基于数据驱动建模的涡脱反馈控制
Feedback control of vortex shedding using data-driven modelling
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中文总结 AI 辅助
本文针对Re=1000的圆柱绕流涡脱，采用带控制的动态模态分解构建降阶模型，设计LQG控制器实现涡脱抑制，在3D DDES中获13.7%阻力降低，证明2D URANS模拟可用于生成控制用降阶模型。
中文摘要 AI 辅助
本文详细介绍了雷诺数Re=1000的圆柱绕流涡脱的数据驱动建模与反馈控制。为控制设计目的，我们研究了降阶模型阶数变化的影响，证明更高阶模型可能导致涡脱抑制效果降低。我们利用伯德积分定理和频域解释表明，性能下降部分源于经典的“水床效应”，该效应会增加未建模动态频带的灵敏度。我们采用2D非定常模拟的训练数据，通过带控制的动态模态分解获取系统的线性降阶状态空间模型。仅使用升力测量，我们表明LQG控制器抑制涡脱至少需要4阶模型，最佳性能仅用9个模态即可实现，而高于14阶的控制器性能显著下降。我们研究了外部干扰、噪声抑制和参数不确定性对控制器性能的影响，实现了升力系数方差降低28.6 dB，阻力降低26%。我们进一步表明，对于具有实际作动带宽的控制设计，闭环控制在3D DDES中实现了显著的13.7%阻力降低，尽管其是用2D URANS训练的，因此我们认为2D URANS模拟足以生成降阶模型和进行控制设计。
英文摘要
This paper details the data-driven modelling and feedback control of vortex shedding past a circular cylinder at a Reynolds number of Re = 1000. We study the effect of varying the order of the reduced model for control design purposes and demonstrate that higher orders can lead to lower suppression of vortex shedding. We use the Bode integral theorem and a frequency-domain interpretation to show that this drop in performance is, in part, due to the classical ``waterbed effect'', which increases sensitivity in frequency bands of unmodelled dynamics. Training data from 2D unsteady simulation is used to obtain linear reduced-order state-space models of the system via dynamic mode decomposition with control. Using only lift measurement, we show that at least a 4th-order model is required for an LQG controller to suppress vortex shedding, with the best performance achieved with as few as 9 modes, whilst higher-order (>14) controllers show a significant decrease in performance. We study the influence of external disturbances, noise rejection, and parameter uncertainty on controller performance. A 28.6 dB reduction in lift coefficient variance is achieved, resulting in a 26% reduction in drag. We further show that, for control design purposes with practical actuation bandwidth, the closed-loop control delivers a significant 13.7% drag reduction within 3D DDES, despite having been trained with 2D URANS and therefore argue that 2D URANS simulation is sufficient for reduced-order model generation and control design.
