Composite structures can lower the weight of an airliner significantly. Due to the higher process complexity and the high material cost, however, the low weight often comes with a significant increase in production cost. The application of cost-effective design strategies is one mean to meet this challenge. In this thesis, a simplified form of direct operating cost is suggested as a comparative value that in combination with multidisciplinary optimization enables the evaluation of a design solution in terms of cost and weight. The proposed cost optimization framework takes into account the manufacturing cost, the non-destructive testing cost and the lifetime fuel consumption based on the weight of the aircraft, thus using a simplified version of the direct operating cost as the objective function. The manufacturing cost can be estimated by means of different techniques. For the proposed optimization framework, feature-based parametric cost models prove to be most suitable. Paper A contains a parametric study in which a skin/stringer panel is optimized for a series of cost/weight ratios (weight penalties) and material configurations. The weight penalty (defined as the specific lifetime fuel burn) is dependent on the fuel consumption of the aircraft, the fuel price and the viewpoint of the optimizer. It is concluded that the ideal choice of the design solution is neither low-cost nor low-weight but rather a combination thereof. Paper B proposes the inclusion of non-destructive testing cost in the design process of composite components, and the adjustment of the design strength of each laminate according to inspection parameters. Hence, the scan pitch of the ultrasonic testing is regarded as a variable, representing an index for the guaranteed material quality. It is shown that the cost for non-destructive testing can be lowered if the quality level of the laminate is assigned and adjusted in an early design stage. In Paper C and Paper D the parameters of the manufacturing processes are upgraded during the cost optimization of the component. In Paper C, the framework is extended by the cost-efficient adaptation of parameters in order to reflect the situation when machining an aluminum component. For different weight penalties, the spar thickness and stringer geometry of the provided case study vary. In addition, another cutter is chosen with regard to the modified shape of the stringer. In Paper D, the methodology is extended to the draping of composite fabrics, thus optimizing not only the stacking layup, but also the draping strategy itself. As in the previous cases, the design alters for different settings of the weight penalty. In particular, one can see a distinct change in fiber layup between the minimum weight and the minimum cost solution. Paper E summarizes the work proposed in Papers A-D and provides a case study on a C-spar component. Five material systems are used for this case study and compared in terms of cost and weight. The case study shows the impact of the weight penalty, the material cost and the labor rate on the choice of the material system. For low weight penalties, for example, the aluminum spar is the most cost-effective solution. For high weight penalties, the RTM system is favorable. The paper also discusses shortcomings with the presented methodology and thereby opens up for future method developments. / QC 20100723 / European Framework Program 6, project ALCAS, AIP4-CT-2003-516092 / Nationella flygtekniska forskningsprogrammet (NFFP) 4, project kostnadseffektiv kompositstruktur (KEKS)

No description available.

The main goal of this thesis is the development of a novel methodology to generate creative solutions at functional level for design tasks without binding solution spaces with designers&rsquo / individual experiences and prejudices. For this purpose, an evolutionary methodology for the conceptual design of engineering products has been proposed. This methodology performs evaluation, combination and modification of the existing solutions repetitively to generate new solution alternatives. Therefore, initially a representation scheme, which is generic enough to cover all alternatives in solution domain, has been defined. Following that, the evolutionary operations have been defined and two evaluation metrics have been proposed. Finally, the computer implementation of the developed theory has been performed. The test-runs of developed software resulted in creative alternatives for the design task. Consequently, the evolutionary design methodology presents a systematic design approach for less experienced or inexperienced designers and establishes a base for experienced designers to conceive many other solution alternatives beyond their experiences.

No description available.

It has been increasingly recognized that realistic problems often involve a tradeoff among many conflicting objectives. Traditional methods aim at satisfying multiple objectives by combining them into a global cost function, which in most cases overlooks the underlying tradeoffs between the conflicting objectives. This raises the issue about how different objectives should be combined to yield a final solution. Moreover, such approaches promise that the chosen overall objective function is optimized over the training samples. However, there is no guarantee on the performance in terms of the individual objectives since they are not considered on an individual basis. Motivated by these shortcomings of traditional methods, the objective in this dissertation is to investigate theory, algorithms, and applications for problems with competing objectives and to understand the behavior of the proposed algorithms in light of some applications. We develop a multi-objective programming (MOP) framework for finding compromise solutions that are satisfactory for each of multiple competing performance criteria. The fundamental idea for our formulation, which we refer to as iterative constrained optimization (ICO), evolves around improving one objective while allowing the rest to degrade. This is achieved by the optimization of individual objectives with proper constraints on the remaining competing objectives. The constraint bounds are adjusted based on the objective functions obtained in the most recent iteration. An aggregated utility function is used to evaluate the acceptability of local changes in competing criteria, i.e., changes from one iteration to the next. Conflicting objectives arise in different contexts in many problems of speech and language technologies. In this dissertation, we consider two applications. The first application is language model (LM) adaptation, where a general LM is adapted to a specific application domain so that the adapted LM is as close as possible to both the general model and the application domain data. Language modeling and adaptation is used in many speech and language processing applications such as speech recognition, machine translation, part-of-speech tagging, parsing, and information retrieval. The second application is automatic language identification (LID), where the standard detection performance evaluation measures false-rejection (or miss) and false-acceptance (or false alarm) rates for a number of languages are to be simultaneously minimized. LID systems might be used as a pre-processing stage for understanding systems and for human listeners, and find applications in, for example, a hotel lobby or an international airport where one might speak to a multi-lingual voice-controlled travel information retrieval system. This dissertation is expected to provide new insights and techniques for accomplishing significant performance improvement over existing approaches in terms of the individual competing objectives. Meantime, the designer has a better control over what is achieved in terms of the individual objectives. Although many MOP approaches developed so far are formal and extensible to large number of competing objectives, their capabilities are examined only with two or three objectives. This is mainly because practical problems become significantly harder to manage when the number of objectives gets larger. We, however, illustrate the proposed framework with a larger number of objectives.

Neste trabalho é desenvolvida uma metodologia de otimização multiobjetivo baseada no NSGA-II (Nondominated Sorting Genetic Algorithm), a qual visa a otimização do projeto de máquinas de indução pentafásicas. A escolha deste tipo de máquina se justifica pelo fato de que elas apresentam vantagens importantes quando comparadas com as trifásicas convencionais, tais como maior potência e maior torque para um mesmo volume de material ativo, além da possibilidade de operar na ocorrência de falhas (perda de uma ou duas fases). Na otimização de máquinas de indução vários objetivos podem ser definidos, sendo estes muitas vezes conflitantes. Neste contexto, este trabalho visa obter soluções que representam um compromisso entre dois objetivos: rendimento e custo do material ativo (ferro e material condutor). O algoritmo de otimização desenvolvido e implementado utiliza dois controles de diversidade da população, um baseado no fenótipo dos indivíduos, que é característico do NSGA-II, e outro adicional que é baseado no genótipo. A geometria do estator e do rotor da máquina e o seu modo de acionamento são parametrizados por 14 variáveis inteiras. O método desenvolvido foi implementado no Matlab R e aplicado a um caso prático de otimização de uma máquina de indução pentafásica considerando os dois objetivos citados. Os resultados práticos mostram que o método é capaz de obter projetos otimizados com maior rendimento e menor custo aproveitando as características particulares deste tipo de máquina. / In this work, it is developed a method of multiobjective optimization based on NSGAII (Nondominated Sorting Genetic Algorithm), which aims at optimizing the design of five-phase induction machines. The choice of this particular type of machine is justified by the fact that they have important advantages over conventional three-phase machines, such as higher power and higher torque for the same volume of material; in addition, they can operate under fault (loss of one or even two phases). When optimizing induction machines, several objectives can be defined, which are often conflicting. In this context, this work aims to obtain solutions that represent a trade-off between two objectives: efficiency and cost of active material (iron and conductor materials). The optimization algorithm that was developed and implemented uses two types of control for the diversity of the population, one based on the phenotype of the individuals, characteristic of the NSGA-II, and another one based on the genotype. The geometrical dimensions of the stator and rotor, together with the driving strategy, are parameterized by 14 integer variables. The developed method was implemented using Matlab R and applied to a practical case of a five-phase induction machine considering the aforementioned objectives. The practical results show that the method can lead to an optimized design with higher efficiency and at a lower cost, accounting for the special characteristics of this type of machine.

Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-10-02T17:57:26Z No. of bitstreams: 1 ÉRICA CRISTINE MEDEIROS NOBRE MACHADO - TESE (PPGRN) 2011.pdf: 17954515 bytes, checksum: b35d0135e20bba39c63bddd9139d873a (MD5) / Made available in DSpace on 2018-10-02T17:57:26Z (GMT). No. of bitstreams: 1 ÉRICA CRISTINE MEDEIROS NOBRE MACHADO - TESE (PPGRN) 2011.pdf: 17954515 bytes, checksum: b35d0135e20bba39c63bddd9139d873a (MD5) Previous issue date: 2011-09-20 / CNPq / Esta tese parte do pressuposto de que, à luz da atual gestão dos recursos hídricos no Brasil, os critérios de determinação da vazão máxima outorgável são bastante restritivos, o que provoca descontentamentos e gera conflitos. Além de basear-se em critérios restritivos, a própria variabilidade do clima impõe modificações nas disponibilidades hídricas, de modo que, em períodos de vazões superiores à outorgável, há um excedente de vazão que fica indisponível para a produção de riquezas sociais. Nesta tese argumenta-se que esta vazão excedente pode ser alocada entre os usuários, minimizando os prejuízos decorrentes da não utilização desta. Dessa forma, além de prover um melhor aproveitamento dos recursos hídricos, a alocação de uma parcela variável da disponibilidade hídrica surge como uma estratégia de adaptação à variabilidade climática. Contudo, admite-se que o desenvolvimento de um sistema de apoio à decisão para alocação da vazão excedente não é tarefa fácil, posto que é necessário o envolvimento e a integração de inúmeras variáveis e métodos, os quais devem ser estruturados e acoplados em um modelo de otimização apropriado; e é necessária a adoção de uma abordagem multiobjetiva integrada aos instrumentos de gestão dos recursos hídricos, de modo a suprir as lacunas existentes sem violar os critérios e preceitos estabelecidos em lei. Nesta tese é proposta uma metodologia para a otimização da alocação interanual da vazão excedente em uma bacia hidrográfica através de um algoritmo evolucionário multiobjetivo, no qual foram inseridas adaptações e operadores de reprodução específicos para incorporar as restrições do problema e contornar os obstáculos apresentados. Além de considerar diferentes usos, conservativos e de diluição, e de ser intertemporal e integrada, tanto quali-quantitativamente quanto espacialmente na bacia, o algoritmo evolucionário foi desenvolvido com o propósito de ser facilmente acoplado a modelos que representem a modelagem hidrometeorológica da bacia hidrográfica, e ser facilmente adaptável para cenários de racionamento (vazão excedente nula ou negativa). A metodologia foi avaliada na bacia hidrográfica do rio Gramame, no Estado da Paraíba e a análise incluiu a aplicação da metodologia em dois cenários hipotéticos de previsão probabilística de precipitação: acima da média histórica e abaixo da média histórica, o que permitiu avaliar o comportamento do modelo de otimização em situações de alocação da vazão excedente e de racionamento da vazão outorgada. Também foi analisado o comportamento do modelo frente à adoção de estratégias com a flexibilização dos valores de vazão ecológica e da concentração de DBO5 admissível nos corpos receptores da bacia. As propostas de alocação encontradas foram ainda avaliadas quanto a sua robustez frente a mudanças nos valores médios históricos das variáveis hidrológicas da bacia, as quais podem ser provocadas por uma alteração climática ou mesmo ser resultantes das incertezas associadas. Os resultados obtidos indicam boas perspectivas de sucesso da metodologia apresentada, uma vez que, tanto nos cenários de racionamento quanto nos cenários de alocação do excedente, o algoritmo progrediu em direção à Fronteira de Pareto, buscando, nesta fronteira, as regiões de viabilidade, quando existia, ou de menores ocorrências de alarmes. / This work assumes that, in Brazil, water rights concession criteria are very restrictive, generating conflicts among users. In addition, the climate variability causes changes in water availability, so that in periods of higher flows, there is an excess discharge that is unavailable for the production of social wealth. This Thesis argues that the discharge surplus could be allocated among users, then minimizing losses due to not using it; the allocation can be defined annually, so that to consider the inter-annual variability of the hydrological variables. The allocation of a variable amount of water availability provides a better utilization of water resources, and is an adaptation strategy to cope with climate variability. However, it is true that the development of a decision support system for allocating the surplus discharge is not simple, because it is necessary the involvement and integration of many variables and methods that must be integrated in an appropriate optimization model. The approach should also be multiobjective and multicriteria and should be integrated with the water resources management law, in order to fill the gaps without violating the existing rules. Thus a methodology is proposed for optimizing the allocation of surplus discharge in a basin through a multiobjective evolutionary algorithm in which adaptations and reproduction operators were inserted to incorporate the specific constraints of the problem and overcome the obstacles presented. In addition this evolutionary algorithm was developed in order to be easily coupled to other models and be easily adaptable to scenarios of rationing (zero or negative surplus discharge). The methodology was evaluated in the Gramame river basin, in the state of Paraiba, which is already presenting evidence of greater demands than availability. The analysis included two hypothetical scenarios of probabilistic forecasts of precipitation: one above-normal forecast and one below-normal forecast; they allowed the assessment of the behavior of the optimization model in situations of allocating the surplus discharge as well as rationing when necessary. We also analyzed the behavior of the model with the adoption of management strategies with the flexibility of environmental flow values and allowable BOD concentration. The robustness of the allocation strategies were evaluated against changes in historical hydrological variables, which may be caused by climate change or even be the result of uncertainties. The results show that the algorithm proved to be adequate, presenting convergence for the most viable regions of Pareto Front.

Neste trabalho é desenvolvida uma metodologia de otimização multiobjetivo baseada no NSGA-II (Nondominated Sorting Genetic Algorithm), a qual visa a otimização do projeto de máquinas de indução pentafásicas. A escolha deste tipo de máquina se justifica pelo fato de que elas apresentam vantagens importantes quando comparadas com as trifásicas convencionais, tais como maior potência e maior torque para um mesmo volume de material ativo, além da possibilidade de operar na ocorrência de falhas (perda de uma ou duas fases). Na otimização de máquinas de indução vários objetivos podem ser definidos, sendo estes muitas vezes conflitantes. Neste contexto, este trabalho visa obter soluções que representam um compromisso entre dois objetivos: rendimento e custo do material ativo (ferro e material condutor). O algoritmo de otimização desenvolvido e implementado utiliza dois controles de diversidade da população, um baseado no fenótipo dos indivíduos, que é característico do NSGA-II, e outro adicional que é baseado no genótipo. A geometria do estator e do rotor da máquina e o seu modo de acionamento são parametrizados por 14 variáveis inteiras. O método desenvolvido foi implementado no Matlab R e aplicado a um caso prático de otimização de uma máquina de indução pentafásica considerando os dois objetivos citados. Os resultados práticos mostram que o método é capaz de obter projetos otimizados com maior rendimento e menor custo aproveitando as características particulares deste tipo de máquina. / In this work, it is developed a method of multiobjective optimization based on NSGAII (Nondominated Sorting Genetic Algorithm), which aims at optimizing the design of five-phase induction machines. The choice of this particular type of machine is justified by the fact that they have important advantages over conventional three-phase machines, such as higher power and higher torque for the same volume of material; in addition, they can operate under fault (loss of one or even two phases). When optimizing induction machines, several objectives can be defined, which are often conflicting. In this context, this work aims to obtain solutions that represent a trade-off between two objectives: efficiency and cost of active material (iron and conductor materials). The optimization algorithm that was developed and implemented uses two types of control for the diversity of the population, one based on the phenotype of the individuals, characteristic of the NSGA-II, and another one based on the genotype. The geometrical dimensions of the stator and rotor, together with the driving strategy, are parameterized by 14 integer variables. The developed method was implemented using Matlab R and applied to a practical case of a five-phase induction machine considering the aforementioned objectives. The practical results show that the method can lead to an optimized design with higher efficiency and at a lower cost, accounting for the special characteristics of this type of machine.

Reconhecendo-se a importância da água como recurso natural limitado e considerando-se a perspectiva de crescimento do contingente populacional urbano, faz-se necessária uma investigação dos sistemas de distribuição de água para abastecimento, por tratarem-se de infra-estruturas básicas comuns aos núcleos populacionais do mundo todo. O planejamento da reabilitação das redes de distribuição de água torna-se de fundamental importância considerando os recursos financeiros limitados e o comportamento operacional desses sistemas que são alterados ao longo do tempo devido ao processo de deterioração de seus componentes. O presente trabalho representa um esforço no sentido de considerar objetivos mais promissores na análise de reabilitação de redes. Dessa maneira, foram considerados: custo, benefício, vazamentos e confiabilidade. Este trabalho apresenta contribuições às análises multiobjetivo via algoritmos genéticos, propriciando um aprimoramento do algoritmo Multiobjective Genetic Algorithm (MOGA) e realizando investigação dos operadores (recombinação e mutação) e dos métodos Non-dominated Sorting Genetic Algorithm (NSGA), Strength Pareto Evolutionary Algorithm (SPEA) e Elitist Non-Dominated Sorting Genetic Algorithm (NSGA II). Do ponto de vista hidráulico, este trabalho introduz tanto perdas por vazamentos como demanda variável com a pressão, proporcionando uma análise mais realística do problema. Os estudos desenvolvidos para redes hipotéticas e para um sistema real, possibilitaram que soluções satisfatórias fossem obtidas, chegando-se inclusive a uma proposição do conceito de programação dinâmica para o caso multiobjetivo. / Recognizing the importance of water as a limited natural resource and considering the prospect of continued population growth, it is important to investigate water distribution systems which are common to all urban infrastructures. Planning of the water distribution network rehabilitation becomes additionally important given economic constraints and operational behavior these systems which modifies in time due to deterioration of water networks. The present work is an effort to consider the multiple objectives in the water network rehabilitation analyses. Four objectives were considered: cost minimization, benefit maximization, leakage minimization and reliability maximization. In addition, it presents some contributions to multiobjective optimization methodology by genetic algorithms, offering an improvement of Multiobjective Genetic Algorithm (MOGA). A detailed investigation is conducted on genetic operators (recombination and mutation) comparing some existing multiobjective optimization methods (Multiobjective Genetic Algorithm - MOGA, Non-dominated Sorting Genetic Algorithm - NSGA, Strength Pareto Evolutionary Algorithm - SPEA and Elitist Non-Dominated Sorting Genetic Algorithm - NSGA II). As regards the hydraulic analysis, this work introduces both leakages and pressure dependent demands in the simulations, providing a more realistic representation of actual field situations. The present study employs hypothetical networks and a real network obtaining satisfactory solutions. Further, dynamic programming concept is also incorporated into the multiobjective optimization framework.

Orientador: Akebo Yamakami / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-11T03:11:24Z (GMT). No. of bitstreams: 1 Rampazzo_PriscilaCristinaBerbert_M.pdf: 1295026 bytes, checksum: ad0738bc161445ec5b9f0db0db565f09 (MD5) Previous issue date: 2008 / Resumo: O objetivo desta dissertação é explorar a utilização de um Sistema Imunológico Artificial, baseado no princípio de Seleção Clonal, na resolução de problemas de Otimização Multiobjetivo. Os Sistemas Imunológicos Artificiais apresentam, em sua estrutura elementar, as principais características requeridas para a resolução de problemas de Otimização Multiobjetivo: exploração, explotação, paralelismo, elitismo, memória, diversidade, mutação e clonagem proporcionais à afinidade e população dinâmica. A abordagem proposta utiliza o conceito de Pareto dominância e factibilidade para identificar os anticorpos (soluções) que devem ser clonados. Nos experimentos, foram consideradas algumas situações importantes que podem aparecer nos problemas reais: presença de restrições (lineares e não-lineares) e formato da Fronteira de Pareto (convexa, côncava, contínua, descontínua, discreta, não-uniforme). Na maioria dos problemas, o algoritmo obteve resultados bons e competitivos quando comparados com as propostas da literatura. Palavras-chave: Otimização Multiobjetivo, Algoritmos Bio-inspirados, Sistemas Imunológicos Artificiais, Seleção Clonal / Abstract: The aim of this work is to explore an Artificial Immune System, based on the Clonal Selection principle, in the solution of Multiobjective Optimization problems. Artificial Immune Systems have, in their elementary structure, the main characteristics required to solve Multiobjective Optimization problems: exploration, exploitation, paralelism, elitism, memory, diversity, mutation and proliferation proportional to the affinity, and dynamic repertorie. The proposed algorithm uses the Pareto dominance concept and feasibility to identify the antibodies (solutions) that must to be cloned. In the experiments, some important situations that occurs in real problems were considered: the presence of constraints (linear and non-linear) and Pareto Front format (convex, concave, continuous, discontinuous, discrete, non-uniforme). In the major part of the problems, the algorithm obtains good and competitive results when compared with approaches from the literature. Keywords: Multiobjective Optimization, Bio-inspired Algorithms, Artificial Immune Systems, Clonal Selection / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica