This paper was presented at CJK-OSM 7, 18–21 June 2012, Huangshan, China.

A set of techniques is developed that allows the incorporation of flight dynamics metrics as an additional discipline in a high-fidelity aerodynamic optimization. Specifically, techniques for including static stability constraints and handling qualities constraints in a high-fidelity aerodynamic optimization are demonstrated. These constraints are developed from stability derivative information calculated using high-fidelity computational fluid dynamics (CFD). Two techniques are explored for computing the stability derivatives from CFD. One technique uses an automatic differentiation adjoint technique (ADjoint) to efficiently and accurately compute a full set of static and dynamic stability derivatives from a single steady solution. The other technique uses a linear regression method to compute the stability derivatives from a quasi-unsteady time-spectral CFD solution, allowing for the computation of static, dynamic and transient stability derivatives. Based on the characteristics of the two methods, the time-spectral technique is selected for further development, incorporated into an optimization framework, and used to conduct stability-constrained aerodynamic optimization. This stability-constrained optimization framework is then used to conduct an optimization study of a flying wing configuration. This study shows that stability constraints have a significant impact on the optimal design of flying wings and that, while static stability constraints can often be satisfied by modifying the airfoil profiles of the wing, dynamic stability constraints can require a significant change in the planform of the aircraft in order for the constraints to be satisfied.

The aim of this study is to design a computer interface, which links the user to commercial Finite Element Analysis (FEA) program, MSC.Marc-Mentat to make automatic FE analysis of an excavator boom by using DELPHI as platform. Parametrization of boom geometry is done to add some flexibility to interface called OPTIBOOM. Parametric FE analysis of a boom shortens the design stages and helps to find the optimum design in terms of stresses and mass.

Les problèmes d'optimisation de forme sont présents naturellement en physique, ingénierie, biologie, etc. Ils visent à répondre à différentes questions telles que:-A quoi une aile d'avion parfaite pourrait ressembler?-Comment faire pour réduire la résistance d'un objet en mouvement dans un gaz ou un fluide?-Comment construire une structure élastique de rigidité maximale?-Quel est le comportement d'un système de cellules en interaction?Pour des exemples précis et autres applications de l'optimisation de forme nous renvoyons à [20] et [69]. Ici, nous traitons les aspects mathématiques théoriques de l'optimisation de forme, concernant l'existence d'ensembles optimaux ainsi que leur régularité. Dans toutes les situations que l'on considère, la fonctionnelle dépend de la solution d'une certaine équation aux dérivées partielles posée sur la forme inconnue. Nous allons parfois se référer à cette fonction comme une fonction d'état.Les fonctions d'état les plus simples, mais qui apparaissent dans beaucoup de problèmes, sont données par les solutions des équations -Δw = 1 et -Δu = λu,qui sont liées à la torsion et aux modes d'oscillation d'un objet donné. Notre étude se concentrera principalement sur ces fonctionnelles de formes, impliquant la torsion et le spectre.[20] D. Bucur, G. Buttazzo: Variational Methods in Shape Optimization Problems. Progress in Nonlinear Differential Equations 65, Birkhauser Verlag, Basel (2005).[69] A. Henrot, M. Pierre: Variation et optimisation de formes: une analyse geometrique. Springer-Berlag, Berlin, 2005. / The shape optimization problems naturally appear in engineering and biology. They aim to answer questions as:-What a perfect wing may look like?-How to minimize the resistance of a moving object in a gas or a fluid?-How to build a rod of maximal rigidity?-What is the behaviour of a system of cells?The shape optimization appears also in physics, mainly in electrodynamics and in the systems presenting both classical and quantum mechanics behaviour. For explicit examples and furtheraccount on the applications of the shape optimization we refer to the books [20] and [69]. Here we deal with the theoretical mathematical aspects of the shape optimization, concerning existence of optimal sets and their regularity. In all the practical situations above, the shape of the object in study is determined by a functional depending on the solution of a given partial differential equation. We will sometimes refer to this function as a state function.The simplest state functions are provided by solutions of the equations−∆w = 1 and −∆u = λu,which usually represent the torsion rigidity and the oscillation modes of a given object. Thus our study will be concentrated mainly on the situations, in which these state functions appear,i.e. when the optimality is intended with respect to energy and spectral functionals. [20] D. Bucur, G. Buttazzo: Variational Methods in Shape Optimization Problems. Progress in Nonlinear Differential Equations 65, Birkhauser Verlag, Basel (2005).[69] A. Henrot, M. Pierre: Variation et optimisation de formes: une analyse geometrique. Springer-Berlag, Berlin, 2005.

The ability to accurately quantify the performance an additively manufactured (AM) product is important for a widespread industry adoption of AM as the design is required to: (1) satisfy geometrical constraints, (2) satisfy structural constraints dictated by its intended function, and (3) be cost effective compared to traditional manufacturing methods. Optimization techniques offer design aids in creating cost-effective structures that meet the prescribed structural objectives. The fundamental problem in existing approaches lies in the difficulty to quantify the structural performance as each unique design leads to a new set of analyses to determine the structural robustness and such analyses can be very costly due to the complexity of in-use forces experienced by the structure. This work develops computationally tractable methods tailored to maximize the structural performance of AM products. A geometry preserving build orientation optimization method as well as data-driven shape optimization approaches to structural design are presented. Proposed methods greatly enhance the value of AM technology by taking advantage of the design space enabled by it for a broad class of problems involving complex in-use loads.

Les méthodes actuelles d’optimisation de formes permettent des gains importants vis-à-vis des fonctions à optimiser. Elles sont largement utilisées par les industriels, notamment dans l’automobile comme chez Renault. Parmi ces approches, l’optimisation de formes paramétriques permet d’obtenir rapidement une géométrie optimale sous réserve que l’espace de conception soit assez restreint pour pouvoir être parcouru en un temps de simulation raisonnable. Ce manuscrit présente deux méthodes d’optimisation de formes paramétriques en grande dimension pour des applications nécessitant des coûts de calculs importants, par exemple en mécanique des fluides. Une manière originale de reconstruire un modèle CAO paramétré à partir d’une surface morte est présentée au préalable. Nous proposons une approche pour identifier les zones interdites de l’espace de conception ainsi que leurs gestions dans une boucle d’optimisation par plans d’expériences. La première méthode s’appuie sur des techniques statistiques pour lever le verrou du nombre de degrés de liberté et utilise une optimisation à deux niveaux de fidélité pour minimiser les temps de calcul. Cette méthode en rupture avec le processus industriel habituel a été appliquée pour optimiser le coefficient de trainée aérodynamique d’un véhicule. La seconde méthode se base sur l’exploitation des gradients fournis par les solveurs adjoints, c’est-à-dire sur les sensibilités du critère (comme l’uniformité d’un écoulement par exemple) par rapport aux degrés de liberté de l’optimisation. Cette méthode innovante et en rupture avec les approches classiques permet de lever très naturellement le verrou du nombre de paramètres. Cependant, les gradients fournis par les logiciels ne sont pas donnés par rapport aux paramètres CAO mais par rapport aux nœuds du maillage. Nous proposons donc une façon d’étendre ces gradients jusqu’aux paramètres CAO. Des exemples académiques ont permis de montrer la pertinence et la validité de notre approche. / Current design loops for shape optimizations allow significant improvements in relation to the functions that need to be optimized. They are widely used in industry, particularly in the car industry like Renault. Among these approaches, parametric shape optimization allows rapid enhancement of the shape, on the condition that the design space is confined enough in order to be explored within a reasonable computational time. This Thesis introduces two CAD-based large-scale shape optimization methods for products requiring significant computational cost, for example in fluid mechanics. An original way to create a parameterized CAD model developed from a dead geometry is presented first. We propose an approach to identify the restricted areas of the design space and their managements in an optimization loop that uses a design of experiments. The first method is based on statistical techniques to circumvent the difficulties of large-scale optimizations and uses a two-level multi-fidelity modelling approach to minimize the computational time. This method, breaking away from the usual industrial process, was applied to optimize the aerodynamic drag coefficient of a car body. The second method is based on the gradients provided by adjoint solvers, that is to say on the sensitivity of the cost function (such as the uniformity deviation for example) with respect to the design points or displacement boundaries. This innovative method breaking away from classical approaches naturally gets over the number of degrees of freedom. However, the sensitivities provided by softwares are not computed with respect to CAD parameters but with respect to the coordinates of the vertices of the surface mesh. Thus, we propose a way to extend these gradients to CAD parameters. Academic test cases have proved the efficiency and accuracy of our method.

Afin d’améliorer la qualité des solutions proposées par l’optimisation dans les processus de conception, il est important de se donner des outils permettant à l’optimiseur de parcourir l’espace de conception le plus largement possible. L’objet de cette Thèse est d’analyser différentes méthodes de paramétrage de formes surfaciques d’une automobile en vue de proposer à Renault un processus d’optimisation efficace. Trois méthodes sont analysées dans cette Thèse. Les deux premières sont issues de l’existant, et proposent de mélanger des formes, afin de créer de la diversité. Ainsi, on maximise l’exploration de l’espace de conception, tout en limitant l’effort de paramétrage des CAO. On montre qu’elles ont un fort potentiel, mais impliquent l’utilisation de méthodes d’optimisation difficiles à mettre en œuvre aujourd’hui. La troisième méthode étudiée consiste à exploiter la formulation de Koiter des équations de coques, qui intègre paramètres de forme et mécanique, et de l’utiliser pour faire de l’optimisation de forme sur critères mécaniques. Cette méthode a par ailleurs pour avantage de permettre le calcul des gradients. D’autre part, nous montrons qu’il est possible d’utiliser les points de contrôles de carreaux de Bézier comme paramètres d’optimisation, et ainsi, de limiter au strict nécessaire le nombre de variables du problème d’optimisation, tout en permettant une large exploration de l’espace de conception. Cependant, cette méthode est non-standard dans l’industrie et implique des développements spécifiques, qui ont été réalisés dans le cadre de cette Thèse. Enfin, nous mettons en place dans cette Thèse les éléments d’un processus d’optimisation de forme surfacique. / To improve optimized solutions quality in the design process, it is important to provide the optimizer tools to navigate the design space as much as possible. The purpose of this thesis is to analyze different parametrization methods for automotive surface shapes, in order to offer Renault an efficient optimization process. Three methods are analyzed in this thesis. The first two are closed to the existing ones, and propose to blend shapes to create diversity. Thus, we are able to maximize the exploration of the design space, while minimizing the effort for CAD setting. We show their high potential, but they involve the use of optimization methods difficult to implement today. The third method is designed to exploit the formulation of Koiter shell equations, which integrates mechanical and shape parameters, and to use it to perform shape optimization with respect to different mechanical criteria. This method also has the advantage of allowing the gradients calculation. On the other hand, we show that it is possible to use the Bezier’s control points as optimization parameters, and thus control the minimum number of variables necessary for the optimization problem, while allowing a broad exploration of the design space. However, this method is non-standard in the industry and involves specific developments that have been made in the context of this thesis. Finally, we implement in this thesis essentials elements of an optimization process for surface shapes.

More and more challenging designs are required everyday in today¿s industries. The traditional trial and error procedure commonly used for mechanical parts design is not valid any more since it slows down the design process and yields suboptimal designs. For structural components, one alternative consists in using shape optimization processes which provide optimal solutions. However, these techniques require a high computational effort and require extremely efficient and robust Finite Element (FE) programs. FE software companies are aware that their current commercial products must improve in this sense and devote considerable resources to improve their codes. In this work we propose to use the Cartesian Grid Finite Element Method, cgFEM as a tool for efficient and robust numerical analysis. The cgFEM methodology developed in this thesis uses the synergy of a variety of techniques to achieve this purpose, but the two main ingredients are the use of Cartesian FE grids independent of the geometry of the component to be analyzed and an efficient hierarchical data structure. These two features provide to the cgFEM technology the necessary requirements to increase the efficiency of the cgFEM code with respect to commercial FE codes. As indicated in [1, 2], in order to guarantee the convergence of a structural shape optimization process we need to control the error of each geometry analyzed. In this sense the cgFEM code also incorporates the appropriate error estimators. These error estimators are specifically adapted to the cgFEM framework to further increase its efficiency. This work introduces a solution recovery technique, denoted as SPR-CD, that in combination with the Zienkiewicz and Zhu error estimator [3] provides very accurate error measures of the FE solution. Additionally, we have also developed error estimators and numerical bounds in Quantities of Interest based on the SPR-CD technique to allow for an efficient control of the quality of the numerical solution. Regarding error estimation, we also present three new upper error bounding techniques for the error in energy norm of the FE solution, based on recovery processes. Furthermore, this work also presents an error estimation procedure to control the quality of the recovered solution in stresses provided by the SPR-CD technique. Since the recovered stress field is commonly more accurate and has a higher convergence rate than the FE solution, we propose to substitute the raw FE solution by the recovered solution to decrease the computational cost of the numerical analysis. All these improvements are reflected by the numerical examples of structural shape optimization problems presented in this thesis. These numerical analysis clearly show the improved behavior of the cgFEM technology over the classical FE implementations commonly used in industry. / Cada d'¿a dise¿nos m'as complejos son requeridos por las industrias actuales. Para el dise¿no de nuevos componentes, los procesos tradicionales de prueba y error usados com'unmente ya no son v'alidos ya que ralentizan el proceso y dan lugar a dise¿nos sub-'optimos. Para componentes estructurales, una alternativa consiste en usar procesos de optimizaci'on de forma estructural los cuales dan como resultado dise¿nos 'optimos. Sin embargo, estas t'ecnicas requieren un alto coste computacional y tambi'en programas de Elementos Finitos (EF) extremadamente eficientes y robustos. Las compa¿n'¿as de programas de EF son conocedoras de que sus programas comerciales necesitan ser mejorados en este sentido y destinan importantes cantidades de recursos para mejorar sus c'odigos. En este trabajo proponemos usar el M'etodo de Elementos Finitos basado en mallados Cartesianos (cgFEM) como una herramienta eficiente y robusta para el an'alisis num'erico. La metodolog'¿a cgFEM desarrollada en esta tesis usa la sinergia entre varias t'ecnicas para lograr este prop'osito, cuyos dos ingredientes principales son el uso de los mallados Cartesianos de EF independientes de la geometr'¿a del componente que va a ser analizado y una eficiente estructura jer'arquica de datos. Estas dos caracter'¿sticas confieren a la tecnolog'¿a cgFEM de los requisitos necesarios para aumentar la eficiencia del c'odigo cgFEM con respecto a c'odigos comerciales. Como se indica en [1, 2], para garantizar la convergencia del proceso de optimizaci'on de forma estructural se necesita controlar el error en cada geometr'¿a analizada. En este sentido el c'odigo cgFEM tambi'en incorpora los apropiados estimadores de error. Estos estimadores de error han sido espec'¿ficamente adaptados al entorno cgFEM para aumentar su eficiencia. En esta tesis se introduce un proceso de recuperaci'on de la soluci'on, llamado SPR-CD, que en combinaci'on con el estimador de error de Zienkiewicz y Zhu [3], da como resultado medidas muy precisas del error de la soluci'on de EF. Adicionalmente, tambi'en se han desarrollado estimadores de error y cotas num'ericas en Magnitudes de Inter'es basadas en la t'ecnica SPR-CD para permitir un eficiente control de la calidad de la soluci'on num'erica. Respecto a la estimaci'on de error, tambi'en se presenta un proceso de estimaci'on de error para controlar la calidad del campo de tensiones recuperado obtenido mediante la t'ecnica SPR-CD. Ya que el campo recuperado es por lo general m'as preciso y tiene un mayor orden de convergencia que la soluci'on de EF, se propone sustituir la soluci'on de EF por la soluci'on recuperada para disminuir as'¿ el coste computacional del an'alisis num'erico. Todas estas mejoras se han reflejado en esta tesis mediante ejemplos num'ericos de problemas de optimizaci'on de forma estructural. Los resultados num'ericos muestran claramente un mejor comportamiento de la tecnolog'¿a cgFEM con respecto a implementaciones cl'asicas de EF com'unmente usadas en la industria. / Nadal Soriano, E. (2014). Cartesian grid FEM (cgFEM): High performance h-adaptive FE analysis with efficient error control. Application to structural shape optimization [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/35620 / TESIS

The objective of this Master’s thesis is to find shape optimal design based on min- imizing friction force of thrust bearing by using genetic algorithm(GA) which is one of an optimization toolbox in Matlab. Reducing the friction force of thrust bearing is one way of making shaft to decreasing friction losses. With four parameters of thrust bearing geometry number of segments(m), angle of running surface(), segment inner radius(R0), and segment outer radius(R1) substitute in Reynolds’ equation. In order to know friction force, it is necessary to generate a connecting variable, oil film thickness(h0) from loading capacity(W ) and revolution per minute(rpm). Friction power loss, as well as weight func- tion conclude the final shape optimization of thrust bearing: m = 7, = 0.1, R0 = 15 mm, and R1 = 20 mm.

Zvýšení efektivity výměny tepla vede k poklesu spotřeby energie, což se následně projeví sníženými provozními náklady, poklesem produkce emisí a potažmo také snížením dopadu na životní prostředí. Běžné způsoby zefektivňování přenosu tepla jako např. přidání žeber či vestaveb do trubek ovšem nemusí být vždy vhodné nebo proveditelné -- zvláště při rekuperaci tepla z proudů s vysokou zanášivostí. Jelikož intenzita přestupu tepla závisí i na charakteru proudění, distribuci toku a zanášení, které lze všechny výrazně ovlivnit tvarem jednotlivých součástí distribučního systému, bylo sestaveno několik zjednodušených modelů pro rychlou a dostatečně přesnou predikci distribuce a také aplikace pro tvarovou optimalizaci distribučních systémů využívající právě tyto modely. Přesnost jednoho z modelů byla dále zvýšena pomocí dat získaných analýzou 282 distribučních systémů v softwaru ANSYS FLUENT. Vytvořené aplikace pak lze využít během návrhu zařízení na výměnu tepla ke zvýšení jejich výkonu a spolehlivosti.