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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Quantitative Gas Property Measurements By Filtered Rayleigh Scattering: A Review

Filtered Rayleigh scattering (FRS) is a laser-based diagnostic technique used to nonintrusively quantify various thermodynamic properties of a light-scattering gas. The backbone of FRS is the molecular filtering of Rayleigh scattered light. This concept was initially introduced by the atmospheric LIDAR community before being adopted within the aerospace research field in the early 1990s. Since then, FRS has matured into a versatile quantitative diagnostic tool and has found use in a variety of flow regimes ranging from sub- to supersonic speeds in both reacting and nonreacting environments. This adoption can be attributed to the wealth of information that can be obtained via FRS, including the gas density, pressure, temperature, velocity, species composition, or, in some cases, several of these properties at once. This article reviews the current state of FRS methodology in recovering such gas properties. As knowledge of the fundamentals of Rayleigh scattering and spectral light filtering is crucial to the design of an FRS experiment, we begin by briefly reviewing these areas. Subsequently, we conduct a survey of experimental design strategies, assumptions, and data reduction methods used to measure different gas properties using FRS. We conclude the review with a short discussion on quantification of experimental uncertainty and future trends in FRS.

filtered Rayleigh scattering↗
DOE OSTI2024

Framework of compressive sensing and data compression for 4D-STEM

Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive materials. However, the field of view of 4D-STEM is relatively small, which in absence of live processing is limited by the data size required for storage. Furthermore, the rectilinear scan approach currently employed in 4D-STEM places a resolution- and signal-dependent dose limit for the study of beam sensitive materials. Improving 4D-STEM data and dose efficiency, by keeping the data size manageable while limiting the amount of electron dose, is thus critical for broader applications. Here we introduce a general method for reconstructing 4D-STEM data with subsampling in both real and reciprocal spaces at high fidelity. The approach is first tested on the subsampled datasets created from a full 4D-STEM dataset, and then demonstrated experimentally using random scan in real-space. The same reconstruction algorithm can also be used for compression of 4D-STEM datasets, leading to a large reduction (100 times or more) in data size, while retaining the fine features of 4D-STEM imaging, for crystalline samples.

4D-STEM↗
DOE OSTI2021

Editorial: Bioconversion and Biorefinery of C1 Compounds

The past decade has seen significant progress in the field of metabolic engineering and synthetic biology. The exponentially growing multi-omics data and technological advances in the development of efficient genetic manipulation tools and techniques have allowed scientists to explore and expand their understanding of microbial metabolisms and further develop sophisticated engineering strategies to realize the use of industrial "workhorses" and non-conventional microorganisms for sustainable bioconversion and biorefinery. There is of great interest for the research community in using C1 compounds (i.e., CO 2 , CO/syngas, methane, methanol) as the next generation feedstocks for microbial cell factories and biocatalysts to promote the sustainable development of a green economy (Figure 1). Considering lowering input costs is also a main consideration for successful business ventures, the use of inexpensive, abundant, and widely accessible C1 compounds is envisioned as a promising route for the sustainable production of fine chemicals, fuels, and other high-value products. Many C1 compounds are waste gases from industrial activities and may have detrimental effects on climate change upon emission into the atmosphere. Therefore, promoting the use of C1 compounds as renewable carbon feedstocks can greatly contribute to the reduction of anthropogenic emission of air pollutants.

09 BIOMASS FUELS↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
NASA NTRS1986

Landsat D Thematic Mapper image dimensionality reduction and geometric correction accuracy

To characterize and quantify the performance of the Landsat thematic mapper (TM), techniques for dimensionality reduction by linear transformation have been studied and evaluated and the accuracy of the correction of geometric errors in TM images analyzed. Theoretical evaluations and comparisons for existing methods for the design of linear transformation for dimensionality reduction are presented. These methods include the discrete Karhunen Loeve (KL) expansion, Multiple Discriminant Analysis (MDA), Thematic Mapper (TM)-Tasseled Cap Linear Transformation and Singular Value Decomposition (SVD). A unified approach to these design problems is presented in which each method involves optimizing an objective function with respect to the linear transformation matrix. From these studies, four modified methods are proposed. They are referred to as the Space Variant Linear Transformation, the KL Transform-MDA hybrid method, and the First and Second Version of the Weighted MDA method. The modifications involve the assignment of weights to classes to achieve improvements in the class conditional probability of error for classes with high weights. Experimental evaluations of the existing and proposed methods have been performed using the six reflective bands of the TM data. It is shown that in terms of probability of classification error and the percentage of the cumulative eigenvalues, the six reflective bands of the TM data require only a three dimensional feature space. It is shown experimentally as well that for the proposed methods, the classes with high weights have improvements in class conditional probability of error estimates as expected.

Ford, G. E.↗
NASA NTRS2005

Chapter 19A – Radar Cross Section

The material in this Section will provide the novice Flight Test Engineer (FTE) with a brief overview of the radar cross section (RCS) concept. Brief details of radar range descriptions, operations, target calibrations, and data reduction are included. It is noted that these range operations concentrate on the "static" test range rather than the much more sophisticated "dynamic" test range. The dynamic test range is what the FTE will probably work with. Radar reflectivity measurement has developed over the last two decades from a relatively simple endeavor involving the measurement of target RCS amplitude statistics to involving wide band, coherent systems that can measure high resolution images of targets as well as, in many cases, the polarization and phasing properties. This rapid growth in RCS technology has occurred because of the increased use of radar in today's commercial and military systems. In general, the goal for commercial systems is to enhance radar reflectivity, whereas the military goal is to reduce radar reflectivity. Also, classification and identification are important for military purposes because in adverse weather radar may be the only system that may be capable of separating enemy targets from friendly ones. This Section will discuss the fundamentals of RCS. Since the flight test techniques associated with RCS measurements are very similar to those of antenna pattern measurements, both will be presented at the same time in Section 19B Antenna Radiation Pattern Measurements.

Robert W Borek↗
NASA NTRS2005

Applicability of Electrical and Electroanalytical Techniques to Detect Water and Characterize the Geochemistry of Undisturbed Planetary Soils

The search for life is a primary goal of NASA s planetary exploration program. The search is, of necessity, tiered in both the detection approach (looking for evidence of microbial fossils or the presence of water in the geological history of a planetary body and/or looking for evidence of water, energy sources, precursors to life, signatures of life and/or life itself in the present day planetary environment) and in the survey method (scale, range, specificity) employed. Terrestrial investigations suggests that life as we know it requires water. Thus, the search for extant microbial life and habitats requires identifying water-bearing soils. Determining Reduction-Oxidation (REDOX) couples present in water, once it is found, provides information on soil geochemistry and identifies potential chemical energy sources for life. Mars offers a near-term target for conducting this search. The identification of gully formation [1], layered deposits [2] and elemental ratios of bromine and chlorine [3] present indirect evidence that water was abundant locally in the Martian past. Additionally, Viking images of polar ice and frost formation on the surface of Mars demonstrate that water can exist in at least some near-surface regions of present-day Mars. Atmospheric pressure data further suggest that liquid water may be stable for short periods of time in the mid-latitudes of the Martian surface. [4] Measurements of the global distribution of hydrogen in the Martian regolith offer tantalizing indirect evidence that water may at least exist in near-surface soils. [5] Evidently, any water to be found is likely to exist as soil mixtures at levels ranging between approx.0.5% and approx.5 %.

Seshadri, S.↗
DOE OSTI2019

Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models

We report that machine learning techniques typically rely on large datasets to create accurate classifiers. However, there are situations when data is scarce and expensive to acquire. This is the case of studies that rely on state-of-the-art computational models which typically take days to run, thus hindering the potential of machine learning tools. In this work, we present a novel classifier that takes advantage of lower fidelity models and inexpensive approximations to predict the binary output of expensive computer simulations. We postulate an autoregressive model between the different levels of fidelity with Gaussian process priors. We adopt a fully Bayesian treatment for the hyper-parameters and use Markov Chain Monte Carlo samplers. We take advantage of the probabilistic nature of the classifier to implement active learning strategies. We also introduce a sparse approximation to enhance the ability of the multi-fidelity classifier to handle a large amount of low fidelity samples. We test these multi-fidelity classifiers against their single-fidelity counterpart with synthetic data, showing a median computational cost reduction of 23% for a target accuracy of 90%. In an application to cardiac electrophysiology, the multi-fidelity classifier achieves an F1 score, the harmonic mean of precision and recall, of 99.6% compared to 74.1% of a single-fidelity classifier when both are trained with 50 samples. In general, our results show that the multi-fidelity classifiers outperform their single-fidelity counterpart in terms of accuracy in all cases. Finally, we envision that this new tool will enable researchers to study classification problems that would otherwise be prohibitively expensive. Source code is available at https://github.com/fsahli/MFclass.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Dimensional Reduction for Sampled Priors and Application to Photometric Redshift Distributions

A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior $p$($q$,$n$|$D$) $\propto$ $\mathcal{L}$($D$|$q$,$n$)$p$(q)$p$($n$), where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n, i.e., one needs to provide a density estimator over the full n space from the provided samples. But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter $\mathcal{L}$. The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1995

The role and status of Euler solvers in impulsive rotor noise computations

Several recent applications (in the last five years) of Euler solvers in the computation of impulsive noise from rotor blades emphasize their emerging role in complementing other methods and experimental work. In the area of high-speed impulsive noise the use of Euler solvers as research tools has become fairly mature with very favorable comparisons with experimental data, especially in hover. The grid sizes and resulting computational times are reasonable when compared to those required for accurate surface aerodynamics alone. Furthermore, Euler solvers have provided a rich database with the resolution and accuracy needed for input to Kirchhoff and acoustic analogy methods for predicting the far-field noise. On the other hand, the application of Euler solvers to calculate blade-vortex interaction noise is still far from mature. The computational resources required for accurate calculations away from the blade are much larger than for high-speed impulsive noise. Current calculations help improve the basic understanding of the phenomena involved, but to date no comparisons with experiment have been made. Fortunately, the use of coupled Euler solver/Kirchhoff methods seems to offer promise for a robust and efficient technique for predicting both high-speed impulsive noise and blade-vortex interaction noise. Finally, a simple model problem of an isolated vortex interacting with an arbitrarily prescribed pitching airfoil demonstrates the feasibility of using Euler solvers to examine noise reduction techniques. The use of simple aerodynamic quasi-static theory and the computed lift time history as feedback to determine the required pitching motion appears sufficient to significantly dampen the unsteady loading and subsequent acoustics by an order of magnitude within a few blade passages.

Baeder, James D.↗
NASA NTRS1998

Evidence for increased cardiac compliance during exposure to simulated microgravity

We measured hemodynamic responses during 4 days of head-down tilt (HDT) and during graded lower body negative pressure (LBNP) in invasively instrumented rhesus monkeys to test the hypotheses that exposure to simulated microgravity increases cardiac compliance and that decreased stroke volume, cardiac output, and orthostatic tolerance are associated with reduced left ventricular peak dP/dt. Six monkeys underwent two 4-day (96 h) experimental conditions separated by 9 days of ambulatory activities in a crossover counterbalance design: 1) continuous exposure to 10 degrees HDT and 2) approximately 12-14 h per day of 80 degrees head-up tilt and 10-12 h supine (control condition). Each animal underwent measurements of central venous pressure (CVP), left ventricular and aortic pressures, stroke volume, esophageal pressure (EsP), plasma volume, alpha1- and beta1-adrenergic responsiveness, and tolerance to LBNP. HDT induced a hypovolemic and hypoadrenergic state with reduced LBNP tolerance compared with the control condition. Decreased LBNP tolerance with HDT was associated with reduced stroke volume, cardiac output, and peak dP/dt. Compared with the control condition, a 34% reduction in CVP (P = 0.010) and no change in left ventricular end-diastolic area during HDT was associated with increased ventricular compliance (P = 0.0053). Increased cardiac compliance could not be explained by reduced intrathoracic pressure since EsP was unaltered by HDT. Our data provide the first direct evidence that increased cardiac compliance was associated with headward fluid shifts similar to those induced by exposure to spaceflight and that reduced orthostatic tolerance was associated with lower cardiac contractility.

Non-NASA Center↗
NASA NTRS1974

A Computing Method for Sound Propagation Through a Nonuniform Jet Stream

Understanding the principles of jet noise propagation is an essential ingredient of systematic noise reduction research. High speed computer methods offer a unique potential for dealing with complex real life physical systems whereas analytical solutions are restricted to sophisticated idealized models. The classical formulation of sound propagation through a jet flow was found to be inadequate for computer solutions and a more suitable approach was needed. Previous investigations selected the phase and amplitude of the acoustic pressure as dependent variables requiring the solution of a system of nonlinear algebraic equations. The nonlinearities complicated both the analysis and the computation. A reformulation of the convective wave equation in terms of a new set of dependent variables is developed with a special emphasis on its suitability for numerical solutions on fast computers. The technique is very attractive because the resulting equations are linear in nonwaving variables. The computer solution to such a linear system of algebraic equations may be obtained by well-defined and direct means which are conservative of computer time and storage space. Typical examples are illustrated and computational results are compared with available numerical and experimental data.

Padula, S. L.↗
DOE OSTI2020

Applying Waveform Correlation to Mining Blasts Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment that uses waveform templates recorded by multiple International Monitoring System stations of the Comprehensive Nuclear-Test-Ban Treaty for up to 10 years prior to detect and identify mining blasts that occur during single weeks of study. We discuss approaches for template selection, threshold setting, and event detection that are specialized for mining blasts and a sparse, global network. We apply the approaches to two different weeks of study for each of two geographic regions, Wyoming and Scandinavia, to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to develop an intuition about potential reduction in analyst workload.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2005

Wake Vortex Tracking Using a 35 GHz Pulsed Doppler Radar

A 35 GHz, pulsed-Doppler radar system has been designed and assembled for wake vortex detection and tracking in low visibility conditions. Aircraft wake vortices continue to be an important factor in determining safe following distances or spacings for aircraft in the terminal area. Currently, under instrument meteorological conditions (IMC), aircraft adhere to conservative, fixed following-distance guidelines based primarily on aircraft weight classifications. When ambient conditions are such that vortices will either drift or dissipate, leaving the flight corridor clear, the prescribed spacings are unnecessarily long and result in decreased airport throughput. There is a potential for significant airport efficiency improvement, if a system can be employed to aid regulators and pilots in setting safe and efficient following distances based on airport conditions. The National Aeronautics and Space Administration (NASA), the Federal Aviation Agency, and Volpe National Transportation Systems Center have promoted and worked to develop systems that would increase airport capacity and provide for safe reductions in aircraft separation. The NASA Aircraft Vortex Spacing System (AVOSS), a wake vortex spacing system that can provide dynamic adjustment of spacings based on real-time airport weather conditions, has demonstrated that Lidar systems can be successfully used to detect and track vortices in clear air conditions. To fill the need for detection capability in low-visibility conditions, a 35 GHz, pulsed-Doppler radar system is being investigated for use as a complimentary, low-visibility sensor for wake vortices. The radar sensor provides spatial and temporal information similar to that provided by Lidar, but under weather conditions that a Lidar cannot penetrate. Currently, we are analyzing the radar design based upon the data and experience gained during the wake vortex Lidar deployment with AVOSS at Dallas/Fort Worth International Airport. As part of this study, two numerical models were utilized in system simulations. The results of this study improve our understanding of the method of detection, resolution requirements for range and azimuth, pulse compression, and performance prediction. Simulations applying pulse compression techniques show that detection is good in heavy fog to greater than 2000 m. Both compressed and uncompressed short pulses show the vortex structure. To explore operational challenges, siting and scanning strategies were also analyzed. Simulation results indicate that excellent wake vortex detection, tracking and classification is possible in drizzle (+15 dBZ) and heavy fog (- 13 dBZ) using short pulse techniques (<99ns) at ranges on the order of 900 m, with a modest power of 500 W output. At 1600 m, detection can be expected at reflectivities as low as -13 dBZ (heavy fog). The radar system, as designed and built, has the potential to support field studies of a wake vortex spacing system in low-visibility conditions ranging from heavy fog to rain, when sited within 2000m of the flight path.

Neece, Robert T.↗
DOE OSTI2024

CO 2 rock physics modeling for reliable monitoring of geologic carbon storage

Monitoring, verification, and accounting (MVA) are crucial to ensure safe and long-term geologic carbon storage. Seismic monitoring is a key MVA technique that utilizes seismic data to infer elastic properties of CO 2 -saturated rocks. Reliable accounting of CO 2 in subsurface storage reservoirs and potential leakage zones requires an accurate rock physics model. However, the widely used CO 2 rock physics model based on the conventional Biot-Gassmann equation can substantially underestimate the influence of CO 2 saturation on seismic waves, leading to inaccurate accounting. We develop an accurate CO 2 rock physics model by accounting for both effects of the stress dependence of seismic velocities in porous rocks and CO 2 weakening on the rock framework. We validate our CO 2 rock physics model using the Kimberlina-1.2 model (a previously proposed geologic carbon storage site in California) and create time-lapse elastic property models with our new rock physics method. We compare the results with those obtained using the conventional Biot-Gassmann equation. Our innovative approach produces larger changes in elastic properties than the Biot-Gassmann results. Using our CO 2 rock physics model can replicate shear-wave speed reductions observed in the laboratory. Our rock physics model enhances the accuracy of time-lapse elastic-wave modeling and enables reliable CO 2 accounting using seismic monitoring.

58 GEOSCIENCES↗
NASA NTRS1994

Evaluation of semiempirical atmospheric density models for orbit determination applications

This paper presents the results of an investigation of the orbit determination performance of the Jacchia-Roberts (JR), mass spectrometer incoherent scatter 1986 (MSIS-86), and drag temperature model (DTM) atmospheric density models. Evaluation of the models was performed to assess the modeling of the total atmospheric density. This study was made generic by using six spacecraft and selecting time periods of study representative of all portions of the 11-year cycle. Performance of the models was measured for multiple spacecraft, representing a selection of orbit geometries from near-equatorial to polar inclinations and altitudes from 400 kilometers to 900 kilometers. The orbit geometries represent typical low earth-orbiting spacecraft supported by the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD). The best available modeling and orbit determination techniques using the Goddard Trajectory Determination System (GTDS) were employed to minimize the effects of modeling errors. The latest geopotential model available during the analysis, the Goddard earth model-T3 (GEM-T3), was employed to minimize geopotential model error effects on the drag estimation. Improved-accuracy techniques identified for TOPEX/Poseidon orbit determination analysis were used to improve the Tracking and Data Relay Satellite System (TDRSS)-based orbit determination used for most of the spacecraft chosen for this analysis. This paper shows that during periods of relatively quiet solar flux and geomagnetic activity near the solar minimum, the choice of atmospheric density model used for orbit determination is relatively inconsequential. During typical solar flux conditions near the solar maximum, the differences between the JR, DTM, and MSIS-86 models begin to become apparent. Time periods of extreme solar activity, those in which the daily and 81-day mean solar flux are high and change rapidly, result in significant differences between the models. During periods of high geomagnetic activity, the standard JR model was outperformed by DTM. Modification of the JR model to use a geomagnetic heating delay of 3 hours, as used in DTM, instead of the 6.7-hour delay produced results comparable to or better than the DTM performance, reducing definitive orbit solution ephermeris overlap differences by 30 to 50 percent. The reduction in the overlap differences would be useful for mitigating the impact of geomagnetic storms on orbit prediction.

Cox, C. M.↗
NASA NTRS2012

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

tropical ecosystems↗
NASA NTRS1996

Development of Low Density Titanium Alloys for Structural Applications

In this report the results of a program designed to reduce the density of titanium by adding magnesium are presented. Because these two elements are immiscible under conventional ingot metallurgy techniques, two specialized powder metallurgy methods namely, mechanical alloying (MA) and physical vapor deposition (PVD) were implemented. The mechanical alloying experiments were done both at the University of Idaho and at the Defense Research Agency in UK. Since titanium is reactive with interstitial elements, a secondary goal of this research was to correlate solubility extensions with interstitial contamination content, especially oxygen and nitrogen. MA was carried out in SPEX 8000 shaker mils and different milling containers were utilized to control the level of contamination. Results showed that solubilities of Mg in Ti were obtained up to 28 at.% (16.4 wt. %) Mg in Ti for Ti-39.6 at. % (25 wt. %) Mg alloys, which greatly exceed those obtained under equilibrium conditions. This reflects a density reduction of approximately 26 %. Contamination of oxygen and nitrogen seemed to increase the solubility of magnesium in titanium in some cases; however, we were not able to make a clear correlation between contamination levels with solubilities. Work at the DRA has emphasized optimization of present PVD equipment, specifically composition and temperature control. Preliminary PVD data has shown Ti-Mg deposits have successfully been made up to 2 mm thick and that solubility extensions were achieved. The potential for density reduction of titanium by alloying with magnesium has been demonstrated; however, this work has only scratched the surface of the development of such low density alloys. Much research is needed before such alloys could be implemented into industry. Further funding is required in order to optimize the MA/PVD processes including contamination control, determination of optimal alloy compositions, microstructure development, and mechanical property determination.

Froes, F. H.↗