The main disadvantage of evolutionary algorithms for airfoil design problems is the high computational costs associated with the use of computational fluid dynamics solvers. This disadvantage was significantly reduced by using an existing technique which combines a genetic algorithm and a neural network to rapidly improve populations. In this technique, both genetic algorithms and a properly trained neural network search the design space, resulting in an interactive process between a genetic algorithm and a neural network that greatly improves the exploration power of the algorithm. Experimental results show that the implemented algorithm discovers the desired solution quickly and significantly reduces the overall costs of solving the problem with little or no reduction in robustness.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Fast evolutionary algorithm for airfoil design via neural network


    Weitere Titelangaben:

    Schneller evolutionärer Algorithmus für den Tragflügelentwurf via neurale Netzwerke


    Beteiligte:

    Erschienen in:

    AIAA Journal (online) ; 45 , 9 ; 2196-2203


    Erscheinungsdatum :

    2007


    Format / Umfang :

    8 Seiten, 7 Bilder, 3 Tabellen, 40 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch






    Firefighter UAV Airfoil Design Via Evolutionary Algorithms

    Hudiono, A. / Defence Science and Technology Organisation (Australia); Royal Aeronautical Society; Institution of Engineers Australia | British Library Conference Proceedings | 2011



    Deep Neural Network for Airfoil Optimization

    Peng, Wenhui / Zhang, Yao / Desmarais, Michel | TIBKAT | 2022