Thematische Bibliographien / Genetic algorithms. Multidisciplinary design optimization

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Auswahl der wissenschaftlichen Literatur zum Thema „Genetic algorithms. Multidisciplinary design optimization“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Genetic algorithms. Multidisciplinary design optimization"

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Farshadnia, Reza. „Genetic Algorithms in Optimization and Computer Aided Design“. Journal of Applied Sciences 1, Nr. 3 (15.06.2001): 289–94. http://dx.doi.org/10.3923/jas.2001.289.294.

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KURAPATI, A., und S. AZARM. „IMMUNE NETWORK SIMULATION WITH MULTIOBJECTIVE GENETIC ALGORITHMS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION“. Engineering Optimization 33, Nr. 2 (Dezember 2000): 245–60. http://dx.doi.org/10.1080/03052150008940919.

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3

Zhang, Jing. „Multidisciplinary Fuzzy Optimization Design of Planar Linkage Mechanism“. Advanced Materials Research 211-212 (Februar 2011): 1016–20. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.1016.

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Based on the fuzzy theory and an idea of multidisciplinary design optimization, a fuzzy optimization model of multidisciplinary design is established. Fuzzy constraints are changed by a fuzzy comprehensive evaluation and an amplification-coefficient method. Using collaborative optimization and genetic algorithms, the multidisciplinary fuzzy optimum of planar linkage mechanism is achieved and a four-bar mechanism is given as an example. Two disciplines are involved in the design optimization of mechanism, i.e., kinematics and control. The numerical results indicate that the optimized mechanism not only satisfies the mechanism and control constraints, but also synthesizes approximate optimum value, and lays a foundation for the solution of more complex mechanical system.
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Huang, Jingjing, Longxi Zheng und Qing Mei. „Design and Optimization Method of a Two-Disk Rotor System“. International Journal of Turbo & Jet-Engines 33, Nr. 1 (01.01.2016): 1–8. http://dx.doi.org/10.1515/tjj-2014-0033.

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AbstractAn integrated analytical method based on multidisciplinary optimization software Isight and general finite element software ANSYS was proposed in this paper. Firstly, a two-disk rotor system was established and the mode, humorous response and transient response at acceleration condition were analyzed with ANSYS. The dynamic characteristics of the two-disk rotor system were achieved. On this basis, the two-disk rotor model was integrated to the multidisciplinary design optimization software Isight. According to the design of experiment (DOE) and the dynamic characteristics, the optimization variables, optimization objectives and constraints were confirmed. After that, the multi-objective design optimization of the transient process was carried out with three different global optimization algorithms including Evolutionary Optimization Algorithm, Multi-Island Genetic Algorithm and Pointer Automatic Optimizer. The optimum position of the two-disk rotor system was obtained at the specified constraints. Meanwhile, the accuracy and calculation numbers of different optimization algorithms were compared. The optimization results indicated that the rotor vibration reached the minimum value and the design efficiency and quality were improved by the multidisciplinary design optimization in the case of meeting the design requirements, which provided the reference to improve the design efficiency and reliability of the aero-engine rotor.
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Lam, Xuan-Binh. „Multidiscilinary design optimization for aircraft wing using response surface method, genetic algorithm, and simulated annealing“. Journal of Science and Technology in Civil Engineering (STCE) - NUCE 14, Nr. 1 (22.01.2020): 28–41. http://dx.doi.org/10.31814/stce.nuce2020-14(1)-03.

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Multidisciplinary Design Optimization (MDO) has received a considerable attention in aerospace industry. The article develops a novel framework for Multidisciplinary Design Optimization of aircraft wing. Practically, the study implements a high-fidelity fluid/structure analyses and accurate optimization codes to obtain the wing with best performance. The Computational Fluid Dynamics (CFD) grid is automatically generated using Gridgen (Pointwise) and Catia. The fluid flow analysis is carried out with Ansys Fluent. The Computational Structural Mechanics (CSM) mesh is automatically created by Patran Command Language. The structural analysis is done by Nastran. Aerodynamic pressure is transferred to finite element analysis model using Volume Spline Interpolation. In terms of optimization algorithms, Response Surface Method, Genetic Algorithm, and Simulated Annealing are utilized to get global optimum. The optimization objective functions are minimizing weight and maximizing lift/drag. The design variables are aspect ratio, tapper ratio, sweepback angle. The optimization results demonstrate successful and desiable construction of MDO framework. Keywords: Multidisciplinary Design Optimization; fluid/structure analyses; global optimum; Genetic Algorithm; Response Surface Method.
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Liu, Yu, Hong Yun Yang und Guo Chao Wang. „Genetic Algorithm Based Multidisciplinary Design Optimization of MEMS Accelerometer“. Applied Mechanics and Materials 101-102 (September 2011): 530–33. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.530.

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Multidisciplinary design optimization of a biaxial capacitive micro-accelerometer with the crab-leg flexural suspension is discussed. Considering the influence of microstructure design, fabrication process and detection circuit, as well as the constraints of fabrication limitations, damp design and adhesion factor, a multidisciplinary optimization model is developed by global criterion method. Furthermore, genetic algorithm is applied to obtain the multi-objective optimum of the proposed multidisciplinary design model.
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Adami, Amirhossein, Mahda Mortazavi und Mehran Nosratollahi. „Multi-modular design optimization and multidisciplinary design optimization“. International Journal of Intelligent Unmanned Systems 3, Nr. 2/3 (11.05.2015): 156–70. http://dx.doi.org/10.1108/ijius-01-2015-0001.

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Purpose – For complex engineering problems, multidisciplinary design optimization (MDO) techniques use some disciplines that need to be run several times in different modules. In addition, mathematical modeling of a discipline can be improved for each module. The purpose of this paper is to show that multi-modular design optimization (MMO) improves the design performances in comparison with MDO technique for complex systems. Design/methodology/approach – MDO framework and MMO framework are developed to optimum design of a complex system. The nonlinear equality and inequality constrains are considered. The system optimizers included Genetic Algorithm and Sequential Quadratic Programming. Findings – As shown, fewer design variables (optimization variables) are needed at the system level for MMO. Unshared variables are optimized in the related module when shared variables are optimized at the system level. The results of this research show that MMO has lower elapsed times (14 percent) with lower F-count (16 percent). Practical implications – The monopropellant propulsion upper-stage is selected as a case study. In this paper, the efficient model of the monopropellant propulsion system is proposed. According to the results, the proposed model has acceptable accuracy in mass model (error < 2 percent), performance estimation (error < 6 percent) and geometry estimation (error < 10 percent). Originality/value – The monopropellant propulsion system is broken down into the three important modules including propellant tank (tank and propellant), pressurized feeding (tank and gas) and thruster (catalyst, nozzle and catalysts bed) when chemical decomposition, aerothermodynamics, mass and configuration, catalyst and structure have been considered as the disciplines. The both MMO and MDO frameworks are developed for the monopropellant propulsion system.
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Xiang, Xianbo, Caoyang Yu, He Xu und Stuart X. Zhu. „Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm“. Complexity 2018 (01.11.2018): 1–12. http://dx.doi.org/10.1155/2018/2024184.

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This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.
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Villanueva, Fredy Marcell, He Linshu und Xu Dajun. „Kick Solid Rocket Motor Multidisciplinary Design Optimization Using Genetic Algorithm“. Journal of Aerospace Technology and Management 5, Nr. 3 (27.08.2013): 293–304. http://dx.doi.org/10.5028/jatm.v5i3.225.

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Nosratollahi, M., M. Mortazavi, A. Adami und M. Hosseini. „Multidisciplinary design optimization of a reentry vehicle using genetic algorithm“. Aircraft Engineering and Aerospace Technology 82, Nr. 3 (18.05.2010): 194–203. http://dx.doi.org/10.1108/00022661011075928.

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Dissertationen zum Thema "Genetic algorithms. Multidisciplinary design optimization"

1

Zhou, Yao. „Study on genetic algorithm improvement and application“. Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-211907/.

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Dingwall, Austin Gregory. „Testing the impact of using cumulative data with genetic algorithms for the analysis of building energy performance and material cost“. Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45952.

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The demand for energy and cost efficient buildings has made architects and contractors more aware of the resources consumed by the built environment. While the actual economic and environmental costs of future construction can never be completely predicted, energy simulations and cost modeling have become accepted ways to guide the design and construction process by comparing possible outcomes. These tools are now commonplace in the construction industry, and researchers are continuing to develop new and innovative strategies to optimize building design and construction. Previous research has proven that genetic algorithms are effective methods to evaluate and optimize building design in situations that contain a large number of possible solutions. The technique makes a computationally difficult multi-optimization process possible but is still a reactive and time consuming process that focuses on evaluation rather than solution generation. This research presented in this paper builds upon established multi-objective optimization techniques that use an energy simulator to estimate a conceptual building’s energy use as well as construction cost. The study compares simulations of a simplified model of a 3-story inpatient hospital located in Atlanta, Georgia using a defined set of variables. A combined global minimum of annual energy consumption and total construction is sought after using a method that utilizes a genetic algorithm. The second phase of this research uses a modified approach that combines the traditional genetic algorithm with a seeding method that utilizes previous results. A new set of simulations were established that duplicates the initial trials using a slightly modified set of design variables. The simulation was altered, and the phase one trials were utilized as the first generation of simulated solutions. The objective of this thesis is to explore one method of making energy use and cost estimating more accessible to the construction industry by combining simulation optimization and indexing. The results indicate that this study’s proposed augmented approach has potential benefits to building design optimization, although more research is required to validate this hypothesis in its entirety. This study concludes that the proposed approach can potentially reduce the time needed for individual optimization exercises by creating a cumulative, robust catalog of previous computations that will inform and seed future analyses. The research was conducted in five general stages. The first part defines the research problem and scope of research to be conducted. In the second part, the concepts of genetic algorithms and energy simulation are explored in a comprehensive literature review. The remaining parts explain the trial simulations performed in this study. Part three explains the experiment’s methodology, and part four describes the simulation results. The fifth and final part looks at what the possible conclusions that can be made from analyzing the study’s results.
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Khalid, Adeel S. „Development and Implementation of Rotorcraft Preliminary Design Methodology using Multidisciplinary Design Optimization“. Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14013.

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A formal framework is developed and implemented in this research for preliminary rotorcraft design using IPPD methodology. All the technical aspects of design are considered including the vehicle engineering, dynamic analysis, stability and control, aerodynamic performance, propulsion, transmission design, weight and balance, noise analysis and economic analysis. The design loop starts with a detailed analysis of requirements. A baseline is selected and upgrade targets are identified depending on the mission requirements. An Overall Evaluation Criterion (OEC) is developed that is used to measure the goodness of the design or to compare the design with competitors. The requirements analysis and baseline upgrade targets lead to the initial sizing and performance estimation of the new design. The digital information is then passed to disciplinary experts. This is where the detailed disciplinary analyses are performed. Information is transferred from one discipline to another as the design loop is iterated. To coordinate all the disciplines in the product development cycle, Multidisciplinary Design Optimization (MDO) techniques e.g. All At Once (AAO) and Collaborative Optimization (CO) are suggested. The methodology is implemented on a Light Turbine Training Helicopter (LTTH) design. Detailed disciplinary analyses are integrated through a common platform for efficient and centralized transfer of design information from one discipline to another in a collaborative manner. Several disciplinary and system level optimization problems are solved. After all the constraints of a multidisciplinary problem have been satisfied and an optimal design has been obtained, it is compared with the initial baseline, using the earlier developed OEC, to measure the level of improvement achieved. Finally a digital preliminary design is proposed. The proposed design methodology provides an automated design framework, facilitates parallel design by removing disciplinary interdependency, current and updated information is made available to all disciplines at all times of the design through a central collaborative repository, overall design time is reduced and an optimized design is achieved.
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Sheng, Lizeng. „Finite Element Analysis and Genetic Algorithm Optimization Design for the Actuator Placement on a Large Adaptive Structure“. Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/30184.

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The dissertation focuses on one of the major research needs in the area of adaptive /intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures -- optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ~ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.
Ph. D.
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Gagliano, Joseph R. „Orbital Constellation Design and Analysis Using Spherical Trigonometry and Genetic Algorithms: A Mission Level Design Tool for Single Point Coverage on Any Planet“. DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1877.

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Recent interest surrounding large scale satellite constellations has increased analysis efforts to create the most efficient designs. Multiple studies have successfully optimized constellation patterns using equations of motion propagation methods and genetic algorithms to arrive at optimal solutions. However, these approaches are computationally expensive for large scale constellations, making them impractical for quick iterative design analysis. Therefore, a minimalist algorithm and efficient computational method could be used to improve solution times. This thesis will provide a tool for single target constellation optimization using spherical trigonometry propagation, and an evolutionary genetic algorithm based on a multi-objective optimization function. Each constellation will be evaluated on a normalized fitness scale to determine optimization. The performance objective functions are based on average coverage time, average revisits, and a minimized number of satellites. To adhere to a wider audience, this design tool was written using traditional Matlab, and does not require any additional toolboxes. To create an efficient design tool, spherical trigonometry propagation will be utilized to evaluate constellations for both coverage time and revisits over a single target. This approach was chosen to avoid solving complex ordinary differential equations for each satellite over a long period of time. By converting the satellite and planetary target into vectors of latitude and longitude in a common celestial sphere (i.e. ECI), the angle can be calculated between each set of vectors in three-dimensional space. A comparison of angle against a maximum view angle, , controlled by the elevation angle of the target and the satellite’s altitude, will determine coverage time and number of revisits during a single orbital period. Traditional constellations are defined by an altitude (a), inclination (I), and Walker Delta Pattern notation: T/P/F. Where T represents the number of satellites, P is the number of orbital planes, and F indirectly defines the number of adjacent planes with satellite offsets. Assuming circular orbits, these five parameters outline any possible constellation design. The optimization algorithm will use these parameters as evolutionary traits to iterate through the solutions space. This process will pass down the best traits from one generation to the next, slowly evolving and converging the population towards an optimal solution. Utilizing tournament style selection, multi-parent recombination, and mutation techniques, each generation of children will improve on the last by evaluating the three performance objectives listed. The evolutionary algorithm will iterate through 100 generations (G) with a population (n) of 100. The results of this study explore optimal constellation designs for seven targets evenly spaced from 0° to 90° latitude on Earth, Mars and Jupiter. Each test case reports the top ten constellations found based on optimal fitness. Scatterplots of the constellation design solution space and the multi-objective fitness function breakdown are provided to showcase convergence of the evolutionary genetic algorithm. The results highlight the ratio between constellation altitude and planetary radius as the most influential aspects for achieving optimal constellations due to the increased field of view ratio achievable on smaller planetary bodies. The multi-objective fitness function however, influences constellation design the most because it is the main optimization driver. All future constellation optimization problems should critically determine the best multi-objective fitness function needed for a specific study or mission.
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Júnior, Paulo Roberto Caixeta. „Otimização multidisciplinar em projeto de asas flexíveis“. Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18135/tde-22122006-111540/.

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A indústria aeronáutica vem promovendo avanços tecnológicos em velocidades crescentes, para sobreviver em mercados extremamente competitivos. Neste cenário, torna-se imprescindível o uso de ferramentas de projeto que agilizem o desenvolvimento de novas aeronaves. Os atuais recursos computacionais permitiram um grande aumento no número de ferramentas que auxiliam o trabalho de projetistas e engenheiros. O projeto de uma aeronave é uma tarefa multidisciplinar por essência, o que logo incentivou o desenvolvimento de ferramentas computacionais que trabalhem com várias áreas ao mesmo tempo. Entre elas se destaca a otimização multidisciplinar em projeto, que une métodos de otimização à modelos matemáticos de áreas distintas de um projeto para encontrar soluções de compromisso. O presente trabalho introduz a otimização multidisciplinar em projeto (Multidisciplinary Design Optimization - MDO) e discorre sobre algumas aplicações possíveis desta metodologia. Foi realizada a implementação de um sistema de MDO para o projeto de asas flexíveis, considerando restrições de aeroelasticidade dinâmica e massa estrutural. Como meta, deseja-se encontrar distribuições ideais de rigidezes flexional e torcional da estrutura da asa, para maximizar a velocidade crítica de flutter e minimizar a massa estrutural. Para tanto, foram utilizados um modelo dinâmico-estrutural baseado no método dos elementos finitos, um modelo aerodinâmico não-estacionário baseado na teoria das faixas e nas soluções bidimensionais de Theodorsen, um modelo de previsão de flutter que utiliza o método K e, por fim, um otimizador baseado no método de algoritmos genéticos (AGs). São apresentados os detalhes empregados em cada modelo, as restrições aplicadas e a maneira como eles interagem ao longo da otimização. É feita uma análise para a escolha dos parâmetros de otimização por AG e em seguida a avaliação de dois casos, para verificação da funcionalidade do sistema implementado. Os resultados obtidos demonstram uma metodologia eficiente, que é capaz de buscar soluções ótimas para problemas propostos, que com devidos ajustes pode ter enorme valor para acelerar o desenvolvimento de novas aeronaves.
The aeronautical industry is always trying to speed up technological advances in order to survive in extremely competitive markets. In this scenario, the use of design tools to accelerate the development of new aircraft becomes essential. Current computational resources allow greater increase in the number of design tools to assist the work of aeronautical engineers. In essence, the design of an aircraft is a multidisciplinary task, which stimulates the development of computational tools that work with different areas at the same time. Among them, the multidisciplinary design optimization (MDO) can be distinguished, which combines optimization methods to mathematical models of distinct areas of a design to find compromise solutions. The present work introduces MDO and discourses on some possible applications of this methodology. The implementation of a MDO system for the design of flexible wings, considering dynamic aeroelasticity restrictions and the structural mass, was carried out. As goal, it is desired to find ideal flexional and torsional stiffness distributions of the wing structure, that maximize the critical flutter speed and minimize the structural mass. To do so, it was employed a structural dynamics model based on the finite element method, a nonstationary aerodynamic model based on the strip theory and Theodorsen’s two-dimensional solutions, a flutter prediction model based on the K method and a genetic algorithm (GA). Details on the model, restrictions applied and the way the models interact to each other through the optimization are presented. It is made an analysis for choosing the GA optimization parameters and then, the evaluation of two cases to verify the functionality of the implemented system. The results obtained illustrate an efficient methodology, capable of searching optimal solutions for proposed problems, that with the right adjustments can be of great value to accelerate the development of new aircraft.
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Júnior, Paulo Roberto Caixeta. „Otimização multidisciplinar em projeto de asas flexíveis utilizando metamodelos“. Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18148/tde-28092011-103532/.

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A Otimização Multidisciplinar em Projeto (em inglês, Multidisciplinary Design Optimization - MDO) é uma ferramenta de projeto importante e versátil e seu uso está se expandindo em diversos campos da engenharia. O foco desta metodologia é unir disciplinas envolvidas no projeto para que trabalhem suas variáveis concomitantemente em um ambiente de otimização, para obter soluções melhores. É possível utilizar MDO em qualquer fase do projeto, seja a fase conceitual, preliminar ou detalhada, desde que os modelos numéricos sejam ajustados às necessidades de cada uma delas. Este trabalho descreve o desenvolvimento de um código de MDO para o projeto conceitual de asas flexíveis de aeronaves, com restrição quanto ao fenômeno denominado flutter. Como uma ferramenta para o projetista na fase conceitual, os modelos numéricos devem ser razoavelmente precisos e rápidos. O intuito deste estudo é analisar o uso de metamodelos para a previsão do flutter de asas de aeronaves no código de MDO, ao invés de um modelo convencional, o que pode alterar significativamente o custo computacional da otimização. Para este fim são avaliados três técnicas diferentes de metamodelagem, que foram escolhidas por representarem duas classes básicas de metamodelos, a classe de métodos de interpolação e a de métodos de aproximação. Para representá-las foram escolhidos o método de interpolação por funções de base radial e o método de redes neurais artificiais, respectivamente. O terceiro método, que é considerado um método híbrido dos dois anteriores, é chamado de redes neurais por funções de bases radiais e é uma tentativa de acoplar as características de ambos em um único metamodelo. Os metamodelos são preparados utilizando um código para solução aeroelástica baseado no método dos elementos finitos acoplado com um modelo aerodinâmico linear de faixas. São apresentados resultados de desempenho dos três metamodelos, de onde se pode notar que a rede neural artificial é a mais adequada para previsão de flutter. O processo de MDO é realizado com o uso de um algoritmo genético multi-objetivo baseado em não-dominância, cujos objetivos são a maximização da velocidade crítica de flutter e a minimização da massa estrutural. Dois estudos de caso são apresentados para avaliar o desempenho do código de MDO, revelando que o processo global de otimização realiza de fato a busca pela fronteira de Pareto.
The Multidisciplinary Design Optimization, MDO, is an important and versatile design tool and its use is spreading out in several fields of engineering. The focus of this methodology is to put together disciplines involved with the design to work all their variables concomitantly, at an optimization environment to obtain better solutions. It is possible to use MDO in any stage of the design process, that is in the conceptual, preliminary or detailed design, as long as the numerical models are fitted to the needs of each of these stages. This work describes the development of a MDO code for the conceptual design of flexible aircraft wings, with restrictions regarding the phenomenon called flutter. As a tool for the designer at the conceptual stage, the numerical models must be fairly accurate and fast. The aim of this study is to analyze the use of metamodels for the flutter prediction of aircraft wings in the MDO code, instead of a conventional model itself, what may affect significantly the computational cost of the optimization. For this purpose, three different metamodeling techniques have been evaluated, representing two basic metamodel classes, that are, the interpolation and the approximation class. These classes are represented by the radial basis function interpolation method and the artificial neural networks method, respectively. The third method, which is considered as a hybrid of the other two, is called radial basis function neural networks and is an attempt of coupling the features of both in single code. Metamodels are prepared using an aeroelastic code based on finite element model coupled with linear aerodynamics. Results of the three metamodels performance are presented, from where one can note that the artificial neural network is best suited for flutter prediction. The MDO process is achieved using a non-dominance based multi-objective genetic algorithm, whose objectives are the maximization of critical flutter speed and minimization of structural mass. Two case studies are presented to evaluate the performance of the MDO code, revealing that overall optimization process actually performs the search for the Pareto frontier.
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Hinds, Christopher Alan. „A PARETO-FRONTIER ANALYSIS OF PERFORMANCE TRENDS FOR SMALL REGIONAL COVERAGE LEO CONSTELLATION SYSTEMS“. DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1342.

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As satellites become smaller, cheaper, and quicker to manufacture, constellation systems will be an increasingly attractive means of meeting mission objectives. Optimizing satellite constellation geometries is therefore a topic of considerable interest. As constellation systems become more achievable, providing coverage to specific regions of the Earth will become more common place. Small countries or companies that are currently unable to afford large and expensive constellation systems will now, or in the near future, be able to afford their own constellation systems to meet their individual requirements for small coverage regions. The focus of this thesis was to optimize constellation geometries for small coverage regions with the constellation design limited between 1-6 satellites in a Walker-delta configuration, at an altitude of 200-1500km, and to provide remote sensing coverage with a minimum ground elevation angle of 60 degrees. Few Pareto-frontiers have been developed and analyzed to show the tradeoffs among various performance metrics, especially for this type of constellation system. The performance metrics focus on geometric coverage and include revisit time, daily visibility time, constellation altitude, ground elevation angle, and the number of satellites. The objective space containing these performance metrics were characterized for 5 different regions at latitudes of 0, 22.5, 45, 67.5, and 90 degrees. In addition, the effect of minimum ground elevation angle was studied on the achievable performance of this type of constellation system. Finally, the traditional Walker-delta pattern constraint was relaxed to allow for asymmetrical designs. These designs were compared to see how the Walker-delta pattern performs compared to a more relaxed design space. The goal of this thesis was to provide both a framework as well as obtain and analyze Pareto-frontiers for constellation performance relating to small regional coverage LEO constellation systems. This work provided an in-depth analysis of the trends in both the design and objective space of the obtained Pareto-frontiers. A variation on the εNSGA-II algorithm was utilized along with a MATLAB/STK interface to produce these Pareto-frontiers. The εNSGA-II algorithm is an evolutionary algorithm that was developed by Kalyanmoy Deb to solve complex multi-objective optimization problems. The algorithm used in this study proved to be very efficient at obtaining various Pareto-frontiers. This study was also successful in characterizing the design and solution space surrounding small LEO remote sensing constellation systems providing small regional coverage.
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Abdalla, Alvaro Martins. „OMPP para projeto conceitual de aeronaves, baseado em heurísticas evolucionárias e de tomadas de decisões“. Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/18/18148/tde-13012011-113940/.

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Este trabalho consiste no desenvolvimento de uma metodologia de otimização multidisciplinar de projeto conceitual de aeronaves. O conceito de aeronave otimizada tem como base o estudo evolutivo de características das categorias imediatas àquela que se propõe. Como estudo de caso, foi otimizada uma aeronave de treinamento militar que faça a correta transição entre as fases de treinamento básico e avançado. Para o estabelecimento dos parâmetros conceituais esse trabalho integra técnicas de entropia estatística, desdobramento da função de qualidade (QFD), aritmética fuzzy e algoritmo genético (GA) à aplicação de otimização multidisciplinar ponderada de projeto (OMPP) como metodologia de projeto conceitual de aeronaves. Essa metodologia reduz o tempo e o custo de projeto quando comparada com as técnicas tradicionais existentes.
This work is concerned with the development of a methodology for multidisciplinary optimization of the aircraft conceptual design. The aircraft conceptual design optimization was based on the evolutionary simulation of the aircraft characteristics outlined by a QFD/Fuzzy arithmetic approach where the candidates in the Pareto front are selected within categories close to the target proposed. As a test case a military trainer aircraft was designed target to perform the proper transition from basic to advanced training. The methodology for conceptual aircraft design optimization implemented in this work consisted on the integration of techniques such statistical entropy, quality function deployment (QFD), arithmetic fuzzy and genetic algorithm (GA) to the weighted multidisciplinary design optimization (WMDO). This methodology proved to be objective and well balanced when compared with traditional design techniques.
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Burger, Christoph Hartfield Roy J. „Propeller performance analys and multidisciplinary optimization using a genetic algorithm“. Auburn, Ala, 2007. http://repo.lib.auburn.edu/2007%20Fall%20Dissertations/Burger_Christoph_57.pdf.

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Bücher zum Thema "Genetic algorithms. Multidisciplinary design optimization"

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Oyama, Akira. Multiobjective optimization of rocket engine pumps using evolutionary algorithm. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Gen, Mitsuo. Genetic algorithms and engineering design. New York: Wiley, 1997.

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Yu-Wang, Chen, Chen Min-Rong, Chen Peng (Optimizaton specialist) und Zeng Guo-Qiang, Hrsg. Extremal optimization: Fundamentals, algorithms, and applications. Boca Raton: Taylor & Francis, CRC Press, 2015.

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Vasiljević, Darko. Classical and evolutionary algorithms in the optimization of optical systems. Boston: Kluwer Academic Publishers, 2002.

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Adeli, Hojjat. Cost optimization of structures: Fuzzy logic, genetic algorithms, and parallel computing. Chichester, England: Wiley, 2006.

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Constraint-handling in evolutionary optimization. Berlin: Springer, 2009.

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Mehrzieloptimierung Betriebswirtschaftlicher Probleme Durch Evolutionare Algorithmen. Peter Lang Publishing, 2005.

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Meng-Sing, Liou, und NASA Glenn Research Center, Hrsg. Multiobjective optimization of rocket engine pumps using evolutionary algorithm. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Yahya, Rahmat-Samii, und Michielssen Eric, Hrsg. Electromagnetic optimization by genetic algorithms. New York: J. Wiley, 1999.

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Rahmat-Samii, Yahya, und Eric Michielssen. Electromagnetic Optimization by Genetic Algorithms. Wiley & Sons, Incorporated, John, 2008.

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Buchteile zum Thema "Genetic algorithms. Multidisciplinary design optimization"

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Hajela, P. „Strategies for Modeling, Approximation, and Decomposition in Genetic Algorithms Based Multidisciplinary Design“. In Emerging Methods for Multidisciplinary Optimization, 257–318. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-2756-8_6.

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Pillai, Ajit C., Philipp R. Thies und Lars Johanning. „Development of a Multi-Objective Genetic Algorithm for the Design of Offshore Renewable Energy Systems“. In Advances in Structural and Multidisciplinary Optimization, 2013–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67988-4_149.

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Hajela, P., E. Lee und C. Y. Lin. „Genetic Algorithms in Structural Topology Optimization“. In Topology Design of Structures, 117–33. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1804-0_10.

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Leupers, Rainer. „Genetic Algorithm Based DSP Code Optimization“. In Evolutionary Algorithms for Embedded System Design, 35–62. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1035-2_2.

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Hajela, P., und E. Lee. „Genetic Algorithms in Topological Design of Grillage Structures“. In Discrete Structural Optimization, 30–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85095-0_4.

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Sefrioui, M., E. Whitney, J. Periaux und K. Srinivas. „Evolutionary Algorithms for Multi-Objective Design Optimization“. In Notes on Numerical Fluid Mechanics and Multidisciplinary Design (NNFM), 224–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44873-0_17.

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Dellino, G., P. Lino, C. Meloni und A. Rizzo. „Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective“. In Hybrid Evolutionary Algorithms, 39–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_3.

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Blessing, Jeffrey. „Efficient Network Design using Heuristic and Genetic Algorithms“. In Telecommunications Optimization: Heuristic and Adaptive Techniques, 35–55. Chichester, UK: John Wiley & Sons, Ltd, 2001. http://dx.doi.org/10.1002/047084163x.ch3.

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Pereira, C. M. N. A., R. Schirru und A. S. Martinez. „Genetic Algorithms Applied to Nuclear Reactor Design Optimization“. In Fuzzy Systems and Soft Computing in Nuclear Engineering, 315–34. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1866-6_14.

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Sáez-Gutiérrez, F. L., F. J. F. Cañavate und A. Guerrero-González. „Review of Industrial Design Optimization by Genetic Algorithms“. In Advances on Mechanics, Design Engineering and Manufacturing II, 336–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12346-8_33.

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Konferenzberichte zum Thema "Genetic algorithms. Multidisciplinary design optimization"

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Perez, Ruben, Joon Chung und Kamran Behdinan. „Aircraft conceptual design using genetic algorithms“. In 8th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4938.

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Anderson, Murray, und Glenn Gebert. „Using Pareto genetic algorithms for preliminary subsonic wing design“. In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4023.

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Stelmack, Marc, Nari Nakashima und Stephen Batill. „Genetic algorithms for mixed discrete/continuous optimization in multidisciplinary design“. In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-4771.

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Doorly, D., J. Peiro und J. P. Oesterle. „Optimisation of aerodynamic and coupled aerodynamic-structural design using parallel Genetic Algorithms“. In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4027.

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Duvigneau, Regis, und Michel Visonneau. „Hybrid Genetic Algorithms and Neural Networks for Fast CFD-Based Design“. In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5465.

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Crossley, William. „Genetic Algorithm approaches for multiobjective design of rotor systems“. In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4025.

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Zhu, Z., und Y. Chan. „An engineering study of genetic algorithms oriented to geometric design applications“. In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-4772.

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Hadim, Hamid A., und Tohru Suwa. „Integrated Thermomechanical Design and Optimization of BGA Packages Using Genetic Algorithm“. In ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56512.

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A new multidisciplinary design and optimization methodology in electronics packaging is presented. A genetic algorithm combined with multi-disciplinary design and multi-physics analysis tools are used to optimize key design parameters. This methodology is developed to improve the electronic package design process by performing multidisciplinary design and optimization at an early design stage. To demonstrate its capability, the methodology is applied to a Ball Grid Array (BGA) package design. Multidisciplinary criteria including thermal, thermal strain, electromagnetic leakage, and cost are optimized simultaneously. A simplified routability analysis criterion is treated as a constraint. The genetic algorithm is used for systematic design optimization while reducing the total computational time. The present methodology can be applied to any electronics product design at any packaging level from the chip level to the system level.
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Dyer, John, Roy Hartfield, Gerry Dozier und John Burkhalter. „Aerospace Design Optimization Using a Steady State Real-Coded Genetic Algorithm“. In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-5921.

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Yamakawa, Hiroshi, und Yuji Takagi. „Multidisciplinary optimization for topology, shape of structural systems and design of control systems using genetic algorithms“. In 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-4846.

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Berichte der Organisationen zum Thema "Genetic algorithms. Multidisciplinary design optimization"

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Ling, Hao. Application of Model-Based Signal Processing and Genetic Algorithms for Shipboard Antenna Design, Placement Optimization. Fort Belvoir, VA: Defense Technical Information Center, Januar 2002. http://dx.doi.org/10.21236/ada399555.

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