Aerodynamic optimization is undoubtedly an important part of design due to its effect on an aircraft’s performance. Objectives of such optimization problem usually involve black-box function of computational simulation, which will not fit the use of conventional gradient-based optimization method as it needs information of derivatives that only well-defined functions are able to provide. The following research presents an airfoil optimization using gradient-free technique called genetic algorithm (GA). The algorithm mimics the concept of genetic inheritance and Darwinian natural selection in living organisms. From a random initial population, GA will generate new individuals iteratively until a desired solution is found. The objective is to minimize the coefficient of drag from a low-speed airfoil of NACA 0012 using PARSEC parameterization technique and a low-fidelity CFD solver XFOIL, with an addition of minimizing the absolute value of coefficient of moment for multi-objective optimization problem. The airfoil is successfully optimized using the GA with the final result of a reduced drag coefficient by almost 50%, and a set of optimum solutions with varying trade-off for each objective is obtained from the multi-objective case.


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    Title :

    Single- and multi-objective optimization of a low-speed airfoil using genetic algorithm


    Contributors:

    Conference:

    7TH INTERNATIONAL SEMINAR ON AEROSPACE SCIENCE AND TECHNOLOGY – ISAST 2019 ; 2019 ; Jakarta, Indonesia


    Published in:

    Publication date :

    2020-04-21


    Size :

    9 pages





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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