Advances in numerical optimization have raised the possibility that efficient and novel aircraft configurations may be ``discovered'' by an algorithm. To begin exploring this possibility, a fast and robust set of tools for aerodynamic shape optimization is developed. Parameterization and mesh-movement are integrated to accommodate large changes in the geometry. This integrated approach uses a coarse B-spline control grid to represent the geometry and move the computational mesh; consequently, the mesh-movement algorithm is two to three orders faster than a node-based linear elasticity approach, without compromising mesh quality. Aerodynamic analysis is performed using a flow solver for the Euler equations. The governing equations are discretized using summation-by-parts finite-difference operators and simultaneous approximation terms, which permit nonsmooth mesh continuity at block interfaces. The discretization results in a set of nonlinear algebraic equations, which are solved using an efficient parallel Newton-Krylov-Schur strategy. A gradient-based optimization algorithm is adopted. The gradient is evaluated using adjoint variables for the flow and mesh equations in a sequential approach. The flow adjoint equations are solved using a novel variant of the Krylov solver GCROT. This variant of GCROT is flexible to take advantage of non-stationary preconditioners and is shown to outperform restarted flexible GMRES. The aerodynamic optimizer is applied to several studies of induced-drag minimization. An elliptical lift distribution is recovered by varying spanwise twist, thereby validating the algorithm. Planform optimization based on the Euler equations produces a nonelliptical lift distribution, in contrast with the predictions of lifting-line theory. A study of spanwise vertical shape optimization confirms that a winglet-up configuration is more efficient than a winglet-down configuration. A split-tip geometry is used to explore nonlinear wake-wing interactions: the optimized split-tip demonstrates a significant reduction in induced drag relative to a single-tip wing. Finally, the optimal spanwise loading for a box-wing configuration is investigated.

Several techniques were studied to select and prioritize technologies for a complex system. Based on the findings, a method called Pareto Optimization and Selection of Technologies (POST) was formulated to efficiently explore the combinatorial technology space. A knapsack problem was selected as a benchmark problem to test-run various algorithms and techniques of POST. A Monte Carlo simulation using the surrogate models was used for uncertainty quantification. The concepts of graph theory were used to model and analyze compatibility constraints among technologies. A probabilistic Pareto optimization, based on the concepts of Strength Pareto Evolutionary Algorithm II (SPEA2), was formulated for Pareto optimization in an uncertain objective space. As a result, multiple Pareto hyper-surfaces were obtained in a multi-dimensional objective space; each hyper-surface representing a specific probability level. These Pareto layers enabled the probabilistic comparison of various non-dominated technology combinations. POST was implemented on a technology exploration problem for a 300 passenger commercial aircraft. The problem had 29 identified technologies with uncertainties in their impacts on the system. The distributions for these uncertainties were defined using beta distributions. Surrogate system models in the form of Response Surface Equations (RSE) were used to map the technology impacts on the system responses. Computational complexity of technology graph was evaluated and it was decided to use evolutionary algorithm for probabilistic Pareto optimization. The dimensionality of the objective space was reduced using a dominance structure preserving approach. Probabilistic Pareto optimization was implemented with reduced number of objectives. Most of the technologies were found to be active on the Pareto layers. These layers were exported to a dynamic visualization environment enabled by a statistical analysis and visualization software called JMP. The technology combinations on these Pareto layers are explored using various visualization tools and one combination is selected. The main outcome of this research is a method based on consistent analytical foundation to create a dynamic tradeoff environment in which decision makers can interactively explore and select technology combinations.

The primary research objective was to develop a methodology to support conceptual design of the Hybrid Wing Body (HWB) configuration. The absence of a horizontal tail imposes new stability and control requirements on the planform, and therefore requiring greater emphasis on control authority assessment than is typical for conceptual design. This required investigations into three primary areas of research. The first was to develop a method for designing an appropriate amount of redundancy. This was motivated widely varying numbers of trailing edge elevons in the HWB literature, and inadequate explanations for these early design decisions. The method identifies stakeholders, metrics of interest, and synthesizes these metrics using the Breguet range equation for system level comparison of control surface layouts. The second area of research was the development trim analysis methods that could accommodate redundant control surfaces, for which conventional methods performed poorly. A new measure of control authority was developed for vehicles with redundant controls. This is accomplished using concepts from the control allocation literature such as the attainable moment subset and the direct allocation method. The result is a continuous measure of remaining control authority suitable for use during HWB sizing and optimization. The final research area integrated performance and control authority to create a HWB sizing environment, and investigations into how to use it for design space exploration and vehicle optimization complete the methodology. The Monte Carlo Simulation method is used to map the design space, identify good designs for optimization, and to develop design heuristics. Finally, HWB optimization experiments were performed to discover best practices for conceptual design.

Dans cette thèse, nous proposons de construire de meilleurs modèles Processus Gaussiens (GPs) en intégrant les connaissances antérieures avec des données expérimentales. En raison du coût élevé de l’exécution d’expériences sur les systèmes physiques, les modèles numériques deviennent un moyen évident de concevoir des systèmes physiques. Traditionnellement, ces modèles ont été construits expérimentalement et itérativement; une méthode plus rentable de construction de modèles consiste à utiliser des algorithmes d’apprentissage automatique. Nous démontrons comment créer des modèles en intégrant une connaissance antérieure en modifiant les fonctions de covariance. Nous proposons des modèles GP pour différents phénomènes physiques en mécanique des fluides.De même, les lois physiques entre plusieurs sorties peuvent être appliquées en manipulant les fonctions de covariance. Pour chaque application, nous comparons le modèle proposé avec le modèle de l’état de l’art et démontrons les gains de coût ou de performance obtenus. / In this thesis, we propose to build better Gaussian Process (GP) modelsby integrating the prior knowledge of Aircraft design with experimental data. Due tothe high cost of performing experiments on physical systems, models become an efficientmeans to designing physical systems. We demonstrate how to create efficient models byincorporating the prior information from engineering design, mainly by changing the covariancefunctions of the GP.We propose GP models to detect onset of non-linearity, detectmodal parameters and interpolate position of shock in aerodynamic experiments. Similarly,physical laws between multiple outputs can be enforced by manipulating the covariancefunctions, we propose to integrate flight-mechanics to better identify loads using thesemodels. For each application we compare the proposed model with the state-of-the-artmodel and demonstrate the cost or performance gains achieved.

L'objectif de ce travail de thèse est de poser, d'analyser et de résoudre le problème multidisciplinaire et multi-objectif de la conception d'avions plus écologiques et plus économiques. Dans ce but, les principaux drivers de l'optimisation des performances d'un avion seront: la géométrie de l'avion, son moteur ainsi que son profil de mission, autrement dit sa trajectoire. Les objectifs à minimiser considérés sont la consommation de carburant, l'impact climatique et le coût d'opération de l'avion. L'étude sera axée sur la stratégie de recherche de compromis entre ces objectifs, afin d'identifier les configurations d'avions optimales selon le critère sélectionné et de proposer une analyse de ces résultats. L'incertitude présente au niveau des modèles utilisés sera prise en compte par des méthodes rigoureusement sélectionnées. Une configuration d'avion hybride est proposée pour atteindre l'objectif de réduction d'impact climatique. / The objective of this PhD work is to pose, investigate, and solve the highly multidisciplinary and multiobjective problem of environmentally efficient aircraft design and operation. In this purpose, the main three drivers for optimizing the environmental performance of an aircraft are the airframe, the engine, and the mission profiles. The figures of merit, which will be considered for optimization, are fuel burn, local emissions, global emissions, and climate impact (noise excluded). The study will be focused on finding efficient compromise strategies and identifying the most powerful design architectures and design driver combinations for improvement of environmental performances. The modeling uncertainty will be considered thanks to rigorously selected methods. A hybrid aircraft configuration is proposed to reach the climatic impact reduction objective.

Ce travail porte sur le développement de méthodes numériques innovantes pour la conception aéro-acoustique optimale de forme des configurations supersoniques. Ce manuscrit présente tout d'abord l'analyse et le développement des approches numériques pour la prévision du bang sonique . Le couplage du calcul CFD tridimensionnel en champ proche prenant en compte la décomposition multipolaire de Fourier et la propagation atmosphérique basée sur un algorithme de tracé de rayons est amélioré par l’intégration d'un processus automatique d' adaptation anisotrope de maillage. La deuxième partie de ce travail se concentre sur l’élaboration et l'application des techniques de conception pour l'optimisation d'une configuration aile-fuselage supersonique. Un module de conception inverse, AIDA , fournit à partir d'une signature acoustique cible au sol à faible bang sonique la géométrie de la configuration correspondante. Pour améliorer a la fois les performances acoustique et aérodynamique, des techniques d'optimisation directes de forme sont utilisées pour résoudre des problèmes d'optimisation mono et multi- disciplinaires et une analyse détaillée est réalisée. Des stratégies innovantes basées sur la coopération et les jeux compétitifs sont enfin appliquées au problème d'optimisation multidisciplinaire offrant une alternative aux algorithmes traditionnels MDO . L’hybridation de ces deux stratégies ouvre la voie a une nouvelle façon d'explorer le front de Pareto de manière efficace. Celle-ci est mise en application sur un cas pratique. / This work addresses the development of original numerical methods for the aero-acoustic optimal shape design of supersonic configurations. The first axis of the present research is the enhancement of numerical approaches for the prediction of sonic boom. The three dimensional CFD near-field prediction matched using a multipole decomposition approach coupled with atmospheric propagation using on a ray-tracing algorithm is improved by the integration of an automated anisotropic mesh adaptation process. The second part of this work focuses on the formulation and development of design techniques for the optimization of a supersonic wing-body configuration. An inverse design module, AIDA, is able to determine an equivalent configuration provided a target shaped signature at ground level corresponding to a low-boom profile. In order to improve both the aerodynamic and the acoustic performance, direct shape optimization techniques are used to solve single and multi-disciplinary optimization problems and a detailed analysis is carried out. At last, innovative strategies based on cooperation and competitive games are then applied to the multi-disciplinary optimization problem providing an alternative to traditional MDO algorithms. Hybridizing the two strategies opens a new efficient way to explore the Pareto front and this is shown on a practical case.

Thesis deals with design of a single-engine aircraft. The purpose of this aircraft design is to carry up nine passengers. The work outlines technical solution of several structural elements. The design starts with aerodynamic calculations, mass analysis and concludes with calculation of flight performance and development cost estimation.

When working with complex aircraft design it is necessary to be able test configurations easily. At Saab this is done in several ways. When looking at the weapons integration on the Gripen Aircraft, clearance analysis (called feasibility studies at Saab) are created to evaluate if new external stores can be mounted. This is done manually in CATIA by measuring and evaluating the result. To ease the work when performing these repetitive tasks, it is possible to take advantage of Knowledge Based Engineering, KBE. This thesis revolves around implementing KBE in the process. The approach is to create a configurator that can handle the different external stores and perform the required tasks. The end result is a configurator with a Graphical User Interface, GUI, in which the user gets help with the feasibility study and which requires light manual input from the user. The configurator performs the study and produces a Word report for the user to examine. The resulting time saving is around 75 to 80 percent from the original process. The conclusion is that the configurator can facilitate the feasibility studies at Saab by reducing the time and lower the threshold for the user. Even though the process is not fully automated, the configurator is judged to perform at a satisfying degree.

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