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Pattern recognition for valve stiction detection with principal component analysis

IFAC Proceedings Volumes 2009年第8期42卷 1438-1443页

作者： M. Stockmanna R. Habera U. Schmitz Department of Process Engineering and Plant Design Laboratory of Process Control Cologne University of Applied Science D-50679 Köln Betzdorfer Str. 2 Germany fax: +49-221-8275-2836 Shell Germany Oil GmbH Rheinland Raffinerie

Static friction (stiction) in control valves is an often unrecognized problem which can lead tooscillating process variables. Therefore it is important to detect stiction at an early stage as the reason for oscillation. This work presents a new and robust method which uses the pattern recognition with principal component analysis for stiction detection.

关键词： valves static friction stiction oscillation statistical analysis principal component analysis hydro-cracker

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A statistical learning approach for the design of polycrystalline materials

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Statistical Analysis and Data Mining 2009年第5期1卷 306-321页

作者： Sundararaghavan, Veera Zabaras, Nicholas Department of Aerospace Engineering University of Michigan Ann Arbor MI 48109 United States Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY 14853-3801 United States

Important physical properties such as yield strength, elastic modulus, and thermal conductivity depend on the material microstructure. Realization of optimal microstructures is important for hardware components in aerospace applications where there is a need to optimize material properties for improved performance. Microstructures can be tailored through controlled deformation or heat treatment. However, identification of the optimal processing path is a non-trivial (and nonunique) problem. Data-mining techniques are eminently suitable for process design since optimal processing paths can be selected based on available information from a large database-relating processes, properties, and microstructures. In this paper, the problem of designing processing stages that lead to a desired microstructure or material property is addressed by mining over a database of microstructural signatures. A hierarchical X-means classifier is designed to match crystallographic orientation features to a class of microstructural signatures within a database. Instead of the conventional distortion minimization algorithm of k-means, X-means maximizes a Bayesian information measure for calculating cluster centers which allows automatic detection of number of classes. Using the microstructural database, an adaptive data-compression technique based on proper orthogonal decomposition (POD) has been designed to accelerate materials design. In this technique, reduced modes selected adaptively from the database are used to speed up auxiliary microstructure optimization algorithms built over the database. © 2009 Wiley Periodicals, Inc.

关键词： Microstructure

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Weighted multibody expansions for computing stable structures of multiatom systems

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Physical Review B 2008年第6期77卷 064101-064101页

作者： Veera Sundararaghavan Nicholas Zabaras Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY 14853-3801 United States

The effect of structural relaxations in alloys is described using a multibody energy expansion formalism. N-body potentials in the multibody expansion are computed from energies of isolated clusters, which, in turn, are calculated from empirical potentials or self-consistent quantum mechanical calculations. Convergence characteristics of multibody expansions (MBEs) are improved by weighting energies obtained from various truncations of many-body expansions in a method called weighted MBE (WMBE). It is shown that multibody expansions of many-atom systems can be efficiently constructed using interpolation of isolated cluster energies from databases. In contrast to the method of cluster expansion, WMBE focuses on positional degrees of freedom and, hence, explicitly handles structural relaxations during computations of stable atom clusters and periodic or amorphous phase structures.

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A coupled thermomechanical, thermal transport and segregation analysis of the solidification of aluminum alloys on molds of uneven topographies

A coupled thermomechanical, thermal transport and segregatio...

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Modeling of Casting, Welding and Advanced Solidification processes - XI

作者： Zabaras, Nicholas Samanta, Deep Tan, Lijian Materials Process Design and Control Laboratory 188 Frank H.T Rhodes Hall Cornell University Ithaca NY 14853-3801 United States

ISBN: (纸本)0873396294

A coupled thermomechanical, thermal transport and segregation analysis of aluminum alloys solidifying on uneven surfaces is presented. The uneven surfaces are modelled as sinusoids with different wavelengths and amplitudes. Phenomena occurring during early stages of solidification play an important role in the formation of surface defects. At the mold-metal interface, formation of air-gaps leads to variations in heat flux from the solidifying shell to the mold. The heat flux is either determined from the contact pressure during perfect contact or through air-gap sizes during imperfect contact (air-gap nucleation). The effects of inverse segregation, arising from shrinkage driven flow in the melt, on nucleation of air-gaps and evolution of stresses in the solidifying shell are examined. The present finite element based numerical model consists of a volume-averaged solidification model coupled with an elasto-viscoplastic deformation model in the solidifying shell with air-gap nucleation and imperfect contact at the metal-mold interface. For different sinusoidal topographies, a particular wavelength that leads to reduction in equivalent stresses and growth front morphology unevenness, in the evolving solid shell, is sought for different aluminum alloys. The current analysis will be extended to include the effects of mold coatings and surface roughness of molds on air-gap formation and stress development.

关键词： Aluminum alloys

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Modeling of microstructure evolution in the solidification of multi-component alloys using level set methods

Modeling of microstructure evolution in the solidification o...

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Modeling of Casting, Welding and Advanced Solidification processes - XI

作者： Zabaras, Nicholas Tan, Lijian Materials Process Design and Control Laboratory 188 Frank H.T Rhodes Hall Cornell University Ithaca NY 14853-3801 United States

ISBN: (纸本)0873396294

A level set method combining features of front tracking methods and fixed domain methods is presented to model microstructure evolution in the solidification of multi-component multi-phase alloy systems. Phase boundaries are explicitly tracked by solving the multi-phase level set equations. Diffused interfaces are constructed by extending a small width in both directions from these explicitly tracked phase boundaries. Based on the constructed artificial diffused interfaces, volume-averaging techniques are applied for energy, species and momentum transport. This sacrifice of accuracy by adopting a diffused interface for computational convenience is small considering that the interfaces are still explicitly tracked. By avoiding explicit application of temperature essential boundary conditions on the freezing front, the numerical scheme is energy conserving and the numerical results insensitive to the mesh size. For the numerical analysis of melt flow, a SUPG (streamline-upwind/Petrov-Galerkin), PSPG (pressure stabilizing/Petrov-Galerkin) and DSPG (Darcy stabilizing/ Petrov-Galerkin) stabilized velocity-pressure finite element algorithm is adopted. Microstructure evolution in multi-component alloy systems is solved directly using input from phase diagrams. This avoids the difficulty of parameter identification needed in most diffused interface models, and allows easy application to the solidification of various practical alloy systems. Comparable accuracy is observed to front tracking and phase field models in a number of examples available in the literature. Computational techniques including fast marching and narrow band computing are utilized to speed up the level set computations. Adaptive mesh refinement in the rapidly varying interface region makes the method practical for coupling phenomena in meso- and macro-scales during the solidification process.

关键词： Microstructure

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An efficient Markov chain Monte Carlo simulation of a stochastic inverse radiation problem

An efficient Markov chain Monte Carlo simulation of a stocha...

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Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

作者： Wang, Jingbo Zabaras, Nicholas Materials Process Design and Control Laboratory 188 Frank H. T. Rhodes Hall Cornell University Ithaca NY 14853-3801 United States

ISBN: (纸本)1563477165

A novel methodology that combines recent advances in computational statistics and reduced-order modeling is presented to explore the application of Bayesian statistical inference to a stochastic inverse problem in radiative heat transfer. The underlying objective of this work is to reveal the potential of using statistical approaches, mainly Bayesian computational statistics and spatial statistics, to solve data-driven stochastic optimization and uncertainty quantification problems raised in various complex continuum system design and control applications when the robustness and reliability requirements are critical. In this work, an unknown transient heat source in a three-dimensional participating medium is reconstructed from the temperature measurements. The heat source is modeled as a stochastic process, of which the joint posterior probability density function (PPDF) is computed using the Bayes' formula. Random errors in thermocouple readings are modeled as Gauss random variables. 'Maximum A Posteriori' (MAP) and posterior mean estimates of the heat source are then computed. A Markov chain Monte Carlo (MCMC) sampler composed of a cycle of symmetric MCMC kernels is designed to explore the posterior state space numerically. To expedite the sampling speed, a proper orthogonal decomposition (POD) based reduced order modeling technique is used in the likelihood computation. Typical heat source profiles are reconstructed using the simulated data to demonstrate the presented methodology. The results indicate that the Bayesian inference method can provide accurate point estimates as well as uncertainty quantification to the solution of the inverse radiation problem. Copyright © 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Machine design

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Simulation of pulping process using neural networks and hybrid model 7th

Simulation of pulping process using neural networks and hybr...

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7th IFAC Symposium on Dynamics and control of process Systems, DYCOPS 2004

作者： Polowski, N.V. Aguiar, H.C. Maciel Filho, R. Laboratory of Optimization Design and Advanced Process Control Faculty of Chemical Engineering State University of Campinas P.O. 6066 Campus -SPZIP: 13081- 970 Brazil

A Neural Network Model is developed for a Kraft digester which is a fundamental stage in the pulp production. A deterministic model is used to describe the main features of the process and will provide the data to verify the performance of the artificial neural network modeling. The paper shows that the artificial neural network is a good way to represent the process since it gives equivalent results when compared to deterministic model, and it is easier and cheaper to be developed. © lhAC Dynamics and control of process Systems, Cambridge, Massachusetts, USA, 2004

关键词： Neural networks

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Simulation of Pulping process Using Neural Networks and Hybrid Model

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IFAC Proceedings Volumes 2004年第9期37卷 691-696页

作者： N.V. Polowski H.C. Aguiar R. Maciel Filho Laboratory of Optimization Design and Advanced Process Control. Faculty of Chemical Engineering State University of Campinas P.O. 6066 – ZIP: 13081-970 – Campus – SP-Brazil

A Neural Network Model is developed for a Kraft digester which is a fundamental stage in the pulp production. A detenninistic model is used to describe the main features of the process and will provide the data to verify the performance of the artificial neural network modeling. The paper shows that the artificial neural network is a good way to represent the process since it gives equivalent results when compared to deterministic model, and it is easier and cheaper to be developed.

关键词： neural networks hybrid model pulp optimization

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A gradient optimization method for efficient design of three‐dimensional deformation processes

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AIP Conference Proceedings 2004年第1期712卷 2074-2079页

作者： Swagato Acharjee Nicholas Zabaras *Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H.T. Rhodes Hall Cornell University Ithaca NY 14853‐3801

In this work, we present a gradient optimization technique for optimizing deformation processes. The optimization is based on the continuum sensitivity method (CSM). CSM involves differentiation of the governing field equations of the direct problem (constitutive, contact and kinematic problems) with respect to the design variables and development of the weak forms for the corresponding continuum sensitivity equations. The present 3D developments involve a novel regularized approach to the contact sensitivity problem that addresses the non‐differentiability of the contact constraints. A relevant 3D die design problem is considered highlighting the features of the metal forming design simulator developed.

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Representation and Classification of Microstructures using Statistical Learning Techniques

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AIP Conference Proceedings 2004年第1期712卷 98-102页

作者： Veeraraghavan Sundararaghavan Nicholas Zabaras *Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T. Rhodes Hall Cornell University Ithaca NY 14853‐3801

Microstructural information is fundamental to determining the critical properties of today’s high performance materials. Hence, there is a need for a representation that can quantify all the microstructural elements through the analysis of digitized images. This paper addresses representation through the creation of a dynamic microstructure library. The paper focuses on the application of machine learning theory for the creation of a library that is trained by experimentally or computationally obtained microstructure snapshots. Support vector machines (SVM) are used to classify microstructure snapshots based on its features into various classes. An incremental‐principal component analysis (PCA) method is employed within image classes to constantly update the microstructure basis and numerically quantify the microstructural features.

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