Otimização do comportamento dinâmico de veículos usando superfície de resposta
Ano de defesa: | 2001 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Uberlândia
Brasil Programa de Pós-graduação em Engenharia Mecânica |
Programa de Pós-Graduação: |
Não Informado pela instituição
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Departamento: |
Não Informado pela instituição
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País: |
Não Informado pela instituição
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Palavras-chave em Português: | |
Link de acesso: | https://repositorio.ufu.br/handle/123456789/30363 http://doi.org/10.14393/ufu.di.2001.60 |
Resumo: | This work describes the application of statistic techniques related to computational model condensation in such a way that sequential optimization procedures can be used in a more efficient way. The application of these techniques is encouraged by the possibility of an important reduction in time and cost associated to vehicle design. A s0phisticated computational model of a Mini—baja vehicle was defined in a virtual environment by means of CAD/CAE software, intending to provide data for dynamic behavior studies and to define the approximated statistic models. The computational model construction deals with the determination of physical and geometric properties, besides obtaining stiffness and damping parameters through experimental procedure. The response surface methodology uses values obtained from dynamic simulations of the original model, intending to establish a polynomial model which approximate the response of interest. These responses are determined by algebraic functions which represent some major aspects of the vehicle dynamics such as: comfort, stability and safety. Finally, results of the sequential optimization procedure applied to the original computational model are compared with those obtained by means of the response surface method, taking into account the difference between the number of object function evaluation of the first approach, and the number of computational experiments necessary to obtain the response surface model. |