In this study, two new techniques are proposed for accelerating the multi-point optimization of an airfoil shape by genetic algorithms. In such multi-point evolutionary optimization, the objective function has to be evaluated several times more than a single-point optimization. Thus, excessive computational time is crucial in these problems particularly, when computational fluid dynamics is used for fitness function evaluation. Two new techniques of preadaptive range operator and adaptive mutation rate are proposed. An unstructured grid Navier–Stokes flow solver with a two-equation k - ɛ turbulence model is used to evaluate the objective function. The new methods are applied for optimum design of a transonic airfoil at two speed conditions. The results show that using the new methods can increase the aerodynamic efficiency of optimum airfoil at each operating condition with about 30% less computational time in comparison with the conventional genetic algorithm approach.


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    Title :

    Multi-point optimization of transonic airfoils using an enhanced genetic algorithm


    Contributors:


    Publication date :

    2018-06-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Multi-point design of transonic airfoils using optimization

    HAGER, J. / EYI, S. / LEE, K. | AIAA | 1992



    Shape Optimization of Airfoils in Transonic Flow Using a Multi-Objective Genetic Algorithm

    Chen, X. / Agarwal, R.K. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2013