Complex industrial processes are represented by data that are well known to be multiscaled due to the variety of events that occur in a process at different time and frequency localizations. Wavelet based multiscale analysis approaches provide an excellent means to examine these events. However, the scope of the existing wavelet based methods in the fields of statistical applications, such as process monitoring and defect identification are still limited. Recent literature contains several wavelet decomposition based multiscale process monitoring approaches including many real life process monitoring applications, such as tool-life monitoring, bearing defect monitoring, and monitoring of ultra-precision processes such as chemical mechanical planarization (CMP) in wafer fabrication. However, all of the above mentioned wavelet based methodologies are offline and depend on the visual observations of the wavelet coefficients and details. The offline analysis paradigm was imposed by the high computation needs of the multiscale analysis, whereas the visual observation based approach was necessitated by the lack of statistical means to identify undesirable events. One of the most recent multiscale application, that deals with detecting delamination in CMP, addressed the need for online analysis by developing a moving window based approach to reduce computation time. This research presents 1) development of a fully online multiscale analysis approach where the speed of wavelet based analysis of the data matches the rate of data generation, 2) development of a statistical tool based on Sequential Probability Ratio Test (SPRT) to detect events of interest, and 3) development of an approach to display the analysis results through real time graphs for ease of process supervisory decision making. The developed methodologies are programmed using MATLAB 6.5 and implemented on several data sets obtained from metal and oxide CMP of wafer fabrication. The results and analysis are presented.

La présence de porosités dans les lingots métalliques représente un problème majeur dans l’industrie des matériaux. En effet, ces porosités altèrent significativement les caractéristiques mécaniques du matériau (ductilité notamment), et sont des sources d’apparition de défauts en mise en forme ou en tenue en service. Pour éliminer ces porosités, les industriels utilisent souvent des procédés de mise forme à chaud tels que le forgeage ou le laminage, mais il est souvent difficile de définir le taux de déformation à appliquer pour refermer entièrement ces porosités. La modélisation numérique s’avère donc être un outil particulièrement intéressant afin d’étudier l’impact des paramètres procédé sur le taux de refermeture de porosités. Dans ce travail, nous avons développé une méthodologie de calibration basée sur des algorithmes d’optimisation et une base de données de 800 simulations à champ complet sur VER, où les paramètres influents sur la refermeture des porosités sont variés (mécaniques et géométriques). Le premier modèle proposé est un modèle scalaire qui s’affranchit de l’hypothèse de chargement axisymétrique, largement utilisée dans la littérature. Le paramètre de Lode a permis avec l’utilisation de la triaxialité des contraintes de définir l’état de contraintes d’une manière unique. Les comparaisons de ce nouveau modèle à trois autres modèles de refermeture de la littérature montrent le gain de précision de ce nouveau modèle scalaire de refermeture. Le deuxième modèle est un modèle tensoriel adapté aux procédés multipasses grâce à l’analyse de la matrice d’inertie de la porosité. Cette matrice sert pour calculer le volume, la forme et l’orientation de la porosité. Ce modèle a été calibré en utilisant une approche basée sur les réseaux de neurones artificiels. La comparaison avec le modèle scalaire et la modélisation en champ complet a montré un gain en précision jusqu’à 35%. Il s’agit là par ailleurs du premier modèle tensoriel proposé dans la littérature. / The presence of voids in ingots is a major issue in the casting industry. These voids decrease materials properties (in particular ductility) and may induce premature failure during metal forming or service life. Hot metal forming processes are therefore used to close these voids and obtain a sound product. However, the amount of deformation required to close these voids is difficult to estimate.Numerical modeling is an interesting tool to study the influence of process parameters on void closure rate. In this work, an optimization-based strategy has been developed to identify the parameters of a mean-field model based on a database of 800 full-field REV simulations with various loading conditions and voids geometry and orientations. The first void closure model is a scalar model that gets rid of the axisymmetric loading hypothesis considered in most models in the literature. The Lode angle, coupled with the stress triaxiality ratio enables to identify the stress state in a unique way. Comparisons of this new model with three other models fromthe literature show the accuracy increase for general loading conditions. In order to address multistages processes, a second model is defined in a tensor version. The ellipsoid void inertia matrix is used to define void’s morphology, orientation and volume. The tensor model predicts the evolution of the inertia terms and its calibration is based on the full-field REV database and on a new Artificial Neural Networks approach. Comparisons were carried out between this tensor model, the scalar model and full-field simulations for multi-stages configurations. These comparisons showed up to 35% accuracy improvement with the tensor model. It is worth mentioning that this is the first attempt to define a void closure tensor model in the literature.

<p>The topic of this thesis is streamline-based integration of dynamic data for porous media systems, particularly in petroleum reservoirs. In the petroleum industry the integration of dynamic data is usually referred to as history matching. The thesis starts out by giving an introduction to streamline-based history-matching methods. Implementations and extensions of two existing methods for streamline-based history matching are then presented.</p><p>The first method pursued is based on obtaining modifications for streamline-effective properties, which subsequently are propagated to the underlying simulation grid for further iterations. For this method, two improvements are proposed to the original existing method. First, the improved approach involves less approximations, enables matching of porosity, and can account for gravity. Second, a multiscale approach is applied for which the data integration is performed on a hierarchy of coarsened grids. The approach proved robust, and gave a faster and better match to the data.</p><p>The second method pursued is the so-called generalized travel-time inversion (GTTI) method, which earlier has proven very robust and efficient for history matching. The key to the efficiency of this method is the quasilinear convergence properties and the use of analytic streamline-based sensitivity coefficients. GTTI is applied together with an efficient multiscale-streamline simulator, where the pressure solver is based on a multiscale mixed finite-element method (MsMFEM). To make the history matching more efficient, a selective work-reduction strategy, based on the sensitivities provided by the inversion method, is proposed for the pressure solver. In addition, a method for improved mass conservation in streamline simulation is applied, which requires much fewer streamlines to obtain accurate production-response curves. For a reservoir model with more than one million grid blocks, 69 producers and 32 injectors, the data integration took less than twenty minutes on a standard desktop computer. Finally, we propose an extension of GTTI to fully unstructured grids, where we in particular address issues regarding regularization and computation of sensitivities on unstructured grids with large differences in cell sizes.</p> / Paper I reprinted with kind permission of Elsevier, sciencedirect.com

<p>This report is a review on aspects of constitutive modelling of high strength steels. Aspects that have been presented are basic crystallography of steel, martensite transformation, thermodynamics and plasticity from a phenomenological point of view. The phenomenon called mechanical twinning is reviewed and the properties of a new material type called TWIP-steel have been briefly presented. Focus has been given on phenomenological models and methods, but an overview over multiscale methods has also been given.</p>

In this dissertation we develop and analyze numerical method to solve general elliptic boundary value problems with many scales. The numerical method presented is intended to capture the small scales effect on the large scale solution without resolving the small scale details, which is done through the construction of a multiscale map. The multiscale method is more effective when the coarse element size is larger than the small scale length. To guarantee a numerical conservation, a finite volume element method is used to construct the global problem. Analysis of the multiscale method is separately done for cases of linear and nonlinear coefficients. For linear coefficients, the multiscale finite volume element method is viewed as a perturbation of multiscale finite element method. The analysis uses substantially the existing finite element results and techniques. The multiscale method for nonlinear coefficients will be analyzed in the finite element sense. A class of correctors corresponding to the multiscale method will be discussed. In turn, the analysis will rely on approximation properties of this correctors. Several numerical experiments verifying the theoretical results will be given. Finally we will present several applications of the multiscale method in the flow in porous media. Problems that we will consider are multiphase immiscible flow, multicomponent miscible flow, and soil infiltration in saturated/unsaturated flow.

The topic of this thesis is streamline-based integration of dynamic data for porous media systems, particularly in petroleum reservoirs. In the petroleum industry the integration of dynamic data is usually referred to as history matching. The thesis starts out by giving an introduction to streamline-based history-matching methods. Implementations and extensions of two existing methods for streamline-based history matching are then presented. The first method pursued is based on obtaining modifications for streamline-effective properties, which subsequently are propagated to the underlying simulation grid for further iterations. For this method, two improvements are proposed to the original existing method. First, the improved approach involves less approximations, enables matching of porosity, and can account for gravity. Second, a multiscale approach is applied for which the data integration is performed on a hierarchy of coarsened grids. The approach proved robust, and gave a faster and better match to the data. The second method pursued is the so-called generalized travel-time inversion (GTTI) method, which earlier has proven very robust and efficient for history matching. The key to the efficiency of this method is the quasilinear convergence properties and the use of analytic streamline-based sensitivity coefficients. GTTI is applied together with an efficient multiscale-streamline simulator, where the pressure solver is based on a multiscale mixed finite-element method (MsMFEM). To make the history matching more efficient, a selective work-reduction strategy, based on the sensitivities provided by the inversion method, is proposed for the pressure solver. In addition, a method for improved mass conservation in streamline simulation is applied, which requires much fewer streamlines to obtain accurate production-response curves. For a reservoir model with more than one million grid blocks, 69 producers and 32 injectors, the data integration took less than twenty minutes on a standard desktop computer. Finally, we propose an extension of GTTI to fully unstructured grids, where we in particular address issues regarding regularization and computation of sensitivities on unstructured grids with large differences in cell sizes. / Paper I reprinted with kind permission of Elsevier, sciencedirect.com

This study introduces a multiscale model for analyzing nonlinear thermo-viscoelastic responses of particulate composites. A simplified micromechanical model consisting of four sub-cells, i.e., one particle and three matrix sub-cells is formulated to obtain the effective thermal and mechanical properties and time-dependent response of the composites. The particle and matrix constituents are made of isotropic homogeneous viscoelastic bodies undergoing small deformation gradients. Perfect bonds are assumed along the sub-cell⁰́₉s interfaces. The coupling between the thermal and mechanical response is attributed to the dissipation of energy due to the viscoelastic deformation and temperature dependent material parameters in the viscoelastic constitutive model. The micromechanical relations are formulated in terms of incremental average field quantities, i.e., stress, strain, heat flux and temperature gradient, in the sub-cells. The effective mechanical properties and coefficient of thermal expansion are derived by satisfying displacement- and traction continuities at the interfaces during the thermo-viscoelastic deformations. The effective thermal conductivity is formulated by imposing heat flux- and temperature continuities at the subcells⁰́₉ interfaces. The expression of the effective specific heat at a constant stress is also established. A time integration algorithm for simultaneously solving the equations that govern heat conduction and thermoviscoelastic deformations of isotropic materials is developed. The algorithm is then incorporated within each sub-cell of the micromechanical model together with the macroscopic energy equation to determine the effective coupled thermoviscoelastic response of the particulate composite. The numerical formulation is implemented within the ABAQUS, general purpose displacement based FE software, allowing for analyzing coupled heat conduction and deformations of composite structures. Experimental data on the effective thermal properties and time dependent responses of particulate composites available in the literature are used to verify the micromechanical model formulation. The multiscale model capability is also examined by comparing the field variables, i.e., temperature, displacement, stresses and strains, obtained from heterogeneous and homogeneous composite structures, during the transient heat conduction and deformations. Examples of coupled thermoviscoelastic analyses of particulate composites and functionally graded structures are also presented. The present micromechanical modeling approach is found to be computationally efficient and shows good agreement with experiments in predicting the effective thermo-mechanical response of particulate composites and functionally graded materials. Our analyses forecast a better design for creep resistant and less dissipative structures using particulate composites and functionally graded materials.

Several methods are presented for the design of satellite formations for science missions in high-eccentricity reference orbits with quantifiable performance criteria specified throughout only a portion the orbit, called the Region of Interest (RoI). A modified form of the traditional average along-track drift minimization condition is introduced to account for the fact that performance criteria are only specified within the RoI, and a robust formation design algorithm (FDA) is defined to improve performance in the presence of formation initialization errors. Initial differential mean orbital elements are taken as the design variables and the Gim-Alfriend state transition matrix (G-A STM) is used for relative motion propagation. Using mean elements and the G-A STM allows for explicit inclusion of J2 perturbation effects in the design process. The methods are applied to the complete formation design problem of the NASA Magnetospheric Multiscale (MMS) mission and results are verified using the NASA General Mission Analysis Tool (GMAT). Since satellite formations in high-eccentricity orbits will spend long times at high altitude, third-body perturbations are an important design consideration as well. A detailed analytical analysis of third-body perturbation effects on satellite formations is also performed and averaged dynamics are derived for the particular case of the lunar perturbation. Numerical results of the lunar perturbation analysis are obtained for the example application of the MMS mission and verified in GMAT.

Mixed Reality experiences, that blend physical and virtual objects, have become commonplace on handheld computing devices. One common application of these technologies is their use in cultural heritage "walking tours." These tours provide information about the surrounding environment in a variety of contexts, to suit the needs and interests of different groups of participants. Using the familiar "campus tour" as a canonical example, this dissertation investigates the technical and cognitive processes involved in transferring this tour from its physical and analog form into Mixed Reality. Using the concept of spatial scale borrowed from cognitive geography, this work identifies the need to create and maintain continuity across different scales of spatial experience as being of paramount importance to successful Mixed Reality walking tours. The concepts of scale transitions, coordination of representations across scales, and scale-matching are shown to be essential to maintaining the continuity of experience. Specific techniques that embody these concepts are also discussed and demonstrated in a number of Mixed Reality examples, including in the context of a successful deployment of a Mixed Reality Tour of the Georgia Tech campus. The potential for a "Language of Mixed Reality" based on the concepts outlined in this work is also discussed, and a general framework, called the Mixed Reality Scale Framework is shown to meet all the necessary criteria for being a cognitive theory of Human-Centered Computing in the context of Mixed Reality.

Water plays a critical role in the function and structure of biological systems. Current techniques to study biologically relevant events that span many length and time scales are limited by the prohibitive computational cost of including accurate effects from the aqueous environment. The aim of this work is to expand the reach of current molecular dynamics techniques by reducing the computational cost for achieving an accurate description of water and its effects on biomolecular systems. This work builds from the assumption that the “local” effect of water (e.g. the local orientational preferences and hydrogen bonding) can be effectively modelled considering only the atomistic detail in a very limited region. A recent adaptive resolution simulation technique (AdResS) has been developed to practically apply this idea; in this work it will be extended to systems of simple hydrophobic solutes to determine a characteristic length for which thermodynamic, structural, and dynamic properties are preserved near the solute. This characteristic length can then be used for simulation of biomolecular systems, specifically those involving protein dynamics in water. Before this can be done, current coarse grain models must be adapted to couple with a coarse grain model of water. This thesis is organized in to five chapters. The first will give an overview of water, and the current methodologies used to simulate water in biological systems. The second chapter will describe the AdResS technique and its application to simple test systems. The third chapter will show that this method can be used to accurately describe hydrophobic solutes in water. The fourth chapter describes the use of coarse grain models as a starting point for targeted search with all-atom models. The final chapter will describe attempts to couple a coarse grain model of a protein with a single-site model for water, and it’s implications for future multi-resolution studies.