Dans cette étude, on s'est intéressé à la modélisation réduite et au contrôle d’organes intervenant dans la réduction des émissions de polluants des véhicules automobiles à basse consommation. Il s’agit des réacteurs catalysés de type « piège à NOx » et SCR, utilisés dans les architectures de post-traitement des gaz d’échappement des véhicules Diesel. Ces systèmes ont en commun la nécessité de contrôler les niveaux des émissions de polluants stockés sur les sites catalytiques et l’optimisation du fonctionnement du GMP en vue d’approcher les futures normes Euro et les nouvelles incitations sur les émissions de gaz à effet de serre.Le piège à NOx est un système catalytique dont la fonction première est de collecter les oxydes d’azote (NOx) des gaz d’échappement afin qu’ils ne soient pas rejetés dans l’environnement. Le réacteur SCR est un système catalytique qui utilise le principe de réduction sélective des NOx par l’ammoniac (NH3), initialment produit et délivré à partir d’un stock d’urée embarqué.La similitude des technologies a permis la mise en œuvre de méthodologies communes de modélisation cinétique et de réduction de modèles, basées sur l’étude thermochimique et cinétique des réseaux réactionnels. Après application aux systèmes respectifs de piège à NOx et SCR, nous avons obtenus des modèles réduits qui ont pu être identifiés, validés et appliqués à l’observation et au contrôle des niveaux de stock des polluants (respectivement NOx et NH3). / The purpose of this study is to develop mathematical reduced order models for components of low consumption motor vehicles : the lean NOx trap and the SCR catalysts, used in the exhaust of Diesel engines and involved in the reduction of pollutants in exhaust emissions. These systems have in common that they aim at controling the boundaries on pollutant emissions in order to achieve the forthcoming Euro standards and they allow the optimization of the aftertreatment systems to reduce greenhouse gases.The lean NOx trap catalyst aims at collecting the NOx in order to avoid the pollution of the environment and the SCR catalyst uses the selective reduction of the NOx by the ammonia (NH3), initially produced by an embedded urea system. The similarity between the two technologies allow the implementation of common methodologies for reduced order modeling of catalytic reactors based on thermochemical and kinetic studies. After application, respectively to the NOx trap and the SCR, we obtain reduced order models which were identified, validated and implemented for the control and diagnosis of the amount of stock of the pollutants (respectively NOx and NH3).

En plus des aspects électrique, magnétique, vibro-acoustique et mécanique, les considérations thermiques doivent être prises en compte lors des phases de conception et d’optimisation des machines électriques. Ce mémoire se porte sur l’analyse et la simulation du comportement thermique des machines électriques Totalement Fermées et Non Ventilées (TFNV) et plus particulièrement sur le cas de la machine Synchro-réluctante (Synchrel), utilisée comme actionneur d’embrayage. Un modèle thermique détaillé (MTD), décrivant le comportement thermique de la machine Synchrel est conçu. Ce MTD proposé est construit grâce à une combinaison de la méthode à Constantes Localisées (CL) et d’une technique numérique de type Mécanique des Fluides Numériques (MFN). La première méthode est dédiée à la modélisation des transferts conductifs et radiatifs. La seconde permet de modéliser le mécanisme de refroidissement par convection naturelle autour de la machine Synchrel. Compte-tenu de l’importance du mode de refroidissement sur l’évolution des températures critiques, l’approche MFN peut apporter plus de précision. Par contre, elle nécessite des temps de calcul importants ce qui freine son utilisation. Afin de surmonter cette problématique, les résultats numériques obtenus pour des points de fonctionnement particuliers sont utilisés afin de définir des relations de corrélation analytiques. Cette analyse numérique est accompagnée d’une démarche expérimentale afin d’élaborer les corrélations expérimentales correspondantes. L’étude montre que les solutions numériques peuvent converger vers des solutions plus précises si l’on tient compte des données d’incertitudes introduites par cette approche. La deuxième problématique traitée est la détermination des Résistances Thermiques de Contact (RTCs) des machines électriques. Elles constituent des paramètres clefs dans la définition du MTD complet. La démarche de détermination des RTCs est basée sur deux approches d’identification paramétrique. La première est basée sur des observations expérimentales du comportement thermique de la machine. La seconde est basée sur une approche mathématique de réduction de modèle. Les valeurs déterminées sont cohérentes avec la littérature, bien que la machine Synchrel diffère en topologie, taille et puissance. En utilisant la corrélation d’origine numérique du phénomène de convection externe, le MTD complet est alors utilisé afin d’évaluer la variation de température due à l’erreur introduite par la MFN. En utilisant la corrélation expérimentale, le MTD complet est validé. Les approches d’identification paramétrique conduisent à la construction de deux modèles thermiques de second ordre de la machine. Ces modèles permettent la surveillance du comportement thermique du bobinage et du carter. Ces deux modèles simplifiés font montre d’une prédictibilité satisfaisante au regard de leur simplicité. / In addition to electrical, magnetic, vibro-acoustic and mechanical aspects, thermal considerations must be taken into account during the design and optimization of electrical machines. This study focuses on the analysis and the simulation of the thermal behavior of Totally Enclosed Non Ventilated (TENV) electric machines, specifically a Synchro-reluctant motor (Synchrel) in the context of an automotive application : a clutch actuator. A detailed thermal model (MTD) describing the thermal behavior of the Synchrel machine is designed. This proposed MTD is based on a combination of the Lumped Parameter Thermal Network method (LPTN) and the Computational Fluid Dynamics (CFD) methods. The first method is dedicated to model the conductive and radiative heat transfers. CFD techniques are dedicated to model the cooling mechanism based on the natural convection around the Synchrel machine. Since the critical temperature is very sensitive to the cooling mode, the CFD approach is used in this study to provide more accurate results. On the other hand, it requires considerable computing time, which prevents its use in design studies based on optimization methods. In order to overcome this problem, only some numerical results obtained for particular operating points are used to define an analytical correlation based on the numerical calculation relations. This numerical analysis goes with an experimental approach in order to elaborate the corresponding experimental correlations. This study shows that numerical solutions can present a good accuracy, if uncertainty data introduced by this approach are taken into account. The second research problem addressed in this study is the determination of the Contact Thermal Resistances (RTCs), which are key parameters in the definition of the MTD. The determination procedure of the RTCs is based on two parametric identification approaches. The first one is experimental and based on some observations of the thermal behavior of the machine. The second one is based on a mathematical model reduction approach. The determined values are consistent with results from literature, although the Synchrel machine differs in topology, size and power. Using the numerical correlations, the MTD is used to evaluate the temperature deviation due to error terms introduced by the CFD approach. Then, using these experimental correlations, the MTD’s quality can be checked and approved. Parametric identification approaches lead to the construction of two secondorder thermal models of the machine. These models allow monitoring the thermal behavior of the winding and the casing. Both simplified models show satisfactory predictability with respect to their great simplicity.

<p>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. </p><p>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.</p><p>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. </p>

A mechatronic system is an assembly of technological components, such as a mechanism, sensors, actuators, and a control unit. Recently, a number of researchers and industrial manufacturers have highlighted the potential advantages of lightweight parallel mechanisms with respect to the accuracy, dynamic performances, construction cost, and transportability issues. The design of a mechatronic system with such a mechanism requires a multidisciplinary approach, where the mechanical deformations have to be considered. This thesis proposes two original contributions in this framework. (i) First, a modular and systematic method is developed for the integrated simulation of mechatronic systems, which accounts for the strongly coupled dynamics of the mechanical and non-mechanical components. The equations of motion are formulated using the nonlinear Finite Element approach for the mechanism, and the block diagram language for the control system. The time integration algorithm relies on the generalized-alpha method, known in structural dynamics. Hence, well-defined concepts from mechanics and from system dynamics are combined in a unified formulation, with guaranteed convergence and stability properties. Several applications are treated in the fields of robotics and vehicle dynamics. (ii) Usual methods in flexible multibody dynamics lead to complex nonlinear models, not really suitable for control design. Therefore, a systematic nonlinear model reduction technique is presented, which transforms an initial high-order Finite Element model into a low-order and explicit model. The order reduction is obtained using the original concept of Global Modal Parameterization: the motion of the assembled mechanism is described in terms of rigid and flexible modes, which have a global physical interpretation in the configuration space. The reduction procedure involves the component-mode technique and an approximation strategy in the configuration space. Two examples are presented: a four-bar mechanism, and a parallel kinematic machine-tool. Finally, both simulation and modeling tools are exploited for the dynamic analysis and the control design of an experimental lightweight manipulator with hydraulic actuators. A Finite Element model is first constructed and validated with experimental data. A reduced model is derived, and an active vibration controller is designed on this basis. The simulation of the closed-loop mechatronic system predicts remarkable performances. The model-based controller is also implemented on the test-bed, and the experimental results agree with the simulation results. The performances and the other advantages of the control strategy demonstrate the relevance of our developments in mechatronics.

This thesis considers the problem of making dynamic models for industrial processes by combining physical modelling with experimental data. The focus is on distributed parameter systems, that is, systems for which the model structure involves partial differential equations (PDE). Distributed parameter systems are important in many applications, e.g., in chemical process systems and in intracellular biochemical processes, and involve for instance all forms of transport and transfer phenomena. For such systems, the postulated model structure usually requires a finite dimensional approximation to enable identification and validation using experimental data. The finite dimensional approximation involves translating the PDE model into a set of ordinary differential equations, and is termed model reduction. The objective of the thesis is two-fold. First, general PDE model reduction methods which are efficient in terms of model order for a given level of accuracy are studied. The focus here is on a class of methods called moving mesh methods, in which the discretization mesh is considered a dynamic degree of freedom that can be used for reducing the model reduction error. These methods are potentially highly efficient for model reduction of PDEs, but often suffer from stability and robustness problems. In this thesis it is shown that moving mesh methods can be cast as standard feedback control problems. Existing moving mesh methods are analyzed based on tools and results available from control theory, and plausible explanations to the robustness problems and parametric sensitivity experienced with these methods are provided. Possible remedies to these problems are also proposed. A novel moving finite element method, Orthogonal Collocation on Moving Finite Elements (OCMFE), is proposed based on a simple estimate of the model reduction error combined with a low order linear feedback controller. The method is demonstrated to be robust, and hence puts only small demands on the user. In the second part of the thesis, the integration of PDE model reduction methods with grey-box modelling tools available for finite dimensional models is considered. First, it is shown that the standard approach based on performing model reduction using some ad hoc discretization method and model order, prior to calibrating and validating the reduced model, has a number of potential pitfalls and can easily lead to falsely validated PDE models. To overcome these problems, a systematic approach based on separating model reduction errors from discrepancies between postulated model structures and measurement data is proposed. The proposed approach is successfully demonstrated on a challenging chromatography process, used for separation in biochemical production, for which it is shown that data collected at the boundaries of the process can be used to clearly distinguish between two model structures commonly used for this process. / QC 20101020

While moment matching can effectively reduce the dimension of a linear, time-invariant system, it can simultaneously fail to improve the stable time-step for the forward Euler scheme. In the context of a semi-discrete heat equation with spatially smooth forcing, the high frequency modes are virtually insignificant. Eliminating such modes dramatically improves the stable time-step without sacrificing output accuracy. This is accomplished by modal filtration, whose computational cost is relatively palatable when applied following an initial reduction stage by moment matching. A bound on the norm of the difference between the transfer functions of the moment-matched system and its modally-filtered counterpart yields an intelligent choice for the mode of truncation. The dual-stage algorithm disappoints in the context of highly nonnormal semi-discrete convection-diffusion equations. There, moment matching can be ineffective in dimension reduction, precluding a cost-effective modal filtering step.

We present a new method for the computation of low rank approximations to the solution of large, sparse, stable Lyapunov equations. It is based on a generalization of the classical Smith method and profits by the usual low rank property of the right hand side matrix. The requirements of the method are moderate with respect to both computational cost and memory. Hence, it provides a possibility to tackle large scale control problems. Besides the efficient solution of the matrix equation itself, a thorough integration of the method into several control algorithms can improve their performance to a high degree. This is demonstrated for algorithms for model reduction and optimal control. Furthermore, we propose a heuristic for determining a set of suboptimal ADI shift parameters. This heuristic, which is based on a pair of Arnoldi processes, does not require any a priori knowledge on the spectrum of the coefficient matrix of the Lyapunov equation. Numerical experiments show the efficiency of the iterative scheme combined with the heuristic for the ADI parameters.

This dissertation concerns the model reduction of linear periodic descriptor systems both in continuous and discrete-time case. In this dissertation, mainly the projection based approaches are considered for model order reduction of linear periodic time varying descriptor systems. Krylov based projection method is used for large continuous-time periodic descriptor systems and balancing based projection technique is applied to large sparse discrete-time periodic descriptor systems to generate the reduce systems. For very large dimensional state space systems, both the techniques produce large dimensional solutions. Hence, a recycling technique is used in Krylov based projection methods which helps to compute low rank solutions of the state space systems and also accelerate the computational convergence. The outline of the proposed model order reduction procedure is given with more details. The accuracy and suitability of the proposed method is demonstrated through different examples of different orders. Model reduction techniques based on balance truncation require to solve matrix equations. For periodic time-varying descriptor systems, these matrix equations are projected generalized periodic Lyapunov equations and the solutions are also time-varying. The cyclic lifted representation of the periodic time-varying descriptor systems is considered in this dissertation and the resulting lifted projected Lyapunov equations are solved to achieve the periodic reachability and observability Gramians of the original periodic systems. The main advantage of this solution technique is that the cyclic structures of projected Lyapunov equations can handle the time-varying dimensions as well as the singularity of the period matrix pairs very easily. One can also exploit the theory of time-invariant systems for the control of periodic ones, provided that the results achieved can be easily re-interpreted in the periodic framework. Since the dimension of cyclic lifted system becomes very high for large dimensional periodic systems, one needs to solve the very large scale periodic Lyapunov equations which also generate very large dimensional solutions. Hence iterative techniques, which are the generalization and modification of alternating directions implicit (ADI) method and generalized Smith method, are implemented to obtain low rank Cholesky factors of the solutions of the periodic Lyapunov equations. Also the application of the solvers in balancing-based model reduction of discrete-time periodic descriptor systems is discussed. Numerical results are given to illustrate the effciency and accuracy of the proposed methods.

[pt] Esta dissertação tem como principal objetivo estudar aplicações da expansão ou decomposição de Karhunen-Loève em dinâmica de estruturas. Esta técnica consiste, basicamente, na obtenção de uma decomposição linear da resposta dinâmica de um sistema qualquer, representado por um campo vetorial estocástico, tendo a importante propriedade de ser ótima, no sentido que dado um certo número de modos, nenhuma outra decomposição linear pode melhor representar esta resposta. Esta capacidade de compressão de informação faz desta decomposição uma poderosa ferramenta para a construção de modelos reduzidos para sistemas mecânicos em geral. Em particular, este trabalho aborda problemas em dinâmica estrutural, onde sua aplicação ainda é bem recente. Inicialmente, são apresentadas as principais hipóteses necessárias à aplicação da expansão de Karhunen-Loève, bem como duas técnicas existentes para sua implementação, com domínios distintos de utilização.É dada especial atenção à relação entre os modos empíricos fornecidos pela expansão e os modos de vibração intrínsecos a sistemas vibratórios lineares, tanto discretos quanto contínuos, exemplificados por uma treliça bidimensional e uma placa retangular. Na mesma linha, são discutidas as vantagens e desvantagens de se usar esta expansão como ferramenta alternativa à análise modal clássica. Como aplicação a sistemas não-lineares, é apresentado o estudo de um sistema de vibroimpacto definido por uma viga em balanço cujo deslocamento transversal é limitado por dois batentes elásticos. Os modos empíricos obtidos através da expansão de Karhunen-Loève são, então, usados na formulação de um modelo de ordem reduzida, através do método de Galerkin, e o desempenho deste novo modelo investigado. / [en] This dissertation has the main objetive of studying applications of the Karhunen-Loève expansion or decomposition in structural dynamics. This technique consists basically in obtaining a linear decomposition of the dynamic response of a general system represented by a stochastic vector field. It has the important property of optimality, meaning that for a given number of modes, no other linear decomposition is able of better representing this response. This information compression capability characterizes this decomposition as powerful tool for the construction of reduced-order models of mechanical systems in general. Particularly, this work deals with structural dyamics problems where its application is still quite new. Initially, the main hypothesis necessary to the application of the Karhunen-Loève expansion are presented, as well as two existing techniques for its implementation that have different domains of use. Special attention is payed to the relation between empirical eigenmodes provided by the expansion and mode shapes intrinsic to linear vibrating systems, both discrete and continuous, exemplified by a bidimensional truss and a rectangular plate. Furthermore, the advantages and disadvantages of using this expansion as an alternative tool for classical modal analysis are discussed. As a nonlinear application, the study of a vibroimpact system consisting of a cantilever beam whose transversal displacement is constrained by two elastic barriers is presented. The empirical eigenmodes provided by the Karhunen-Loève expansion are then used to formulate a reduced-order model through Galerkin projection and the performance of this new model is investigated.

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