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At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Two-dimensional hygrothermal diffusions into a finite width composite laminate

Two dimensional hygroscopic diffusion into composite laminates with free edges was obtained through the use of a second order finite difference scheme. A computer program was developed for the transient nonlinear coupled hygrothermal diffusion into finite width laminates with varying surface conditions along two edges. The formulation permits the diffusion coefficient to be a function of temperature, moisture concentration, and fiber orientation. The moisture distributions thus obtained are necessary for analysis of the moisture induced interlaminar stresses in the vicinity of free edges. It is shown that large diffusion times tend to eliminate the significance of stacking sequence, but gradients within and between layers are significant for all diffusion times.

Farley, G. L.↗
NASA NTRS1978

A critical evaluation of the minimization techniques as applied to nonlinear structural analyses

This paper identifies the potential for unconstrained minimization algorithms of mathematical programming to be cost-effective with other conventional techniques of transient nonlinear structural analysis. With this in mind the authors have attempted to critically evaluate a few of the most commonly used algorithms for their effectiveness in solving structural problems involving geometric and material nonlinearities. The algorithms have been categorized as being zeroth order, first order and second order depending upon the order of the derivative of the function called for by the algorithm. The sensitivity of these algorithms to the accuracy of derivatives derived on the basis of finite difference operations clearly suggest using analytically derived derivatives to obtain better control on the number of minimizations required for convergence to the exact solution.

Kamat, M. P.↗
NASA NTRS1982

Dynamical electrophotoconductivity in polycrystalline thin films

Polycrystalline cadmium sulfide (CdS) films were deposited on lithium niobate (LiNbO3) substrates by vacuum evaporation and annealed to obtain high photosensitivity. The change in photoconductivity of these films due to the penetration of electric fields associated with elastic waves propagating on their substrates was demonstrated and studied. The relationship between the acoustic electric field and the induced change in film conductivity was found to be a nonlinear one. The fractional change in conductivity is strongly dependent on the light intensity and the film temperature, showing a prominent maximum as a function of these quantities. The largest recorded fractional change in conductivity was about 25% at electric fields of the order of 1,000 volts per centimeter. A phenomological model was developed based on the interaction between the space charge created by the electric field and the electron trapping states in the photoconductor.

Kowel, S. T.↗
NASA NTRS1983

Numerical solutions for spin-up from rest in a cylinder

A set of three-dimensional flow-field data for the region around a cylinder impulsively spun-up from rest was derived with a numerical model based on the Navier-Stokes equations. Laser-Doppler anemometer data in the azimuthal direction was employed to test the model predictions, and data was developed for a flowfield with Ekman numbers from 9.18/1,000,000 to 9.18/10,000. The contributions of inviscid and viscous terms were determined as functions of radius and time. It was found that immediately after start-up viscous diffusion is the dominant factor, which is replaced by nonlinear radial advection. The Coriolis force dominates in the later stages of spin-up. The inward radial flow is a maximum near the front, where the vertical velocity is small, but features strong radial gradients, as it does at the edge of the Ekman layer.

Hyun, J. M.↗
NASA NTRS1985

Remote sensing of earth terrain

Progress on the investigation of the anisotropy of the terrain media, such as vegetation canopy and sea ice, and the study of the fluctuation-dissipation theorem in conjunction with the application of strong fluctuation theory for passive remote sensing of snowpacks is reported. The Feynman diagrammatic technique is used to derive the Dyson equation for the mean field and the Bethe-Salpeter equation for the correlation or the covariance of the field for electromagnetic wave propagation and scattering in an anisotropic random medium. With the random permittivity expressed in a general form, the bilocal and the nonlinear approximations are employed to solve the Dyson equation and the ladder approximation to the Bethe-Salpeter equation. The mean dyadic Green's function for a two layer anisotropic random medium with arbitrary three dimensional correlation function was investigated with the zeroth-order solutions to the Dyson equation under the four characteristic waves associated with the coherent vector fields propagating in an anisotropic random medium layer, which are the ordinary and extraordinary waves with upward and downward propagating vectors.

Kong, J. A.↗
NASA NTRS1987

Calculating Sonic-Boom Propagation

Nonlinear effects included, enabling more-realistic modeling. Modified Method of Characteristics Sonic Boom Extrapolation Program (MMOC) is computer program for sonic-boom propagation that includes shock coalescence and incorporates effects of asymmetries due to volume and lift. Numerically integrates nonlinear governing equations using data on initial data line approximately one body length from aircraft and yields sonic-boom pressure at ground as function of time or of position at given time. MMOC written in FORTRAN IV.

Darden, C. M.↗
NASA NTRS1989

Contact solution algorithms

Two algorithms for obtaining static contact solutions are described in this presentation. Although they were derived for contact problems involving specific structures (a tire and a solid rubber cylinder), they are sufficiently general to be applied to other shell-of-revolution and solid-body contact problems. The shell-of-revolution contact algorithm is a method of obtaining a point load influence coefficient matrix for the portion of shell surface that is expected to carry a contact load. If the shell is sufficiently linear with respect to contact loading, a single influence coefficient matrix can be used to obtain a good approximation of the contact pressure distribution. Otherwise, the matrix will be updated to reflect nonlinear load-deflection behavior. The solid-body contact algorithm utilizes a Lagrange multiplier to include the contact constraint in a potential energy functional. The solution is found by applying the principle of minimum potential energy. The Lagrange multiplier is identified as the contact load resultant for a specific deflection. At present, only frictionless contact solutions have been obtained with these algorithms. A sliding tread element has been developed to calculate friction shear force in the contact region of the rolling shell-of-revolution tire model.

Tielking, John T.↗
NASA NTRS1990

Magnetic reconnection driven by current repulsion

The evolution of an equilibrium consisting of two magnetic islands with oppositely directed currents embedded in a strong magnetic field is investigated, using numerical simulation methods. The rapid development of an ideal magnetohydrodynamic instability is observed, which first rotates and then expels the islands. The growth rate is on the order of the inverse of the Alfven transit time and is much higher than that for magnetic island coalescence. In the nonlinear stage, resistivity becomes important as the reconnection process ensues and dissipates the magnetic energy. The growth rate of the instability is a weak function of the plasma beta and other plasma parameters such as S, the magnetic Reynolds number. An energy principle analysis, based on eigenfunctions obtained from the simulation, confirms the existence of the instability.

Richard, R. L.↗
NASA NTRS1993

Estimating snow grain size using AVIRIS data

Estimates of snow grain size for the near-surface snow layer were calculated for the Tioga Pass region and Mammoth Mountain in the Sierra Nevada, California, using an inversion technique and data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The Tioga Pass and Mammoth Mountain single-band AVIRIS radiance images were atmospherically corrected to obtain surface reflectance. A discrete-ordinate model was used to calculate directional reflectance as a function of snowpack grain size for a wide range of snow grain radii. The resulting radius vs. reflectance curves were each fit using a nonlinear least squares technique which provided a means of transforming surface reflectance in each AVIRIS image to optically equivalent grain size on a per-pixel basis. The model results and grain size estimates derived from the AVIRIS data show that, for solar incidence angles between 0 and 30, the technique provides good estimates of grain size. This work provides the first quantitative estimates for grain size using data acquired from an airborne remote sensing instrument and is an important step in improving our ability to retrieve snow physical properties independent of field measurements.

Nolin, Anne W.↗
NASA NTRS1994

A Fully Associative, Non-Linear Kinematic, Unified Viscoplastic Model for Titanium Based Matrices

Specific forms for both the Gibb's and complementary dissipation potentials are chosen such that a complete (i.e., fully associative) potential based multiaxial unified viscoplastic model is obtained. This model possesses one tensorial internal state variable that is associated with dislocation substructure, with an evolutionary law that has nonlinear kinematic hardening and both thermal and strain induced recovery mechanisms. A unique aspect of the present model is the inclusion of non-linear hardening through the use of a compliance operator, derived from the Gibb's potential, in the evolution law for the back stress. This non-linear tensorial operator is significant in that it allows both the flow and evolutionary laws to be fully associative (and therefore easily integrated) and greatly influences the multiaxial response under non-proportional loading paths. In addition to this nonlinear compliance operator, a new consistent, potential preserving, internal strain unloading criterion has been introduced to prevent abnormalities in the predicted stress-strain curves, which are present with nonlinear hardening formulations, during unloading and reversed loading of the external variables. Specification of an experimental program for the complete determination of the material functions and parameters for characterizing a metallic matrix, e.g., TIMETAL 21S, is given. The experiments utilized are tensile, creep, and step creep tests. Finally, a comparison of this model and a commonly used Bodner-Partom model is made on the basis of predictive accuracy and numerical efficiency.

Arnold, S. M.↗
NASA NTRS1999

The Prospect for Remote Sensing of Cirrus Clouds with a Submillimeter-Wave Spectrometer

Given the substantial radiative effects of cirrus clouds and the need to validate cirrus cloud mass in climate models, it is important to measure the global distribution of cirrus properties with satellite remote sensing. Existing cirrus remote sensing techniques, such as solar reflectance methods, measure cirrus ice water path (IWP) rather indirectly and with limited accuracy. Submillimeter/wave radiometry is an independent method of cirrus remote sensing based on ice particles scattering the upwelling radiance emitted by the lower atmosphere. A new aircraft instrument, the Far Infrared Sensor for Cirrus (FIRSC), is described. The FIRSC employs a Fourier Transform Spectrometer (FTS). which measures the upwelling radiance across the whole submillimeter region (0.1 1.0-mm wavelength). This wide spectral coverage gives high sensitivity to most cirrus particle sizes and allows accurate determination of the characteristic particle size. Radiative transfer modeling is performed to analyze the capabilities of the submillimeter FTS technique. A linear inversion analysis is done to show that cirrus IWP, particle size, and upper-tropospheric temperature and water vapor may be accurately measured, A nonlinear statistical algorithm is developed using a database of 20000 spectra simulated by randomly varying most relevant cirrus and atmospheric parameters. An empirical orthogonal function analysis reduces the 500-point spectrum (20 - 70/cm) to 15 "pseudo-channels" that are then input to a neural network to retrieve cirrus IWP and median particle diameter. A Monte Carlo accuracy study is performed with simulated spectra having realistic noise. The retrieval errors are low for IWP (rms less than a factor of 1.5) and for particle sizes (rins less than 30%) for IWP greater than 5 g/sq m and a wide range of median particle sizes. This detailed modeling indicates that there is good potential to accurately measure cirrus properties with a submillimeter FTS.

Evans, K. Franklin↗
DOE OSTI2022

Cost function for low-dimensional manifold topology assessment

Abstract In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.

42 ENGINEERING↗
DOE OSTI2021

Probing nonlinear velocity profiles of shear-thinning, nematic platelet dispersions in Couette flow using x-ray photon correlation spectroscopy

In this study, we report experiments employing x-ray photon correlation spectroscopy (XPCS) to characterize the velocity profiles of complex fluids in Couette flow. The approach involves modeling the XPCS correlation functions obtained with the incident x-ray beam passing tangentially through the Couette cell gap at various distances from the inner wall. We first demonstrate the technique with measurements on a dilute colloidal dispersion in the Newtonian liquid glycerol, where the expected linear velocity profiles are recovered. We then employ the technique to map the shear-rate-dependent velocity profiles of a shear-thinning dispersion of nematically ordered Gibbsite platelets. The nonlinear velocity profiles of the Gibbsite dispersion include a narrow slip region adjacent to the outer wall and a band with a small velocity gradient in the interior of the gap that evolves into a region increasingly resembling plug flow with increasing shear rate. Variations in the velocity profile along the vorticity direction indicate an instability in the interface between this region of small velocity gradient and a region of high velocity gradient near the inner wall. The analysis of the small-angle scattering patterns provides information about the spatial and temporal variations in the nematic order of the Gibbsite dispersion and their coupling to the velocity profile. Additional potential applications of this XPCS-based technique and comparisons with established methods for characterizing velocity profiles are discussed.

42 ENGINEERING↗
DOE OSTI2021

Charged Ferroelectric Domain Walls for Deterministic ac Signal Control at the Nanoscale

The direct current (dc) conductivity and emergent functionalities at ferroelectric domain walls are closely linked to the local polarization charges. Depending on the charge state, the walls can exhibit unusual dc conduction ranging from insulating to metallic-like, which is leveraged in domain-wall-based memory, multilevel data storage, and synaptic devices. In contrast to the functional dc behaviors at charged walls, their response to alternating currents (ac) remains to be resolved. Here, we reveal ac characteristics at positively and negatively charged walls in ErMnO 3 , distinctly different from the response of the surrounding domains. By combining voltage-dependent spectroscopic measurements on macroscopic and local scales, we demonstrate a pronounced nonlinear response at the electrode-wall junction, which correlates with the domain-wall charge state. The dependence on the ac drive voltage enables reversible switching between uni- and bipolar output signals, providing conceptually new opportunities for the application of charged walls as functional nanoelements in ac circuitry.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Nonlinear dynamics, avalanches, and noise for driven Wigner crystals

Here we consider the driven dynamics of Wigner crystals interacting with random disorder. Using numerical simulations, we find a rich variety of transport phenomena as a function of charge density, drive, and pinning strength. For weak pinning, the system forms a defect-free crystal that depins elastically. When the pinning is stronger, a pinned glass phase appears that depins into a filamentary flow state, transitions at higher drives into a disordered flow phase, and finally forms a moving smectic. Within the filamentary flow phase, the conduction curves can show switching dynamics as well as negative differential conductivity in which switching events cause some flow channels to be blocked. The velocity noise in the filamentary flow regime exhibits narrow band characteristics due to the one-dimensional nature of the motion, while the moving smectic has narrow band velocity noise with a washboard frequency. In the disordered flow state, the noise power reaches a peak value and the noise has a 1/$\mathcal{f}$ character. Our transport results are consistent with recent experimental transport studies in systems where Wigner crystal states are believed to be occurring. Below the conduction threshold, we find that avalanches with a power-law size distribution appear when there are sudden local rearrangements of charges from one pinned configuration to another.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1986

An adaptive characteristic Petrov-Galerkin finite element method for convection-dominated linear and nonlinear parabolic problems in one space variable

The present adaptive FEM technique for convection-dominated problems is based on a Petrov-Galerkin scheme for spatial approximation, whose typical time-step employs test functions chosen to yield an approximate solution coinciding with the exact solutions at the finite element grid nodes. The derivation of truly local a posteriori error estimates is made possible by this procedure, which is also shown to be a very effective solver by the numerical examples presented.

Demkowicz, L.↗
NASA NTRS1998

Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data

Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.

Klein, Vladislav↗
NASA NTRS2009

Hybrid NN/SVM Computational System for Optimizing Designs

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan↗