![]() ![]() 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. For example, airfoils for use on the wings of low-speed airplanes are generally thicker (in terms of their thickness-to-chord ratio) and have more curvature or. Outboard chord has been tested until 70mm (), 100mm is more common. 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. A review is given of some theoretical and experi- mental research on the aerodynamic characteristics of airfoil sections for low speed flows. One of the most popular airfoils for swept wings due to low drag (speed) and very low Re-Numbers you need. From a random initial population, GA will generate new individuals iteratively until a desired solution is found. The algorithm mimics the concept of genetic inheritance and Darwinian natural selection in living organisms. The following research presents an airfoil optimization using gradient-free technique called genetic algorithm (GA). 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. ![]() Aerodynamic optimization is undoubtedly an important part of design due to its effect on an aircraft’s performance. ![]()
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