Simulating the complex physics and dynamics associated with unsteady aeroelastic systems is often attempted with high-fidelity numerical models. While these high-fidelity approaches are powerful in terms of capturing the main physical features, they may not discern the role of underlying phenomena that are interrelated in a complex manner. This often makes it difficult to characterize the relevant causal mechanisms of the observed features. Besides, the extensive computational resources and time associated with the use these tools could limit the capability of assessing different configurations for design purposes. These shortcomings present the need for the development of simplified and reduced-order models that embody relevant physical aspects and elucidate the underlying phenomena that help in characterizing these aspects. In this work, different fluid and aeroelastic systems are considered and reduced-order models governing their behavior are developed. In the first part of the dissertation, a methodology, based on the method of multiple scales, is implemented to show its usefulness and effectiveness in the characterization of the physics underlying the system, the implementation of control strategies, and the identification of high-impact system parameters. In the second part, the unsteady aerodynamic aspects of flapping micro air vehicles (MAVs) are modeled. This modeling is required for evaluation of performance requirements associated with flapping flight. The extensive computational resources and time associated with the implementation of high-fidelity simulations limit the ability to perform optimization and sensitivity analyses in the early stages of MAV design. To overcome this and enable rapid and reasonably accurate exploration of a large design space, a medium-fidelity aerodynamic tool (the unsteady vortex lattice method) is implemented to simulate flapping wing flight. This model is then combined with uncertainty quantification and optimization tools to test and analyze the performance of flapping wing MAVs under varying conditions. This analysis can be used to provide guidance and baseline for assessment of MAVs performance in the early stages of decision making on flapping kinematics, flight mechanics, and control strategies. / Ph. D.

In this thesis, we apply nonlinear Koopman mode analysis to decompose the swing dynamics of a power system into modes of oscillation, which are identified by analyzing the Koopman operator, a linear infinite-dimensional operator that may be defined for any nonlinear dynamical system. Specifically, power system modes of oscillation are identified through spectral analysis of the Koopman operator associated with a particular observable. This means that they can be determined directly from measurements. These modes, referred to as Koopman modes, are single-frequency oscillations, which may be extracted from nonlinear swing dynamics under small and large disturbances. They have an associated temporal frequency and growth rate. Consequently, they may be viewed as a nonlinear generalization of eigen-modes of a linearized system. Koopman mode analysis has been also applied to identify coherent swings and coherent groups of machines of a power system. This will allow us to carry out a model reduction of a large-scale system and to derive a precursor to monitor the loss of transient stability. / Master of Science

Due to the complexity, multi-scale, and multi-physics nature of the mathematical models for fires, current numerical models require too much computational effort to be useful in design and real-time decision making, especially when dealing with fires over large domains. To reduce the computational time while retaining the complexity of the domain and physics, our research has focused on several reduced-order modeling techniques. Our contributions are improving wildland fire reduced-order models (ROMs), creating new ROM techniques for nonlinear systems, and preserving optimality when discretizing a continuous-time ROM. Currently, proper orthogonal decomposition (POD) is being used to reduce wildland fire-spread models with limited success. We use a technique known as the discrete empirical interpolation method (DEIM) to address the slowness due to the nonlinearity. We create new methods to reduce nonlinear models, such as the Burgers' equation, that perform better than POD over a wider range of input conditions. Further, these ROMs can often be constructed without needing to capture full-order solutions a priori. This significantly reduces the off-line costs associated with creating the ROM. Finally, we investigate methods of time-discretization that preserve the optimality conditions in a certain norm associated with the input to output mapping of a dynamical system. In particular, we are able to show that the Crank-Nicholson method preserves the optimality conditions, but other single-step methods do not. We further clarify the need for these discrete-time ROMs to match at infinity in order to ensure local optimality. / Ph. D.

Generally, large-scale dynamical systems pose tremendous computational difficulties when applied in numerical simulations. In order to overcome these challenges we use several model reduction techniques. For stable linear models these techniques work very well and provide good approximations for the full model. However, large-scale unstable systems arise in many applications. Many of the known model reduction methods are not very robust, or in some cases, may not even work if we are dealing with unstable systems. When approximating an unstable system by a reduced order model, accuracy is not the only concern. We also need to consider the structure of the reduced order model. Often, it is important that the number of unstable poles in the reduced system is the same as the number of unstable poles in the original system. The Iterative Rational Krylov Algorithm (IRKA) is a robust model reduction technique which is used to locally reduce stable linear dynamical systems optimally in the ℋ₂-norm. While we cannot guarantee that IRKA reduces an unstable model optimally, there are no numerical obstacles to the reduction of an unstable model via IRKA. In this thesis, we investigate IRKA's behavior when it is used to reduce unstable models. We also consider systems for which we cannot obtain a first order realization of the transfer function. We can use Realization-independent IRKA to obtain a reduced order model which does not preserve the structure of the original model. In this paper, we implement a structure preserving algorithm for systems with nonlinear frequency dependency. / Master of Science

Mathematical models of physical systems are pervasive in engineering. These models can be used to analyze properties of the system, to simulate the system, or synthesize controllers. However, many of these models are too complex or too large for standard analysis and synthesis methods to be applicable. Hence, there is a need to reduce the complexity of models. In this thesis, techniques for reducing complexity of large linear time-invariant (lti) state-space models and linear parameter-varying (lpv) models are presented. Additionally, a method for synthesizing controllers is also presented. The methods in this thesis all revolve around a system theoretical measure called the H2-norm, and the minimization of this norm using nonlinear optimization. Since the optimization problems rapidly grow large, significant effort is spent on understanding and exploiting the inherent structures available in the problems to reduce the computational complexity when performing the optimization. The first part of the thesis addresses the classical model-reduction problem of lti state-space models. Various H2 problems are formulated and solved using the proposed structure-exploiting nonlinear optimization technique. The standard problem formulation is extended to incorporate also frequency-weighted problems and norms defined on finite frequency intervals, both for continuous and discrete-time models. Additionally, a regularization-based method to account for uncertainty in data is explored. Several examples reveal that the method is highly competitive with alternative approaches. Techniques for finding lpv models from data, and reducing the complexity of lpv models are presented. The basic ideas introduced in the first part of the thesis are extended to the lpv case, once again covering a range of different setups. lpv models are commonly used for analysis and synthesis of controllers, but the efficiency of these methods depends highly on a particular algebraic structure in the lpv models. A method to account for and derive models suitable for controller synthesis is proposed. Many of the methods are thoroughly tested on a realistic modeling problem arising in the design and flight clearance of an Airbus aircraft model. Finally, output-feedback H2 controller synthesis for lpv models is addressed by generalizing the ideas and methods used for modeling. One of the ideas here is to skip the lpv modeling phase before creating the controller, and instead synthesize the controller directly from the data, which classically would have been used to generate a model to be used in the controller synthesis problem. The method specializes to standard output-feedback H2 controller synthesis in the lti case, and favorable comparisons with alternative state-of-the-art implementations are presented.

High Redundancy Actuation (HRA) is a novel type of fault tolerant actuator. By comprising a relatively large number of actuation elements, faults in the elements can be inherently accommodated without resulting in a failure of the complete actuation system. By removing the possibility of faults detection and reconfiguration, HRA can provide high reliability and availability. The idea is motivated by the composition of human musculature. Our musculature can sustain damage and still function, sometimes with reduced performance, and even complete loss of a muscle group can be accommodated through kinematics redundancy, e.g. the use of just one leg. Electro-mechanical actuation is used as single element inside HRA. This thesis is started with modelling and simulation of individual actuation element and two basic structures to connect elements, in series and in parallel. A relatively simple HRA is then modelled which engages a two-by-two series-in-parallel configuration. Based on this HRA, position feedback controllers are designed using both classical and optimal algorithms under two control structures. All controllers are tested under both healthy and faults injected situations. Finally, a hardware demonstrator is set up based simulation studies. The demonstrator is controlled in real time using an xPC Target system. Experimental results show that the HRA can continuously work when one element fails, although performance degradation can be expected.

This dissertation deals with some computational and analytic challenges for dynamic process operations using first-principles models. For processes with significant spatial variations, spatially distributed first-principles models can provide accurate physical descriptions, which are crucial for offline dynamic simulation and optimization. However, the large amount of time required to solve these detailed models limits their use for online applications such as nonlinear model predictive control (NMPC). To cope with the computational challenge, we develop computationally efficient and accurate dynamic reduced order models which are tractable for NMPC using temporal and spatial model reduction techniques. Then we introduce an input and state blocking strategy for NMPC to further enhance computational efficiency. To improve the overall economic performance of process systems, one promising solution is to use economic NMPC which directly optimizes the economic performance based on first-principles dynamic models. However, complex process models bring challenges for the analysis and design of stable economic NMPC controllers. To solve this issue, we develop a simple and less conservative regularization strategy with focuses on a reduced set of states to design stable economic NMPC controllers. In this thesis, we study the operation problems of a solid sorbent-based CO2 capture system with bubbling fluidized bed (BFB) reactors as key components, which are described by a large-scale nonlinear system of partial-differential algebraic equations. By integrating dynamic reduced models and blocking strategy, the computational cost of NMPC can be reduced by an order of magnitude, with almost no compromise in control performance. In addition, a sensitivity based fast NMPC algorithm is utilized to enable the online control of the BFB reactor. For economic NMPC study, compared with full space regularization, the reduced regularization strategy is simpler to implement and lead to less conservative regularization weights. We analyze the stability properties of the reduced regularization strategy and demonstrate its performance in the economic NMPC case study for the CO2 capture system.

La nécessité de simuler des systèmes complexes et multi-physiques est de plus en plus courante dans l’industrie, en particulier avec l’avènement de la conception mécatronique. Ce phénomène couplé à la pression économique poussant les industriels dans la voie de l’optimisation de leurs produits conduit à une augmentation forte des temps de simulation que les progrès techniques ne parviennent pas à compenser. Les travaux menés lors de cette thèse ont permis de proposer une approche hybride analytique/éléments finis pour la simulation temporelle de la dynamique des transmissions par engrenages en présence de non-linéarités de contact. Couplée à une réduction des modèles éléments finis, cette approche permet la résolution rapide de la dynamique d’un réducteur et offre ainsi la possibilité d’intégrer le réducteur comme composant d’un système complexe tel qu’un système mécatronique. La résolution de la dynamique du réducteur peut être menée en parallèle des autres physiques en prenant en compte des couplages forts. L’optimisation d’un tel système est abordée sur un exemple pour clore cette étude. / The need to simulate complex and multi-physics systems is increasingly common in the industry, especially with the advent of mechatronic design. This coupled with economic pressure pushing the industry towards optimizing their products led to a strong increase in simulation time that technological advances can not compensate. An hybrid method analytical/finite element has been developed for the time domain simulation of gear transmissions involving contact non-linearities. Coupled with a reduction of finite element models, this approach allows fast resolution of the dynamics of a gearbox. Consequently, it is possible to integrate a gearbox as a part of a more complex mechatronic system. All physical phenomena involved in such a complex product are solved at the same time allowing strong coupling to be considered. The optimization of such a system is discussed with an example to conclude this study.

La perturbation d’un système peut donner lieu à de la propagation d’onde. Une façon classique d’appréhender ce phénomène est de rechercher les modes propres de vibration du milieu. Mathématiquement, trouver ces modes consiste à rechercher les valeurs et fonctions propres de l’opérateur de propagation. Cependant, d’un point de vue numérique, l’opération peut s’avérer coûteuse car les matrices peuvent avoir de très grandes tailles. En outre, dans la plupart des applications, des incertitudes sont inévitablement associées à notre modèle. La question se pose alors de savoir s’il faut attribuer d’importantes ressources de calcul pour une simulation dont la précision du résultat n’est pas assurée. Nous proposons dans cette thèse une démarche qui permet à la fois de mieux comprendre l’influence des incertitudes sur la propagation et de réduire considérablement les coûts de calcul pour la propagation des infrasons dans l’atmosphère. L’idée principale est que tous les modes n’ont pas la même importance et souvent, seule une poignée d’entre eux suffit à décrire le phénomène sans perte notable de précision. Ces modes s’avèrent être ceux qui sont les plus sensibles aux perturbations atmosphériques. Plus précisément, l’analyse de sensibilité permet d’identifier les structures de l’atmosphère les plus influentes, les groupes de modes qui leur sont associés et les parties du signal infrasonore qui leur correspondent. Ces groupes de modes peuvent être spécifiquement ciblés dans un calcul de spectre au moyen de techniques de projection sur des sous-espace de Krylov, ce qui implique un gain important en coût de calcul. Cette méthode de réduction de modèle peut être appliquée dans un cadre statistique et l’estimation de l’espérance et de la variance du résultat s’effectue là aussi sans perte notable de précision et avec un coût réduit. / The perturbation of a system can give rise to wave propagation. A classical approach to understand this phenomenon is to look for natural modes of vibration of the medium. Mathematically, finding these modes requires to seek the eigenvalues and eigenfunctions of the propagation operator. However, from a numerical point of view, the operation can be costly because the matrices can be of very large size. Furthermore, in most applications, uncertainties are inevitably associated with our model. The question then arises as to whether we should allocate significant computational resources for simulation while the accuracy of the result is not guaranteed. We propose in this thesis an approach that allows both a better understanding of the influence of uncertainties on the propagation and a significant decrease of computational costs for infrasound propagation in the atmosphere. The main idea is that all modes do not have the same importance and only a few of them is often sufficient to account for the phenomenon without a significant loss of accuracy. These modes appear to be those which are most sensitive to atmospheric disturbances. Specifically, a sensitivity analysis is used to identify the most influential structures of the atmosphere, the associated groups of modes and their associated parts of the infrasound signal. These groups of modes can be specifically targeted in a spectrum calculation with the projection of the operator onto Krylov subspaces, that allows a significant decrease of the computational cost. This method of model reduction can be applied in a statistical framework as well and estimations of the expectation and the variance of the results are carried out without a significant loss of accuracy and still with a low cost.

Les simulations à grande dimension jouent un rôle crucial dans l'étude d'une grande variété de phénomènes physiques complexes, entraînant souvent des demandes écrasantes sur les ressources informatiques. La gestion de ces demandes constitue la principale motivation pour la réduction du modèle : produire des modèles de commande réduite plus simples, qui permettent une simulation plus rapide et moins coûteuse tout en se rapprochant avec précision du comportement du modèle d'origine. La présence des systèmes avec multiples entrées et multiples sorties (MIMO) rend le processus de réduction encore plus difficile. Dans cette thèse, nous nous intéressons aux méthodes de réduction de modèles à grande dimension en utilisant la projection sur des sous-espaces de Krylov tangentielles. Nous nous penchons sur le développement de techniques qui utilisent l'interpolation tangentielle. Celles-ci présentent une alternative efficace et intéressante à la troncature équilibrée qui est considérée comme référence dans le domaine et tout particulièrement la réduction pour les systèmes linéaire à temps invariants. Enfin, une attention particulière sera portée sur l'élaboration de nouveaux algorithmes efficaces et sur l'application à des problèmes pratiques. / Large-scale simulations play a crucial role in the study of a great variety of complex physical phenomena, leading often to overwhelming demands on computational resources. Managing these demands constitutes the main motivation for model reduction : produce simpler reduced-order models, which allow for faster and cheaper simulation while accurately approximating the behaviour of the original model. The presence of multiple inputs and outputs (MIMO) systems, makes the reduction process even more challenging. In this thesis we are interested in methods of reducing large-scale models, using projection on tangential Krylov subspaces. We are looking at the development of techniques using tangential interpolation. These present an effective and interesting alternative to the balanced truncation which is considered as a reference in the field and especially for the reduction of linear time invariant systems. Finally, special attention will be focused on the development of new efficient algorithms and application to practical problems.