 {mu}m region ({Delta}J = -0.31 {+-} 0.05). As with other L/T transition binaries, this reversal likely reflects the depletion of condensate opacity in the T dwarf, with the contrast enhanced by the thick clouds present in the photosphere of the L dwarf primary. The 1 {mu}m flux from the T dwarf most likely emerges from gaps in its cloud layer, as suggested by the significant optical variability detected from this source by Gillon et al. Component mass measurements of the WISE J1049-5319AB system through astrometric and component radial velocity monitoring may resolve the current debate as to whether the loss of photospheric condensate clouds at the L dwarf/T dwarf boundary is a slow or rapid process, a conceivable endeavor given its proximity, brightness, small separation (3.1 {+-} 0.3 AU), and reasonable orbital period (20-30 yr)

79 ASTRONOMY AND ASTROPHYSICS↗