Image coding by coding contours
Two dimensional data array contour tracing algorithm for reduction of image coding bits number
Wilkins, L. C.↗
SEARCH · Engineering Papers
Results for “Dimensionality Reduction”
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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Preliminary assessment of the potential impact of solid-fueled rocket engines in the stratosphere
Free chlorine atoms produced from HCl deposited by solid-fueled rocket engines react to destroy odd oxygen in the stratosphere. Using a one-dimensional photochemical model, we have computed the reduction of ozone likely to be caused by long-term operation of such vehicles powered by large solid-fueled rocket engines. It is likely that ozone reduction will not be greater than a few tenths of a per cent, although the value is dependent upon ambient NO(x) concentrations.
Whitten, R. C.↗
Drag reduction effects in turbulent boundary layers over wavy walls
Two dimensional incompressible flow over wavy surfaces are analyzed numerically by spectral methods. Algorithms for periodic flows (Fourier modes in the periodic flow direction and Chebycheff modes in the normal direction), and inflow-outflow boundary conditions (Chebycheff modes used in both directions) are described. Results obtained using both codes are reported for laminar flows. Comparisons with known theoretical and experimental results are made.
Balasubramanian, R.↗
Recent development of a jet-diffuser ejector
The paper considers thrust augmenting ejectors in which the processes of mixing and diffusion are partly carried out downstream of the ejector solid surfaces. A jet sheet surrounding the periphery of a widely diverging diffuser prevents separation and forms a gaseous, curved surface to provide effective diffuser ratio and additional length for mixing of primary and induced flows. Three-dimensional potential flow methods achieved a large reduction in the length of the associated solid surface; primary nozzle design further reduced the volume required by the jet-diffuser ejectors, resulting in thrust augmentation in excess of two, and an overall length of about 2 1/2 times the throat width.
Alperin, M.↗
On the control canonical structure of a class of scalar input systems
A discrete finite dimensional system, nonharmonic Fourier series and controllability, reduction to canonical form, and spectral synthesis are considered. The extent to which the eigenvalue associated with a controllable pair of a certain type may be modified via continuous linear state feedback is demonstrated.
Teglas, R.↗
On the control canonical structure of a class of scalar input systems
A discrete finite dimensional system, nonharmonic Fourier series and controllability, reduction to canonical form, and spectral synthesis are considered. The extent to which the eigenvalue associated with a controllable pair of a certain type may be modified via continuous linear state feedback is demonstrated. Previously announced in STAR as N83-28925
Teglas, R. G.↗
Laser Doppler anemometry
The material in this NASA TM is to appear as a chapter on Laser Doppler Anemometry (LDA) in the AGARDograph entitled, A Survey of Measurements and Measuring Techniques in Rapidly Distorted Compressible Turbulent Boundary Layers. The application of LDA (specifically, the dual-beam, burst-counter approach) to compressible flows is discussed. Subjects treated include signal processing, particle light scattering and tracking, data reduction and sampling bias, and three-dimensional measurements.
Johnson, Dennis A.↗
The potential impact on atmospheric ozone and temperature of increasing trace gas concentrations
The response of the atmosphere to emissions of chlorofluorocarbons (CFCs) and other chlorocarbons, and to increasing concentrations of other radiatively active trace gases such as CO2, CH4, and N2O is calculated by a coupled chemical-radiative transport one-dimensional model. It is shown that significant reductions in the ozone concentration and in the temperature are expected in the upper stratosphere as a result of increasing concentrations of active chlorine produced by photodecomposition of the CFCs. The ozone content is expected to increase in the troposphere, as a consequence of increasing concentrations of methane and nitrogen oxides. Due to enhanced greenhouse effects, the Earth's surface should warm up by several degrees. The amplitude and even the sign of future changes in the ozone column are difficult to predict as they are strongly scenario-dependent. An early detection system to prevent noticeable ozone changes as a result of increasing concentrations of source gases should thus be based on a continuous monitoring of the ozone amount in the upper stratosphere rather than on measurements of the ozone column only. Measurements of NOx, Clx, and HOx are also required for unambiguous trend detection and interpretation.
Brasseur, G.↗
Antarctic ozone hole - Possible implications for ozone trends in the Southern Hemisphere
Satellite-borne instruments (the Total Ozone Mapping Spectrometer and the Solar Backscattered Ultraviolet Instrument) show that, compared to 1979, total column ozone has a year-round decrease of more than 5 percent in the neighborhood of 60 deg S. The meteorological conditions (warmer temperatures, the absence of polar stratospheric clouds) at these latitudes do not seem to favor heterogeneous chemistry as the direct cause for the observed year-round ozone reduction. A mechanism involving the seasonal transport of ozone-poor air from within the polar vortex to lower latitudes (the so-called 'dilution effect') is proposed as a possible explanation for the observed year-round ozone reduction in subpolar regions. A two-dimensional model with an imposed springtime Antarctic ozone depletion is used to study the post-ozone hole impact on the spatial and temporal distributions of column ozone at latitudes north of 60 deg S. It is found that the time constant associated with the dilution effect in the latitude region 40-60 deg S is about 1 year, long enough to contribute to the observed year-round decrease of total ozone in that region.
Sze, N. D.↗
Effects of Fineness Ratio and Reynolds Number on the Low-Speed Crosswind Drag Characteristics of Circular and Modified-Square Cylinders
A wind-tunnel investigation has been made on modified-square and circular cylinders to determine the effects of fineness ratio and Reynolds numbers on the crosswind drag characteristics. Fineness ratios from 2 to 14 were investigated over a Reynolds number range from approximately 300,000 to 1,650,000 which corresponded to Mach numbers from 0.057 to 0.377.The result of the investigation show that at supercraft Reynolds numbers the drag coefficient of the circular cylinder increases with increasing Reynolds number for all fineness ratios but at low fineness ratios this effect is considerably less than at higher fineness ratios. For circular cylinders in the high fineness-ratio range there is a reduction in drag as the fineness ratio is decreased except for Reynolds numbers of 900,000 and 1,000,000, whereas at low fineness ratios the opposite trend generally occurs. The addition of hemispherical ends to the circular cylinder gave a substantial decrease in drag at a fineness ratio of 3.27 but the effect was negligible at fineness ratios of 5.27 and 10. The finite-length modified-square cylinder gave the reduction in drag over the two-dimensional modified-square cylinder for the complete range of test Reynolds numbers with the lowest fineness ratio giving the lowest drag at Reynolds numbers above 3O0,OOO.
McKinney, Linwood W.↗
Projection pursuit adaptation on polynomial chaos expansions
Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.
97 MATHEMATICS AND COMPUTING↗
Data reduction through optimized scalar quantization for more compact neural networks
Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.
97 MATHEMATICS AND COMPUTING↗
Numerical and experimental study of Lamb wave propagation in a two-dimensional acoustic black hole
The propagation of laser-generated Lamb waves in a two-dimensional acoustic black-hole structure was studied numerically and experimentally. The geometrical acoustic theory has been applied to calculate the beam trajectories in the region of the acoustic black hole. The finite element method was also used to study the time evolution of propagating waves. An optical system based on the laser-Doppler vibration method was assembled. The effect of the focusing wave and the reduction in wave speed of the acoustic black hole has been validated.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Platinum Deposited Nitrogen-Doped Vertically Aligned Carbon Nanofibers as Methanol Tolerant Catalyst for Oxygen Reduction Reaction with Improved Durability
Nitrogen doping in carbon materials can modify the employed carbon material’s electronic and structural properties, which helps in creating a stronger metal-support interaction. In this study, the role of nitrogen doping in improving the durability of Pt catalysts supported on a three-dimensional vertically aligned carbon nanofiber (VACNF) array towards oxygen reduction reaction (ORR) was explored. The nitrogen moieties present in the N-VACNF enhanced the metal-support interaction and contributed to a reduction in the Pt particle size from 3.1 nm to 2.3 nm. The Pt/N-VACNF catalyst showed better durability when compared to Pt/VACNF and Pt/C catalysts with similar Pt loading. DFT calculations validated the increase in the durability of the Pt NPs with an increase in pyridinic N and corroborated the molecular ORR pathway for Pt/N-VACNF. Moreover, the Pt/N-VACNF catalyst was found to have excellent tolerance towards methanol crossover.
Elangovan, Ayyappan↗
Small Engine Technology (SET) - Task 4, Regional Turboprop/Turbofan Engine Advanced Combustor Study
Under the SET Program Task 4 - Regional Turboprop/Turbofan Engine Advanced Combustor Study, a total of ten low-emissions combustion system concepts were evaluated analytically for three different gas turbine engine geometries and three different levels of oxides of nitrogen (NOx) reduction technology, using an existing AlliedSignal three-dimensional (3-D) Computational Fluid Dynamics (CFD) code to predict Landing and Takeoff (LTO) engine cycle emission values. A list of potential Barrier Technologies to the successful implementation of these low-NOx combustor designs was created and assessed. A trade study was performed that ranked each of the ten study configurations on the basis of a number of manufacturing and durability factors, in addition to emissions levels. The results of the trade study identified three basic NOx-emissions reduction concepts that could be incorporated in proposed follow-on combustor technology development programs aimed at demonstrating low-NOx combustor hardware. These concepts are: high-flow swirlers and primary orifices, fuel-preparation cans, and double-dome swirlers.
Reynolds, Robert↗
Weak Lie symmetry and extended Lie algebra
The concept of weak Lie motion (weak Lie symmetry) is introduced. Applications given exhibit a reduction of the usual symmetry, e.g., in the case of the rotation group. In this context, a particular generalization of Lie algebras is found ('extended Lie algebras') which turns out to be an involutive distribution or a simple example for a tangent Lie algebroid. Riemannian and Lorentz metrics can be introduced on such an algebroid through an extended Cartan-Killing form. Transformation groups from non-relativistic mechanics and quantum mechanics lead to such tangent Lie algebroids and to Lorentz geometries constructed on them (1-dimensional gravitational fields).
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Reduction of Large Dynamical Systems by Minimization of Evolution Rate
Reduction of a large system of equations to a lower-dimensional system of similar dynamics is investigated. For dynamical systems with disparate timescales, a criterion for determining redundant dimensions and a general reduction method based on the minimization of evolution rate are proposed.
Girimaji, Sharath S.↗
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional O(10 n ), n ≥ 2, stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The “curse of dimensionality” can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). Here, we refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.
42 ENGINEERING↗