Multidisciplinary Design Optimization with Mixed Integer Quasiseparable Subsystems

Numerous hierarchical and nonhierarchical decomposition strategies for the optimization of large scale systems, comprised of interacting subsystems, have been proposed. With a few exceptions, all of these strategies have proven theoretically unsound. Recent work considered a class of optimization problems, called quasiseparable, narrow enough for a rigorous decomposition theory, yet general enough to encompass many large scale engineering design problems. The subsystems for these problems involve local design variables and global system variables, but no variables from other subsystems. The objective function is a sum of a global system criterion and the subsystems' criteria. The essential idea is to give each subsystem a budget and global system variable values, and then ask the subsystems to independently maximize their constraint margins. Using these constraint margins, a system optimization then adjusts the values of the system variables and subsystem budgets. The subsystem margin problems are totally independent, always feasible, and could even be done asynchronously in a parallel computing context. An important detail is that the subsystem tasks, in practice, would be to construct response surface approximations to the constraint margin functions, and the system level optimization would use these margin surrogate functions. The present paper extends the quasiseparable necessary conditions for continuous variables to include discrete subsystem variables, although the continuous necessary and sufficient conditions do not extend to include integer variables

Mission-Based Multidisciplinary design optimization methodologies for unmanned aerial vehicles with morphing technologies

One of the most challenging aspects of aircraft design is to synthesize the mutual interactions among disciplines in order to achieve enhanced design solutions from the earliest stages of the design process. The complexity of the aircraft physics and the multiple couplings between disciplines complicates this task. The advance of design tools and optimization methods alongside with the computer’s exponential increase in data handling capacity is paving the way for the development of comprehensive multidisciplinary design codes that gradually contribute to a paradigm change, leading to a revolution in the design methodologies. The research work presented in this thesis features two unmanned aerial vehicles preliminary design optimization methodologies - a Parametric Design Analysis and a Multilevel Design Optimization. A specific code has been developed for each methodology, with low-fidelity models being used for the main design disciplines, namely the aerodynamics, propulsion, weight, static stability and dynamic stability. To increase the usability of the codes a graphical user interface for both programs has also been developed. The first methodology is called Parametric AiRcRaft design OpTimization (PARROT) and relies on a parametric study that optimizes the wing layout for one of two different goals: surveillance mission or maximum payload. Whereas in the former the goal is to maximize the flight range or endurance, the latter’s objective is to maximize the useful payload lifted. Constraints include the take-off distance, climb rate, bank angle, cruise velocity, among others. The results have shown to be in line with some experimental benchmarking data and to allow the user to easily evaluate the impact of varying two key design variables (wing mean chord and wingspan) on multiple performance metrics, thus significantly contributing to help the designer’s decision-making process. The second methodology is called MulTidisciplinary design OPtimization (MTOP) and adopts the Enhanced Collaborative Optimization (ECO) architecture, together with a gradient-based optimization algorithm. As the goal is to minimize the energy consumption for the specified mission profile, it results in an unconstrained system problem which aims to assure compatibility between subspaces and dully constrained subspace level problems, which aims to minimize the energy consumption. Instead of each subspace representing the traditional design disciplines (e.g. aerodynamics, structures, stability, etc), the author has chosen to make a different subspace out of each flight stage (e.g. take-off, climb, cruise, etc). The main reason for this choice was the inclusion of morphing technologies as part of the optimization process, namely a variable span wing (VSW), a variable camber flap (VCF) and a variable propeller pitch (VPP). The software final output is the combination of design variables that better suits the objective function subjected to the design constraints. The results have shown how the selection of the optimum combination of morphing/adaptive technologies highly depends on the mission profile. Moreover, the morphing mechanisms weight has a strong impact on the overall performance, which is not easily grasped without an optimization methodology like the one presented. Globally, these two methodologies foster a more efficient and effective preliminary design stage by feeding the designer’s decision-making process with a large set of relevant data.Um dos aspetos mais desafiantes do projeto de aeronaves é a gestão das múltiplas interações entre disciplinas, com vista à obtenção de soluções de projeto otimizadas desde os primeiros estágios do projeto de aeronaves. A complexidade da física aeronáutica e os múltiplos acoplamentos entre disciplinas complicam esta tarefa. Com o desenvolvimento de ferramentas de projeto e metodologias de otimização aliadas ao aumento exponencial da capacidade de processamento dos computadores e o desenvolvimento de abrangentes códigos de otimização multidisciplinar estão a contribuir para uma mudança de paradigma, que se espera vir a revolucionar os atuais processos de projeto aeronáutico. Esta investigação inclui duas metodologias de otimização de projeto preliminar de veículos aéreos não-tripulados - uma otimização paramétrica e uma otimização multinível. Foi desenvolvido um código para cada metodologia, tendo sido utilizados modelos de baixa-fidelidade para as várias disciplinas de projeto, nomeadamente aerodinâmica, propulsão, peso, estabilidade estática e dinâmica. Para aumentar o leque de utilizadores, foi desenvolvido um interface gráfico para ambos os programas. A primeira metodologia denomina-se Parametric AiRcRaft design OpTimization (PARROT) e segue uma abordagem paramétrica que otimiza a geometria da asa para um de dois objetivos: missão de vigilância ou máximo peso. Enquanto na primeira o objetivo passa por otimizar o alcance ou autonomia, na segunda o foco passa por maximizar o peso útil sustentado. Constrangimentos incluem a distância de descolagem, a velocidade de subida, o ângulo de pranchamento, a velocidade cruzeiro, entre outros. Os resultados mostraram estar em linha com resultados experimentais de referência e ainda permitir ao utilizador avaliar o impacto da variação de duas variáveis-chave (corda média aerodinâmica e envergadura) em diversas métricas de desempenho, desta forma contribuindo significativamente para auxiliar o processo decisório do engenheiro de projeto. A segunda metodologia chama-se MulTidisciplinary design OPtimization (MTOP) e adota a arquitetura Enhanced Collaborative Optimization (ECO), juntamente com um algoritmo de otimização do tipo gradiente. Uma vez que o objetivo passa por minimizar a energia consumida para um perfil de missão específico, cinge-se a um problema de otimização não constrangido ao nível do sistema, a solução do qual visa a compatibilidade entre subespaços, e um problema devidamente constrangido com o objetivo de minimizar a energia consumida ao nível dos subespaços. Ao invés de cada subespaço representar as disciplinas tradicionais de projeto (e.g. aerodinâmica, estruturas, estabilidade, etc), o autor decidiu criar um subespaço diferente para cada estágio da missão (e.g. descolagem, subida, cruzeiro, etc). A principal razão para esta escolha foi a inclusão de metodologias adaptativas como parte do processo de otimização, nomeadamente uma asa de envergadura variável (VSW), um perfil alar com curvatura variável através de um flap (VCF) e um hélice de passo variável (VPP). O resultado final é a combinação de variáveis que melhor se adequa à função objetivo, sujeitos aos constrangimentos de projeto. Os resultados mostraram que a seleção da combinação de tecnologias adaptativas adequada está altamente dependente do tipo de missão. Além disso, o peso das tecnologias adaptativas tem um elevado impacto que não é facilmente percecionado sem uma metodologia de otimização como a que é apresentada. Globalmente, estas duas metodologias contribuem para um projeto preliminar mais eficaz e eficiente, alimentando a tomada de decisão do projetista com muita informação relevante

Domination and Decomposition in Multiobjective Programming

During the last few decades, multiobjective programming has received much attention for both its numerous theoretical advances as well as its continued success in modeling and solving real-life decision problems in business and engineering. In extension of the traditionally adopted concept of Pareto optimality, this research investigates the more general notion of domination and establishes various theoretical results that lead to new optimization methods and support decision making. After a preparatory discussion of some preliminaries and a review of the relevant literature, several new findings are presented that characterize the nondominated set of a general vector optimization problem for which the underlying domination structure is defined in terms of different cones. Using concepts from linear algebra and convex analysis, a well known result relating nondominated points for polyhedral cones with Pareto solutions is generalized to nonpolyhedral cones that are induced by positively homogeneous functions, and to translated polyhedral cones that are used to describe a notion of approximate nondominance. Pareto-oriented scalarization methods are modified and several new solution approaches are proposed for these two classes of cones. In addition, necessary and sufficient conditions for nondominance with respect to a variable domination cone are developed, and some more specific results for the case of Bishop-Phelps cones are derived. Based on the above findings, a decomposition framework is proposed for the solution of multi-scenario and large-scale multiobjective programs and analyzed in terms of the efficiency relationships between the original and the decomposed subproblems. Using the concept of approximate nondominance, an interactive decision making procedure is formulated to coordinate tradeoffs between these subproblems and applied to selected problems from portfolio optimization and engineering design. Some introductory remarks and concluding comments together with ideas and research directions for possible future work complete this dissertation

Optimisation multidisciplinaire sous incertitude en phase conceptuelle avion

Ces travaux de recherche concernent l'optimisation multidisciplinaire déployée lors de la conception de systèmes complexes. Ils sont tout particulièrement centrés sur la conception avion. À ce stade de la conception les incertitudes engendrées sont significatives. De nouvelles méthodes efficaces de modélisation et de propagation des incertitudes sont donc proposées afin de concevoir un système fiable et robuste. Elles font appel à des techniques de modélisation adaptatives, à des algorithmes d'optimisation classiques et à des techniques basées sur l'intelligence artificielle (systèmes multi-agent).These researches concern multidisciplinary optimization deployed in the design of complex systems. They are particularly focused on aircraft design. At this stage of the design, the uncertainties are significant. Effective new methods of modeling and uncertainty propagation are proposed to develop a reliable and robust system. They use techniques of adaptive modeling, optimization algorithms and classical techniques based on artificial intelligence (multi-agent systems)

Application of Multidisciplinary Design Optimisation Frameworks for Engine Mapping and Calibration

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.UK Technology Strategy Board (TSB