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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Icing Test Results on an Advanced Two-Dimensional High-Lift Multi-Element Airfoil

An experimental study has been conducted to investigate ice accretions on a high-lift, multi-element airfoil in the Icing Research Tunnel at the NASA Lewis Research Center. The airfoil is representative of an advanced transport wing design. The experimental work was conducted as part of a cooperative program between McDonnell Douglas Aerospace and the NASA Lewis Research Center to improve current understanding of ice accretion characteristics on the multi-element airfoil. The experimental effort also provided ice shapes for future aerodynamic tests at flight Reynolds numbers to ascertain high-lift performance effects. Ice shapes documented for a landing configuration over a variety of icing conditions are presented along with analyses.

Shin, Jaiwon↗
NASA NTRS1994

Ultra high purity, dimensionally stable INVAR 36

An INVAR 36 material having long-term dimensional stability is produced by sintering a blend of powders of nickel and iron under pressure in an inert atmosphere to form an alloy containing less than 0.01 parts of carbon and less than 0.1 part aggregate and preferably 0.01 part individually of Mn, Si, P, S and Al impurities. The sintered alloy is heat treated and slowly and uniformly cooled to form a material having a coefficient of thermal expansion of less than 1 ppm/C and a temporal stability of less than 1 ppm/year.

Sokolowski, Witold M.↗
NASA NTRS1995

Ultra high purity, dimensionally stable INVAR 36

An INVAR 36 material having long-term dimensional stability is produced by sintering a blend of powders of nickel and iron under pressure in an inert atmosphere to form an alloy containing less than 0.01 parts of carbon and less than 0.1 part aggregate and preferably 0.01 part individually of Mn, Si, P, S and Al impurities. The sintered alloy is heat treated and slowly and uniformly cooled to form a material having a coefficient of thermal expansion of less than 1 ppm/C and a temporal stability of less than 1 ppm/year.

Sokolowski, Witold M.↗
NASA NTRS1998

Three-Dimensional High-Lift Analysis Using a Parallel Unstructured Multigrid Solver

A directional implicit unstructured agglomeration multigrid solver is ported to shared and distributed memory massively parallel machines using the explicit domain-decomposition and message-passing approach. Because the algorithm operates on local implicit lines in the unstructured mesh, special care is required in partitioning the problem for parallel computing. A weighted partitioning strategy is described which avoids breaking the implicit lines across processor boundaries, while incurring minimal additional communication overhead. Good scalability is demonstrated on a 128 processor SGI Origin 2000 machine and on a 512 processor CRAY T3E machine for reasonably fine grids. The feasibility of performing large-scale unstructured grid calculations with the parallel multigrid algorithm is demonstrated by computing the flow over a partial-span flap wing high-lift geometry on a highly resolved grid of 13.5 million points in approximately 4 hours of wall clock time on the CRAY T3E.

Mavriplis, Dimitri J.↗
NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗
NASA NTRS1995

Micromachined Silicon Waveguide Circuits

Rectangular waveguides are commonly used as circuit elements in remote-sensing heterodyne receivers at millimeter wavelengths. The advantages of waveguides are low loss and mechanical tunability. However, conventional machining techniques for waveguide components operating above a few hundred GHz are complicated and costly. Waveguides micromachined from silicon however would have several important advantages including low-cost; small size for very high frequency (submillimeter wave) operation; high dimensional accuracy (important for high-Q circuits); atomically smooth walls, thereby reducing rf losses; and the ability to integrate active and passive devices directly in the waveguide on thin membranes, thereby solving the traditional problem of mounting thin substrates.

Silicon Waveguide↗
NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗
NASA NTRS1984

The status of analytical preparation for 2-dimensional testing at high transonic speeds in the University of Southampton transonic self-streamlining wind tunnel

Validation data from the Transonic Self-Streamlining Wind Tunnel has proved the feasibility of streamlining two dimensional flexible walls at low speeds and up to transonic speeds, the upper limit being the speed where the flexible walls are just supercritical. At this condition, breakdown of the wall setting strategy is evident in that convergence is neither as rapid nor as stable as for lower speeds, and wall streamlining criteria are not always completely satisfied. The only major step necessary to permit the extension of two dimensional testing into higher transonic speeds is the provision of a rapid algorithm to solve for mixed flow in the imagery flow fields. The status of two dimensional high transonic testing in the Transonic Self-Streamlining Wind Tunnel is outlined and, in particular, the progress of adapting an algorithm, which solves the Transonic Small Perturbation Equation, for predicting the imagery flow fields is detailed.

Lewis, M. C.↗
NASA NTRS1993

Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

Becus, Georges A.↗
NASA NTRS1992

Quality Function Deployment for Large Systems

Quality Function Deployment (QFD) is typically applied to small subsystems. This paper describes efforts to extend QFD to large scale systems. It links QFD to the system engineering process, the concurrent engineering process, the robust design process, and the costing process. The effect is to generate a tightly linked project management process of high dimensionality which flushes out issues early to provide a high quality, low cost, and, hence, competitive product. A pre-QFD matrix linking customers to customer desires is described.

Dean, Edwin B.↗
NASA NTRS2022

Resolving the fine structure in the energy landscapes of repeat proteins

Ankyrin (ANK) repeat proteins are coded by tandem occurrences of patterns with around 33 amino acids. They often mediate protein–protein interactions in a diversity of biological systems. These proteins have an elongated non-globular shape and often display complex folding mechanisms. This work investigates the energy landscape of representative proteins of this class made up of 3, 4 and 6 ANK repeats using the energy-landscape visualisation method (ELViM). By combining biased and unbiased coarse-grained molecular dynamics AWSEM simulations that sample conformations along the folding trajectories with the ELViM structure-based phase space, one finds a three-dimensional representation of the globally funnelled energy surface. In this representation, it is possible to delineate distinct folding pathways. We show that ELViMs can project, in a natural way, the intricacies of the highly dimensional energy landscapes encoded by the highly symmetric ankyrin repeat proteins into useful low-dimensional representations. These projections can discriminate between multiplicities of specific parallel folding mechanisms that otherwise can be hidden in oversimplified depictions.

Murilo N. Sanches↗
NASA NTRS1984

Body-turbulence interaction

The paper reviews the area of body-turbulence interaction with particular emphasis upon the influence of the body upon an incident turublent field. Cases considered include two-dimensional (high and low fineness ratio, porous, and impervious) and three-dimensional bodies in-stream, adjacent to, and attached to walls. Particular physics common to several geometric and incident flow configurations include (1) eddy severing at relatively sharp leading edges, (2) production of vorticity of the opposite sense on bluff bodies, and (3) body region production of control vortices which affect the incident turbulence field for the order of 100 boundary-layer thicknesses downstream. The major local effects of the body upon the incident turbulent field include (1) a blocking effect, (2) influence of the body momentum deficit/near wake, (3) distortion due to the body time-averaged flow field, and (4) unsteady body circulation. The review may be of particular interest for turbulence alteration/control using fixed geometry in applications such as drag reduction, separation control, noise reduction, and augmentor optimization.

Bushnell, D. M.↗
NASA NTRS1974

Study of genetic direct search algorithms for function optimization

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Zeigler, B. P.↗
NASA NTRS1985

The investigation of tethered satellite system dynamics

A progress report is presented that deals with three major topics related to Tethered Satellite System Dynamics. The SAO rotational dynamics computer code was updated. The program is now suitable to deal with inclined orbits. The output has been also modified in order to show the satellite Euler angles referred to the rotating orbital frame. The three-dimensional high resolution computer program SLACK3 was developed. The code simulates the three-dimensional dynamics of a tether going slack taking into account the effect produced by boom rotations. Preliminary simulations on the three-dimensional dynamics of a recoiling slack tether are shown in this report. A program to evaluate the electric potential around a severed tether is immersed in a plasma. The potential is computed on a three-dimensional grid axially symmetric with respect to the tether longitudinal axis. The electric potential variations due to the plasma are presently under investigation.

Lorenzini, E.↗
NASA NTRS1989

Low-dimensional chaos in turbulence

Direct numerical simulations are being performed on two different fluid flows in an attempt to discover the mechanism underlying the transition to turbulence in each. The first system is Taylor-Couette flow; the second, two-dimensional flow over an airfoil. Both flows exhibit a gradual transition to high-dimensional turbulence through low-dimensional chaos. The hope is that the instabilities leading to chaos will be easier to relate to physical processes in this case, and that the understanding of these mechanisms can then be applied to a wider array of turbulent systems.

Vastano, John A.↗