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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 595 records · Page 33

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NASA NTRS1991

Voyager 2 test of the radar time-delay effect

By analyzing radio range measurements generated during the solar conjunction of the Voyager 2 spacecraft in 1985, the relativistic time-delay effect has been tested accurately with a spacecraft at a distance of 3 x 10 to the 9th km from the earth. Because the range measurements were generated simultaneously at two frequencies on the downlink, it was possible to derive calibrations for the delay resulting from intervening solar plasma. In addition, a record of attitude thruster firings was used to model small accelerations of the spacecraft that could have produced a range error. It was thereby possible to determine the parameterized post-Newtonian parameter gamma to an accuracy of 3 percent with only a sparse amount of range data.

Krisher, Timothy P.↗

NASA NTRS1993

Real-time qualitative reasoning for telerobotic systems

This paper discusses the sensor-based telerobotic driving of a car in a-priori unknown environments using 'human-like' reasoning schemes implemented on custom-designed VLSI fuzzy inferencing boards. These boards use the Fuzzy Set theoretic framework to allow very vast (30 kHz) processing of full sets of information that are expressed in qualitative form using membership functions. The sensor-based and fuzzy inferencing system was incorporated on an outdoor test-bed platform to investigate two control modes for driving a car on the basis of very sparse and imprecise range data. In the first mode, the car navigates fully autonomously to a goal specified by the operator, while in the second mode, the system acts as a telerobotic driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right, speed up, slow down, stop, or back up depending on the obstacles perceived by the sensors. Indoor and outdoor experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Sample results are presented that illustrate the feasibility of developing autonomous navigation modules and robust, safety-enhancing driver's aids for telerobotic systems using the new fuzzy inferencing VLSI hardware and 'human-like' reasoning schemes.

Pin, Eancois G.↗

NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗

NASA NTRS1994

Superhumps in cataclysmic binaries. 3: V795 Herculis

We report photometry of the cataclysmic variable V795 Herculis during 1990-1994. Comparison of recent with previously published data shows that the 2.78-hr periodicity has essentially disappeared; the amplitude has diminished by a factor of at least 10. In its place, there is a low-amplitude signal near the radial-velocity (orbital?) period of 2.60 hr. During 1983-1989, the star did show an obvious 2.78-hr signal. We study the published data on this signal, together with our own data under the (questionable) assumption that the signal still exists with an amplitude too small to survive the rigors of unbiased period detection. The data are really too sparse and too poorly distributed to decide the issue of phase stability. But since the photometric signal is transient and exceeds the likely orbital period by approximately 8%, the photometric waves are likely to be yet another example of 'superhumps', the phenomenon made famous by dwarf novae in superoutburst. At high frequencies, the star shows a quasi-periodic signal with P approximately 1160 s, and possibly also a stable signal with P = 1310.2 s (or 1330.4 s, the one-day alias). The beat period between these signals could be the orbital period, or that of the vanished superhump.

Patterson, Joseph↗

NASA NTRS1999

Development of Prototype Micro-Lidar using Narrow Linewidth Semiconductor Lasers for Mars Boundary Layer Wind and Dust Opacity Profiles

We have developed a compact Doppler lidar concept which utilizes recent developments in semiconductor diode laser technology in order to be considered suitable for wind and dust opacity profiling in the Mars lower atmosphere from a surface location. The current understanding of the Mars global climate and meteorology is very limited, with only sparse, near-surface data available from the Viking and Mars Pathfinder landers, supplemented by long-range remote sensing of the Martian atmosphere. The in situ measurements from a lander-based Doppler lidar would provide a unique dataset particularly for the boundary layer. The coupling of the radiative properties of the lower atmosphere with the dynamics involves the radiative absorption and scattering effects of the wind-driven dust. Variability in solar irradiance, on diurnal and seasonal time scales, drives vertical mixing and PBL (planetary boundary layer) thickness. The lidar data will also contribute to an understanding of the impact of wind-driven dust on lander and rover operations and lifetime through an improvement in our understanding of Mars climatology. In this paper we discuss the Mars lidar concept, and the development of a laboratory prototype for performance studies, using, local boundary layer and topographic target measurements.

Menzies, Robert T.↗

NASA NTRS2000

Spacebased Observations of the Oceanic Responses to Monsoons in South China Sea and Arabian Sea

A large percentage of the world's population and their agrarian economy must endure the vagaries of the monsoons over the tropical oceans between Africa and the Philippines. We know very little about the oceanic responses to changes of the monsoon in the South China Sea (SCS), which is under the influence of the East Asian Monsoon System, and the Arabian Sea (AS), which is dominated by the Indian Monsoon System; oceanic observations are sparse in both regions. Data from spaceborne microwave scatterometers and radiometers have been used to estimate the two major atmospheric forcing, momentum flux and latent heat flux (LHF), which change with the monsoon winds. Spaceborne sensors also observed the surface signatures of the oceanic response: SST and sea level changes (SLC. Sufficient durations of these data have recently become available to allow the meaningful studies of the annual cycles and interannual anomalies. In SCS, the winter monsoon is strong and steady but the summer monsoon is weak and has large intraseasonal fluctuations. In AS, the summer monsoon is much stronger than the winter monsoon. Significant correlations between LHF and SST tendency, and between curl of wind stress and SLC are found in both oceans. In the north SCS, winds are strong and dry, LHF is high, and ocean cooling is also large in fall; LHF is low and the ocean warms up in spring. In AS, LHF and SST tendency have a semi annual period; LHF is high in summer when the wind is strong and in winter when the wind is dry. Along the coast of Oman, the strong summer southwest monsoon causes intense upwelling, low SST and LHF in summer; such wind-driven SST changes is not as obvious along the Vietnam coast because of the weaker summer monsoon. The negative correlation between curl of wind stress and SLC found in the central basins of both SCS and AS agrees with a simple Ekman pumping scenario. Cyclonic winds drive surface divergence and upwelling in the ocean; the rise of the thermocline causes lower sea levels. Anticyclonic winds cause higher SLC. The exceptions (positive correlations) are found in the coastal regions in the north and the south of SCS, off the west coast of India between 5N and 10N, and along the coast of Somalia.

Xie, Xiao-Su↗

NASA NTRS2000

QuikSCAT and TRMM Reveal the Interplay Between Dynamic and Hydrologic Parameters in Hurricane Floyd

Over the ocean, in situ observations in a hurricane are extremely sparse, and conventional satellite data provide only cloud imagery at the top of the storm.

Hurricane Wind Rain↗

NASA NTRS2011

Isolation Effects on the Moon: High Topographic Slope Observations from the LRO and LOLA Instruments

The extremely low temperatures in the Moon's polar permanent shadow regions (PSR) has long been considered a unique factor necessary for entrapping volatile Hydrogen (H). However, recent discoveries indicate some H concentrations lie outside PSR, suggesting other geophysical factors may also influence H distributions. In this study we consider insolation and its resulting thermal effects as a loss/redistribution process influencing the Moon's near-surface < 1m volatile H budget. To isolate regional (5deg latitude band) insolation effects we correlate two data sets collected from the ongoing, 1.5 year long mapping mission of the Lunar Reconnaissance Orbiter (LRO). Epithermal neutron mapping data from the Lunar Exploration Neutron Detector (LEND) is registered and analyzed in the context of slope derivations from Lunar topography maps produced by the Lunar Observing Laser Altimeter (LOLA). Lunar epithermal neutrons are inferred to be direct geochemical evidence for near-surface H due to the correlated suppression of surface leakage fluxes of epithermal neutrons with increased H concentration. Regional suppressions of neutrons seen in LEND maps are considered localized evidence of H concentration increase in the upper 1 m of the Lunar surface. To quantify spatially localized insolation effects, LEND data are averaged from sparsely distributed pixels, classed as a function of the LOLA slope derivations.

McClanahan, T. P.↗

NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗

NASA NTRSDate not supplied

Recent Progress and Development of Self-Aligned Focusing Schlieren

A broad range of developments and applications of self-aligned focusing schlieren (SAFS)systems is presented. The replacement of the Ronchi ruling (RR) with a𝜇LCD was demonstrated with digital SAFS, which provides the ability to modulate the pattern and orientation without physical intervention. Additionally, replacement of the traditional RR with spectral-spatial filters was demonstrated to yield the ability to simultaneously image horizontal and vertical index of refraction gradients using two separate wavelengths. The application of the Scheimpflug principle to SAFS systems enables off-axis imaging and allowed for extended fields-of-view to be captured using a two-camera system. The addition of a plenoptic camera to a baseline SAFS system enables refocusing capabilities, which is demonstrated on two jets offset along the optical axis. Furthermore, application of an event-based camera to SAFS was shown to enable acquisition of sparse, spatio-temporal data. Beyond the listed developments, SAFS was applied to difficult and constraining facilities, such as the National Full-Scale Aerodynamics Complex(NFAC) 80- by 120-ft Wind Tunnel and the Thermal Acoustic Engines (TAE) rig at NASA Ames Research Center and NASA Glenn Research Center, respectively. Behavior of Ronchi rulings, Rochon prisms, and the use of quarter-wave plates versus quarter-wave films are evaluated to provide insight for future experimental design of SAFS-systems.

Self-Aligned Focusing Schlieren↗

NASA NTRSDate not supplied

Recent Progress and Development of Self-Aligned Focusing Schlieren

Self-Aligned Focusing Schlieren↗

DOE OSTI2022

Randomized Functional Sparse Tucker Tensor for Compression and Fast Visualization of Scientific Data.

Abstract not provided.

Rai, Prashant↗

DOE OSTI2025

Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems

Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist, however each model is only useful in a certain regime and often limited by computational complexity. To ensure data-driven methods align with theory, they must be consistent and predictable when acting on data whose governing dynamics are known. Weak Sparse Identification of Nonlinear Dynamics (WSINDy) is a recently developed data-driven method that has shown promise in learning governing dynamics from data with high noise levels [1]. This work examines how WSINDy acts on ideal MHD test problems as the initial conditions are varied and specifies limiting requirements for successful equation identification. Furthermore, it is hard to recover the governing dynamics from data that emphasize a single dominant behavior. In these low information cases, Shannon information entropy is able to pick up on the redundancies in the data that affect recoverability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

Abstract There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1987

Computer Sciences and Data Systems, volume 1

Topics addressed include: software engineering; university grants; institutes; concurrent processing; sparse distributed memory; distributed operating systems; intelligent data management processes; expert system for image analysis; fault tolerant software; and architecture research.

Source record↗

DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

NASA NTRSDate not supplied

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗

DOE OSTI2024

$\overline{TKE}$ Parameterization and $\bar{v}$ Uncertainty Analysis for CGMF

Previous work was performed on tuning CGMF parameters for 235 U, 238 U, and Plutonium isotopes. Now work is being done to tune minor uranium isotopes. However, uranium isotopes like 232 U and 236 U have almost no experimental data. We are applying cross-isotope models to extrapolate and tune CGMF on isotopes that lack experimental data. There exist several internal CGMF physics quantities that affect the output of CGMF—multi-chance fission probability, excitation energy sharing, spin-cutoff factor, spin scaling, and fragment total kinetic energy to name a few. The mean fragment total kinetic energy, $\overline{TKE}$, is particularly interesting because of its strong anti-correlation with $\bar{v}$. We are most interested in the mean fragment total kinetic energy before neutron emissions. $\overline{TKE}$ is assumed to be pre-neutron emission unless otherwise stated. Currently in CGMF, the $\overline{TKE}$ model for 233,234,235,238 U are tuned independently to reproduce ν for the associated isotopes. In this report, we will tune a cross-isotope $\overline{TKE}$ model to experimental $\overline{TKE}$ data for 232,233,234,235,236,238 U. Because of the unreliable and sparse nature of $\overline{TKE}$ experimental data, future work will use more reliable experimental $\bar{v}$ data to infer the $\overline{TKE}$ model (and likely other internal CGMF parameters) for uranium isotopes. Such work has been performed previously using a sensitivity analysis and Kalman filter methods.

07 ISOTOPE AND RADIATION SOURCES↗