Neural network prediction and control of three-dimensional unsteady separated flowfields
Neural network prediction and control of three-dimensional unsteady separated flowfields
复制标题
三维非定常分离流场的神经网络预测与控制
DOI：
10.2514/3.46866
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发表时间：
1995
影响因子：
2.2
通讯作者：
M. Luttges
中科院分区：
文献类型：
--
作者：
W. Faller;S. Schreck;M. Luttges
利用人工神经网络（ANN）控制非定常空气动力学的一种方法是建立实时模型，在给定作动器控制信号的情况下，预测非定常流场机翼干扰。这些模型的流翼相互作用，然后可以用来作为基础上，开发自适应控制系统。本文支持这一概念，使用三维非定常表面压力拓扑收集从一个矩形机翼通过静态失速角在七个无量纲俯仰率俯仰。非定常表面压力的神经网络模型的开发，通过训练人工神经网络对这七个数据集的五个。在训练之后，模型所需的唯一输入是瞬时迎角和角速度。这些网络预测的非定常表面压力的时间历程进行了比较直接的实验压力数据。然后，利用压力模型设计了机翼运动历程的神经网络控制器。结果表明，控制器执行器信号可靠地产生运动历史，从而产生测量的升阻比（LID）时间历史。此外，结果表明，对于任何期望的LID要求，可以生成优化的运动历史。
Using artificial neural networks (ANN), one approach to the control of unsteady aerodynamics is to develop real-time models which, given the actuator control signals, anticipate the unsteady flowfield wing interactions. These models of flow-wing interactions can then be used as the foundation upon which to develop adaptive control systems. This article supports this concept using three-dimensional unsteady surface pressure topologies collected from a rectangular wing pitched through the static stall angle at seven nondimensional pitch rates. A neural network model of the unsteady surface pressures was developed by training an ANN on five of these seven data sets. Following training, the only inputs required for the model were instantaneous angle of attack and angular velocity. These network-predicted unsteady surface pressure time histories were compared directly to the experimental pressure data. Then, a neural network controller for the wing motion history was developed using the pressure model. The results indicated that the controller actuator signals reliably yielded motion histories that generated the measured lift to drag ratio (LID) time histories. Further, the results suggest that for any desired LID requirement optimized motion histories can be generated.