Nous proposons dans ce manuscrit une nouvelle méthode pour prendre en compte l’effet du gradient en Fretting-fatigue. Les champs mécaniques présents à proximité du front de contact sont décrits à travers des facteurs d’intensité non locaux. L’objectif est d’aboutir à une description du champ de vitesse sous la forme d’une somme de termes exprimés chacun comme le produit d’un facteur d’intensité (Is, Ia, Ic), qui dépend des chargements macroscopiques appliqués à l’ensemble et d’une fonction de forme (ds, da, dc), qui est liée à la géométrie locale du contact. Cette description est obtenue à travers un processus non intrusif de post-processing des résultats obtenus avec des calculs à éléments finis. De plus, elle a été pensée pour être implémentée dans un contexte industriel. En pratique, pour chaque chargement macroscopique et pour chaque géométrie, il est possible de calculer un ensemble de facteurs d’intensité non locaux qui permettent de décrire les champs mécaniques locaux près du front de contact. Cette description non locale a l’avantage d’être (i) indépendante de la géométrie du contact employé et (ii) utilisable dans des modèles à éléments finis utilisés dans l’industrie qui sont caractérisés par des maillages plus grossiers par rapport à ceux utilisés pour étudier le fretting-fatigue dans des milieux académiques. Une étude est menée pour vérifier que les facteurs d’intensité non locaux peuvent être utilisés pour transposer les résultats expérimentaux d’une géométrie à une autre. / In this manuscript a new method to describe the stress gradient effect in fretting-fatigue is proposed. It is based on the description of the mechanical fields arising close to the contact edges through nonlocal intensity factors. For this purpose, the kinetic field around the contact ends is partitioned into a summation of multiple terms, each one expressed as the product between intensity factors, Is, Ia, Ic, depending on the macroscopic loads applied to the mechanical assembly, and spatial reference fields, ds, da, dc, depending on the local geometry of the part. This description is obtained through nonintrusive post-processing of FE computation and is conceived in order to be easily implementable in the industrial context. As a matter of fact, for any given macroscopic load and geometry, a set of nonlocal intensity factors is computed that permits to characterize the mechanical fields close to the contact edges. Such nonlocal description has the advantage of being (i) geometry independent so that the nonlocal intensity factors can be used to compare laboratory test with real-scale industrial assembly, (ii) applicable to industrial FE models usually characterized by rougher meshes compared to the ones used to describe fretting-fatigue in the academic context. The procedure is applied to fretting-fatigue test data in order to verify whether the nonlocal intensity factors can be used to transpose experimental results to different contact geometries from the one in which they have been obtained.

No description available.

Methods in hydrodynamic stability, systems and control theory are applied to spatially developing flows, where the flow is not required to vary slowly in the streamwise direction. A substantial part of the thesis presents a theoretical framework for the stability analysis, input-output behavior, model reduction and control design for fluid dynamical systems using examples on the linear complex Ginzburg-Landau equation. The framework is then applied to high dimensional systems arising from the discretized Navier–Stokes equations. In particular, global stability analysis of the three-dimensional jet in cross flow and control design of two-dimensional disturbances in the flat-plate boundary layer are performed. Finally, a parametric study of the passive control of two-dimensional disturbances in a flat-plate boundary layer using streamwise streaks is presented. / QC 20101103

No description available.

In this thesis we apply linear feedback control to spatially evolving flows in order to minimize disturbance growth. The dynamics is assumed to be described by the linearized Navier--Stokes equations. Actuators and sensor are designed and a Kalman filtering technique is used to reconstruct the unknown flow state from noisy measurements. This reconstructed flow state is used to determine the control feedback which is applied to the Navier--Stokes equations through properly designed actuators. Since the control and estimation gains are obtained through an optimization process, and the Navier--Stokes equations typically forms a very high-dimensional system when discretized there is an interest in reducing the complexity of the equations. One possible approach is to perform Fourier decomposition along (almost) homogeneous spatial directions and another is by constructing a reduced order model by Galerkin projection on a suitable set of vectors. The first strategy is used to control the evolution of a range of instabilities in the classical family of Falkner--Skan--Cooke flows whereas the second is applied to a more complex cavity type of geometry. / QC 20101122

Dynamical systems are a commonly used and studied tool for simulation, optimization and design. In many applications such as inverse problem, optimal control, shape optimization and uncertainty quantification, those systems typically depend on a parameter. The need for high fidelity in the modeling stage leads to large-scale parametric dynamical systems. Since these models need to be simulated for a variety of parameter values, the computational burden they incur becomes increasingly difficult. To address these issues, parametric reduced models have encountered increased popularity in recent years. We are interested in constructing parametric reduced models that represent the full-order system accurately over a range of parameters. First, we define a global joint error mea- sure in the frequency and parameter domain to assess the accuracy of the reduced model. Then, by assuming a rational form for the reduced model with poles both in the frequency and parameter domain, we derive necessary conditions for an optimal parametric reduced model in this joint error measure. Similar to the nonparametric case, Hermite interpolation conditions at the reflected images of the poles characterize the optimal parametric approxi- mant. This result extends the well-known interpolatory H2 optimality conditions by Meier and Luenberger to the parametric case. We also develop a numerical algorithm to construct locally optimal reduced models. The theory and algorithm are data-driven, in the sense that only function evaluations of the parametric transfer function are required, not access to the internal dynamics of the full model. While this first framework operates on the continuous function level, assuming repeated transfer function evaluations are available, in some cases merely frequency samples might be given without an option to re-evaluate the transfer function at desired points; in other words, the function samples in parameter and frequency are fixed. In this case, we construct a parametric reduced model that minimizes a discretized least-squares error in the finite set of measurements. Towards this goal, we extend Vector Fitting (VF) to the parametric case, solving a global least-squares problem in both frequency and parameter. The output of this approach might lead to a moderate size reduced model. In this case, we perform a post- processing step to reduce the output of the parametric VF approach using H2 optimal model reduction for a special parametrization. The final model inherits the parametric dependence of the intermediate model, but is of smaller order. A special case of a parameter in a dynamical system is a delay in the model equation, e.g., arising from a feedback loop, reaction time, delayed response and various other physical phenomena. Modeling such a delay comes with several challenges for the mathematical formulation, analysis, and solution. We address the issue of transient behavior for scalar delay equations. Besides the choice of an appropriate measure, we analyze the impact of the coefficients of the delay equation on the finite time growth, which can be arbitrary large purely by the influence of the delay. / Ph. D.

Simulation, design, and control of dynamical systems play an important role in numerous scientific and industrial tasks. The need for detailed models leads to large-scale dynamical systems, posing tremendous computational difficulties when employed in numerical simulations. In order to overcome these challenges, we perform model reduction, replacing the large-scale dynamics with high-fidelity reduced representations. There exist a plethora of methods for reduced order modeling of linear systems, including the Iterative Rational Krylov Algorithm (IRKA), Balanced Truncation (BT), and Hankel Norm Approximation. However, these methods generally target stable systems and the approximation is performed over an infinite time horizon. If we are interested in a finite horizon reduced model, we utilize techniques such as Time-limited Balanced Truncation (TLBT) and Proper Orthogonal Decomposition (POD). In this dissertation we establish interpolation-based optimality conditions over a finite horizon and develop an algorithm, Finite Horizon IRKA (FHIRKA), that produces a locally optimal reduced model on a specified time-interval. Nonetheless, the quantities being interpolated and the interpolant are not the same as in the infinite horizon case. Numerical experiments comparing FHIRKA to other algorithms further support our theoretical results. Next, we discuss model reduction for nonlinear dynamical systems. For models with unstructured nonlinearities, POD is the method of choice. However, POD is input dependent and not optimal with respect to the output. Thus, we use operator splitting to integrate the best features of system theoretic approaches with trajectory based methods such as POD in order to mitigate the effect of the control inputs for the approximation of nonlinear dynamical systems. We reduce the linear terms with system theoretic methods and the nonlinear terms terms via POD. Evolving the linear and nonlinear terms separately yields the reduced operator splitting solution. We present an error analysis for this method, as well as numerical results that illustrate the effectiveness of our approach. While in this dissertation we only pursue the splitting of linear and nonlinear terms, this approach can be implemented with Quadratic Bilinear IRKA or Balanced Truncation for Quadratic Bilinear systems to further diminish the input dependence of the reduced order modeling. / Doctor of Philosophy / Simulation, design, and control of dynamical systems play an important role in numerous scientific and industrial tasks such as signal propagation in the nervous system, heat dissipation, electrical circuits and semiconductor devices, synthesis of interconnects, prediction of major weather events, spread of fires, fluid dynamics, machine learning, and many other applications. The need for detailed models leads to large-scale dynamical systems, posing tremendous computational difficulties when applied in numerical simulations. In order to overcome these challenges, we perform model reduction, replacing the large-scale dynamics with high-fidelity reduced representations. Reduced order modeling helps us to avoid the outstanding burden on computational resources. Numerous model reduction techniques exist for linear models over an infinite horizon. However, in practice we usually are interested in reducing a model over a specific time interval. In this dissertation, given a reduced order, we present a method that finds the best local approximation of a dynamical system over a finite horizon. We present both theoretical and numerical evidence that supports the proposed method. We also develop an algorithm that integrates operator splitting with model reduction to solve nonlinear models more efficiently while preserving a high level of accuracy.

Bilinear systems are a class of nonlinear dynamical systems that arise in a variety of applications. In order to obtain a sufficiently accurate representation of the underlying physical phenomenon, these models frequently have state-spaces of very large dimension, resulting in the need for model reduction. In this work, we introduce two new methods for the model reduction of bilinear systems in an interpolation framework. Our first approach is to construct reduced models that satisfy multipoint interpolation constraints defined on the Volterra kernels of the full model. We show that this approach can be used to develop an asymptotically optimal solution to the H_2 model reduction problem for bilinear systems. In our second approach, we construct a solution to a bilinear system realization problem posed in terms of constructing a bilinear realization whose kth-order transfer functions satisfy interpolation conditions in k complex variables. The solution to this realization problem can be used to construct a bilinear system realization directly from sampling data on the kth-order transfer functions, without requiring the formation of the realization matrices for the full bilinear system. / Ph. D.

The optimal estimation theory of the Wiener-Kalman filter is extended to cover the situation in which the number of memory elements in the estimator is restricted. A method, based on the simultaneous diagonalisation of two symmetric positive definite matrices, is given which allows the weighted least square estimation error to be minimised. A control system design method is developed utilising this estimator, and this allows the dynamic controller in the feedback path to have a low order. A 12-order once-through boiler model is constructed and the performance of controllers of various orders generated by the design method is investigated. Little cost penalty is found even for the one-order controller when compared with the optimal Kalman filter system. Whereas in the Kalman filter all information from past observations is stored, the given method results in an estimate of the state variables which is a weighted sum of the selected information held in the storage elements. For the once-through boiler these weighting coefficients are found to be smooth functions of position, their form illustrating the implicit model reduction properties of the design method. Minimal-order estimators of the Luenberger type also generate low order controllers and the relation between the two design methods is examined. It is concluded that the design method developed in this thesis gives better plant estimates than the Luenberger system and, more fundamentally, allows a lower order control system to be constructed. Finally some possible extensions of the theory are indicated. An immediate application is to multivariable control systems, while the existence of a plant state estimate even in control systems of very low order allows a certain adaptive structure to be considered for systems with time-varying parameters.

L’objectif est de construire un modèle d’écoulement qui se prête bien à des problèmes de contrôle, en associant un faible nombre de degrés de liberté à la possibilité de décrire la dynamique d’un écoulement relativement complexe. Dans ce travail nous considérons un écoulement bidimensionnel laminaire autour d’un cylindre carré. Des actionneurs placés sur le cylindre permettent un contrôle actif par sou?age et aspiration. Ce contrôle peut être dé?ni par rétroaction, exploitant des mesures de la vitesse dans le sillage du cylindre. Nous construisons un modèle d’ordre réduit (ROM) des équations de Navier-Stokes incompressibles, basé sur la technique de décomposition orthogonale aux valeurs propres (POD). Une façon classique de construire un tel modèle est de réaliser une projection Galerkin des équations sur le sous-espace réduit obtenu par POD. Un tel modèle peut cependant être peu précis, voire instable. Une technique de calibration est alors mise en place pour assurer la bonne représentativité dynamique du modèle. Nous dé?nissons ensuite une stratégie pour mettre à jour le modèle au cours d’un processus d’optimisation. La méthode est en?n appliquée pour réduire la di?érence entre l’écoulement contrôlé et la solution stationnaire instable à Re = 150. / The aim is to build a ?ow model adapted for control applications combining a low number of degrees of freedom with the possibility of describing relatively complex ?ows. In this work a two-dimensional laminar ?ow past a square cylinder is considered. Actuators placed on the cylinder enable active control by blowing and suction. Proportional feedback control can then be applied using velocity measurements taken in the cylinder wake. The proper orthogonal decom- position (POD) approach is used to build a low order model of the incompressible Navier-Stokes equations. A classical way of obtaining a Reduced-Order Model (ROM) is to perform a Galerkin projection of the equations onto the subspace spanned by the POD modes. Such a model can however be inaccurate, even unstable. A calibration technique is therefore applied, leading to a model that is accurate and robust to variations of the control parameters. A strategy is then de?ned to update the model within an optimisation loop. The method is tested at Re = 150 for reducing the di?erence between the actuated ?ow ?eld and the steady unstable solution.