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首页> 外文期刊>Journal of Aircraft >Aeroelastic Optimization of Adaptive Bumps for Yaw Control
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Aeroelastic Optimization of Adaptive Bumps for Yaw Control

机译：偏航控制的自适应碰撞的气动弹性优化

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This paper captures the aeroelastic behavior of adaptable bumps on morphing wings using trained neural networks. Parameters of the bump such as the height, size, location, and shape play a major aerodynamic and structural role. Though other issues such as wave drag minimization, boundary layer control are discussed, the primary problem addressed here is the generation of the lateral directional moment. The shape of the bump is optimized so that it produces maximum yaw moment with a minimum actuation energy spent in creating the adaptive bump. The analysis of fluid separation on the airfoil surface due to various types of bumps and its effects on the aerodynamic forces is performed using the computational fluid dynamics (CFD) software FLUENT~(TM). This analysis is performed at a low speed of Mach 0.3 and at a transonic speed of Mach 0.7. Structural analysis is performed using the finite element software ANSYS~(TM) using a nonlinear beam model of the bump. In order to perform an aeroelastic analysis, the softwares FLUENT and ANSYS have to be interconnected so that the changing aerodynamic pressure with bump deformation is reflected in the structural analysis. Direct coupling of two such numerical codes, one based on CFD and the other based on finite element modeling (FEM), is computationally expensive. Hence, artificial neural networks are trained from these aerodynamic and structural analysis. Two neural networks are trained, one for the aerodynamic pressure and the other for the structural loads and strain energy. These two neural networks serve as an efficient decoupler, that facilitates an aeroelastic optimization procedure to evaluate the best bump shape for maximum drag for providing micro-yaw control while using the minimum actuation energy and minimizing the loss in the lift.
机译：本文使用训练有素的神经网络捕获了变型​​机翼上的自适应凸块的气动弹性行为。凸点的参数（例如高度，大小，位置和形状）在空气动力学和结构上起着重要作用。尽管讨论了诸如最小化波浪阻力，边界层控制之类的其他问题，但这里要解决的主要问题是横向力矩的产生。优化了凸块的形状，以使其产生最大的偏航力矩，而在创建自适应凸块上花费的驱动能量最小。使用计算流体动力学（CFD）软件FLUENT〜（TM）对由于各种类型的颠簸引起的翼型表面流体分离及其对空气动力的影响进行分析。该分析以0.3马赫的低速和0.7马赫的跨音速进行。使用有限元软件ANSYSTM使用凸块的非线性梁模型进行结构分析。为了进行气动弹性分析，必须将软件FLUENT和ANSYS互连，以便在结构分析中反映出随着凸块变形而变化的气动压力。直接耦合两个这样的数字代码，一个基于CFD，另一个基于有限元建模（FEM），在计算上非常昂贵。因此，从这些空气动力学和结构分析中训练出了人工神经网络。训练了两个神经网络，一个用于气动压力，另一个用于结构载荷和应变能。这两个神经网络充当有效的解耦器，有助于进行空气弹性优化程序，以评估最佳的碰撞形状以获得最大阻力，从而在使用最小促动能量并最大程度降低升程损失的同时提供微偏航控制。

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