Orientador: Antonio Roberto Balbo / Resumo: A produção de energia eólica tem se destacado no Brasil e mostrado grande importância na questão ambiental, pois auxilia na redução da emissão dos gases poluentes na atmosfera, provenientes de outras fontes de energia. Neste trabalho é proposta uma modelagem matemática de otimização multiobjetivo a qual explora a produção de energia eólica em um problema de despacho econômico e ambiental termo-eólico. O principal objetivo é mostrar que uma metodologia determinística envolvendo os métodos de otimização multiobjetivo de restrições canalizadas progressivas e de técnicas de programação por metas ponderadas, em conjunto com o método de pontos interiores, é eficiente à resolução deste problema. É proposta uma nova técnica, a qual é uma combinação entre os métodos de otimização multiobjetivo citados. As soluções dos subproblemas gerados por estes métodos serão determinadas através de pacotes computacionais, onde são apresentados resultados de casos distintos de produção de energia, mostrando a insuficiência da energia eólica nos custos operacionais da geração e no impacto ambiental / Abstract: The production of wind energy has stood out in Brazil and has shown great importance in the environmental issue, as it assists to reduce of polluting gases in the atmosphere arising out of other sources of energy. In this work a mathematical modeling of optimization multiobjective is proposed, which explores the wind energy production in a thermal-wind environmental and economic dispatch problem. The main objective is to show that a deterministic methodology involving the multiobjective optimization methods, progressive bounded constraints and weighted goal programming techniques, together with an interior point method, is e cient to solve this problem. A new technique is proposed, which is a combination of the mentioned multiobjective optimization methods. The solutions of the generated subproblems by these methods will be determined through of computational package and the results of distinct cases of energy production will be presented, showing the in uence of the wind power on the generation and on the environmental impact. / Doutor

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Natural and predictable changes in consumer needs often require the development of new products. Providing solutions that anticipate, account for, and allow for these changes over time is a significant challenge to manufacturers and design engineers. Products that adapt to these changes through the addition of modules reduce production costs through product commonality and provide a set of products that cater to customization and adaptation. In this thesis, a multiobjective optimization design method using s-Pareto frontiers – sets of non-dominated designs from disparate design models - is developed and used to identify a set of optimal adaptive product designs that satisfy changing consumer needs. The novel intent of the method is to design a product that adapts to changing consumer needs by moving from one location on the s-Pareto frontier to another through the addition of a module and/or reconfiguration. The six-step method is described as follows: (A) Characterize the multiobjective design space. (B) Identify the anticipated regions of interest within the search space based on predicted future needs. (C) Identify the platform design variables that minimize the performance losses due to commonality across the anticipated regions of interest. (D) Assemble the s-Pareto frontier within each region of interest. (E) Determine the values of all design variables for the optimal product design in each region of interest by multiobjective optimization. (F) Identify the module design variables, and identify the platform and module designs by constrained module design. An example of the design of a simple unmanned air vehicle is used to demonstrate application of the method for a single Pareto frontier case. The design of a manual irrigation pump is used to demonstrate application of the method for a s-Pareto frontier case. In addition, these examples show the ability of the method to design a product that adapts to changing consumer needs by traversing the s-Pareto frontier.

One of the most important decisions in the product development process is the selection of a promising design concept because of the large influence it has on the final product. A thorough search for the best design is a significant challenge to designers, who are trying to balance the objective and subjective performance of the designs they create. In this thesis, a computationally-assisted design methodology is developed and used in the early stages of design to more thoroughly search for designs that perform well according to objective physics-based models and subjective designer-specific preference-based models. The method presented herein uses an initial pool of user-created designs that is parameterized and used in a numerical search that recombines design features to form new designs in a semi-automated way. Designs are then evaluated quantitatively by objective performance calculations and evaluated qualitatively by human designers. Designer preference is interactively gathered when visual representations of new computer-created designs are presented to the designer for subjective evaluation. A mathematical model is then formed using statistical probability methods to approximate the designer's preference and incrementally updated after the designer subjectively evaluates a new set of designs at each iteration of the automated search process. The methodology uses a multiobjective approach to search for optimally performing designs, treating both the physics-based models and the preference-based models as objectives. The methodology couples the speed of computational searches with the ability of human designers to subjectively evaluate unmodeled objectives. The method is demonstrated with two product examples to find optimal designs that designers may not have otherwise discovered among the vast number of possible combinations of features. The proposed methodology brings the ability to search for and find numerous, optimal solutions across a wide solution space, in an efficient and human-centered way, and does so in the early stages of design.

Collaborative products are created by combining components from two or more products to result in a new product that performs previously unattainable tasks. The resulting reduction in cost, weight, and size of a set of products needed to perform a set of functions makes collaborative products useful in the developing world. In this thesis, multiobjective optimization is used to design a set of products for optimal individual and collaborative performance. This is introduced through a nine step method which simultaneously optimizes multiple products both individually and collaboratively. The method searches through multiple complex design spaces while dealing with various trade-offs between products in order to optimize their collaborative performance. An example is provided to illustrate this method and demonstrate its usefulness in designing collaborative products for both the developed and developing world. We conclude that the presented method is a novel, useful approach for designing collaborative products while balancing the inherent trade-offs between the performance of collaborative products and the product sets used to create them.

In the design of mechanical components, numerical simulations and experimental methods are commonly used for design creation (or modification) and design optimization. However, a major challenge of using simulation and experimental methods is that they are timeconsuming and often cost-prohibitive for the designer. In addition, the simultaneous interactions between aerodynamic, thermodynamic and mechanical integrity objectives for a particular component or set of components are difficult to accurately characterize, even with the existing simulation tools and experimental methods. The current research and practice of using numerical simulations and experimental methods do little to address the simultaneous “satisficing” of multiple and often conflicting design objectives that influence the performance and geometry of a component. This is particularly the case for gas turbine systems that involve a large number of complex components with complicated geometries. Numerous experimental and numerical studies have demonstrated success in generating effective designs for mechanical components; however, their focus has been primarily on optimizing a single design objective based on a limited set of design variables and associated values. In this research, a multiobjective design optimization framework to solve a set of userspecified design objective functions for mechanical components is proposed. The framework integrates a numerical simulation and a nature-inspired optimization procedure that iteratively perturbs a set of design variables eventually converging to a set of tradeoff design solutions. In this research, a gas turbine engine system is used as the test application for the proposed framework. More specifically, the optimization of the gas turbine blade internal cooling channel configuration is performed. This test application is quite relevant as gas turbine engines serve a iv critical role in the design of the next-generation power generation facilities around the world. Furthermore, turbine blades require better cooling techniques to increase their cooling effectiveness to cope with the increase in engine operating temperatures extending the useful life of the blades. The performance of the proposed framework is evaluated via a computational study, where a set of common, real-world design objectives and a set of design variables that directly influence the set of objectives are considered. Specifically, three objectives are considered in this study: (1) cooling channel heat transfer coefficient, which measures the rate of heat transfer and the goal is to maximize this value; (2) cooling channel air pressure drop, where the goal is to minimize this value; and (3) cooling channel geometry, specifically the cooling channel cavity area, where the goal is to maximize this value. These objectives, which are conflicting, directly influence the cooling effectiveness of a gas turbine blade and the material usage in its design. The computational results show the proposed optimization framework is able to generate, evaluate and identify thousands of competitive tradeoff designs in a fraction of the time that it would take designers using the traditional simulation tools and experimental methods commonly used for mechanical component design generation. This is a significant step beyond the current research and applications of design optimization to gas turbine blades, specifically, and to mechanical components, in general.

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Made available in DSpace on 2014-12-17T14:55:48Z (GMT). No. of bitstreams: 1 LeonardoWTS.pdf: 2629101 bytes, checksum: b5455ce80c5ec1bb8ee09a9f3502cbd4 (MD5) Previous issue date: 2006-02-17 / Antenna arrays are able to provide high and controlled directivity, which are suitable for radiobase stations, radar systems, and point-to-point or satellite links. The optimization of an array design is usually a hard task because of the non-linear characteristic of multiobjective, requiring the application of numerical techniques, such as genetic algorithms. Therefore, in order to optimize the electronic control of the antenna array radiation pattem through genetic algorithms in real codification, it was developed a numerical tool which is able to positioning the array major lobe, reducing the side lobe levels, canceling interference signals in specific directions of arrival, and improving the antenna radiation performance. This was accomplished by using antenna theory concepts and optimization methods, mainly genetic algorithms ones, allowing to develop a numerical tool with creative genes codification and crossover rules, which is one of the most important contribution of this work. The efficiency of the developed genetic algorithm tool is tested and validated in several antenna and propagation applications. 11 was observed that the numerical results attend the specific requirements, showing the developed tool ability and capacity to handle the considered problems, as well as a great perspective for application in future works. / Os arranjos de antenas podem fornecer uma diretividade elevada e control?vel, que ? ?til em esta??es r?dio base, sistemas de radares e enlaces ponto-a-ponto ou de sat?lite. A otimiza??o do projeto do arranjo ? uma tarefa usualmente dif?cil, devido ? caracter?stica n?o-linear de m?ltiplos objetivos, requisitando o uso de ferramentas computacionais, tais como os algoritmos gen?ticos. Nesse contexto, com o prop?sito de otimizar o controle eletr?nico do diagrama de radia??o de arranjos de antenas, atrav?s de algoritmos gen?ticos com codifica??o real, foi desenvolvida uma ferramenta computacional capaz de posicionar o l?bulo principal, reduzir o n?vel dos l?bulos laterais, rejeitar interfer?ncias com dire??es de chegada conhecidas e melhorar a ?rea de cobertura da antena. Para tanto, foram empregados conceitos de teoria de antenas e m?todos de otimiza??o, com ?nfase nos algoritmos gen?ticos, permitindo desenvolver a ferramenta com formas criativas de codifica??o e recombina??o, o que ? uma das mais importantes contribui??es deste trabalho. A efici?ncia da ferramenta desenvolvida ? testada e validada em aplica??es de antenas e propaga??o. Foi observado que os resultados num?ricos atendem aos requisitos especificados, demonstrando a habilidade e capacidade da ferramenta desenvolvida para lidar com os problemas considerados, como tamb?m uma grande perspectiva para aplica??es em trabalhos futuros.

Master of Science / Department of Architectural Engineering and Construction Science / Kimberly W. Kramer / The basic tenets of performance based seismic design (PBSD) are introduced. This includes a description of the underlying philosophy of PBSD, the concept of performance objectives, and a description of hazard levels and performance indicators. After establishing the basis of PBSD, analysis procedures that fit well within the PBSD framework are introduced. These procedures are divided into four basic categories: linear static, linear dynamic, nonlinear static, and nonlinear static. Baseline FEMA requirements are introduced for each category. Each analysis category is then expanded to include a detailed description of and variations on the basic procedure. Finally, optimization procedures that mesh well with a PBSD framework are introduced and described. The optimization discussion focuses first on the solution tools needed to effectively execute a PBSD multi-objective optimization procedure, namely genetic and evolutionary strategies algorithms. Next, multiple options for defining objective functions and constraints are presented to illustrate the versatility of structural optimization. Taken together, this report illustrates the unique aspects of PBSD. As PBSD moves to the forefront of design methodology, the subjects discussed serve to familiarize engineers with the advantages, possibilities, and finer workings of this powerful new design methodology.

Problemas com múltiplos objetivos são muito frequentes nas áreas de Otimização, Economia, Finanças, Transportes, Engenharia e várias outras. Como os objetivos são, geralmente, conflitantes, faz-se necessário o uso de técnicas apropriadas para obter boas soluções. A área que trata de problemas deste tipo é chamada de Otimização Multiobjetivo. Neste trabalho, estudamos os problemas dessa área e alguns dos métodos existentes para resolvê-los. Primeiramente, alguns conceitos relacionados ao conjunto de soluções são definidos, como o de eficiência, no intuito de entender o que seria a melhor solução para este tipo de problema. Em seguida, apresentamos algumas condições de otimalidade de primeira ordem, incluindo as do tipo Fritz John para problemas de Otimização Multiobjetivo. Discutimos ainda sobre algumas condições de regularidade e total regularidade, as quais desempenham o mesmo papel das condições de qualificação em Programação Não-Linear, propiciando a estrita positividade dos multiplicadores de Lagrange associados às funções objetivo. Posteriormente, alguns dos métodos existentes para resolver problemas de Otimização Multiobjetivo são descritos e comparados entre si. Ao final, aplicamos a teoria e métodos de Otimização Multiobjetivo nas áreas de Compressed Sensing e Otimização de Portfolio. Exibimos então testes computacionais realizados com alguns dos métodos discutidos envolvendo problemas de Otimização de Portfolio e fazemos uma análise dos resultados. / Problems with multiple objectives are very frequent in areas such as Optimization, Economy, Finance, Transportation, Engineering and many others. Since the objectives are usually conflicting, there is a need for appropriate techniques to obtain good solutions. The area that deals with problems of this type is called Multiobjective Optimization. The aim of this work is to study the problems of such area and some of the methods available to solve them. Firstly, some basic concepts related to the feasible set are defined, for instance, efficiency, in order to comprehend which solution could be the best for this kind of problem. Secondly, we present some first-order optimality conditions, including the Fritz John ones for Multiobjective Optimization. We also discuss about regularity and total regularity conditions, which play the same role in Nonlinear Multiobjective Optimization as the constraint qualifications in Nonlinear Programming, providing the strict positivity of the Lagrange multipliers associated to the objective functions. Afterwards, some of the existing methods to solve Multiobjective Optimization problems are described and compared with each other. At last, the theory and methods of Multiobjective Optimization are applied into the fields of Compressed Sensing and Portfolio Optimization. We, then, show computational tests performed with some of the methods discussed involving Portfolio Optimization problems and we present an analysis of the results.