This thesis presents two methods for initial design propeller optimization using constrained non- linear programming. The process uses the Nelder-Mead simplex algorithm. The Wageningen B-series optimal propeller selection is presented along with the combined annular momentum theory and blade element theory optimization. Both techniques require preliminary hull and engine design characteristics, but do not necessitate extensive background knowledge of pro- pellers and their calculations. A comparison of the two methods shows the combined annular momentum theory and blade element theory optimization produces the more e cient propeller. The optimization programs were designed with a graphic user interface implemented in the programming language Python.

Indiana University-Purdue University Indianapolis (IUPUI) / Computer Numerical Control (CNC) is widely used in many industries that needs high speed machining of the parts with high precision, accuracy and good surface finish. In order to avail this the generation of the CNC part program size will be immensely big and leads to an inefficient process, which increases the delivery time and cost of products. This work presents the automation of high-accuracy CNC tool trajectory planning from CAD to G-code generation through optimal NURBs surface approximation. The proposed optimization method finds the minimum number of NURBS control points for a given admissible theoretical cord error between the desired and manufactured surfaces. The result is a compact part program that is less sensitive to data starvation than circular and spline interpolations with potential better surface finish. The proposed approach is demonstrated with the tool path generation of an involute gear profile and a topologically optimized structure is developed using this approach and then finally it is 3D printed.

The calibration of model parameters is an important step in model development. Commonly, system output is measured, and model parameters are iteratively varied until the model output is a good match to the measured system output. Optimization algorithms are often used to identify the model parameter values. The presence of noise is difficult to avoid when physical processes are used to calibrate models due to measurement error, model structure error, and errors arising from numerical techniques and approximate solutions. Our study focuses on the effects of noise in parameter identification optimization problems. We generate six test problems, including five perturbations of a smooth problem. A previously studied groundwater parameter identification problem serves as our seventh test problem. We test the Nelder-Mead Algorithm, a combination of the Nelder-Mead Algorithm and Simulated Annealing, and the Shuffled Complex Evolution Method on these test problems. Comparison of optimization results for these problems reveals the effects of noise on optimization performance, including an increase in fitness values and a decrease in the number of fit evaluations. We vary the values of the internal algorithmic parameters to determine the effects of different values and present numerical results that indicate that changing the values of the algorithmic parameters can cause profound differences in optimization results for all three algorithms. A variation of the generally accepted parameter values for the Nelder-Mead Algorithm is recommended, and we determine that the Nelder-Mead/Simulated Annealing Hybrid and Shuffled Complex Evolution Method are too problem dependent for general recommendations for parameter values. Finally, we prove new convergence results for the Nelder-Mead/Simulated Annealing Hybrid in both smooth and noisy cases. / Ph. D.

Submitted by Liliane Ferreira (ljuvencia30@gmail.com) on 2018-04-04T11:42:16Z No. of bitstreams: 2 Dissertação - Caio José Fernandes Póvoa - 2018.pdf: 6079271 bytes, checksum: 5efa21665d3c5f3bf6b4a58652fff6b4 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-04-04T13:23:10Z (GMT) No. of bitstreams: 2 Dissertação - Caio José Fernandes Póvoa - 2018.pdf: 6079271 bytes, checksum: 5efa21665d3c5f3bf6b4a58652fff6b4 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-04-04T13:23:10Z (GMT). No. of bitstreams: 2 Dissertação - Caio José Fernandes Póvoa - 2018.pdf: 6079271 bytes, checksum: 5efa21665d3c5f3bf6b4a58652fff6b4 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-03-22 / This dissertation of master degree describes methods of optimizing routes and allocating towers of overhead power lines, with the objective of meeting technical, structural and constructive constraints, and reducing financial costs. The generated solutions are graphically presented through the transmission line profile and its 3-dimension representation upon the elevation map of the area. For the projects evaluation, elements of structural analysis are used, highlighting the Matrix Structural Analysis for the study of efforts and deformations in the towers and their components. Three methods are proposed, each one using different approaches. First, it will be shown an optimization algorithm based on Evolutionary Computation, characterized by the application of natural selection on individuals generated from mutations and genetic crossover. The second algorithm was inspired by the well-known Nelder-Mead optimization method. The triangular transformations addressed in the original method were adapted and physically implemented to transmission lines. The last optimization algorithm presented is a hybridization of the two previous methods. Finally, a performance comparison of the algorithms, in which each one of them will be applied to three different cases, will be carried out in order to validate them. / Esta dissertação de mestrado descreve métodos de otimização de trajetos e alocação de torres de linhas aéreas de transmissão de energia elétrica, com o objetivo de obedecer a restrições técnicas, estruturais e construtivas, e de reduzir custos financeiros. As soluções encontradas são apresentadas graficamente a partir da plotagem do perfil da linha de transmissão, e da sua representação em três dimensões sobre o mapa de relevo da região. Para a avaliação dos projetos, utilizam-se elementos de análise estrutural, destacando-se a Análise Estrutural Matricial para o estudo dos esforços e deformações nas torres e seus componentes. São propostos três métodos que utilizam abordagens diferentes. Primeiramente, será considerado um algoritmo de otimização baseado na Computação Evolucionária, caracterizando-se pela aplicação da seleção natural ao longo de gerações, em indivíduos gerados a partir de mutações e recombinações. O segundo algoritmo apresentado é inspirado no consagrado método de Nelder-Mead, sendo as transformações triangulares, por ele apresentadas, adaptadas e implementadas fisicamente a linhas de transmissão. O último método de otimização é uma hibridação dos dois métodos anteriores. Por fim, será feita uma comparação de desempenho dos algoritmos apresentados, a partir da aplicação de cada um deles a três estudos de caso distintos para validá-los.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-08-05T14:42:09Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE PPGEP (Romero Luiz M. Sales Filho).pdf: 3453451 bytes, checksum: be76714c0d9a1e907faa85d15041f6ca (MD5) / Made available in DSpace on 2016-08-05T14:42:09Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE PPGEP (Romero Luiz M. Sales Filho).pdf: 3453451 bytes, checksum: be76714c0d9a1e907faa85d15041f6ca (MD5) Previous issue date: 2016-02-24 / CAPEs / In recent years, a family of probability distributions based on Nonextensive Statistical Mechanics, known as q-distributions, has experienced a surge in terms of applications to several fields of science and engineering. In this work the _-Exponential distribution will be studied in detail. One of the features of this distribution is the capability of modeling data that have a power law behavior, since it has a heavy-tailed probability density function (PDF) for particular values of its parameters. This feature allows us to consider this distribution as a candidate to model data sets with extremely large values (e.g. cycles to failure). Once the analytical expressions for the maximum likelihood estimates (MLE) of _-Exponential are very difficult to be obtained, in this work, we will obtain the MLE for the parameters of the _- Exponential using two different optimization methods: particle swarm optimization (PSO) and Nelder-Mead (NM), which are also coupled with parametric and non-parametric bootstrap methods in order to obtain confidence intervals for these parameters; asymptotic intervals are also derived. Besides, we will make inference about a useful performance metric in system reliability, the called index __(_, where the stress _ and strength are independent q-Exponential random variables with different parameters. In fact, when dealing with practical problems of stress-strength reliability, one can work with fatigue life data and make use of the well-known relation between stress and cycles until failure. For some materials, this kind of data can involve extremely large values and the capability of the q- Exponential distribution to model data with extremely large values makes this distribution a good candidate to adjust stress-strength models. In terms of system reliability, the index _ is considered a topic of great interest, so we will develop the maximum likelihood estimator (MLE) for the index _ and show that this estimator is obtained by a function that depends on the parameters of the distributions for and _. The behavior of the MLE for the index _ is assessed by means of simulated experiments. Moreover, confidence intervals are developed based on parametric and non-parametric bootstrap. As an example of application, we consider two experimental data sets taken from literature: the first is related to the analysis of high cycle fatigue properties of ductile cast iron for wind turbine components, and the second one evaluates the specimen size effects on gigacycle fatigue properties of high-strength steel. / Nos últimos anos, tem sido notado em diversas áreas da ciência e engenharia, um aumento significativo na aplicabilidade da família q de distribuições de probabilidade que se baseia em Mecânica Estatística Não Extensiva. Uma das características da distribuição q-Exponencial é a capacidade de modelar dados que apresentam comportamento de lei de potência, uma vez que tal distribuição possui uma função densidade de probabilidade (FDP) que apresenta cauda pesada para determinados valores de parâmetros. Esta característica permite-nos considerar tal distribuição como candidata para modelar conjuntos de dados que apresentam valores extremamente grandes (Ex.: ciclos até a falha). Uma vez que expressões analíticas para os estimadores de máxima verossimilhança dos parâmetros não são facilmente encontradas, neste trabalho, iremos obter as estimativas de máxima verossimilhança dos parâmetros através de dois métodos de otimização: particle swarm optimization (PSO) e Nelder-Mead (NM), que além das estimativas pontuais, irão nos fornecer juntamente com abordagens bootstrap, intervalos de confiança para os parâmetros da distribuição; intervalos assintóticos também serão derivados. Além disso, faremos inferência sobre um importante índice de confiabilidade, o chamado Índice __(_, onde Y (estresse) e X (força) são variáveis aleatórias independentes. De fato, quando tratamos de problemas práticos de força-estresse, podemos trabalhar com dados de fadiga e fazer uso da bem conhecida relação entre estresse e ciclos até a falha. Para alguns materiais, esse tipo de variável pode apresentar dados com valores muito grandes e a capacidade da q-Exponencial em modelar esse tipo de dado torna essa uma distribuição a ser considerada para ajustar modelos de força-estresse. Em termos de confiabilidade de sistemas, o índice R é considerado um tópico de bastante interesse, assim iremos desenvolver os estimadores de máxima verossimilhança para esse índice e mostrar que esse estimador é obtido através de uma função que depende dos parâmetros da distribuição de X e Y. O comportamento do estimador é investigado através de experimentos simulados. Intervalos de confiança são desenvolvidos através de bootstrap paramétrico e nãoparamétrico. Duas aplicações envolvendo dados de ciclos até a falha e retiradas da literatura são consideradas: a primeira para ferro fundido e a segunda para aço de alta resistência.

This thesis focuses on deterministic chaos and selected methods of chaos control. It briefly describes the matter of deterministic chaos and presents commonly used tools of analysis of dynamical systems exhibiting chaotic behavior. A list of frequently studied chaotic systems is presented and followed by a description of methods of chaos control and the optimization of these methods. The practical part is dedicated to the stabilization of two model systems and one real system with described methods.

[pt] A crescente demanda pela explotação de óleo em águas cada vez mais profundas representa um desafio para o projeto seguro e eficiente de risers. Tipicamente, o projeto deste elemento estrutural exige um elevado número de análises numéricas e, muitas vezes, se baseia na experiência do projetista, especificamente no que diz respeito à obtenção de uma configuração inicial estruturalmente viável. A automação de tarefas nas fases preliminares de projeto pode proporcionar uma maior eficiência e segurança na seleção de configurações viáveis, além de reduzir o tempo necessário para a realização desta tarefa. Este trabalho propõe uma ferramenta computacional para a otimização de configurações iniciais de risers, satisfazendo requisitos estruturais, operacionais e econômicos. A ferramenta se baseia no método Globalized Bounded Nelder-Mead (GBNM), que não requer o cálculo de gradientes, demanda poucas avaliações da função objetivo por iteração, ou seja, reduz o número de análises dinâmicas realizadas usando o Método dos Elementos Finitos, e permite a implementação de uma estratégia eficiente de paralelização do problema. Para validar e demonstrar a eficiência da ferramenta proposta, são apresentados estudos de caso que reproduzem as condições encontradas em fases iniciais de projetos de risers, bem como uma comparação dos resultados com os obtidos utilizando outros métodos de otimização conhecidos na literatura. / [en] The increasing demand for oil exploitation in deeper water represents a challenge for the safe and efficient design of risers. Typically, the design of this structural member requires a large number of numerical analyses and is often based on the designer’s experience, specifically with respect to obtaining a structurally feasible initial configuration. The automation of tasks in the preliminary stages of design can provide greater efficiency and safety in the selection of feasible configurations, in addition to reducing the time required to perform this task. This work proposes a computational tool to optimize initial riser configurations, satisfying structural, operational and economic requirements. The tool is based on the Globalized Bounded Nelder-Mead method (GBNM), which does not require the computation of gradients, it requires few evaluations of the objective function per iteration, i.e., it reduces the number of dynamic analyses performed using the Finite Element Method, and allows the implementation of an efficient strategy to parallelize the problem. In order to validate and demonstrate the efficiency of the proposed tool, case studies that reproduce the conditions found during the initial stages of the design of risers are presented, as well as a comparison of the results with those obtained using other optimization methods established in the literature.

A number of parallel gradient-free local optimization methods are investigated in application to problems of voyage planning for maritime ships. Two optimization algorithms are investigated, a parallel version of the Nelder-Mead Simplex method and the Subplex method with Nelder-Mead Simplex as its inner solver. Additionally, two new formulations of the optimization problem are suggested which together with an improved implementation of the objective function increases the overall performance of the model. Numerical results show the efficiency of these methods in comparison with the earlier introduced Grid search method and solvers from an open-source optimization library.

Dans cette thèse, des stratégies de maintenance conditionnelle sont proposées pour les systèmes de production de biens et de services. Ces stratégies sont développées en tenant compte de la dégradation de l'environnement causée par la détérioration du système de production. Le système de production est sujet à une dégradation aléatoire qui affecte la qualité de l'environnement. Les modèles de maintenance proposés consistent à réduire la dégradation de l'environnement en contrôlant la détérioration du système. Pour contrôler la détérioration du système, des inspections sont effectuées à des dates précises. L'objectif est de déterminer les dates optimales d'inspection qui optimisent un critère de performance donné. Deux modèles mathématiques d'optimisation sont alors proposés dans cette thèse. Le premier modèle considère le coût total moyen par unité de temps, tandis que le deuxième modèle considère la disponibilité stationnaire du système. L'optimisation sous contrainte a été aussi étudiée où l'objectif est de minimiser le coût total moyen par unité de temps sous une contrainte de disponibilité stationnaire ou alors de maximiser la disponibilité stationnaire du système sous une contrainte de budget. Basé sur la méthode de Nelder-Mead, des algorithmes sont développés pour calculer les dates optimales d'inspection pour chaque critère d'optimisation. Des exemples numériques sont présentés pour montrer l'applicabilité des stratégies de maintenance conditionnelle proposées / In this thesis, condition-based maintenance strategies are proposed for production systems of goods and services. These strategies are developed while taking into account the environmental degradation caused by the deterioration of the production system. The system is subjected to random deterioration which impacts the environmental quality. The proposed maintenance models aim to assess the system deterioration in such a way to reduce the environmental degradation. To control this deterioration, inspections are performed on specific dates. The objective is to determine optimal inspection dates which optimize a given performance criterion. Two mathematical optimization models are then proposed in this thesis. The first model considers the average total cost per time unit, while the second model considers the stationary availability of the system. The constrained optimization has also been discussed in this work where the objective is to minimize the average total cost per time unit under an availability constraint or to maximize the stationary availability of the system under a budget constraint. On the basis of Nelder-Mead method, inspection dates as optimal solutions are computed for each optimization criterion. Numerical examples are provided to show the applicability of the proposed condition-based maintenance strategies

<p>Before a new flight control system is released for flight, a huge number of simulations are evaluated to find weaknesses of the system. This process is called flight clearance. Flight clearance is a very important but time consuming process. There is a need of better flight clearance methods and one of the most promising methods is the use of optimization. In this thesis the flight clearance of a simulation model of JAS 39 Gripen is examined. Two flight clearance algorithms using two different optimization methods are evaluated and compared to each other and to a traditional flight clearance method.</p><p>In this thesis the flight clearance process is separated into three cases: search for the worst flight condition, search for the worst manoeuvre and search for the worst flight condition including parameter uncertainties. For all cases the optimization algorithms find a more dangerous case than the traditional method. In the search for worst flight condition, both with and without uncertainties, the optimization algorithms are to prefer to the traditional method with respect to the clearance results and the number of objective function calls. The search for the worst manoeuvre is a much more complex problem. Even as the algorithms find more dangerous manoeuvres than the traditional method, it is not certain that they find the worst manoeuvres. If not other methods should be used the problem has to be rephrased. For example other optimization variables or a few linearizations of the optimization problem could reduce the complexity.</p><p>The overall impression is that the need of information and problem characteristics define which method that is most suitable to use. The information required must be weighed against the cost of objective function calls. Compared to the traditional method, the optimization methods used in this thesis give extended information about the problems examined and are better to locate the worst case.</p>