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Active flow separation control by a position-based iterative learning control algorithm with experimental validation

机译:通过基于位置的迭代学习控制算法进行主动流分离控制,并进行实验验证

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摘要

A novel iterative learning control (ILC) algorithm was developed and applied to an active flow control problem. The technique uses pulsed air jets to delay flow separation on a two-element high-lift wing. The ILC algorithm uses position-based pressure measurements to update the actuation. The method was experimentally tested on a wing model in a 0.9 m × 0.6 m low-speed wind tunnel at the University of Southampton. Compressed air and fast switching solenoid valves were used as actuators to excite the flow, and the pressure distribution around the chord of the wing was measured as a feedback control signal for the ILC controller. Experimental results showed that the actuation was able to delay the separation and increase the lift by approximately 10%-15%. By using the ILC algorithm, the controller was able to find the optimum control input and maintain the improvement despite sudden changes of the separation position.
机译:开发了一种新颖的迭代学习控制(ILC)算法,并将其应用于主动流控制问题。该技术使用脉冲空气喷射来延迟两元件高升力机翼上的气流分离。 ILC算法使用基于位置的压力测量来更新致动。该方法在南安普敦大学的0.9 m×0.6 m低速风洞的机翼模型上进行了实验测试。压缩空气和快速切换电磁阀用作促动器来激发气流,并且测量机翼弦周围的压力分布作为ILC控制器的反馈控制信号。实验结果表明,该致动能够延迟分离并提高升程约10%-15%。通过使用ILC算法,尽管分离位置突然改变,控制器仍能够找到最佳控制输入并保持改进。

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