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Results for “Reduced Order Modeling”

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 1,459 records · Page 81
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NASA NTRS1978

Computerized power supply analysis: State equation generation and terminal models

To aid engineers that design power supply systems two analysis tools that can be used with the state equation analysis package were developed. These tools include integration routines that start with the description of a power supply in state equation form and yield analytical results. The first tool uses a computer program that works with the SUPER SCEPTRE circuit analysis program and prints the state equation for an electrical network. The state equations developed automatically by the computer program are used to develop an algorithm for reducing the number of state variables required to describe an electrical network. In this way a second tool is obtained in which the order of the network is reduced and a simpler terminal model is obtained.

Garrett, S. J.↗
NASA NTRS2009

Thermal Transport Model for Heat Sink Design

A document discusses the development of a finite element model for describing thermal transport through microcalorimeter arrays in order to assist in heat-sinking design. A fabricated multi-absorber transition edge sensor (PoST) was designed in order to reduce device wiring density by a factor of four. The finite element model consists of breaking the microcalorimeter array into separate elements, including the transition edge sensor (TES) and the silicon substrate on which the sensor is deposited. Each element is then broken up into subelements, whose surface area subtends 10 10 microns. The heat capacity per unit temperature, thermal conductance, and thermal diffusivity of each subelement are the model inputs, as are the temperatures of each subelement. Numerical integration using the Finite in Time Centered in Space algorithm of the thermal diffusion equation is then performed in order to obtain a temporal evolution of the subelement temperature. Thermal transport across interfaces is modeled using a thermal boundary resistance obtained using the acoustic mismatch model. The document concludes with a discussion of the PoST fabrication. PoSTs are novel because they enable incident x-ray position sensitivity with good energy resolution and low wiring density.

Chervenak, James A.↗
DOE OSTI2017

Model's sparse representation based on reduced mixed GMsFE basis methods

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.

60 APPLIED LIFE SCIENCES↗
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 OSTI2023

Mean field model parameterization to recover coverage-dependent kinetics

Lateral interactions between adsorbates introduce coverage dependence into adsorption energies and activation barriers of surface reactions. Lattice-based kinetic Monte Carlo (kMC) simulations can capture these interactions quantitatively but are laborious to parameterize and solve. Mean field models are more tractable, but protocols to construct and parameterize them are unclear. Here we explore the ability of a coverage-aware mean-field model to map to a lattice-kMC model of a generic two-step reaction network, including quasi-equilibrated adsorption and rate-limiting dissociation steps. We derive expressions for mean-field and coverage-dependent adsorption energies and dissociation barriers and parameterize against lattice-kMC predictions. We show that the parameterized mean-field rates correlate with ground truth lattice-kMC results across a wide range of reaction conditions and identify regions where the mean field fails. The mean field model similarly captures kMC-derived rate-order, Arrhenius and Sabatier plots at a greatly reduced computational cost. Further, the results provide guidance for parameterizing mean-field models, benchmarked against explicit lattice-based approaches for incorporating the influence of coverage effects.

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

Measurement of the inclusive and fiducial t t &#x00AF;<!-- ¯ --> production cross-sections in the lepton+jets channel in pp collisions at s = 8 &#xA0; TeV with the ATLAS detector

The inclusive and fiducial t t &#x00AF;<!-- ¯ --> production cross-sections are measured in the lepton+jets channel using 20.2 &#xA0; fb &#x2212;<!-- - --> 1 of proton–proton collision data at a centre-of-mass energy of 8 TeV recorded with the ATLAS detector at the LHC. Major systematic uncertainties due to the modelling of the jet energy scale and b-tagging efficiency are constrained by separating selected events into three disjoint regions. In order to reduce systematic uncertainties in the most important background, the W +\,jets process is modelled using Z+ jets events in a data-driven approach. The inclusive t t &#x00AF;<!-- ¯ --> cross-section is measured with a precision of 5.7% to be &#x03C3;<!-- σ --> inc ( t t &#x00AF;<!-- ¯ --> ) = 248.3 &#x00B1;<!-- ± --> 0.7 ( s t a t . ) &#x00B1;<!-- ± --> 13.4 ( s y s t . ) &#x00B1;<!-- ± --> 4.7 ( l u m i . ) &#xA0; pb , assuming a top-quark mass of 172.5 GeV. The result is in agreement with the Standard Model prediction. The cross-section is also measured in a phase space close to that of the selected data. The fiducial cross-section is &#x03C3;<!-- σ --> fid ( t t &#x00AF;<!-- ¯ --> ) = 48.8 &#x00B1;<!-- ± --> 0.1 ( s t a t . ) &#x00B1;<!-- ± --> 2.0 ( s y s t . ) &#x00B1;<!-- ± --> 0.9 ( l u m i . ) &#xA0; pb with a precision of 4.5%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

Turbulence modelling of flow fields in thrust chambers

Following the consensus of a workshop in Turbulence Modelling for Liquid Rocket Thrust Chambers, the current effort was undertaken to study the effects of second-order closure on the predictions of thermochemical flow fields. To reduce the instability and computational intensity of the full second-order Reynolds Stress Model, an Algebraic Stress Model (ASM) coupled with a two-layer near wall treatment was developed. Various test problems, including the compressible boundary layer with adiabatic and cooled walls, recirculating flows, swirling flows, and the entire SSME nozzle flow were studied to assess the performance of the current model. Detailed calculations for the SSME exit wall flow around the nozzle manifold were executed. As to the overall flow predictions, the ASM removes another assumption for appropriate comparison with experimental data to account for the non-isotropic turbulence effects.

Chen, C. P.↗
NASA NTRS1993

Development of an algebraic stress/two-layer model for calculating thrust chamber flow fields

Following the consensus of a workshop in Turbulence Modeling for Liquid Rocket Thrust Chambers, the current effort was undertaken to study the effects of second-order closure on the predictions of thermochemical flow fields. To reduce the instability and computational intensity of the full second-order Reynolds Stress Model, an Algebraic Stress Model (ASM) coupled with a two-layer near wall treatment was developed. Various test problems, including the compressible boundary layer with adiabatic and cooled walls, recirculating flows, swirling flows and the entire SSME nozzle flow were studied to assess the performance of the current model. Detailed calculations for the SSME exit wall flow around the nozzle manifold were executed. As to the overall flow predictions, the ASM removes another assumption for appropriate comparison with experimental data, to account for the non-isotropic turbulence effects.

Chen, C. P.↗
NASA NTRS1972

A study of pilot modeling in multi-controller tasks

A modeling approach, which utilizes a matrix of transfer functions to describe the human pilot in multiple input, multiple output control situations, is studied. The approach used was to extend a well established scalar Wiener-Hopf minimization technique to the matrix case and then study, via a series of experiments, the data requirements when only finite record lengths are available. One of these experiments was a two-controller roll tracking experiment designed to force the pilot to use rudder in order to coordinate and reduce the effects of aileron yaw. One model was computed for the case where the signals used to generate the spectral matrix are error and bank angle while another model was computed for the case where error and yaw angle are the inputs. Several anomalies were observed to be present in the experimental data. These are defined by the descriptive terms roll up, break up, and roll down. Due to these algorithm induced anomalies, the frequency band over which reliable estimates of power spectra can be achieved is considerably less than predicted by the sampling theorem.

Whitbeck, R. F.↗
DOE OSTI2026

A point-particle-based hydride shell-shedding model for ejecta particle transport in reactive environments

A shock wave passing over a rough or perturbed metal surface will induce a limiting case of Richtmyer–Meshkov instability and will cause small particles to eject from the surface and transport into the surrounding medium. These particles are known as ejecta and can be either solid or liquid in nature. Recent experiments have shown that liquid cerium ejecta clouds exhibit unexpected non-monotonic acceleration behaviors as well as temperature plateaus after a brief temperature rise if they are transporting in a chemically reactive, hydrogen-based medium while they act as expected in an inert medium. This work details a point-particle model developed for reactive cerium ejecta transport, which attempts to account for these new physics through the behavior of a developing solid hydride shell, which is believed to form as a product of the reaction. The overall model incorporates the effects of the reaction on the particle properties as well as the effects of potential shedding of the shell into sub-micrometer scale flakes and potential phase change of the hydride if the ejecta particles reach the melt point of the hydride layer. The model is tested by performing simulations of the original motivating experiments and comparing quantities, such as ejected mass, velocimetry, and temperature profiles, against the experimental data. While the model is able to capture many general features of the observed anomalies, some inaccuracies still exist. These point to both missing physics in the model (such as a deuterium adsorption mechanism on the hydride layer) as well as a lack of knowledge of certain material properties (such as the strength of cerium hydride to determine dynamic fracture thicknesses) needed to fully reduce the uncertainties in the model by up to an order of magnitude and perform a true attempt at model validation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

LDRD-DR: Accelerating Nuclear Fuel Qualification through Integrated Multiscale and Multiphysics Models [Slides]

Nuclear fuel qualification implies ensuring that a new fuel can safely operate in a reactor under both normal and accident conditions. Historically, extensive irradiation campaigns probe fuel performance relationships and interconnections in an integral sense, which is very time-consuming and costly. The presentation focus is on introducing advanced modeling and simulation into the qualification process in order to reduce the qualification timeline by targeting fewer integral tests

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

ACCELERATED FITTING OF STELLAR SPECTRA

Stellar spectra are often modeled and fitted by interpolating within a rectilinear grid of synthetic spectra to derive the stars’ labels: stellar parameters and elemental abundances. However, the number of synthetic spectra needed for a rectilinear grid grows exponentially with the label space dimensions, precluding the simultaneous and self-consistent fitting of more than a few elemental abundances. Shortcuts such as fitting subsets of labels separately can introduce unknown systematics and do not produce correct error covariances in the derived labels. In this paper we present a new approach—Convex Hull Adaptive Tessellation (chat)—which includes several new ideas for inexpensively generating a sufficient stellar synthetic library, using linear algebra and the concept of an adaptive, data-driven grid. A convex hull approximates the region where the data lie in the label space. A variety of tests with mock data sets demonstrate that chat can reduce the number of required synthetic model calculations by three orders of magnitude in an eight-dimensional label space. The reduction will be even larger for higher dimensional label spaces. In chat the computational effort increases only linearly with the number of labels that are fit simultaneously. Around each of these grid points in the label space an approximate synthetic spectrum can be generated through linear expansion using a set of “gradient spectra” that represent flux derivatives at every wavelength point with respect to all labels. These techniques provide new opportunities to fit the full stellar spectra from large surveys with 15–30 labels simultaneously.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Foreground bias from parametric models of far-IR dust emission

We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB + foreground emission to precision 0.1% or better. The Rayleigh–Jeans approximation to the dust spectrum biases the fitted dust spectral index by Δβ{sub d}=0.2 and the inflationary B-mode amplitude by Δr=0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by Δr>0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple superposition of emission at different temperatures, but fails at the level Δr=0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

Performance improvement of robots using a learning control scheme

Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

Krishna, Ramuhalli↗
NASA NTRS1991

Computational modelling of dump combustors flowfield

A computational model aimed at predicting the flowfield of dump combustors is presented. The turbulent combustion model is based on the conserved scalar approach and on a convenient specification of its probability density function, which reduces the computation of the mean density to a closed form. Turbulence is modeled by means of the k-epsilon model. The averaged conservation equations are solved by a technique based on a staggered grid and on the SIMPLE solver. The computational model is applied to a simple dump combustor to assess the computer time requirements and accuracy. The turbulent combustion model is shown to reduce the computer time by an order of magnitude when compared to evaluating the mean density by numerical quadrature.

Lentini, D.↗
NASA NTRS1992

The three-point function in an ensemble of numerical simulations

The three-point function in Fourier space is evaluated for an ensemble of 2D numerical simulations with nine initial power spectra distinguished by spectral index and cutoff. To remove the main dependences on scale and time, results are presented as the reduced amplitude Q in the hierarchical model. To lowest nonvanishing order in perturbation theory, Q is a constant, independent of length scale, of time, and of initial spectrum. The results show that in the nonlinear regime of evolution, normalizing to Q does remove the main variations, but systematic dependences on spectral index and cutoff remain at late times that depend on properties of the initial spectrum. Models with initial power cut off on a scale that has been evolved far into the nonlinear regime are indistinguishable from models with no cutoff.

Fry, J. N.↗