Desenvolvimento de perfis aerodinâmicos a partir de suas características utilizando redes neurais artificiais

• Ano de defesa: 2013
• Autor(a) principal: Diniz, Bruno da Cunha
• Orientador(a): Freire Júnior, Raimundo Carlos Silvério
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia Mecânica
• Departamento: Tecnologia de Materiais; Projetos Mecânicos; Termociências
• País: BR
• Palavras-chave em Português: Perfis aerodinâmicos. Características aerodinâmicas. Redes neurais artificiais. Arquiteturas de rede
• Palavras-chave em Inglês: Aerodynamic airfoils. Aerodynamic characteristics. Artificial neural networks. Network architectures
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/15701

One of the current major concerns in engineering is the development of aircrafts that have low power consumption and high performance. So, airfoils that have a high value of Lift Coefficient and a low value for the Drag Coefficient, generating a High-Efficiency airfoil are studied and designed. When the value of the Efficiency increases, the aircraft s fuel consumption decreases, thus improving its performance. Therefore, this work aims to develop a tool for designing of airfoils from desired characteristics, as Lift and Drag coefficients and the maximum Efficiency, using an algorithm based on an Artificial Neural Network (ANN). For this, it was initially collected an aerodynamic characteristics database, with a total of 300 airfoils, from the software XFoil. Then, through the software MATLAB, several network architectures were trained, between modular and hierarchical, using the Back-propagation algorithm and the Momentum rule. For data analysis, was used the technique of cross- validation, evaluating the network that has the lowest value of Root Mean Square (RMS). In this case, the best result was obtained for a hierarchical architecture with two modules and one layer of hidden neurons. The airfoils developed for that network, in the regions of lower RMS, were compared with the same airfoils imported into the software XFoil