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arXiv 2410.03330physics.flu-dynmath.DS
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圆柱尾流的降阶建模与闭环控制

Reduced-Order Modelling and Closed-Loop Control of the Cylinder Wake

Tea Vojković, Dimitris Boskos, Abel-John Buchner

AI总结:

本文通过推广 Stuart-Landau 降阶模型并设计模型预测输出反馈控制器,在 Re=50 下实现了圆柱尾流涡脱落振荡的闭环抑制。

摘要:

我们提出一种基于模型的方法,用于对圆柱尾流中的涡脱落进行闭环控制。控制目标是抑制流动的非定常性,该非定常性在临界雷诺数 $Re_c$ 处通过超临界 Hopf 分岔产生。在 $Re_c$ 附近,流动可由通过全局弱非线性分析导出的受迫 Stuart-Landau 方程很好地描述。该 Stuart-Landau 方程支配全局模态振幅 $A$ 在慢时间尺度上的演化。在本文中,我们将 [Sipp 2012] 中的方法进行了推广,该方法考虑固定振幅的谐波强迫,而我们允许强迫振幅 E0 在慢时间尺度上变化。这使得能够为多个替代 Stuart-Landau 模型设计闭环控制器,这些模型是针对不同类别的强迫频率获得的。当这些频率接近 $Re_c$ 处的全局模态振荡频率时,我们可以将 $A$ 和 $E'$ 同时降至零,从而完全抑制流动的非定常部分。我们还表明,在该频率附近,最优强迫结构沿伴随全局模态的方向。假设流动的速度测量为部分测量,我们设计了一种输出反馈控制律来稳定流动。该方法的核心是针对替代模型的模型预测控制器,该控制器利用全阶模型测量来确定必要的强迫振幅,同时尊重建模约束。我们在 $Re=50$ 时通过空间密集的体积强迫和两点速度测量实现了尾流振荡的抑制。

英文摘要:

We present a model-based approach for the closed-loop control of vortex shedding in the cylinder wake. The control objective is to suppress the unsteadiness of the flow, which arises at a critical Reynolds number $Re_c$ through a supercritical Hopf bifurcation. In the vicinity of $Re_c$ the flow is well described by a forced Stuart-Landau equation derived via a global weakly nonlinear analysis. This Stuart-Landau equation governs the evolution of the amplitude $A$ of the global mode on the slow time scale. In this paper, we generalize the approach from [Sipp 2012], which considers a fixed-amplitude harmonic forcing, by allowing the forcing amplitude E0 to vary on the slow time scale. This enables the design of closed-loop controllers for multiple surrogate Stuart-Landau models, which we obtain for different classes of forcing frequencies. When these frequencies are near the global mode oscillation frequency at Rec, we can bring both $A$ and $E'$ to zero, which fully suppresses the unsteady part of the flow. We also show that near this frequency, the optimal forcing structure is in the direction of the adjoint global mode. Assuming partial velocity measurements of the flow, we design an output-feedback control law that stabilizes the flow. The approach hinges on a model predictive controller for the surrogate model, which exploits the full-order model measurements to determine the necessary forcing amplitudes while respecting the modelling constraints. We achieve suppression of the wake oscillations with spatially dense volume forcing and two-point velocity measurement at $Re=50$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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