Here, we investigate a method for the inverse design of airfoil sections using artificial neural networks (ANNs). The aerodynamic force coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A feedforward neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. In this paper, we explore different strategies for training this neural network. From our test, the most promising backpropagation strategy is to initially use steepest descent algorithm and then continue with linear and nonlinear constraint in the algorithm. Results indicate that our combined approach optimally trains artificial neural network and may accurately predict airfoil profile.


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    Titel :

    Design of Airfoil Using Backpropagation Training with Combined Approach


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Bajpai, Ram P. (Herausgeber:in) / Chandrasekhar, U. (Herausgeber:in) / Arankalle, Avinash R. (Herausgeber:in) / Thinakaran, K. (Autor:in) / Rajasekar, R. (Autor:in)


    Erscheinungsdatum :

    03.05.2014


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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