Diffusion is a phenomenon in which particles move from regions of higher density to regions of lower density. Many physical systems, in fields as diverse as plant biology and finance, are known to involve diffusion phenomena. Typically, diffusion systems are modeled by partial differential equations (PDEs), which include certain parameters. These parameters characterize a given diffusion system. Therefore, for both modeling and simulation of a diffusion system, one has to either know or determine these parameters. Moreover, as PDEs are infinite order dynamic systems, for computational purposes one has to approximate them by a finite order model. In this thesis, we investigate these two issues of model reduction and parameter estimation by considering certain specific cases of heat diffusion systems. We first address model reduction by considering two specific cases of heat diffusion systems. The first case is a one-dimensional heat diffusion across a homogeneous wall, and the second case is a two-dimensional heat diffusion across a homogeneous rectangular plate. In the one-dimensional case we construct finite order approximations by using some well known PDE solvers and evaluate their effectiveness in approximating the true system. We also construct certain other alternative approximations for the one-dimensional diffusion system by exploiting the different modal structures inherently present in it. For the two-dimensional heat diffusion system, we construct finite order approximations first using the standard finite difference approximation (FD) scheme, and then refine the FD approximation by using its asymptotic limit. As for parameter estimation, we consider the same one-dimensional heat diffusion system, as in model reduction. We estimate the parameters involved, first using the standard batch estimation technique. The convergence of the estimates are investigated both numerically and theoretically. We also estimate the parameters of the one-dimensional heat diffusion system recursively, initially by adopting the standard recursive prediction error method (RPEM), and later by using two different recursive algorithms devised in the frequency domain. The convergence of the frequency domain recursive estimates is also investigated.

The objective of this thesis is to propose modeling techniques that enable the design and optimization of material systems which require descriptions via molecular simulations. These kinds of systems are quite common in materials and engineering research. The first step in performing design and optimization tasks on such systems is the development of accurate simulation models from experimental data. In the first part of this thesis, we present a novel simulation model for the hyperbranched polymerization process of difunctional A2 oligomers, and B3 monomers. Unlike the previous models developed by other groups, our model is able to simulate the evolution of the polymer structure development under a wide range of synthesis routes, and in the presence of cyclization and endcapping reactions. Furthermore, our results are in agreement with the experimental data, and add insight into the underlying kinetic mechanisms of this polymerization process. The second major step in our work is the development of reduced order process models that are suitable for design and optimization tasks, using simulation data. We illustrate our approach on a stochastic simulation model of epitaxial thin film deposition process. Compared to the widely used approach called equation-free modeling, our method requires fewer assumptions about the dynamic system. The assumptions required in equation-free modeling include a wide separation between the time scales of low and high order moments describing the system state, and the accuracy of the time derivatives of system properties computed from molecular simulation data, despite the potentially large amount of fluctuations in stochastic simulations. Unlike the recent similar studies, our study also includes the analysis of prediction error which is important to evaluate the predictions of the reduced order model, compared to the high dimensional molecular simulations. Hence, we address two major issues in this thesis: development of simulation models from molecular experimental data, and derivation of reduced order models from molecular simulation data. These two aspects of modeling are both necessary to design and optimize processing conditions of materials for which continuum level descriptions are not available or accurate enough.

Uncertainties in both loading and structural characteristics can adversely affect the response and reliability of a structure. Parameter uncertainties in structural dynamics can arise due to several sources. These include variations due to intrinsic material property variability, measurement errors, manufacturing and assembly errors, differences in modeling and solution procedures. Problems of structural dynamics with randomly distributed spatial inhomogeneities in elastic, mass, and damping properties, have been receiving wide attention. Several mathematical and computational issues include discretization of random fields, characterization of random eigensolutions, inversion of random matrices, solutions of stochastic boundary-value problems, and description of random matrix products. Difficulties are encountered when one has to include interaction between nonlinear and stochastic system characteristics, or if one is interested in controlling the system response. The study of structural systems including the effects of system nonlinearity in the presence of parameter uncertainties presents serious challenges and difficulties to designers and reliability engineers. In the analysis of large structures, the situation for substructuring frequently arises due to the repetition of identical assemblages (substructures), within a structure, and the general need to reduce the size of the problem, particularly in the case of non-linear inelastic dynamic analysis. A small reduction in the model size can have a large effect on the storage and time requirement. A primary structural dynamic system may be coupled to subsystems such as piping systems in a nuclear reactor or in a chemical plant. Usually subsystem in itself is quite complex and its modeling with finite elements may result in a large number of degrees of freedom. The reduced subsystem model should be of low-order yet capturing the essential dynamics of the subsystem for useful integration with the primary structure. There are two major issues to be studied: one, techniques for analyzing a complex structure into component subsystems, analyzing the individual sub-system dynamics, and from thereon determining the dynamics of the structure after assembling the subsystems. The nonlinearity due to support gap effects such as supports for piping system in nuclear reactors further complicates the problem. The second is the issue of reviewing the methods for reducing the model-order of the component subsystems such that the order of the global dynamics, after assembly, is within some predefined limits. In the reliability analysis of complex engineering structures, a very large number of the system parameters have to be considered as random variables. The parameter uncertainties are modeled as random variables and are assumed to be time independent. Here the problem would be to reduce the number of random variables without sacrificing the accuracy of the reliability analysis. The procedure involves the reduction of the size of the vector of random variables before the calculation of failure probability. The objectives of this thesis are: 1.To use the available model reduction techniques in order to effectively reduce the size of the finite element model, and hence, compare the dynamic responses from such models. 2.Study of propagation of uncertainties in the reduced order/coupled stochastic finite element dynamic models. 3.Addressing the localized nonlinearities due to support gap effects in the built up structures, and also in cases of sudden change in soil behaviour under the footings. The irregularity in soil behaviour due to lateral escape of soil due to failure of quay walls/retaining walls/excavation in neighbouring site, etc. 4.To evolve a procedure for the reduction of size of the vector containing the random variables before the calculation of failure probability. In the reliability analysis of complex engineering structures, a very large number of the system parameters are considered to be random variables. Here the problem would be to reduce the number of random variables without sacrificing the accuracy of the reliability analysis. 5.To analyze the reduced nonlinear stochastic dynamic system (with phase space reduction), and effectively using the network pruning technique for the solution, and also to use filter theory (wavelet theory) for reducing the input earthquake record to save computational time and cost. It is believed that the techniques described provide highly useful insights into the manner structural uncertainties propagate. The cross-sectional area, length, modulus of elasticity and mass density of the structural components are assumed as random variables. Since both the random and design variables are expressed in a discretized parameter space, the stochastic sensitivity function can be modeled in a parallel way. The response of the structures in frequency domain is considered. This thesis is organized into seven chapters. This thesis deals with the reduced order models of the stochastic structural systems under deterministic/random loads. The Chapter 1 consists of a brief introduction to the field of study. In Chapter 2, an extensive literature survey based on the previous works on model order reduction and the response variability of the structural dynamic systems is presented. The discussion on parameter uncertainties, stochastic finite element method, and reliability analysis of structures is covered. The importance of reducing mechanical models for dynamic response variability, the systems with high-dimensional variables and reduction in random variables space, nonlinearity issues are discussed. The next few chapters from Chapter 3 to Chapter 6 are the main contributions in this thesis, on model reduction under various situations for both linear and nonlinear systems. After forming a framework for model reduction, local nonlinearities like support gaps in structural elements are considered. Next, the effect of reduction in number of random variables is tackled. Finally influence of network pruning and decomposition of input signals into low and high frequency parts are investigated. The details are as under. In Chapter 3, the issue of finite element model reduction is looked into. The generalized finite element analysis of the full model of a randomly parametered structure is carried out under a harmonic input. Different well accepted finite element model reduction techniques are used for FE model reduction in the stochastic dynamic system. The structural parameters like, mass density and modulus of elasticity of the structural elements are considered to be non-Gaussian random variables. Since the variables considered here are strictly positive, the probabilistic distribution of the random variables is assumed to be lognormal. The sensitivities in the eigen solutions are compared. The response statistics based on response of models in frequency domain are compared. The dynamic responses of the full FE model, separated into real and imaginary parts, are statistically compared with those from reduced FE models. Monte Carlo simulation is done to validate the analysis results from SFEM. In Chapter 4, the problem of coupling of substructures in a large and complex structure, and FE model reduction, e.g., component mode synthesis (CMS) is studied in the stochastic environment. Here again, the statistics of the response from full model and reduced models are compared. The issues of non-proportional damping, support gap effects and/local nonlinearity are considered in the stochastic sense. Monte Carlo simulation is done to validate the analysis results from SFEM. In Chapter 5, the reduction in size of the vector of random variables in the reliability analysis is attempted. Here, the relative entropy/ K-L divergence/mutual information, between the random variables is considered as a measure for ranking of random variables to study the influence of each random variable on the response/reliability of the structure. The probabilistic distribution of the random variables is considered to be lognormal. The reliability analysis is carried out with the well known Bucher and Bourgund algorithm (1990), along with the probabilistic model reduction of the stochastic structural dynamic systems, within the framework of response surface method. The reduction in number of random variables reduces the computational effort required to construct an approximate closed form expression in response surface approach. In Chapter 6, issues regarding the nonlinearity effects in the reduced stochastic structural dynamic systems (with phase space reduction), along with network pruning are attempted. The network pruning is also adopted for reduction in computational effort. The earthquake accelerogram is decomposed using Fast Mallat Algorithm (Wavelet theory) into smaller number of points and the dynamic analysis of structures is carried out against these reduced points, effectively reducing the computational time and cost. Chapter 7 outlines the contributions made in this thesis, together with a few suggestions made for further research. All the finite element codes were developed using MATLAB5.3. Final pages of the thesis contain the references made in the preparation of this thesis.

A practical procedure based on implicit time integration methods applied to the differential Lyapunov equations arising in the square root balanced truncation method is presented. The application of high order time integrators results in indefinite right-hand sides of the algebraic Lyapunov equations that have to be solved within every time step. Therefore, classical methods exploiting the inherent low-rank structure often observed for practical applications end up in complex data and arithmetic. Avoiding the additional effort treating complex quantities, a symmetric indefinite factorization of both the right-hand side and the solution of the differential Lyapunov equations is applied.

Modeling and simulation of granular matter has important applications in both natural science and industry. One widely used method is the discrete element method (DEM). It can be used for simulating granular matter in the gaseous, liquid as well as solid regime whereas alternative methods are in general applicable to only one. Discrete element analysis of large systems is, however, limited by long computational time. A number of solutions to radically improve the computational efficiency of DEM simulations are developed and analysed. These include treating the material as a nonsmooth dynamical system and methods for reducing the computational effort for solving the complementarity problem that arise from implicit treatment of the contact laws. This allow for large time-step integration and ultimately more and faster simulation studies or analysis of more complex systems. Acceleration methods that can reduce the computational complexity and degrees of freedom have been invented. These solutions are investigated in numerical experiments, validated using experimental data and applied for design exploration of iron ore pelletising systems. / <p>This work has been generously supported by Algoryx Simulation, LKAB (dnr 223-</p><p>2442-09), Umeå University and VINNOVA (2014-01901).</p>

It has been known for decades that fatigue crack propagation in elastic-plastic media is very sensitive to load history since the nonlinear behavior of the material can have a great influence on propagation rates. However, the raw computation of millions of fatigue cycles with nonlinear material behavior on tridimensional structures would lead to prohibitive calculation times. In this respect, we propose a global model reduction strategy, mixing both the a posteriori and a priori approaches in order to drastically decrease the computational cost of these types of problems. First, the small scale yielding hypothesis is assumed, and an a posteriori model reduction of the plastic behavior of the cracked structure is performed. This reduced model provides incrementally the plastic state in the vicinity of the crack front, from which the instantaneous crack growth rate is inferred. Then an additional a priori model reduction technique is used to accelerate even more the time to solution of the whole problem. This a priori approach consists in building incrementally and without any previous calculations a reduced basis specific to the considered test-case, by extracting information from the evolving displacement field of the structure. Then the displacement solutions of the updated crack geometries are sought as linear combinations of those few basis vectors. The numerical method chosen for this work is the finite element method. Hence, during the propagation the spatial discretization of the model has to be updated to be consistent with the evolving crack front. For this purpose, a specific mesh morphing technique is used, that enables to discretize the evolving model geometry with meshes of the same topology. This morphing method appears to be a key component of the model reduction strategy. Finally, the whole strategy introduced above is embedded inside an adaptive approach, in order to ensure the quality of the results with respect to a given accuracy. The accuracy and the efficiency of this global strategy have been shown through several examples; either in bidimensional and tridimensional cases for model crack propagation, including the industrial example of a helicopter structure.

Today in the scientific and technological world, physical and artificial processes are often described by mathematical models which can be used for simulation, optimization or control. As the mathematical models get more detailed and different coupling effects are required to include, usually the dimension of these models become very large. Such large-scale systems lead to large memory requirements and computational complexity. To handle these large models efficiently in simulation, control or optimization model order reduction (MOR) is essential. The fundamental idea of model order reduction is to approximate a large-scale model by a reduced model of lower state space dimension that has the same (to the largest possible extent) input-output behavior as the original system. Recently, the system-theoretic method Balanced Truncation (BT) which was believed to be applicable only to moderately sized problems, has been adapted to really large-scale problems. Moreover, it also has been extended to so-called descriptor systems, i.e., systems whose dynamics obey differential-algebraic equations. In this thesis, a BT algorithm is developed for MOR of index-1 descriptor systems based on several papers from the literature. It is then applied to the setting of a piezo-mechanical system. The algorithm is verified by real-world data describing micro-mechanical piezo-actuators. The whole algorithm works for sparse descriptor form of the system. The piezo-mechanical original system is a second order index-1 descriptor system, where mass, damping, stiffness, input and output matrices are highly sparse. Several techniques are introduced to reduce the system into a first order index-1 descriptor system by preserving the sparsity pattern of the original models. Several numerical experiments are used to illustrate the efficiency of the algorithm.

This Thesis is concerned with the approximation of high order controllers or the controller reduction problem. We firstly consider approximating high-order controllers by low order controllers based on the closed-loop system approximation. By approximating the closed-loop system transfer function, we derive a new parametrized double-sided frequency weighted model reduction problem. The formulas for the input and output weights are derived using three closed-loop system configurations: (i) by placing a controller in cascade with the plant, (ii) by placing a controller in the feedback path, and (iii) by using the linear fractional transformation (LFT) representation. One of the weights will be a function of a free parameter which can be varied in the resultant frequency weighted model reduction problem. We show that by using standard frequency weighted model reduction techniques, the approximation error can be easily reduced by varying the free parameter to give more accurate low order controllers. A method for choosing the free parameter to get optimal results is being suggested. A number of practical examples are used to show the effectiveness of the proposed controller reduction method. We have then considered the relationships between the closed-loop system con gurations which can be expressed using a classical control block diagram or a modern control block diagram (LFT). Formulas are derived to convert a closed-loop system represented by a classical control block diagram to a closed-loop system represented by a modern control block diagram and vice versa.

Représentations séparées pour la simulation multi-échelle du comportementmécanique et de l’endommagement des matériaux composites.Résumé: Le développement de méthodes numériques performantes pour simuler les structurescomposites est un défi en raison de la nature multi-échelle et de la complexité des mécanismed’endommagement de ce type de matériaux. Les techniques classiques de discrétisationvolumique conduisent à des coûts de calcul importants et sont restreintes en pratique à despetites structures.Dans cette thèse, un nouvelle stratégie basée sur une représentation séparée de la solution estexplorée. L’objectif est de proposer un cadre numérique efficace et fiable pour analyser les endommagementsdans les composites stratifiés sous chargements statiques et dynamiques. Ladécomposition propre généralisée (PGD) est utilisée pour construire la solution.Pour traiter l’endommagement, et plus particulière le délaminage, un modèle de zone cohésivea été implémenté dans la PGD. Une approches multi-échelle innovante est également proposéepour simuler le comportement mécanique des composites à microstructure périodique. L’idéeprincipale est de séparer deux échelles : l’échelle du motif périodique (microstructure) et l’échellemacroscopique. Les résultats de la PGD sont très proches des résultats obtenus par la méthodeéléments fini classique. Finalement, la PGD permet de réduire significativement la complexitédes modèles tout en gardant une précision satisfaisante. / Separated representations for the multiscale simulation of the mechanicalbehavior and damages of composite materials.Abstract: The development of efficient simulations for composite structures is very challengingdue to the multiscale nature and the complex damage process of this materials. When usingstandard 3D discretization techniques with advanced models for large structures, the computationalcosts are generally prohibitive.In this thesis, a new strategy based on a separated represenation of the solution is explored todevelop a computationally efficient and reliable numerical framework for the analysis of damagesin laminated composites subjected to quasi-static and dynamic loading. The PGD (Proper GeneralizedDecomposition) is used to build the solution.To treat damage, and especially delamination, a cohesive zone model has been implemented inthe PGD solver. A novel multiscale approach is also proposed to compute the mechanical behaviorof composites with periodic microstructure. The idea is to separate two scales: the scaleof periodic pattern and the macroscopic scale. The PGD results have been compared with theresults obtained with the classcial finite element method. A close agreement is found between thetwo approach and the PGD has significantly reduced the model complexity.

Dans le cadre d’une Cifre avec Airbus Helicopters, le projet a pour but le développement d’un modèle pour le pré-dimensionnement de pièces lamifiées élastomère-métal dont le rôle est critique en termes de conception et de sécurité pour les architectures de rotors d’hélicoptères. Pour cela, un premier volet de la thèse a consisté à caractériser le comportement élasto-dissipatif du matériau d’étude (un butadiène chargé de noir de carbone) via une campagne d’essais statiques et dynamiques, sous différents modes de sollicitations (uniaxiales et biaxiales) et sur un spectre assez large de fréquences, d’amplitudes et de températures. A partir de ces observations, un modèle phénoménologique de comportement hyper-viscoélastique est proposé. Sur la base d’un modèle de Maxwell généralisé, il permet de traduire les phénomènes observés sur la gamme de sollicitations visées. Un accent particulier a été porté sur la prise en compte de l’effet Payne en adoptant une approche originale. Les paramètres du modèle sont identifiés par une méthode robuste et rapide. Le modèle est ensuite développé à la fois dans un code commercial de calcul par éléments finis et dans un outil de calcul basé sur une méthode de réduction de modèles. Enfin, une étude du comportement en fatigue est réalisée à travers une campagne d’essais originaux servant de point de départ à la proposition d’une loi d’endommagement continu. / In association with Airbus Helicopters, the aim of the project is to develop a model to pre-size elastomer-metal laminated devices whose role is critical in terms of design and safety for helicopters rotor architectures. To do so, the first part of this thesis consisted in characterizing the elasto-dissipatice behavior of the studied material (a carbon black filled butadiene rubber) thanks to static and dynamic tests, with several kind of loading (uni-axial and bi-axial) and over a wide range of frequences, amplitudes and temperatures. From these observations, a phenomenological hyper-viscoelastic model is proposed. Based on a generalized Maxwell model, it is able to describe the phenomena over the loading range of concern. A particular focus is made to take in account the Payne effect thanks to an original approach. The model parameters are identified with a fast and robust method. The model is then implemented in a commercial finite element code and in a tool based on a model reduction method. Last, a study of the behaviour in fatigue is performed with an original characterization campaign from which a continuous damage law is proposed.