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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Decoding polymer self-dynamics using a two-step approach

The self-correlation function and corresponding self-intermediate scattering function in Fourier space are important quantities for describing the molecular motions of liquids. This work draws attention to a largely overlooked issue concerning the analysis of these space-time density-density correlation functions of polymers. Here, we show that the interpretation of non-Gaussian behavior of polymers is generally complicated by intrachain averaging of distinct self-dynamics of different segments. By the very nature of the mathematics involved, the averaging process not only conceals critical dynamical information, but also contributes to the observed non-Gaussian dynamics. To fully expose this issue and provide a thorough benchmark of polymer self-dynamics, we perform analyses of coarse-grained molecular dynamics simulations of linear and ring polymer melts as well as several theoretical models using a “two-step” approach, where interchain and intrachain averagings of segmental self-dynamics are separated. While past investigations primarily focused on the average behavior, our results indicate that a more nuanced approach to polymer self-dynamics is clearly required.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Statistical properties of two sine waves in Gaussian noise.

A detailed study is presented of some statistical properties of a stochastic process that consists of the sum of two sine waves of unknown relative phase and a normal process. Since none of the statistics investigated seem to yield a closed-form expression, all the derivations are cast in a form that is particularly suitable for machine computation. Specifically, results are presented for the probability density function (pdf) of the envelope and the instantaneous value, the moments of these distributions, and the relative cumulative density function (cdf).

Esposito, R.↗
DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗
DOE OSTI2018

Sparse modeling of space- and time-varying diffraction response of a progressively loaded aluminum alloy

Highlights: • Plasticity induces crystal orientation and lattice spacing heterogeneity. • High energy x-ray diffraction spot broadening typically accompanies plasticity. • Method quantifies spot broadening in difficult, mixed microstructure data. • Algorithm finds sparse combination of entries from dictionary of peak functions. • Approach showed asymmetric plastic response in preprocessed AA7075 T651 sample. - Abstract: High energy X-ray diffraction data collected in situ during loading experiments permits probing of the crystal structure of a plastically deforming material sample. An elastoplastic deformation is associated with heterogeneity in both crystal orientation and lattice spacing—each manifesting as azimuthal broadening and radial broadening of diffraction peaks respectively. Quantifying the spreading effect is challenging, especially in cases where the sample has a granularity between that of a single crystal and fine grain or powder material. The approach developed in this paper begins by modeling the intensity signal in the vicinity of a Debye-Scherrer ring as a sparse, nonnegative superposition of Gaussian basis functions drawn from an over-complete dictionary. Processing automatically selects from the dictionary basis functions whose widths and amplitudes are representative of the data. The chosen basis functions are used to compute scalar measures of radial and azimuthal broadening for the set of diffraction peaks composing a Debye-Scherrer ring. To demonstrate the utility of the approach, we show that analysis of diffraction peak shape information from an aluminum alloy 7075 T651 (AA7075 T651) sample provides insight into the origin of material state that would have otherwise have been difficult if not impossible to extract using conventional methods. Furthermore, the extracted information revealed a deformation response linked to the processing history of the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correspondence between neuroevolution and gradient descent

Abstract We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learning process, neuroevolution is equivalent to gradient descent on the loss function. We use numerical simulation to show that this correspondence can be observed for finite mutations, for shallow and deep neural networks. Our results provide a connection between two families of neural-network training methods that are usually considered to be fundamentally different.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Bimodal Control of Heat Transport at Graphene–Metal Interfaces Using Disorder in Graphene

Thermal energy transport across the interfaces of physically and chemically modified graphene with two metals, Al and Cu, was investigated by measuring thermal conductance using the time-domain thermoreflectance method. Graphene was processed using a He 2+ ion-beam with a Gaussian distribution or by exposure to ultraviolet/O 3 , which generates structural or chemical disorder, respectively. Hereby, we could monitor changes in the thermal conductance in response to varying degrees of disorder. We find that the measured conductance increases as the density of the physical disorder increases, but undergoes an abrupt modulation with increasing degrees of chemical modification, which decreases at first and then increases considerably. Moreover, we find that the conductance varies inverse proportionally to the average distance between the structural defects in the graphene, implying a strong in-plane influence of phonon kinetics on interfacial heat flow. We attribute the bimodal results to an interplay between the distinct effects on graphene’s vibrational modes exerted by graphene modification and by the scattering of modes.

Science & Technology - Other Topics↗
DOE OSTI2015

Role of excited state solvent fluctuations on time-dependent fluorescence Stokes shift

We explore the connection between the solvation dynamics of a chromophore upon photon excitation and equilibrium fluctuations of the solvent. Using molecular dynamics simulations, fluorescence Stokes shift for the tryptophan in Staphylococcus nuclease was examined using both nonequilibrium calculations and linear response theory. When the perturbed and unperturbed surfaces exhibit different solvent equilibrium fluctuations, the linear response approach on the former surface shows agreement with the nonequilibrium process. This agreement is excellent when the perturbed surface exhibits Gaussian statistics and qualitative in the case of an isomerization induced non-Gaussian statistics. However, the linear response theory on the unperturbed surface breaks down even in the presence of Gaussian fluctuations. Experiments also provide evidence of the connection between the excited state solvent fluctuations and the total fluorescence shift. These observations indicate that the equilibrium statistics on the excited state surface characterize the relaxation dynamics of the fluorescence Stokes shift. Our studies specifically analyze the Gaussian fluctuations of the solvent in the complex protein environment and further confirm the role of solvent fluctuations on the excited state surface. The results are consistent with previous investigations, found in the literature, of solutes dissolved in liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Radiation of a charge flying in a partially loaded dielectric section of a waveguide

We consider the electromagnetic field of a point charged particle moving along the axis of a cylindrical waveguide from a homogeneously filled area to a dielectric loading area having an axially symmetrical channel. We are interested in studying the Cherenkov radiation excited in the bilayer area. The solution is performed by expanding the field in each area in a series of orthogonal eigenmodes. The main attention is focused on investigation of the wave field in the bilayer section. We show that, at a given observation point, the "reduced wakefield" is simplified with time (the number of modes decreases). The obtained results are generalized for the case of a bunch with Gaussian longitudinal profile. The typical numerical results for wakefield formation process are presented. These results agree with simulations done by the industry standard electromagnetic code CST Particle Studio.

Grigoreva, Aleksandra A.↗
NASA NTRS1974

Ion and electron temperatures in the SUMMA mirror device by emission spectroscopy

Ion temperatures were obtained in the SUMMA mirror device by observing the Doppler-broadened charge-exchange component of the 667.8and 587.6-nm He lines in He plasma and the H alpha and H beta lines in H2 plasma. The second moment of the line profiles was used as the parameter to determine ion temperature. Corrections for magnetic splitting, fine structure, monochromator slit function, and variation in charge-exchange cross section with energy are derived and included. Even for constant cross section, no magnetic splitting or fine structure, and infinitely narrow slit function, the line profile is not Gaussian, because the excitation results from a change-exchange process. Comparison is made with temperatures from a neutral particle analyzer.-

Patch, R. W.↗
NASA NTRS1994

MEM application to IRAS CPC images

A method for applying the Maximum Entropy Method (MEM) to Chopped Photometric Channel (CPC) IRAS additional observations is illustrated. The original CPC data suffered from problems with repeatability which MEM is able to cope with by use of a noise image, produced from the results of separate data scans of objects. The process produces images of small areas of sky with circular Gaussian beams of approximately 30 in. full width half maximum resolution at 50 and 100 microns. Comparison is made to previous reconstructions made in the far-infrared as well as morphologies of objects at other wavelengths. Some projects with this dataset are discussed.

Marston, A. P.↗
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
DOE OSTI2016

On the determination of the crystal-vapor surface free energy, and why a Gaussian expression can be accurate for a system far from Gaussian

The crystal-vapor surface free energy γ is an important physical parameter governing physical processes, such as wetting and adhesion. We explore exact and approximate routes to calculate γ based on cleaving an intact crystal into non-interacting sub-systems with crystal-vapor interfaces. We do this by turning off the interactions, ΔV, between the sub-systems. Using the soft-core scheme for turning off ΔV, we find that the free energy varies smoothly with the coupling parameter λ, and a single thermodynamic integration yields the exact γ. We generate another exact method, and a cumulant expansion for γ by expressing the surface free energy in terms of an average of e{sup −βΔV} in the intact crystal. The second cumulant, or Gaussian approximation for γ is surprisingly accurate in most situations, even though we find that the underlying probability distribution for ΔV is clearly not Gaussian. We account for this fact by developing a non-Gaussian theory for γ and find that the difference between the non-Gaussian and Gaussian expressions for γ consist of terms that are negligible in many situations. Exact and approximate methods are applied to the (111) surface of a Lennard-Jones crystal and are also tested for more complex molecular solids, the surface of octane and nonadecane. Alkane surfaces were chosen for study because their crystal-vapor surface free energy has been of particular interest for understanding surface freezing in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1976

A non-gaussian model of continuous atmospheric turbulence for use in aircraft design

A non-Gaussian model of atmospheric turbulence is presented and analyzed. The model is restricted to the regions of the atmosphere where the turbulence is steady or continuous, and the assumptions of homogeneity and stationarity are justified. Also spatial distribution of turbulence is neglected, so the model consists of three independent, stationary stochastic processes which represent the vertical, lateral, and longitudinal gust components. The non-Gaussian and Gaussian models are compared with experimental data, and it is shown that the Gaussian model underestimates the number of high velocity gusts which occur in the atmosphere, while the non-Gaussian model can be adjusted to match the observed high velocity gusts more satisfactorily. Application of the proposed model to aircraft response is investigated, with particular attention to the response power spectral density, the probability distribution, and the level crossing frequency. A numerical example is presented which illustrates the application of the non-Gaussian model to the study of an aircraft autopilot system. Listings and sample results of a number of computer programs used in working with the model are included.

Reeves, P. M.↗
DOE OSTI2017

Probabilistic Cosmological Mass Mapping from Weak Lensing Shear

We infer gravitational lensing shear and convergence fields from galaxy ellipticity catalogs under a spatial process prior for the lensing potential. We demonstrate the performance of our algorithm with simulated Gaussian-distributed cosmological lensing shear maps and a reconstruction of the mass distribution of the merging galaxy cluster Abell 781 using galaxy ellipticities measured with the Deep Lens Survey. Given interim posterior samples of lensing shear or convergence fields on the sky, we describe an algorithm to infer cosmological parameters via lens field marginalization. In the most general formulation of our algorithm we make no assumptions about weak shear or Gaussian-distributed shape noise or shears. Because we require solutions and matrix determinants of a linear system of dimension that scales with the number of galaxies, we expect our algorithm to require parallel high-performance computing resources for application to ongoing wide field lensing surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Diffraction and thermal effect of a Bessel-Gaussian laser for Ag nanoparticle deposition

Nanoparticles are known to sinter at much lower temperatures than the corresponding bulk or micro size particles. A laser-assisted sintering process is considered in this study to sinter Ag nanoparticles by dispensing Ag paste onto an indium tin oxide-coated Si substrate. The Gaussian beam of a CO 2 laser source is propagated through axicon and biconvex lenses, and the resulting hollow beam is focused on the Ag paste with a hollow parabolic mirror. A Bessel-Gaussian irradiance distribution is obtained at the focal plane of the parabolic mirror due to the interference of the hollow laser cone. The Fresnel diffraction approximation is considered to determine the phasor of the laser and an analytical approach is implemented to calculate the irradiance distribution of the Bessel-Gaussian beam. This irradiance distribution is utilized as a heat source in a heat conduction model and the temperature distribution is analyzed for thin Ag films formed during the laser sintering of Ag nanoparticles. An analytical expression is obtained for the temperature distribution by solving the heat conduction equation using Fourier transform for finite media. The widths of the deposited Ag lines are predicted from the temperature profiles and the model predictions compare well with the experimental results. The isotherms are found to be geometrically noncongruent with convex and concave tips depending on the locally maximum and minimum irradiances of the Bessel-Gaussian beam, respectively. The convex and concave tips, however, appear in the same isotherm for sufficiently high substrate speed relative to the laser beam.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools.

60 APPLIED LIFE SCIENCES↗