There are many challenges facing the aerospace industry that can be addressed with new concepts, technologies, and materials. However, current design methods make it difficult to include these new ideas early in the design of aircraft. This is especially true in the structures discipline, which often uses weight-based methods based upon statistical regressions of historical data. A way to address this is to use physics-based structural analysis and design to create more detailed structural data. Thus, the overall research objective of this dissertation is to develop a physics-based structural analysis method to incorporate new concepts, technologies, and materials into the conceptual design phase. The design space of physics-based structural design problem is characterized as highly multimodal with numerous discontinuities; thus, a large number of alternatives must be explored. Current physics-based structural design methods tend to use high fidelity modeling and analysis tools that are computationally expensive. This dissertation proposes a modeling & simulation environment based on classical structural analysis methods. Using classical structural analysis will enable increased exploration of the design space by reducing the overall run time necessary to evaluate one alternative. The use of physics-based structural optimization using classical structural analysis is tested through experimentation. First the underlying hypotheses are tested in a canonical example by comparing different optimization algorithms ability to locate a global optimum identified through design space exploration. Then the proposed method is compared to a method based on higher fidelity finite element analysis as well as a method based on weight-based empirical data to validate the overall research objective.

Rotorcraft optimization is a challenging problem due to its conflicting requirements among many disciplines and highly coupled design variables affecting the overall design. Also, the design process for a composite rotor blade is often ambiguous because of its design space. Furthermore, analytical tools do not produce acceptable results compared with flight test when it comes to aerodynamics and aeroelasticity unless realistic models are used, which leads to excessive computer time per iteration. To comply these requirements, computationally efficient yet realistic tools for rotorcraft analysis, such as VABS and DYMORE were used as analysis tools. These tools decompose a three-dimensional problem into a two-dimensional cross-sectional and a one-dimensional beam analysis. Also, to eliminate the human interaction between iterations, a previously VABS-ANSYS macro was modified and automated. The automated tool shortened the computer time needed to generate the VABS input file for each analysis from hours to seconds. MATLAB was used as the wrapper tool to integrate VABS, DYMORE and the VABS-ANSYS macro into the methodology. This methodology uses Genetic Algorithm and gradient-based methods as optimization schemes. The baseline model is the rotor system of generic Georgia Tech Helicopter (GTH), which is a three-bladed, soft-in-plane, bearingless rotor system. The resulting methodology is a two-level optimization, global and local. Previous studies showed that when stiffnesses are used as design variables in optimization, these values act as if they are independent and produce design requirements that cannot be achieved by local-level optimization. To force design variables at the global level to stay within the feasible design space of the local level, a surrogate model was adapted into the methodology. For the surrogate model, different ``design of experiments" (DOE) methods were tested to find the most computationally efficient DOE method. The response surface method (RSM) and Kriging were tested for the optimization problem. The results show that using the surrogate model speeds up the optimization process and the Kriging model shows superior performance over RSM models. As a result, the global-level optimizer produces requirements that the local optimizer can achieve.

Cette thèse s’inscrit dans le cadre de la programmation par contraintes quantifiées, un formalisme étendantla programmation par contraintes classique en ajoutant aux variables des quantificateurs existentiels ouuniversels, ce qui apporte en théorie une expressivité suffisante pour modéliser des problèmes avec adversaireou incertitude sur certains paramètres sous forme de problèmes appelés QCSP (Quantified Constraintsatisfaction Problem).Nous commençons par apporter une réponse aux difficultés de modélisation de problèmes réels dont estfrappée la programmation par contraintes quantifiées en introduisant une extension aux QCSP permettantd’expliciter les actions possibles de l’agent principal et de son adversaire. Puis, nous décrivons différentproblèmes grâce à ce formalisme, et discutons de la place de cette extension parmi les formalismes voisins créésen réponse à cette même difficulté de modélisation. Enfin, nous nous intéressons à la notion d’optimisationdans le cas des contraintes quantifiées, et apportons un formalisme d’optimisation de contraintes quantifiéespermettant d’exprimer des problèmes multi-niveaux non linéaires. / This thesis presents works in the research area of quantified constraint programming, which extends theconstraint programming framework by setting (existential and universal) quantifiers to the problem’s variables.This framework is theoretically expressive enough to model problems where an opponent or uncertainparameters are involved, under the form of Quantified Constraint Safisfaction Problems (QCSP).QCSPs suffer from a modeling difficulty that we solve by presenting an extension to this framework, in whichpossible moves for the principal agent and its opponent may be explicitely declared. Then, we describe realproblems using this extention, and discuss of its pros and cons against neighbour framework thar were createdto solve the same difficulty. Finally, we focus on quantifies optimization problems, and present a quantifiedoptimization framework thet allows the modeling of nonlinear multi-level problems.

Made available in DSpace on 2016-08-17T14:52:45Z (GMT). No. of bitstreams: 1 Valeska Martins Souza.pdf: 622283 bytes, checksum: 075dc5eb2d1ecc78b4ecd96ae57ab70e (MD5) Previous issue date: 2002-02-13 / In this dissertation a new methodology of based convex optimization in linear matrix inaqualities is proposal as basic instrument for the synthesis of robust controllers of discrete and linear dynamic systems that take care of to the specifications of perturbations of worse case. / Nesta dissertação é proposta uma nova metodologia de otimização convexa baseada em desigualdades matriciais lineares como instrumento básico para a síntese de controladores robustos de sistemas dinâmicos discretos e lineares que atendam às especificações de pertubações de pior caso.

Multidisciplinary design optimization (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously for reasons of efficiency. When performing MDO, a large number of designs need to be rated. Detailed simulation models used to assess automotive design proposals are often computationally expensive to evaluate. A useful MDO process must distribute work to the groups involved and be computationally efficient. In this thesis, MDO methods are assessed in relation to the characteristics of automotive structural applications. Single-level optimization methods have a single optimizer, while multi-level optimization methods have a distributed optimization process. Collaborative optimization and analytical target cascading are possible choices of multi-level optimization methods for automotive structures. They distribute the design process, but are complex. One approach to handle the computationally demanding simulation models involves metamodel-based design optimization (MBDO), where metamodels are used as approximations of the detailed models during optimization studies. Metamodels can be created by individual groups prior to the optimization process, and therefore also offer a way of distributing work. A single-level optimization method in combination with metamodels is concluded to be the most straightforward way of implementing MDO into the development of automotive structures.

La conception d'un système électrique est une tâche très complexe qui relève d’expertises dans différents domaines de compétence. Dans un contexte compétitif où l’avance technologique est un facteur déterminant, l’industrie cherche à réduire les temps d'étude et à fiabiliser les solutions trouvées par une approche méthodologique rigoureuse fournissant une solution optimale systémique.Il est alors nécessaire de construire des modèles et de mettre au point des méthodes d'optimisation compatibles avec ces préoccupations. En effet, l’optimisation unitaire de sous-systèmes sans prendre en compte les interactions ne permet pas d'obtenir un système optimal. Plus le système est complexe plus le travail est difficile et le temps de développement est important car il est difficile pour le concepteur d'appréhender le système dans toute sa globalité. Il est donc nécessaire d'intégrer la conception des composants dans une démarche systémique et globale qui prenne en compte à la fois les spécificités d’un composant et ses relations avec le système qui l’emploie.Analytical Target Cascading est une méthode d'optimisation multi niveaux de systèmes complexes. Cette approche hiérarchique consiste à décomposer un système complexe en sous-systèmes, jusqu’au niveau composant dont la conception relève d’algorithmes d'optimisation classiques. La solution optimale est alors trouvée par une technique de coordination qui assure la cohérence de tous les sous-systèmes. Une première partie est consacrée à l'optimisation de composants électriques. L'optimisation multi niveaux de systèmes complexes est étudiée dans la deuxième partie où une chaîne de traction électrique est choisie comme exemple / The design of an electrical system is a very complex task which needs experts from various fields of competence. In a competitive environment, where technological advance is a key factor, industry seeks to reduce study time and to make solutions reliable by way of a rigorous methodology providing a systemic solution.Then, it is necessary to build models and to develop optimization methods which are suitable with these concerns. Indeed, the optimization of sub-systems without taking into account the interaction does not allow to achieve an optimal system. More complex the system is more the work is difficult and the development time is important because it is difficult for the designer to understand and deal with the system in its complexity. Therefore, it is necessary to integrate the design components in a systemic and holistic approach to take into account, in the same time, the characteristics of a component and its relationship with the system it belongs to.Analytical Target Cascading is a multi-level optimization method for handling complex systems. This hierarchical approach consists on the breaking-down of a complex system into sub-systems, and component where their optimal design is ensured by way of classical optimization algorithms. The optimal solution of the system must be composed of the component's solutions. Then a coordination strategy is needed to ensure consistency of all sub-systems. First, the studied and proposed optimization algorithms are tested and compared on the optimization of electrical components. The second part focuses on the multi-level optimization of complex systems. The optimization of railway traction system is taken as a test case

Dans le contexte actuel de globalisation des marchés, le processus classique de conception par essais et erreurs n'est plus capable de répondre aux exigences de plus en plus accrues en termes de délais très courts, réduction des coûts de production, etc. L'outil d'optimisation propose une réponse à ces questions, en accompagnant les ingénieurs dans la tâche de conception optimale.L'objectif de cette thèse est centré sur la conception optimale des systèmes complexes. Deux approches sont abordées dans ce travail: l'optimisation par modèles de substitution et la conception optimale basée sur la décomposition des systèmes complexes.L'utilisation de la conception assistée par ordinateur (CAO) est devenue une pratique régulière dans l’industrie. La démarche d'optimisation basée sur modèles de substitution est destinée à répondre à l'optimisation des dispositifs bénéficiant d’une telle modélisation précise, mais couteuse en temps de calcul.Les chaînes de traction ferroviaire sont trop complexes pour être traités comme un tout. La décomposition de ces systèmes s’impose en vue de simplifier le problème et de repartir la charge de calcul. Des stratégies appropries à la résolution de telles structures ont été analysées dans ce travail. Ces approches permettent à chaque équipe de spécialistes de travailler de façon autonome à l'objet de leur expertise.Les approches d'optimisation développées au sein de ce travail ont été appliquées pour résoudre plusieurs problèmes d'optimisation électromagnétiques, ainsi que la conception optimale d’un système de traction ferroviaire de la Société Alstom / Within a globalized market context, the classical trial-and-error design process is no longer capable of answering to the ever-growing demands in terms of short deadlines, reduced production costs, etc. The optimization tool presents itself as an answer to these issues, accompanying the engineers in the optimal design task.The focus of this thesis is centered on the optimal design of complex systems. Two main optimization approaches are addressed within this work: the metamodel-based design optimization and the decomposition-based complex systems optimal design.The use of computer-aided design/engineering (CAD/CAE) software has become a regular practice in the engineering design process. The metamodel-based optimization approach is intended to address the optimization of devices represented by such accurate but computationally expensive simulation models, as the finite element analysis (FEA) in electromagnetics.Engineering systems such as railway traction systems are too complex to be addressed as a whole. The decomposition-based optimization strategies are intended to address the optimal design of such systems. The decomposition of such systems is required in order to simplify the problem and to distribute the computational burden across the decomposed structure. Appropriate multi-level strategies have been identified and analyzed within this work. Such approaches allow each team of specialists to work independently at the object of their expertise.The optimization approaches developed within this work are applied for solving several electromagnetic optimization problems and a railway traction system optimal design problem of the Alstom Company

Le système de santé français est confronté au défi d’augmentation permanente de la demande en soins, sous une forte pression financière. Dans la stratégie nationale de santé, une des grandes orientations est de développer une base de coopération impliquant l’ensemble des acteurs et de leur engagement. Ces enjeux demandent au génie hospitalier de rechercher une efficience dans une échelle encore plus globale, ce qui demande d’intégrer les problèmes locaux et leurs outils d’optimisation qui présentent en général un haut degré de fragmentation, afin de contribuer à l’amélioration globale du système. Dans ce contexte-là, initialisé par un projet de conception du système de soins en réseau avec ressource de production mutualisée, nous proposons à travers ce mémoire de thèse une méthode générique pour résoudre le problème d’optimisation multi-niveaux dans lequel les décisions interdépendantes doivent être prises à différents niveaux dans une structure hiérarchique, ou aux étapes successives. Les décisions faites sont souvent corrélées, surtout pour une topologie de décisions enchaînées en hiérarchique que nous définissons sous le terme de « sous-structure optimale feedback ». La résolution de ce type de problème doit s’adapter pour prendre en compte autant que possible les implications liées aux décisions corrélées. La méthode proposée est basée sur la méta-heuristique PSO, elle utilise une procédure récursive pour définir le transfert des paramètres des sous-problèmes descendant et des évaluations ascendant à travers de multiples espaces de recherche, en assurant la cohérence de la convergence du problème global. Les applications et les analyses ont montrées que la méthode est assez générique et capable de produire la performance et la qualité de résolution proche de celles de la littérature / French healthcare system confronts the challenges of permanent increase in demand for healthcare, under heavy financial pressure. In the national healthcare strategy, a key focus is to develop a cooperation framework involving all organizations and units. These challenges require healthcare engineering to find efficiency in a more global scale, which means to integrate local optimization problems and decision tools that have generally a high degree of fragmentation in order to contribute to the overall improvement of the system. In this thesis, initiated by a shared unit-dose drug distribution system design project, a generic method was developed to solve the multi-level optimization problem in which interdependent decisions are made at different levels in a hierarchical structure, or at successive stages. The decisions made are often correlated, particularly for decisions in hierarchical topologies that we define by the term "optimal substructure with feedback". The resolution of this problem must be adapted to take into account all implications for correlated decisions. The proposed method is based on the meta-heuristic PSO, it uses a recursive procedure to define the top-down transfer of parameters and the bottom-up feedback of fitness through multiple search spaces, and ensures the consistency of global problem convergence. Our applications and analyzes have shown that this method is generic and is able to provide similar resolution performance and quality compared to the literature references