Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-07T15:01:41Z No. of bitstreams: 1 alexandrecancadocardoso.pdf: 3207141 bytes, checksum: 46de44194b8a9a99093ecb73f332eacd (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-07T15:07:15Z (GMT) No. of bitstreams: 1 alexandrecancadocardoso.pdf: 3207141 bytes, checksum: 46de44194b8a9a99093ecb73f332eacd (MD5) / Made available in DSpace on 2017-03-07T15:07:15Z (GMT). No. of bitstreams: 1 alexandrecancadocardoso.pdf: 3207141 bytes, checksum: 46de44194b8a9a99093ecb73f332eacd (MD5) Previous issue date: 2016-09-16 / A estratégia de dividir para conquistar é comum a diversos ramos de atuação, indo do projeto de algoritmos à politica e sociologia. Em engenharia, é utilizada, dentre outras aplicações, para auxiliar na resolução de problemas de criação de um projeto (general desing problems) ou de um projeto ótimo (optimal design problems) de sistemas grandes, complexos ou multidisciplinares. O presente, trabalho apresenta um método para divisão, decomposição destes problemas em sub-problemas menores a partir de informação apenas do seu modelo (model-based decomposition). Onde a extração dos padrões de relação entre as variáveis, funções, simulações e demais elementos do modelo é realizada através de algoritmos de aprendizado não supervisionado em duas etapas. Primeiramente, o espaço dimensional é reduzido a fim de ressaltar as relações mais significativas, e em seguida utiliza-se a técnica de detecção de comunidade oriunda da área de redes complexas ou técnicas de agrupamento para identificação dos sub-problemas. Por fim, o método é aplicado a problemas de otimização de projeto encontrados na literatura de engenharia estrutural e mecânica. Os sub-problemas obtidos são avaliados segundo critérios comparativos e qualitativos. / The divide and conquer strategy is common to many fields of activity, ranging from the algorithms design to politics and sociology. In engineering, it is used, among other applications, to assist in solving general design problems or optimal design problems of large, complex or multidisciplinary systems. The present work presents a method for splitting, decomposition of these problems into smaller sub-problems using only information from its model (model-based decomposition). Where the pattern extraction of relationships between variables, functions, simulations and other model elements is performed using unsupervised learning algorithms in two steps. First, the dimensional space is reduced in order to highlight the most significant relationships, and then we use the community detection technique coming from complex networks area and clustering techniques to identify the sub-problems. Finally, the method is applied to design optimization problems encountered in structural and mechanical engineering literature. The obtained sub-problems are evaluated against comparative and qualitative criteria.

Dans cette Thèse, la conception robuste avec un modèle incertain d'un système électromécanique avec vibro-impact est fait. Le système électromécanique est constitué d'un chariot, dont le mouvement est excité par un moteur à courant continu et un marteau embarqué dans ce chariot. Le marteau est relié au chariot par un ressort non linéaire et par un amortisseur linéaire, de façon qu'un mouvement relatif existe entre eux. Une barrière flexible linéaire, placé à l'extérieur du chariot limite les mouvements de marteau. En raison du mouvement relatif entre le marteau et la barrière, impacts peuvent se produire entre ces deux éléments. Le modèle du système développé prend en compte l'influence du courant continu moteur dans le comportement dynamique du système. Certains paramètres du système sont incertains, tels comme les coefficients de rigidité et d'amortissement de la barrière flexible. L'objectif de la Thèse est de réaliser une optimisation de ce système électromécanique par rapport aux paramètres de conception afin de maximiser l'impact puissance sous la contrainte que la puissance électrique consommée par le moteur à courant continu est inférieure à une valeur maximale. Pour choisir les paramètres de conception dans le problème d'optimisation, une analyse de sensibilité a été réalisée afin de définir les paramètres du système les plus sensibles. L'optimisation est formulée dans le cadre de la conception robuste en raison de la présence d'incertitudes dans le modèle. Les lois de probabilités liées aux variables aléatoires du problème sont construites en utilisant le Principe du Maximum l'Entropie et les statistiques de la réponse stochastique du système sont calculées en utilisant la méthode de Monte Carlo. L'ensemble d'équations non linéaires sont présentés, et un solveur temporel adapté est développé. Le problème d'optimisation non linéaire stochastique est résolu pour différents niveaux d'incertitudes, et aussi pour le cas déterministe. Les résultats sont différents, ce qui montre l'importance de la modélisation stochastique / In this Thesis, the robust design with an uncertain model of a vibro-impact electromechanical system is done. The electromechanical system is composed of a cart, whose motion is excited by a DC motor (motor with continuous current), and an embarked hammer into this cart. The hammer is connected to the cart by a nonlinear spring component and by a linear damper, so that a relative motion exists between them. A linear flexible barrier, placed outside of the cart, constrains the hammer movements. Due to the relative movement between the hammer and the barrier, impacts can occur between these two elements. The developed model of the system takes into account the influence of the DC motor in the dynamic behavior of the system. Some system parameters are uncertain, such as the stiffness and the damping coefficients of the flexible barrier. The objective of the Thesis is to perform an optimization of this electromechanical system with respect to design parameters in order to maximize the impact power under the constraint that the electric power consumed by the DC motor is lower than a maximum value. To chose the design parameters in the optimization problem, an sensitivity analysis was performed in order to define the most sensitive system parameters. The optimization is formulated in the framework of robust design due to the presence of uncertainties in the model. The probability distributions of random variables are constructed using the Maximum Entropy Principle and statistics of the stochastic response of the system are computed using the Monte Carlo method. The set of nonlinear equations are presented, and an adapted time domain solver is developed. The stochastic nonlinear constrained design optimization problem is solved for different levels of uncertainties, and also for the deterministic case. The results are different and this show the importance of the stochastic modeling

L’objectif ultime de ce travail de thèse est d’évaluer la possibilité de valider et d’optimiser un processus de fabrication en utilisant la simulation numérique en tenant compte des incertitudes irréductibles sur le procédé, les matériaux et la géométrie du produit fabriqué. La prise en compte des incertitudes nécessite de quantifier les effets des variations des paramètres du modèle sur les sorties de celui-ci, en propageant ces variations via la simulation numérique pour évaluer leurs effets sur les sorties. Dans ce travail nous avons proposé une procédure pour déterminer le seuil de sensibilité du modèle numérique afin de construire des plans d’expériences numériques cohérents avec ce seuil. Nous avons également montré que, compte tenu des incertitudes sur les matériaux et la géométrie du produit, il est possible d’optimiser certains paramètres du procédé pour contrôler les effets des incertitudes sur les variations dimensionnelles et morphologiques du produit. Pour cela, nous avons proposé une procédure d’optimisation basée sur un algorithme NSGA-II et une méta-modélisation du procédé. L’application à l’emboutissage d’une tôle en U, retour élastique inclus, montre qu’il s’agit d’un problème de conception robuste pour lequel nous obtenons l’ensemble des compromis entre l’écart à la moyenne et l’écart type d’une fonction « performance » du procédé correctement choisie. Finalement l’analyse de ces résultats nous permet de quantifier le lien entre la notion de robustesse d’une solution optimisée du procédé et les critères de mesure de la qualité du produit / The ultimate objective of this thesis is to evaluate the possibility to validate and optimize a manufacturing process using numerical simulation and taking into account the irreducible uncertainties in the process, materials and geometry of manufactured product. Taking into account the uncertainties requires quantifying the effects of variations of model parameters on the outputs, by propagating these variations via computer simulation to assess their effects on the outputs. In this work, we have proposed a procedure to determine the sensitivity threshold of the numerical model to build numerical Design of Experiments consistent with this threshold. We have also shown that, given the uncertainties in the materials and the geometry of the product, it is possible to optimize certain process parameters to control the effects of uncertainties on the dimensional and morphological variations of the product. For this, we have proposed an optimization procedure based on NSGA-II algorithm and a meta-modeling of the process. The application for deep drawing of a U-shaped sheet metal part, springback included shows that it is a robust design problem for which we get all the compromise between the deviation from the mean and standard deviation of a "performance" depending on the process correctly chosen. Finally, the analysis of these results allows us to quantify the relationship between the notion of robustness of an optimized solution of the process and criteria for measuring the quality of the product

La conception de lanceurs est un problème d’optimisation multidisciplinaire dont l’objectif est de trouverl’architecture du lanceur qui garantit une performance optimale tout en assurant un niveau de fiabilité requis.En vue de l’obtention de la solution optimale, les phases d’avant-projet sont cruciales pour le processus deconception et se caractérisent par la présence d’incertitudes dues aux phénomènes physiques impliqués etaux méconnaissances existantes sur les modèles employés. Cette thèse s’intéresse aux méthodes d’analyse et d’optimisation multidisciplinaire en présence d’incertitudes afin d’améliorer le processus de conception de lanceurs. Trois sujets complémentaires sont abordés. Tout d’abord, deux nouvelles formulations du problème de conception ont été proposées afin d’améliorer la prise en compte des interactions disciplinaires. Ensuite, deux nouvelles méthodes d’analyse de fiabilité, permettant de tenir compte d’incertitudes de natures variées, ont été proposées, impliquant des techniques d’échantillonnage préférentiel et des modèles de substitution. Enfin, une nouvelle technique de gestion des contraintes pour l’algorithme d’optimisation ”Covariance Matrix Adaptation - Evolutionary Strategy” a été développée, visant à assurer la faisabilité de la solution optimale. Les approches développées ont été comparées aux techniques proposées dans la littérature sur des cas tests d’analyse et de conception de lanceurs. Les résultats montrent que les approches proposées permettent d’améliorer l’efficacité du processus d’optimisation et la fiabilité de la solution obtenue. / Launch vehicle design is a Multidisciplinary Design Optimization problem whose objective is to find the launch vehicle architecture providing the optimal performance while ensuring the required reliability. In order to obtain an optimal solution, the early design phases are essential for the design process and are characterized by the presence of uncertainty due to the involved physical phenomena and the lack of knowledge on the used models. This thesis is focused on methodologies for multidisciplinary analysis and optimization under uncertainty for launch vehicle design. Three complementary topics are tackled. First, two new formulations have been developed in order to ensure adequate interdisciplinary coupling handling. Then, two new reliability techniques have been proposed in order to take into account the various natures of uncertainty, involving surrogate models and efficient sampling methods. Eventually, a new approach of constraint handling for optimization algorithm ”Covariance Matrix Adaptation - Evolutionary Strategy” has been developed to ensure the feasibility of the optimal solution. All the proposed methods have been compared to existing techniques in literature on analysis and design test cases of launch vehicles. The results illustrate that the proposed approaches allow the improvement of the efficiency of the design process and of the reliability of the found solution.

Cette thèse s’inscrit dans le cadre des travaux menés par PSA Peugeot Citroën pour l’allègement de ses véhicules. Les optimisations masse multi-prestations réalisées sur le périmètre de la structure contribuent directement à cette démarche en recherchant une allocation d’épaisseurs de tôles à masse minimale qui respectent des spécifications physiques relatives à différentes prestations (choc, vibro-acoustique, etc.). Ces spécifications sont généralement évaluées à travers des modèles numériques à très haute-fidélité qui présentent des temps de restitution particulièrement élevés. Le recours à des fonctions de substitution, connues sous le nom de métamodèles, reste alors la seule alternative pour mener une étude d’optimisation tout en respectant les délais projet. Cependant la prestation qui nous intéresse, à savoir le choc frontal, présente quelques particularités (grande dimensionnalité, fortes non-linéarités, dispersions physique et numérique) qui rendent sa métamodélisation difficile.L’objectif de la thèse est alors de proposer une approche d’optimisation basée sur des métamodèles adaptatifs afin de dégager de nouveaux gains de masse. Cela passe par la prise en compte du choc frontal dont le caractère chaotique est exacerbé par la présence d’incertitudes. Nous proposons ainsi une méthode d’optimisation fiabiliste avec l’introduction de quantiles comme mesure de conservatisme. L’approche est basée sur des modèles de krigeage avec enrichissement adaptatif afin de réduire au mieux le nombre d’appels aux modèles éléments finis. Une application sur un véhicule complet permet de valider la méthode. / One of the most challenging tasks in modern engineering is that of keeping the cost of manufactured goods small. With the advent of computational design, prototyping for instance, a major source of expenses, is reduced to its bare essentials. In fact, through the use of high-fidelity models, engineers can predict the behaviors of the systems they design quite faithfully. To be fully realistic, such models must embed uncertainties that may affect the physical properties or operating conditions of the system. This PhD thesis deals with the constrained optimization of structures under uncertainties in the context of automotive design. The constraints are assessed through expensive finite element models. For practical purposes, such models are conveniently substituted by so-called surrogate models which stand as cheap and easy-to-evaluate proxies. In this PhD thesis, Gaussian process modeling and support vector machines are considered. Upon reviewing state-of-the-art techniques for optimization under uncertainties, we propose a novel formulation for reliability-based design optimization which relies on quantiles. The formal equivalence of this formulation with the traditional ones is proved. This approach is then coupled to surrogate modeling. Kriging is considered thanks to its built-in error estimate which makes it convenient to adaptive sampling strategies. Such an approach allows us to reduce the computational budget by running the true model only in regions that are of interest to optimization. We therefore propose a two-stage enrichment scheme. The first stage is aimed at globally reducing the Kriging epistemic uncertainty in the vicinity of the limit-state surface. The second one is performed within iterations of optimization so as to locally improve the quantile accuracy. The efficiency of this approach is demonstrated through comparison with benchmark results. An industrial application featuring a car under frontal impact is considered. The crash behavior of a car is indeed particularly affected by uncertainties. The proposed approach therefore allows us to find a reliable solution within a reduced number of calls to the true finite element model. For the extreme case where uncertainties trigger various crash scenarios of the car, it is proposed to rely on support vector machines for classification so as to predict the possible scenarios before metamodeling each of them separately.

With the demand of computing power from electronic chips on a constant rise, innovative methods are needed for effective and efficient thermal management. Forced convection cooling through an array of micro pin-fins acts not only as a heat sink, but also allows for the electrical interconnection between stacked layers of integrated circuits. This work performs a multi-objective optimization of three shapes of pin-fins to maximize the efficiency of this cooling system. An inverse design approach that allows for the design of cooling configurations without prior knowledge of thermal mapping was proposed and validated. The optimization study showed that pin-fin configurations are capable of containing heat flux levels of next generation electronic chips. It was also shown that even under these high heat fluxes the structural integrity is not compromised. The inverse approach showed that configurations exist that are capable of cooling heat fluxes beyond those of next generation chips. Thin film heat spreaders made of diamond and graphene nano-platelets were also investigated and showed that further reduction in maximum temperature, increase in temperature uniformity and reduction in thermal stresses are possible.

Tato diplomová práce se zabývá tématem výměny tepla v zásobníku tepla typu ”packed bed”. Cílem je popsat přenos tepla v zásobníku tepla obsahující kamínky malých průměrů, skrz který proudí horký vzduch. Toto je modelováno v prostředí MATLAB. Na začátku je krátký úvod do problematiky zahrnující ukládání tepla a jeho možné využití. Dále je uveden krátký přehled o základech přenosu tepla, typech přenosu tepla a termofyzikální vlastnosti systému vzduch-kámen. Ve třetí kapitole je představen zásobník tepla typu ”packed bed” a rozličné modely a dané podmínky jsou vysvětleny. Další kapitola se zabývá s numerickými metodami, převážně s metodou konečných diferencí použitou v této práci. Pátá kapitola se zaměřuje na obecnou optimalizaci daného problému přenosu tepla. Populačně založený metaheuristický optimalizační algoritmus zvaný Genetický algoritmus je popsán. Sestavení modelu je ukázáno v šesté kapitole, stejně jako prezentace výsledků získaných z programu MATLAB. V poslední kapitole je pak diskutován závěr a doporučení.

ABSTRACT Multidisciplinary Design Optimization of an Extreme Aspect Ratio HALE UAV Bryan J. Morrisey Development of High Altitude Long Endurance (HALE) aircraft systems is part of a vision for a low cost communications/surveillance capability. Applications of a multi payload aircraft operating for extended periods at stratospheric altitudes span military and civil genres and support battlefield operations, communications, atmospheric or agricultural monitoring, surveillance, and other disciplines that may currently require satellite-based infrastructure. Presently, several development efforts are underway in this field, including a project sponsored by DARPA that aims at producing an aircraft that can sustain flight for multiple years and act as a pseudo-satellite. Design of this type of air vehicle represents a substantial challenge because of the vast number of engineering disciplines required for analysis, and its residence at the frontier of energy technology. The central goal of this research was the development of a multidisciplinary tool for analysis, design, and optimization of HALE UAVs, facilitating the study of a novel configuration concept. Applying design ideas stemming from a unique WWII-era project, a “pinned wing” HALE aircraft would employ self-supporting wing segments assembled into one overall flying wing. The research effort began with the creation of a multidisciplinary analysis environment comprised of analysis modules, each providing information about a specific discipline. As the modules were created, attempts were made to validate and calibrate the processes against known data, culminating in a validation study of the fully integrated MDA environment. Using the NASA / AeroVironment Helios aircraft as a basis for comparison, the included MDA environment sized a vehicle to within 5% of the actual maximum gross weight for generalized Helios payload and mission data. When wrapped in an optimization routine, the same integrated design environment shows potential for a 17.3% reduction in weight when wing thickness to chord ratio, aspect ratio, wing loading, and power to weight ratio are included as optimizer-controlled design variables. Investigation of applying the sustained day/night mission requirement and improved technology factors to the design shows that there are potential benefits associated with a segmented or pinned wing. As expected, wing structural weight is reduced, but benefits diminish as higher numbers of wing segments are considered. For an aircraft consisting of six wing segments, a maximum of 14.2% reduction in gross weight over an advanced technology optimal baseline is predicted.

Current airspace regulations require the remote pilot-in-command of an unmanned aircraft systems (UAS) to maintain visual line of sight with the vehicle for situational awareness. The future of UAS will not have these constraints as technology improves and regulations are changed. An operational model for the future of UAS is proposed where a remote operator will monitor remote vehicles with the capability to intervene if needed. One challenge facing this future operational concept is the ability for a flight data system to effectively communicate flight status to the remote operator. A system architecture has been developed to facilitate the implementation of such a flight data system. Utilizing the system architecture framework, a Phase I prototype was designed and built for two vehicles in the Autonomous Flight Laboratory (AFL) at Cal Poly. The project will continue to build on the success of Phase I, culminating in a fully functional command and control system for remote UAS operational testing.

The application of optimization algorithms in the design of many economical and industrial problems currently represents a significant assignment. The development of high-powered computers allows an application of difficult mathematical techniques and physical phenomena to simulate real problems with sufficient accuracy. The optimization techniques used in engineering designs are mostly represented by modified mathematical programming methods with extension of their usability. The aim of the presented thesis "Using Optimization´s Algorithms by Designing of Structures" is to analyze the applicability of optimization procedures which are available in the widely used computing system ANSYS in civil and mechanical engineering practice. The numerical analyses were performed within the frame of multi-extreme, one to three dimensional optimization problems, multi-dimensional problems expressed by minimizing the weight of a truss beam and efficient design of air gap location in wooden studs from the point of view of thermal features of the structure. The analyzed optimization processes are in plurality verified with accurate manual computing and graphical solutions and the accent is put on optimization methods´ possibilities to improve robustness, efficiency and accuracy of the optimization algorithms in civil engineering problems´ designs. The optimization methods represent a suitable approach to improve the efficient design of a wide range of civil and mechanical engineering structures and elements. By combination of their advantages and FEM/FEA method it is possible to achieve very good results, although robustness of the solutions is not guaranteed. The robustness and accuracy of the procedure could be increased by competent exploration of design space and suitable selections of optimization methods´ features.