• DocumentCode
    2155111
  • Title

    Intelligent aerodynamic design for airfoil based on Artificial Neural Network Method

  • Author

    Jie, Chen ; Gang, Sun ; Xin, Jin

  • Author_Institution
    Dept. of Mech. & Eng. Sci., Fudan Univ., Shanghai, China
  • Volume
    5
  • fYear
    2010
  • fDate
    26-28 Feb. 2010
  • Firstpage
    289
  • Lastpage
    293
  • Abstract
    The Artificial Neural Network (ANN) method is applied to intelligent aerodynamic design for airfoils. A Self-Organizing Map (SOM) network is demonstrated selecting referenced airfoils which mostly meet or close to the design requirement from airfoil database. Then a Back-Propagation (BP) network automatically learns the relationship between referenced airfoil geometry and aerodynamic performance by means of supervised learning approach. After a set of training, the BP network is able to estimate airfoil aerodynamic characteristics using knowledge and criteria learned before. Design results indicate that trained network can give effective prediction and excellent aerodynamic efficiency for airfoil.
  • Keywords
    aerodynamics; aerospace components; aerospace computing; backpropagation; intelligent design assistants; self-organising feature maps; airfoil database; artificial neural network method; back propagation network; design requirement; intelligent aerodynamic design; self-organizing map network; supervised learning approach; training; Aerodynamics; Artificial intelligence; Artificial neural networks; Automotive components; Data mining; Intelligent networks; Learning systems; Neurons; Shape; Spatial databases; BP; SOM; artificial neural network; intelligent airfoil design;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-5585-0
  • Electronic_ISBN
    978-1-4244-5586-7
  • Type

    conf

  • DOI
    10.1109/ICCAE.2010.5451445
  • Filename
    5451445