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Large-aspect-ratio wings and composite structures both have been considered for the next-generation civil transport aircraft to achieve improved aerodynamic efficiency and to save aircraft structural weight. The use of the large-aspect-ratio and the light-weight composite wing can lead to an enhanced flexibility of the aircraft wing, which may cause many aeroelastic problems such as large deflections, increased drag, onset of flutter, loss of control authority, etc. Aeroelastic tailoring, internal structural layout design and aerodynamic wing shape morphing are all considered to address these aeroelastic problems through multidisciplinary design, analysis and optimization (MDAO) studies in this work. Performance Adaptive Aeroelastic Wing (PAAW) program was initiated by NASA to leverage the flexibility associated with the use of the large-aspect-ratio wings and light-weight composite structures in a beneficial way for civil transport aircraft wing design. The biologically inspired SpaRibs concept is used for aircraft wing box internal structural layout design to achieve the optimal stiffness distribution to improve the aircraft performance. Along with the use of the active aeroelastic wing concept through morphing wing shape including the wing jig-shape, the control surface rotations and the aeroelastic tailoring scheme using composite laminates with ply-drop for wing skin design, a MDAO framework, which has the capabilities in total structural weight minimization, total drag minimization during cruise, ground roll distance minimization in takeoff and load alleviation in various maneuver loads by morphing its shape, is developed for designing models used in the PAAW program. A bilevel programming (BLP) multidisciplinary design optimization (MDO) architecture is developed for the MDAO framework. The upper-level optimization problem entails minimization of weight, drag and ground roll distance, all subjected to both static constraints and the global dynamic requirements including flutter mode and free vibration modes due to the specified control law design for body freedom flutter suppression and static margin constraint. The lower-level optimization is conducted to minimize the total drag by morphing wing shape, to minimize wing root bending moment by scheduling flap rotations (a surrogate for weight reduction), and to minimize the takeoff ground roll distance. Particle swarm optimization and gradient-based optimization are used, respectively, in the upper-level and the lower-level optimization problems. Optimization results show that the wing box with SpaRibs can further improve the aircraft performances, especially in a large weight saving, as compared to the wing with traditional spars and ribs. Additionally, the nonuniform chord control surface associated with the wing with SpaRibs achieve further reductions in structural weight, total drag and takeoff ground roll distance for an improved aircraft performance. For a further improvement of the global wing skin panel design, an efficient finite element approach is developed in designing stiffened composite panels with arbitrarily shaped stiffeners for buckling and vibration analyses. The developed approach allows the finite element nodes for the stiffeners and panels not to coincide at the panel-stiffeners interfaces. The stiffness, mass and geometric stiffness matrices for the stiffeners can be transformed to those for the panel through the displacement compatibility at their interfaces. The method improves the feasible model used in shape optimizing by avoiding repeated meshing for stiffened plate. Also, it reduces the order of the finite element model, a fine mesh typically associated with the skin panel stiffened by many stiffeners, for an efficient structural analysis. Several benchmark cases have been studied to verify the accuracy of the developed approach for stiffened composite panel structural analyses. Several parametric studies are conducted to show the influence of stiffener shape/placement/depth-ratio on panel's buckling and vibration responses. The developed approach shows a potential benefit of using gradient-based optimization for stiffener shape design. / Ph. D.

<p>This thesis investigates the second-best toll pricing and capacity expansion problems when stated as mathematical programs with equilibrium constraints (MPEC). Three main questions are rised: First, whether conventional descent methods give sufficiently good solutions, or whether global solution methods are to prefer. Second, how the performance of the considered solution methods scale with network size. Third, how a discretized stochastic mathematical program with equilibrium constraints (SMPEC) formulation of a stochastic network design problem can be practically solved. An attempt to answer these questions is done through a series ofnumerical experiments.</p><p>The traffic system is modeled using the Wardrop’s principle for user behavior, separable cost functions of BPR- and TU71-type. Also elastic demand is considered for some problem instances.</p><p>Two already developed method approaches are considered: implicit programming and a cutting constraint algorithm. For the implicit programming approach, several methods—both local and global—are applied and for the traffic assignment problem an implementation of the disaggregate simplicial decomposition (DSD) method is used. Regarding the first question concerning local and global methods, our results don’t give a clear answer.</p><p>The results from numerical experiments of both approaches on networks of different sizes shows that the implicit programming approach has potential to solve large-scale problems, while the cutting constraint algorithm scales worse with network size.</p><p>Also for the stochastic extension of the network design problem, the numerical experiments indicate that implicit programming is a good approach to the problem.</p><p>Further, a number of theorems providing sufficient conditions for strong regularity of the traffic assignment solution mapping for OD connectors and BPR cost functions are given.</p>

This thesis investigates the second-best toll pricing and capacity expansion problems when stated as mathematical programs with equilibrium constraints (MPEC). Three main questions are rised: First, whether conventional descent methods give sufficiently good solutions, or whether global solution methods are to prefer. Second, how the performance of the considered solution methods scale with network size. Third, how a discretized stochastic mathematical program with equilibrium constraints (SMPEC) formulation of a stochastic network design problem can be practically solved. An attempt to answer these questions is done through a series ofnumerical experiments. The traffic system is modeled using the Wardrop’s principle for user behavior, separable cost functions of BPR- and TU71-type. Also elastic demand is considered for some problem instances. Two already developed method approaches are considered: implicit programming and a cutting constraint algorithm. For the implicit programming approach, several methods—both local and global—are applied and for the traffic assignment problem an implementation of the disaggregate simplicial decomposition (DSD) method is used. Regarding the first question concerning local and global methods, our results don’t give a clear answer. The results from numerical experiments of both approaches on networks of different sizes shows that the implicit programming approach has potential to solve large-scale problems, while the cutting constraint algorithm scales worse with network size. Also for the stochastic extension of the network design problem, the numerical experiments indicate that implicit programming is a good approach to the problem. Further, a number of theorems providing sufficient conditions for strong regularity of the traffic assignment solution mapping for OD connectors and BPR cost functions are given.

In the push for clean and renewable fuels, timber derived biomass is a promising frontier for biofuel production in the United States. This thesis approaches the established timberlands biofuel implementation problem with three different mathematical programming studies, each testing feasibility and sustainability in different economic and supply related situations. In the first study, a competitive game theory approach was utilized to provide new insights into the behavior within a timberlands supply chain. We utilized Stackelberg game theory modeled with bilevel programming to represent the competing harvesting and manufacturing sectors. In the second study, the initial bilevel model was utilized in a larger two stage multiperiod model with parameter uncertainty. In this more realistic model, the first stage contained logistical decisions around biorefinery investments, such as location and capacity, while the second stage was composed of multiple discrete bilevel scenarios representing potential situations in the timberlands system. The final study focused on long term land management strategies for the timberlands supply chain. Introduction of a new biorefinery investment meant that management strategies must be altered to ensure consistent material flows to manufacturers as well as sustain the new production facility. A modified cyclic scheduling formulation was used to model a timberlands system and its planting and harvesting schedule to accommodate a new biorefinery. This cyclic model added an initial startup period to initiate biofuel production and provide time to adapt land management. The overall contribution of these studies was to analyze a biorefinery's impact on the established behavior in a timberlands supply chain. In particular, the goals of these models were to develop introductory decision making tools for timberlands supply chain managers.

Orientador: José Roberto Sanches Mantovani / Resumo: Neste trabalho, o modelo matemático do problema de fluxo de potência ótimo básico não linear é analisado e manipulado algebricamente para obter um modelo de programação convexa, do tipo cônico de segunda ordem. O conceito de envelopes convexos é apresentado para tratar a não linearidade e não convexidade da restrição trigonométrica inversa que surge ao escrever o modelo de FPO como um modelo cônico. Aplicando duas proposições apresentadas neste trabalho a restrição trigonométrica é resolvida em um pré-processamento por um solver de otimalidade local, neste caso o KNITRO, que enumera todas as possibilidades dos pontos de KKT para obter os envelopes convexos e tornar o modelo de FPO totalmente convexo. O modelo é implementado no AMPL e é resolvido com solvers de otimalidade global com sistemas testes da literatura, nesta tese usam-se os sistemas testes IEEE 14, 30, 57 e 118 barras. Os resultados obtidos são validados comparando-os com resultados fornecidos pelo Matpower, que é um simulador para FPO. Como contribuição desta tese, o modelo convexo de FPO obtido é utilizado como exemplo de aplicações no problema de despacho ótimo de potência ativa e reativa, considerando competições via programação binível. São apresentados dois modelos biníveis e dois modelos uníveis. O modelo iterativo convexo utiliza-se do modelo proposto de FPO convexo e as não linearidades são convexificadas fazendo uso dos envelopes de McCormick. O conceito de dualidade forte é empregado afim de obter um mod... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In this work, the basic nonlinear mathematical model for the optimal power flow (OPF) problem is analyzed and manipulated algebraically in order to obtain a second-order conic convex programming model. The concept of convex envelopes is presented to deal with the nonlinearity and nonconvexity of the inverse trigonometric constraint that arises when transforming the nonconvex OPF model into an equivalent conic model. By applying two propositions presented in this work, the trigonometric constraint is solved in a pre-processing stage by a local optimization solver, in this case, the KNITRO solver, which considers all the possibilities of the KKT points to obtain the convex envelopes and find a completely convex OPF model, is used. The model is implemented in AMPL and is solved via global optimization solvers while to show the effectiveness of the model several IEEE systems such as the IEEE 14-, 30-, 57-, and 118-bus systems are used. The obtained results are validated by comparing them with the results provided by Matpower, which is an OPF solver. As a contribution of this thesis, the obtained convex OPF model is used as an application in the active and reactive optimal power dispatch problem, considering competition via bilevel programming. Two bilevel models and two single-level models are presented. The convex iterative model uses the proposed convex OPF model, and the nonlinearities are convexified using McCormick envelopes. The concept of strong duality is employed to obta... (Complete abstract click electronic access below) / Doutor

Made available in DSpace on 2016-12-23T14:33:41Z (GMT). No. of bitstreams: 1 dissertacao.pdf: 1222375 bytes, checksum: 25701e5d822c85de67ae48d04a4d24df (MD5) Previous issue date: 2010-03-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work gives a rigorous approach of bilevel problems, especially the linear case. Proofs of known results in the literature are reproduced or remade. As motivation for the reader, classic problems are reformulated as bilevel problems. In theoretical point of view, some contributions are the formalization of relations between models of literature; their extensions to multilevel problems; the result that complements the equivalence between optimal solutions of the models in linear optimistic case; and the generalization of the method of Calamai and Vicente for generation of linear test problems. In practical point of view, the contribution is a new algorithm for local optimal solutions of linear problems, which differs from other methods in generality: treat unlimited problems, and only requires that the problem s polyhedron does not have degenerate faces. / Este trabalho aborda de forma rigorosa o problema de dois níveis, sobretudo o caso linear. Resultados conhecidos da literatura tiveram suas demonstrações reproduzidas, ou refeitas. Como motivaçãoo para o leitor, formulações de problemas clássicos como problemas de dois níveis foram expostas. No aspecto teórico, destacam-se como contribuições a formalizaçãoo das relações entre os modelos usualmente encontrados na literatura; suas extensões para problemas multinível; o resultado que complementa a equivalência entre soluções ótimas dos modelos para o caso linear otimista; e a generalização do método de Calamai e Vicente para geração de problemas-teste lineares. No aspecto prático, destaca-se o novo método para soluções ótimas locais de problemas lineares, cujo diferencial diante de outros métodos é a generalidade: engloba ilimitabilidade, e exige apenas que o poliedro do problema não tenha faces degeneradas.

Em um problema de natureza hierárquica, o nível mais influente toma certas decisões que afetam o comportamento dos níveis inferiores. Cada decisão do nível mais influente é considerada como fixa pelos níveis inferiores, que, com tais informações, tomam decisões que maximizam seus objetivos. Essas decisões podem influenciar os resultados obtidos pelo nível superior, que, por sua vez, também anseia pela decisão ótima. Em programação matemática, este problema é modelado como um problema de programação em níveis. Neste trabalho, consideramos uma classe particular de problemas de programação em níveis: os problemas de programação matemática em dois níveis. Estudamos uma técnica de resolução que consiste em substituir o problema do nível inferior por suas condições necessárias de primeira ordem, que podem ser formuladas de diversas maneiras, conforme as restrições de complementaridade são modificadas. O novo problema torna-se um problema de programação não linear e pode ser resolvido com algoritmos clássicos de otimização. Com o auxílio de condições de otimalidade de primeira e segunda ordem mostramos as relações entre o problema original e o problema reformulado. Aplicamos a técnica a problemas encontrados na literatura, analisamos o seu comportamento e apresentamos estratégias para eliminar certos inconvenientes encontrados. / In problems of hierarchical nature, the choices made by the most influential level - the so-called leader - affect the behavior of the lower levels. For each one of the leader\'s decisions there is a response from the lower levels, which maximizes the value of their respective objectives. These optimal choices, in return, may have influence in the results achieved by the leader, which also wants to make the optimal choices. In mathematical programming, this kind of problem is described as a multilevel programming problem. The present work considers a specific kind of multilevel problem: the bilevel mathematical problem. We study a resolution technique which consists in replacing the lower level problem by its necessary first order conditions, which can be formulated in various ways, as complementarity constraints occur and are modified. The new reformulated problem is a nonlinear programming problem which can be solved by classical optimization methods. Using first and second order optimality conditions, we show the relations between the original bilevel problem and the reformulated problem. We apply the described technique to solve a set of bilevel problems taken from the literature, analyse their behavior and discuss strategies to prevent undesirable difficulties that may arise.

In der Dissertation werden Zwei-Ebenen-Optimierungsaufgaben mit spezieller Struktur untersucht. Von Interesse sind hierbei für den sogenannten pessimistischen Lösungszugang Existenzresultate für Lösungen, die Eckpunkteigenschaft einer Lösung, eine Regularisierungstechnik, Optimalitätsbedingungen sowie für den linearen Fall ein Verfahren zur Bestimmung einer global pessimistischen Lösung. Beim optimistischen Lösungszugang wird zunächst eine Verallgemeinerung des Lösungsbegriffes angegeben. Anschließend finden sich Betrachtungen zur Komplexität des Problems, zu Optimalitätsbedingungen sowie ein Abstiegs- und Branch&Bound-Verfahren für den linearen Fall wieder. Den Abschluss der Arbeit bilden ein Anwendungsbeispiel und numerische Testrechnungen.