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Aerodynamic Optimization of a Morphing Leading Edge Airfoil with a Constant Arc Length Parameterization

机译:恒定弧长参数化变形前缘翼型的空气动力学优化

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The paper presents the aerodynamic optimization of a morphing leading edge airfoil using a parameterization based on the class/shape transformation (CST) technique associated with a dedicated procedure to keep the arc length of the curve constant in order to limit the axial stress of the deformed shapes. The optimization is performed with a standard methodology based on genetic algorithms, comparing the results for three different aerodynamic models. Whereas the solutions obtained with the third model are standard droop nose shapes, those found via transitional models show an uncommon deformation with an upward leading edge deflection. A metamodel-assisted optimization loop is used to solve a known problem, showing that an artificial neural network is able to provide a reduction of the convergence effort when approximating the highly nonlinear relationship between the constant arc length parameterization and the aerodynamic behavior predicted with two of the models. (c) 2017 American Society of Civil Engineers.
机译:本文介绍了基于类/形状变换(CST)技术的参数化与变形过程的前缘翼型的空气动力学优化,该技术与专用过程相关联,以保持曲线的弧长恒定以限制变形的轴向应力形状。使用基于遗传算法的标准方法进行优化,比较三种不同空气动力学模型的结果。使用第三种模型获得的解是标准下垂的鼻形,而通过过渡模型发现的解则显示出不常见的变形,并具有向上的前缘挠度。使用元模型辅助的优化循环来解决一个已知问题,这表明当逼近恒定弧长参数化和通过以下两个预测的空气动力学行为之间的高度非线性关系时,人工神经网络能够减少收敛工作。模型。 (c)2017年美国土木工程师学会。

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