Дисертації з теми "Multidisciplinary Design Optimisation"

Automotive engines are becoming increasingly technically complex and associated legal emissions standards more restrictive, making the task of identifying optimum actuator settings to use significantly more difficult. Given these challenges, this research aims to develop a process for engine calibration optimisation by exploiting advanced mathematical methods. Validation of this work is based upon a case study describing a steady-state Diesel engine calibration problem. The calibration optimisation problem seeks an optimal combination of actuator settings that minimises fuel consumption, while simultaneously meeting or exceeding the legal emissions constraints over a specified drive cycle. As another engineering target, the engine control maps are required as smooth as possible. The Multidisciplinary Design Optimisation (MDO) Frameworks have been studied to develop the optimisation process for the steady state Diesel engine calibration optimisation problem. Two MDO strategies are proposed for formulating and addressing this optimisation problem, which are All At Once (AAO), Collaborative Optimisation. An innovative MDO formulation has been developed based on the Collaborative Optimisation application for Diesel engine calibration. Form the MDO implementations, the fuel consumption have been significantly improved, while keep the emission at same level compare with the bench mark solution provided by sponsoring company. More importantly, this research has shown the ability of MDO methodologies that manage and organize the Diesel engine calibration optimisation problem more effectively.

Studies are undertaken using Multidisciplinary Design Optimisation (MDO) on the incorporation of an outboard morphing wing system, with two partitions that are variable in twist and dihedral angle, onto an existing conventionally designed commercial passenger jet. For this intent, an optimisation suite is created, incorporating a high end, low fidelity aero-structural control analysis together with a full engine model and integrated operational performance algorithm. Initial studies, focusing on the single objective of specific air range improvement for a number of flight phases, reveal increases of approximately 6.5-7.5% over the base line aircraft with wing fences across each case. Studies analyse the effects of the wing system on additional operational performance metrics, such as take-off, initial climb, approach-climb and landing performance parameters, in order to ascertain a truly holistic representation of the benefits of morphing wings. Further effort is expended to couple the effects of each phase within a multiobjective framework. Refined studies are performed, incorporating multiobjective optimisation methods and a critical phase, aero-structural wing sizing tool into the MDO suite. Results maintain strong improvements in cruise performance throughout the entire flight envelope and across multiple stage lengths. High fidelity computational and experimental analysis is performed upon a similarly modelled conventional aircraft wing. Results are generated with the intention of drawing meaningful comparisons with trends in aerodynamic and structural efficiency observed in the multidisciplinmy optimisation studies. Computational results are obtained with the DLR-Tau computational fluid dynamics code and experimental testing is performed in the University of Bristol 7' x 5' low speed wind tulmel. Outer twist variation of ±3 ° and dihedral angles from planar up to 90° are tested for a range of incidence angles. Results demonstrate varying levels of agreement between each form of analysis method and offer insight into the aerodynamic and structural trade-off required to select an optimal configuration. The work in this thesis numerically and experimentally outlines and provides justification for the feasible performance gains through the utilisation of morphing wing technology.

The implementation of key technologies in the initial stages of the aircraft wing design process has always represented a substantial challenge for aircraft designers. The lack of reliable and accessible wing mass prediction methods ¬which allow assessment of the relative benefits of new technologies for reducing structural wing weight - is of significant importance. This necessitates the development of new and generally applicable wing mass estimation methods. This thesis aims to create a new framework for estimating the mass of metallic and composite transport aircraft wings via finite element multidisciplinary analysis, and design optimisation techniques. To this end, the multidisciplinary static strength and stiffness, dynamic aeroelastic stability, and manufacturing constraints are simultaneously addressed within an optimisation environment through a gradient-based search algorithm. A practical optimisation procedure is presented as part of the sizing optimisation process, with enhanced features in solving large-scale nonlinear structural optimisation problems, incorporating an effective initial design variable value generation scheme based on the concept of the fully stressed design. The applicability and accuracy of the proposed approaches is accomplished by conducting a number of case studies in which the wingbox structure of the public domain NASA wing - commonly referred to as the Common Research Model (CRM) - is optimised to produce a minimum mass design. The results of a case study examining minimisation of the mass of the CRM wingbox structures designed using four different models of increasing structural fidelity prove that the multidisciplinary design optimisation framework can successfully calculate the mass of realistic real-world aircraft wing designs. This provides an insight into the competence of certain wingbox models in predicting the mass of the metallic and composite primary wing structures to an acceptable level of accuracy, and in demonstrating the relative merits of the wingbox structural complexity models under consideration and the computational resources necessary to achieving the required degree of accuracy ... [cont.].

Coronary stents are tubular type scaffolds that are deployed, using an inflatable balloon on a catheter, most commonly to recover the lumen size of narrowed (diseased) arterial segments. Even though numerous stent designs, of varying geometrical and material complexity, are used in clinical practice today, the adverse biological responses post-stenting are not completely eliminated. In-stent restenosis (IR), reduction in lumen size due to neointima formation within 12 months of procedure, and stent thrombosis (ST), formation of a blood clot inside a stented vessel, are the two most common adverse responses to stents. Such adverse responses are multifactorial and their causes are not completely understood. However, the geometric design of a stent, which is a common differentiating factor between the numerous commercially available stents, is known to be a key factor influencing adverse responses. In light of the above, this thesis exploits stent geometry parameterisation in both constrained and multiobjective optimisation. Gaussian process surrogate modelling is used to cost effectively (a) understand the influence of stent geometry parameters on metrics indicating adverse response, and (b) obtain families of stent designs which are potentially more resistant to such responses. Various computational models are developed to evaluate the efficacy of a stent in terms of the factors influencing the adverse responses. In particular, two finite element analysis (FEA) models and two computational fluid dynamics (CFD) models are developed. The FEA models are used to simulate the balloon-expansion of stents in a representative coronary artery and bending of stents on application of bending moments. On the other hand, the CFD models simulate haemodynamic flow in the stented artery and the associated drug-release into the tissue. The expansion FEA models are validated against manufacturer provided pressure-diameter relationship and the flexibility FEA models are validated against the numerical studies found in literature. The numerical models are then used to extract metrics which are related to the adverse responses. Six metrics are formulated: (i) acute recoil, which measures the radial strength of the stent; (ii) volume average stress, which measures potential arterial injury caused by the stenting procedure; (iii) haemodynamic low and reverse index, which measures the haemodynamic alteration relevant to IR; (iv) volume average drug, which measures the amount of anti-proliferative drug delivered into the tissue; (v) drug deviation, which measures the uniformity of drug-distribution in the tissue; and (vi) flexibility metric, which measures the deliverability of the stent. These metrics are then used to compare the performance of different geometric stent designs. Two parameterisation techniques – one for a generic ring and link topology of stents, and one for the commercial CYPHER (Cordis corporation, Johnson & Johnson company) – are proposed to study the effect of geometrical variation in stent design on the formulated metrics of efficacy. These techniques are then combined with surrogate modelling to perform stent design optimisation studies and study the effect of stent geometry on the evaluation metrics. Finally, three paradigms to choose optimal stent designs from a set of non-dominated solutions, in terms of the evaluation metrics, are proposed, and optimal designs under such paradigms are identified. The last part of this thesis concerns surrogate assisted optimisation, and is not specific to the problem of stent design. Here, the use of analytically available gradient information in widely used Kriging predictors is explored. A search algorithm to locate all stationary points of a Krig, using a combination of an iterative sequence of the Krig derivative and a low-discrepancy sequence is proposed.

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions.

Multidisciplinary design optimisation incorporates several disciplines in one integrated optimisation problem. The benefi t of considering all requirements at once rather than in individual optimisations is that synergies between disciplines can be exploited to fi nd superior designs to what would otherwise be possible. The main obstacle for the use of multidisciplinary design optimisation in an industrial setting is the related computational cost which may become prohibitively large. This work is focused on the development of a multidisciplinary design optimisation framework that extends the existing trust-region based optimisation method known as the mid-range approximation method. The main novel contribution is an approach to solving multidisciplinary design optimisation problems using metamodels built in sub-spaces of the design variable space. Each metamodel is built in the sub-space relevant to the corresponding discipline while the optimisation problem is solved in the full design variable space. Since the metamodels are built in a space of reduced dimensionality, the computational budget for building them can be reduced without compromising their quality. Furthermore, a method for efficiently building kriging metamodels is proposed. This is done by means of a two-step hyper parameter tuning strategy. The fi rst step is a line search where the set of tuning parameters is treated as a single variable. The solution of the fi rst step is used in the second step, a gradient based hyper parameter optimisation where partial derivatives are obtained using the adjoint method. The framework is demonstrated on two examples, a multidisciplinary design optimisation of a thin-walled beam section subject to static and impact requirements, and a multidisciplinary design optimisation of an aircraft wing subject to static and bird strike requirements. In both cases the developed technique demonstrates a reduced computational effort compared to what would typically be achieved by existing methods.

This study investigates a novel methodology for the preliminary design of aeroengines. This involves the modelling of the disciplines that affect the engine's requirements and constraints, their implementation in software format and their coupling into a single unit. Subsequently, this unit is interfaced with an optimiser software. The resulting multidisciplinary optimisation (MDO) tool allows the automation of the traditional, human-based preliminary design process. The investigation of the above-mentioned novel methodology is carried out through the development of a "pilot" MDO tool and its subsequent utilisation in three case studies, characterised by different optimisation scenarios. The selection of each case study is motivated by current research questions, such as aviation's contribution to climate change or the attractiveness of specific novel propulsion concepts. The outcome of the pilot MDO study is considered successful and has been well received by several academic and industrial aero-engine organisations. The choice of the disciplines and of their modelling fidelity allowed a realistic representation of the main disciplinary interactions and tradeoffs that characterise the important phase of preliminary design. The computational effort involved in the solution of the optimisation studies was found to be acceptable, and no major reprogramming was required when different optimisation scenarios were considered. The case studies were investigated with an ease and comprehensiveness that would not have been achievable through a human-based parametric analysis. The positive experience with the pilot MDO tool suggests that an automated methodology for the preliminary design of aero-engines is feasible, applicable and valuable. Its adoption can provide substantial advantages over the traditional human-based approach, such as a reduction in human effort, costs and risk. From this perspective, the pilot study constitutes a first step towards the development of a full-scale MDO tooL usable by aero-engine manufacturers. In the near future, issues like climate change could drive significant modifications in airframe and engine design. A preliminary design MDO tool is therefore timely, and has the potential of making a significant contribution.

Envisioned in the next 15 to 30 years in the aviation industry, hybrid-electric propulsion offers theopportunity to integrate new technology bricks providing additional degrees of freedom to improveoverall aircraft performance, limit the use of non-renewable fossil resources and reduce the aircraftenvironmental footprint. Today, hybrid-electric technology has mainly been applied to groundbased transports, cars, buses and trains, but also ships. The feasibility in the air industry has to beestablished and the improvement in aircraft performance has still to be demonstrated. This thesisaims to evaluate the energy savings enabled by electric power in the case of a 70-seat regionalaircraft. First, energy saving opportunities are identified from the analysis of the propulsion andaerodynamic efficiencies of a conventional twin turboprop aircraft. The potential benefits comingfrom the variation of the size of prime movers and the new power managements with the use ofbatteries are studied. Also, possible aerodynamic improvements enabled by new propellerintegrations are considered. For each topic, simplified analyses provide estimated potential ofenergy saving. These results are then used to select four electrified propulsion systems that arestudied in more detail in the thesis: a parallel-hybrid, a turboelectric with distributed propulsion, apartial-turboelectric with high-lift propellers and an all-electric. Evaluating the selected hybrid-electric aircraft is even more challenging that the sizing of the different components, the energymanagement strategies and the mission profiles one can imagine are many and varied. Inaddition, the overall aircraft design process and the evaluation tools need to be adaptedaccordingly. The Airbus in-house Multidisciplinary Design Optimisation platform named XMDO,which includes most of the required modifications, is eventually selected and further developedduring the thesis. For examples, new parametric component models (blown wing, electrical motor,gas turbine, propeller, etc…) are created, a generic formulation for solving the propulsion systemequilibrium is implemented, and simulation models for take-off and landing are improved. In orderto evaluate the energy efficiency of the hybrid-electric aircraft, a reference aircraft equipped with aconventional propulsion system is first optimised with XMDO. Different optimisation algorithms aretested, and the consistency of the new design method is checked. Then, all the hybrid-electricconfigurations are optimised under the same aircraft design requirements as the reference. Forthe electrical components, two levels of technology are defined regarding the service entry date ofthe aircraft. The optimisation results for the turboelectric and the partial-turboelectric are used tobetter understand the potential aerodynamic improvements identified in the first part of the thesis.Optimisations for the parallel-hybrid, including different battery recharge scenarios, highlight thebest energy management strategies when batteries are used as secondary energy sources. All theresults are finally compared to the reference in terms of fuel and energy efficiencies, for the twoelectrical technology levels. The last part of the thesis focuses on the all-electric aircraft, and aimsat identifying the minimum specific energy required for batteries as a function of the aircraft designrange. A trade study is also carried-out in accordance with the service entry date for the otherelectrical components

Nowadays, the product design process relies on computer simulations more than ever. Compared to the experimental tests, they allow substantially more designs to be evaluated. Moreover, computer simulations allow a search for the optimum. That is why a fast and efficient transition from one design iteration to the next is necessary.  For design evaluation in the aerospace industry, Computational Fluid Dynamics tools are used, where finite volume meshes are computationally expensive to create. Instead of recreating them for each product design during an optimisation process, it is much faster to morphone design into the next one. Here an algorithm for mesh morphing based on radial basis functions is presented. Its implementation is evaluated for mesh quality and performance. Mesh morphing of NURBS surfaces, a continuous representation of a given model geometry, together with discrete meshes is proposed next. Lastly, the implementation of the morphing algorithm is linked with a fluid flow solverand an optimisation suite. All three programs are used together to optimise a product coming from the aerospace industry.

La conception de lanceurs est un problème d'optimisation multidisciplinaire (MDO) complexe qui a la particularité d'intégrer une optimisation de trajectoire très contrainte, difficile à résoudre et fortement couplée à toutes les autres disciplines entrant en jeu dans le processus de conception (e.g. propulsion, aérodynamique, structure, etc.). Cette thèse s'intéresse aux méthodes permettant d'intégrer judicieusement l'optimisation de la trajectoire au sein du processus d'optimisation des variables de conception. Une nouvelle méthode, appelée "Stage-Wise decomposition for Optimal Rocket Design" (SWORD), a été proposée. Celle-ci décompose le processus de conception suivant les différentes phases de vol et transforme le problème d'optimisation de lanceur multiétage en un problème de coordination des optimisations de chacun des étages, plus facile à résoudre. La méthode SWORD a été comparée à la méthode MDO classique (Multi Discipline Feasible) dans le cas d'une optimisation globale d'un lanceur tri-étage. Les résultats montrent que la méthode SWORD permet d'améliorer l'efficacité du processus d'optimisation, tant au niveau de la vitesse de recherche de l'espace de solutions faisables que de la qualité de l'optimum trouvé en temps de calcul limité. Afin d'améliorer la vitesse de convergence de la méthode tout en ne requérant pas de connaissance a priori de l'utilisateur au niveau de l'initialisation et l'espace de recherche, une stratégie d'optimisation dédiée à la méthode SWORD a été développée.

The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.

Master of Engineering (Research)
The overall objective of this research was to realise the practical application of Hierarchical Asynchronous Parallel Evolutionary Algorithms for Multi-objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using high fidelity analysis tools. The research looked at the assumed aerodynamics and structures of two production UAV wings and attempted to optimise these wings in isolation to the rest of the vehicle. The project was sponsored by the Asian Office of the Air Force Office of Scientific Research under contract number AOARD-044078. The two vehicles wings which were optimised were based upon assumptions made on the Northrop Grumman Global Hawk (GH), a High Altitude Long Endurance (HALE) vehicle, and the General Atomics Altair (Altair), Medium Altitude Long Endurance (MALE) vehicle. The optimisations for both vehicles were performed at cruise altitude with MTOW minus 5% fuel and a 2.5g load case. The GH was assumed to use NASA LRN 1015 aerofoil at the root, crank and tip locations with five spars and ten ribs. The Altair was assumed to use the NACA4415 aerofoil at all three locations with two internal spars and ten ribs. Both models used a parabolic variation of spar, rib and wing skin thickness as a function of span, and in the case of the wing skin thickness, also chord. The work was carried out by integrating the current University of Sydney designed Evolutionary Optimiser (HAPMOEA) with Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) tools. The variable values computed by HAPMOEA were subjected to structural and aerodynamic analysis. The aerodynamic analysis computed the pressure loads using a Boeing developed Morino class panel method code named PANAIR. These aerodynamic results were coupled to a FEA code, MSC.Nastran® and the strain and displacement of the wings computed. The fitness of each wing was computed from the outputs of each program. In total, 48 design variables were defined to describe both the structural and aerodynamic properties of the wings subject to several constraints. These variables allowed for the alteration of the three aerofoil sections describing the root, crank and tip sections. They also described the internal structure of the wings allowing for variable flexibility within the wing box structure. These design variables were manipulated by the optimiser such that two fitness functions were minimised. The fitness functions were the overall mass of the simulated wing box structure and the inverse of the lift to drag ratio. Furthermore, six penalty functions were added to further penalise genetically inferior wings and force the optimiser to not pass on their genetic material. The results indicate that given the initial assumptions made on all the aerodynamic and structural properties of the HALE and MALE wings, a reduction in mass and drag is possible through the use of the HAPMOEA code. The code was terminated after 300 evaluations of each hierarchical level due to plateau effects. These evolutionary optimisation results could be further refined through a gradient based optimiser if required. Even though a reduced number of evaluations were performed, weight and drag reductions of between 10 and 20 percent were easy to achieve and indicate that the wings of both vehicles can be optimised.

Cette thèse traite de l'optimisation sous incertitude de fonctions coûteuses dans le cadre de la conception de systèmes aéronautiques. Nous développons dans un premier temps une stratégie d'optimisation robuste multiobjectifs par modèles de substitution. Au delà de fournir une représentation plus rapide des fonctions initiales, ces modèles facilitent le calcul de la robustesse des solutions par rapport aux incertitudes du problème. L'erreur de modélisation est maîtrisée grâce à une approche originale d'enrichissement de plan d'expériences qui permet d'améliorer conjointement plusieurs modèles au niveau des régions de l'espace possiblement optimales. Elle est appliquée à la minimisation des émissions polluantes d'une chambre de combustion de turbomachine dont les injecteurs peuvent s'obstruer de façon imprévisible. Nous présentons ensuite une méthode heuristique dédiée à l'optimisation robuste multidisciplinaire. Elle repose sur une gestion locale de la robustesse au sein des disciplines exposées à des paramètres incertains, afin d'éviter la mise en place d'une propagation d'incertitudes complète à travers le système. Un critère d'applicabilité est proposé pour vérifier a posteriori le bien-fondé de cette approche à partir de données récoltées lors de l'optimisation. La méthode est mise en oeuvre sur un cas de conception avion où la surface de l'empennage vertical n'est pas connue avec précision.

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

L'optimisation multidisciplinaire (MDO) à base de gradients est efficace et très utilisée pour le dimensionnement structural d'ailes flexibles. Cependant, dans le contexte de simulations numériques haute-fidélité, le calcul efficace des gradients reste un défi majeur. L'objectif de ce travail est d'étudier les approches les mieux adaptées aux spécificités du calcul de sensibilité des efforts aéroélastiques par rapport à des paramètres structuraux.Deux techniques de calcul de gradient haute-fidélité adaptées aux systèmes aéroélastiques fortement couplés sont proposées. La technique la plus intrusive repose sur les formulations directe et adjointe qui nécessitent un effort d'implémentation logicielle substantiel. Alternativement, nous proposons une approche découplée et non-intrusive, moins lourde à implémenter et cependant capable de fournir une approximation précise des gradients. Ces deux techniques ont été intégrées dans le logiciel CFD elsA de l'Onera.La précision, l'efficience et l'applicabilité de ces méthodes sont démontrées sur le cas-test avion de transport civil Common Research Model (CRM). Nous résolvons un problème inverse dont l'objectif est de retrouver, en conditions de vol de croisière, une loi cible de vrillage voilure. Ces deux méthodes s'avèrent comparables en matière de précision et de coût. Elles offrent ainsi une souplesse supplémentaire de mise en œuvre en fonction du niveau d'intégration recherché dans le processus MDO
To improve the structural design of flexible wings, gradient based Multidisciplinary Design Optimization (MDO) techniques are effective and widely used. However, gradients calculation is not trivial and can be costly when high-fidelity models are considered. Our objective is to study different suitable approaches to compute gradients of aeroelastic loads with respect to structural design parameters.To this end, two high-fidelity aero-structure gradient computation techniques for strongly coupled aeroelastic systems are proposed. The most intrusive technique includes the well-established direct and adjoint formulations that require substantial implementation effort. In contrast, we propose an alternative uncoupled non-intrusive approach easier to implement and yet capable of providing accurate gradients approximations. Both techniques have been implemented in the Onera elsA CFD software.Accuracy, efficiency and applicability of these methods are demonstrated on the civil transport aircraft Common Research Model (CRM) test-case. More specifically, an inverse design problem is set up with the objective of matching an in-flight target twist law distribution. These two methods prove to be comparable in terms of accuracy and cost. Thus they offer additional operational flexibility depending on the level of integration sought in the MDO process

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

Les produits mécatroniques sont complexes et multidisciplinaires par nature. Les exigences pour les concevoir sont souvent contradictoires et doivent être validées par les différentes équipes d'ingénierie disciplinaire (ID). Pour répondre à cette complexité et réduire le temps de conception, les ingénieurs disciplinaires ont besoin de collaborer dynamiquement, de résoudre les conflits interdisciplinaires et de réutiliser les connaissances de projets antérieurs. De plus, ils ont besoin de collaborer en permanence avec l’équipe d’ingénierie systèmes (IS) pour avoir un accès direct aux exigences et l’équipe d’optimisation multidisciplinaire (OMD) pour valider le système dans sa globalité.Nous proposons d'utiliser des techniques de gestion des connaissances pour structurer les connaissances générées lors des activités de collaboration afin d'harmoniser le cycle de conception. Notre principale contribution est une approche d'unification qui explique comment IS, ID et OMD se complètent et peuvent être utilisés en synergie pour un cycle de conception intégré et continu. Notre méthodologie permet de centraliser les connaissances nécessaires à la collaboration et au suivi des exigences. Elle assure également la traçabilité des échanges entre les ingénieurs grâce à la théorie des graphes. Cette connaissance formalisée du processus de collaboration permet de définir automatiquement un problème OMD
Mechatronic products are complex and multidisciplinary in nature. The requirements to design them are often contradictory and must be validated by the various disciplinary engineering (DE) teams. To address this complexity and reduce design time, disciplinary engineers need to collaborate dynamically, resolve interdisciplinary conflicts, and reuse knowledge from previous projects. In addition, they need to work seamlessly with the Systems Engineering (SE) team to have direct access to requirements and the Multidisciplinary Design Optimization (MDO) team for global validation. We propose to use Knowledge Management techniques to structure the knowledge generated during collaboration activities and harmonize the overall design cycle. Our primary contribution is a unification approach, elaborating how SE, DE, and MDO complement each-other and can be used in synergy for an integrated and continuous design cycle. Our methodology centralizes the product knowledge necessary for collaboration. It ensures traceability of the exchange between disciplinary engineers using graph theory. This formalized process knowledge facilitates MDO problem definition

La variété des concepts d'aéronef à voilure tournante n'a d'égal que l'étendue de leur hamp applicatif. Dès lors, se pose une question essentielle : quel concept est le plus adapté face à un certain nombre de missions ou de spécifications ? Une partie essentielle de la réponse réside dans l'étude des performances de vol et des impacts environnementaux de l'appareil. Le projet de recherche fédérateur C.R.E.A.T.I.O.N. pour " Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network " a pour but de mettre en place une plateforme numérique de calculs multidisciplinaires et multiniveaux capables d'évaluer de tels critères. La multidisciplinarité fait écho aux différentes disciplines associées à l'évaluation des giravions tandis que l'aspect multi-niveaux reflète la possibilité d'étudier un concept quelque soit l'état des connaissances sur ce dernier. La thèse s'inscrit dans ce projet. Une première implication est le développement de modèles de performances de vol et leur intégration dans des boucles de calcul multidisciplinaires. Au-delà de cet aspect de modélisation physique, la multidisciplinarité touche aussi le champ des mathématiques appliquées. Les méthodes d'optimisation multi objectifs multi paramètres, l'aide à la décision pour la sélection d'un optimum de meilleur compromis, l'exploration de bases de données, la création de modèles réduits sont autant de thématiques explorées dans cette thèse.

Depuis la fermeture de Phénix en 2010 le CEA ne possède plus de réacteur au sodium. Vus les enjeux énergétiques et le potentiel de la filière, le CEA a lancé un programme de démonstrateur industriel appelé ASTRID (Advanced Sodium Technological Reactor for Industrial Demonstration), réacteur d’une puissance de 600MW électriques (1500 MW thermiques). L’objectif du prototype est double, être une réponse aux contraintes environnementales et démontrer la viabilité industrielle :• De la filière RNR-Na, avec un niveau de sureté au moins équivalent aux réacteurs de 3ème génération, du type de l’EPR. ASTRID intégrera dès la conception le retour d’expérience de Fukushima ;• Du retraitement des déchets (transmutation d’actinide mineur) et de la filière qui lui serait liée.La sûreté de l’installation est prioritaire, aucun radioélément ne doit être rejeté dans l’environnement, et ce dans toutes les situations. Pour atteindre cet objectif, il est impératif d’anticiper l’impact des nombreuses sources d’incertitudes sur le comportement du réacteur et ce dès la phase de conception. C’est dans ce contexte que s’inscrit cette thèse dont l’ambition est le développement de nouvelles méthodes d’optimisation des cœurs des RNR-Na. L’objectif est d’améliorer la robustesse et la fiabilité des réacteurs en réponse à des incertitudes existantes. Une illustration sera proposée à partir des incertitudes associées à certains régimes transitoires dimensionnant. Nous utiliserons le modèle ASTRID comme référence pour évaluer l’intérêt des nouvelles méthodes et outils développés.L’impact des incertitudes multi-Physiques sur le calcul des performances d’un cœur de RNR-Na et l’utilisation de méthodes d’optimisation introduisent de nouvelles problématiques :• Comment optimiser des cœurs « complexes » (i.e associés à des espaces de conception de dimensions élevée avec plus de 20 paramètres variables) en prenant en compte les incertitudes ?• Comment se comportent les incertitudes sur les cœurs optimisés par rapport au cœur de référence ?• En prenant en compte les incertitudes, les réacteurs sont-Ils toujours considérés comme performants ?• Les gains des optimisations obtenus à l’issue d’optimisations complexes sont-Ils supérieurs aux marges d’incertitudes (qui elles-Mêmes dépendent de l’espace paramétrique) ?La thèse contribue au développement et à la mise en place des méthodes nécessaires à la prise en compte des incertitudes dans les outils de simulation de nouvelle génération. Des méthodes statistiques pour garantir la cohérence des schémas de calculs multi-Physiques complexes sont également détaillées.En proposant de premières images de cœur de RNR-Na innovants, cette thèse présente des méthodes et des outils permettant de réduire les incertitudes sur certaines performances des réacteurs tout en les optimisant. Ces gains sont obtenus grâce à l’utilisation d’algorithmes d’optimisation multi-Objectifs. Ces méthodes permettent d’obtenir tous les compromis possibles entre les différents critères d’optimisations comme, par exemple, les compromis entre performance économique et sûreté
Since Phenix shutting down in 2010, CEA does not have Sodium Fast Reactor (SFR) in operating condition. According to global energetic challenge and fast reactor abilities, CEA launched a program of industrial demonstrator called ASTRID (Advanced Sodium Technological Reactor for Industrial Demonstration), a reactor with electric power capacity equal to 600MW. Objective of the prototype is, in first to be a response to environmental constraints, in second demonstrates the industrial viability of:• SFR reactor. The goal is to have a safety level at least equal to 3rd generation reactors. ASTRID design integrates Fukushima feedback;• Waste reprocessing (with minor actinide transmutation) and it linked industry.Installation safety is the priority. In all cases, no radionuclide should be released into environment. To achieve this objective, it is imperative to predict the impact of uncertainty sources on reactor behaviour. In this context, this thesis aims to develop new optimization methods for SFR cores. The goal is to improve the robustness and reliability of reactors in response to existing uncertainties. We will use ASTRID core as reference to estimate interest of new methods and tools developed.The impact of multi-Physics uncertainties in the calculation of the core performance and the use of optimization methods introduce new problems:• How to optimize “complex” cores (i.e. associated with design spaces of high dimensions with more than 20 variable parameters), taking into account the uncertainties?• What is uncertainties behaviour for optimization core compare to reference core?• Taking into account uncertainties, optimization core are they still competitive? Optimizations improvements are higher than uncertainty margins?The thesis helps to develop and implement methods necessary to take into account uncertainties in the new generation of simulation tools. Statistical methods to ensure consistency of complex multi-Physics simulation results are also detailed.By providing first images of innovative SFR core, this thesis presents methods and tools to reduce the uncertainties on some performance while optimizing them. These gains are achieved through the use of multi-Objective optimization algorithms. These methods provide all possible compromise between the different optimization criteria, such as the balance between economic performance and safety

Pour améliorer son offre de tramway, la société ALSTOM Transport a décidé de développer une batterie mécanique qui assure l’autonomie énergétique d’un véhicule entre deux stations.Mais même si les volants d’inertie existent essentiellement depuis les années 1950 pour les applications mobiles, il n’en existe aucun qui soit conçu pour assurer 100% de l’apport énergétique pour des applications mobiles. La batterie doit donc tenir peu de place, peser le moins possible, être sûr et répondre au cahier des charges.Suite à une analyse des données bibliographiques, nous avons entrepris de développer une méthode de pré-dimensionnement multi-physique des batteries mécaniques en prenant en compte les interactions entre les différents organes majeurs de conception, alors que jusque là, les méthodes consistaient essentiellement à développer les batteries mécaniques partie par partie.Pour cela, nous avons développé un outil de conception intégré qui prend en compte les phénomènes énergétiques (énergie et puissance), mécaniques (résistance des matériaux, dynamique des rotors), électromagnétiques (moteur électrique) et géométriques (gabarit d’intégration,création des volumes). Nous avons également dû développer une méthode de sélection des matériaux pour l’usage des volants d’inertie, à partir de laquelle nous avons dressé une liste de matériaux pertinents.Nous avons montré que la conception intégrée apportait des solutions plus performantes en terme d’intégration et d’équilibre entre la partie mécanique et électromagnétique. Nous avons aussi montré que les matériaux composites ne sont pas forcément le meilleur choix de conception et que les matériaux comme les aciers hautes performances sont de très bons candidats suivant le domaine d’étude qui nous intéresse (la frontière déterminante étant la vitesse de rotation de 30000 tr/min). Nous avons montré qu’il est possible d’atteindre des domaines stables de fonctionnement, même s’il sera sans doute inévitable de passer des vitesses critiques au démarrage. La méthode de conception que nous avons développée, fait en sorte que les seuls modes dynamiques excités soient les modes de paliers, ce qui permet de traiter le problème à l’interface avec le bâti assez facilement. Elle permet également de représenter la configuration du système, de faire une analyse statique des contraintes, d’étudier les phénomènes dynamiques d’une batterie mécanique et enfin, cette stratégie permet de réaliser une optimisation global edu système par la méthode d’organisation des solutions de Kohon en.Les résultats obtenus sont significatifs car la méthode de conception systématique optimisée que nous avons développée, peut s’appliquer à tous les cas de figure. Elle permet de savoir quels matériaux choisir suivant la configuration voulue, présente graphiquement les résultats comportementaux des systèmes étudiés et apporte une connaissance du système en cours de développement. Cela permet notamment d’anticiper les évolutions potentielles de conception.Il s’agit donc d’un outil d’aide à la compréhension et à la décision en conception des batteries mécaniques.Le chemin scientifique que nous avons emprunté est celui préconisé par le Professeur Italien Giancarlo Genta, spécialiste du domaine, à la fin de ses propres études. Cette approche évolutive a permis d’accroître la connaissance des batteries mécaniques et de mieux les concevoir
To improve its tram offer, ALSTOM Transport has decided to develop a mechanical batterythat provides energy of a vehicle between two stations. But even if the flywheels are essentiallydevelopped since the 1950s for mobile applications, none of them is designed to ensure 100%of energy for mobile applications. The battery must be light, weigh as little as possible, besafe and respect the specifications.Following a bibliographic data analysis, we undertook to develop a method of pre-sizingmechanical battery by taking into account interactions between different major organs, whereaspreviously, methods were concentrated on developing mechanical batteries part by part.For this, we developed an integrated design tool that takes into account the energy (energyand power), mechanic (strength of materials, rotor dynamics), electromagnetic (electric motor)and geometric (template integration, creation of volumes). We also develop a method forselecting the right materials for flywheels, from which we have compiled a list of relevantmaterials.We have shown that the integrated design is more efficient in terms of integration andbalance between the mechanical and electromagnetic.We also showed that composite materialsare not necessarily the best design choices and materials such as high performance steels areexcellent candidates according to the study area of interest (the threshold being the criticalrotational speed 30000 rpm). We have shown that it is possible to achieve stable areas ofoperation, although it will probably be inevitable to pass critical speeds at startup. The designmethod we developed ensures that the only modes excited are the dynamic modes of bearings,which can be treaten quite easily. It can also represent the system configuration, make a staticstress analysis, study the dynamic phenomena of a mechanical battery, and finally, this methodallows an overall system optimization by the Kohonen method.The results are significant because the systematic method we developed can be applied toevery cases. It helps to know what materials to choose, the configuration you want, presentsgraphically the results of behavioral systems studied and brings a knowledge of the systemunder development. This allows us to anticipate potential changes in design. It is therefore atool for understanding and making during the design process.The scientific path that we have taken is the one advocated by Professor Giancarlo Genta,Italian specialist in the field, at the end of his own studies. This evolutionary approach hasled to increased knowledge of batteries and better mechanical design.Keywords

Le processus actuel de conception avion est centré sur l'optimisation des performances du véhicule (minimiser la traînée de l'avion, réduire les niveaux de bruit, maximiser le Rayon d'Action, réduire les émissions polluantes, etc. ) et traite toutes les autres disciplines séquentiellement et en tant que contraintes de design. Parmi ces contraintes, la discipline de Stabilité & Contrôle est certainement la plus importante, bien qu'elle ne soit pas considérée comme telle. Outre le dimensionnement des stabilisateurs et des organes de contrôle de l'appareil, la Stabilité & Contrôle est intimement liée aux Performances, à la sécurité et aux aspects de certification de l'avion. Dans la première étape du cycle de conception avion (phase de Design Conceptuel), la Stabilité & Contrôle n'est traitée que partiellement et consiste simplement à exploiter des relations statistiques (coefficients de volume) et à mener quelques analyses statiques (Diagramme en Ciseaux). Ce n'est que plus tard dans le processus de développement avion (phase de Design Détaillée et essais en vol) que cette discipline joue un rôle dominant et que les choix de design faits à l'étape conceptuelle sont validés ou non. Bien que cette méthodologie de conception des stabilisateurs et des gouvernes fonctionne convenablement quand on considère des avions ayant une forme typique (voilure + fuselage tubulaire + empennage arrière), elle échoue lorsque l'on examine des avions ayant une forme non conventionnelle. De plus, la façon "simple" d'appréhender la discipline de Stabilité & Contrôle en phase conceptuelle conduit à des aéronefs sous-optimaux ayant souvent des problèmes de stabilité et/ou de contrôle qui non seulement sont coûteux à rectifier, mais qui peuvent également dégrader les performances du véhicule et compromettre sa sécurité. Ce travail de recherche a pour objectif d'établir une méthodologie de Design Conceptuel, à la fois générique, alternative et rapide, capable de dimensionner et d'optimiser tout type de configuration avion en accordant une importance particulière à la discipline de Stabilité & Contrôle. Dans cette méthodologie, le caractère séquentiel du processus de design conceptuel actuel est remplacé par une approche simultanée et intégrée d'Optimisation Multidisciplinaire (MDO), dans laquelle la discipline de Stabilité & Contrôle, en particulier, est considérée au même niveau que les Performances. L'approche de design proposée dans cette thèse vise à déterminer la forme avion satisfaisant, entre autres, un ensemble générique (i. E. Indépendant de la configuration avion) d'exigences de stabilité et de contrôle, tout en possédant les meilleures performances opérationnelles tout au long d'un profil de mission type. Comparé au processus de design avion traditionnel, le problème d'optimisation qui en résulte est davantage contraint d'un point de vue de la Stabilité & Contrôle et considère non seulement des critères statiques mais également des exigences dynamiques et de manœuvre. Cette méthodologie s'apparente à une démarche "inverse" puisque les caractéristiques souhaitées de stabilité et de contrôle sont imposées par avance en tant que contraintes du problème d'optimisation. Le concepteur cherche ainsi à déterminer la forme avion ayant des qualités de vol prédéfinies. L'intégration de la discipline de Stabilité & Contrôle dans l'étape conceptuelle de design requiert la mise à disposition d'un outil intégré et modulaire, servant de plateforme pour mener des analyses MDO. Un tel outil n'est cependant pas disponible à l'heure actuelle dans l'industrie civile. Pour pallier à ce problème, un outil simple permettant d'émuler l'environnement requis a été développé. La méthodologie présentée est illustrée avec deux configurations avion radicalement différentes mais ayant une taille semblable. Bien que le travail exposé ci-dessous ne constitue qu'une première étape pour résoudre le problème complet, on démontre cependant que la méthode est viable et que l'on peut obtenir des gains supplémentaires en performances en prenant en compte la Stabilité & Contrôle dès le stade de Design Conceptuel. L'approche permet également de comparer des configurations avions entre elles sur des bases entièrement physiques et objectives et non pas sur des opinions variées et subjectives, comme c'est le cas actuellement
The current aircraft design process focuses on Performance optimization (minimize the airframe drag, reduce noise levels, maximize Range, reduce pollutant emissions, etc. ) and treats all other disciplines sequentially and as design constraints. Among the constraint disciplines, Stability & Control is the most important one, although not always recognized as such. Indeed, in addition to being responsible for equipping the airframe with stabilizers and controls that ensure the proper handling of the vehicle, Stability & Control is strongly tied to Performance, safety, and aircraft certification aspects. In the earliest aircraft design stage (Conceptual Design phase), the Stability & Control discipline is only partially considered and consists of little more than statistical relationships (volume coefficients) and static analyses (Scissors Plot). It is not until later in the aircraft development process (Detailed Design phase and flight tests), that the Stability & Control discipline plays a dominant role and where the airplane design choices made at the conceptual level are validated or not. Although this traditional design procedure proved to be "successful" when sizing the stabilizers and controls of typical airplanes (wing + tubular fuselage + rear empennage), it fails when the aircraft layout under study deviates from the conventional one. Furthermore, the wide discrepancy between the rather sophisticated manner in which the Stability & Control discipline is considered in the Detailed Design phase, compared to the relatively simple approach used during the Conceptual Design level, results in "sub-optimal" airplane designs with stability and/or deficiencies that not only are expensive to fix, but can also be detrimental to the vehicle performance characteristics and jeopardize its safety. This research work introduces a generic, alternative, and fast aircraft conceptual design methodology capable of sizing and optimizing any aircraft configuration by giving further importance to the Stability & Control discipline. In this methodology, the sequential character of the current conceptual design process is replaced with a simultaneous and integrated Multi-Disciplinary Optimization (MDO) approach in which the discipline of Stability & Control, in particular, is considered at the same level as Performance. The proposed design procedure aims at deriving the airplane outer shape satisfying, among others, a set of generic (i. E. Independent of the aircraft configuration) stability and control requirements, while possessing the best operational performance throughout a typical mission profile. Compared to the traditional aircraft design methodology, the derived optimization problem is much more constrained from the Stability & Control perspective and considers not only static requirements, but also dynamic and maneuver criteria. The methodology resembles an "inverse" (or "reverse engineering") design process since the desired stability and control features of the airplane are imposed in advance as constraints of the optimization problem. The conceptual designer therefore seeks to determine the aircraft shape having specific handling characteristics. Integrating the Stability & Control discipline at conceptual level requires a modular and integrated tool, capable of performing MDO, that is not yet available within the civilian aviation industry. To counter this, a "simple" tool was created for partially mimicking the environment required. The methodology is illustrated with two different aircraft configurations of similar size. Although the work presented herein represents only a small fraction of the whole research challenge, the methodology is demonstrated to be viable and it is shown that further performance benefits can be extracted if the stability and control constraints are taken into account from the early design stages. This approach also enables to compare different aircraft configurations from a physical and rational basis and not on subjective and disparate opinions, as is currently the case

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This paper presents a calibration optimization study for a Gasoline Direct Injection engine based on a multidisciplinary design optimization (MDO) framework. The paper presents the experimental framework used for the GDI engine mapping, followed by an analysis of the calibration optimization problem. The merits of the MDO approach to calibration optimization are discussed in comparison with a conventional two-stage approach based on local trade-off optimization analysis, focused on a representative emissions drive cycle (NEDC) and limited part load engine operation. The benefits from using the MDO optimisation framework are further illustrated with a study of relative effectiveness of different camshaft timing control strategies (twin independent Versus fixed timing, exhaust only, inlet only and fixed overlap / dual equal) for the reference GDI engine based on the part load test data. The main conclusion is that the MDO structure offers an effective framework for the GDI steady state calibration optimization analysis.

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This paper presents the development of an Analytical Target Cascading (ATC) Multidisciplinary Design Optimization (MDO) framework for a steady-state engine calibration optimization problem. The implementation novelty of this research is the use of the ATC framework to formulate the complex multi-objective engine calibration problem, delivering a considerable enhancement compared to the conventional 2-stage calibration optimization approach [1]. A case study of a steady-state calibration optimization of a Gasoline Direct Injection (GDI) engine was used for the calibration problem analysis as ATC. The case study results provided useful insight on the efficiency of the ATC approach in delivering superior calibration solutions, in terms of “global” system level objectives (e.g. improved fuel economy and reduced particulate emissions), while meeting “local” subsystem level requirements (such as combustion stability and exhaust gas temperature constraints). The ATC structure facilitated the articulation of engineering preference for smooth calibration maps via the ATC linking variables, with the potential to deliver important time saving for the overall calibration development process.