机器学习辅助的相位-振幅缩减用于具有约束波动的翼型尾流快速同步

中文摘要 AI 辅助

本研究考虑利用稀疏传感器信息，在升力系数波动约束下，快速改变翼型周围流动的尾流脱落频率。这通过将相位-振幅缩减与基于非线性机器学习的稀疏传感器重构相结合来实现。我们推导了时变的相位和振幅灵敏度场，这些场仅从三个传感器即可识别出最优的致动空间位置和时序。通过灵敏度场，我们解析地获得了用于快速同步尾流脱落频率的最优波形，同时最小化气动响应的振幅偏差。所提出的方法在多种NACA翼型在多个失速后攻角下的流动中进行了评估，所有这些流动均表现出非定常周期性涡脱落。利用识别出的最优强迫，尾流频率的改变比标准正弦致动快得多。此外，与无振幅惩罚的最优强迫相比，振幅惩罚强迫实现了升力系数波动20%的抑制。当前的振幅惩罚技术可能为快速流动修改提供一条高效路径，而不会在具有流固耦合的周期性气动和气动弹性系统中引起有害波动。

英文摘要

This study considers rapidly modifying the wake shedding frequency of the flow around an airfoil using sparse sensor information, subject to constraints on the lift coefficient fluctuations. This is achieved by combining phase-amplitude reduction with nonlinear machine-learning-based sparse sensor reconstruction. We derive time-varying phase and amplitude sensitivity fields that identify the optimal spatial locations and timing for actuation from merely three sensors. Through the sensitivity fields, we analytically obtain the optimal waveform for fast synchronization of wake shedding frequency while minimizing amplitude deviation of aerodynamic responses. The proposed approach is evaluated using flows over various NACA airfoils at several post-stall angles of attack, all of which exhibit unsteady periodic vortex shedding. With the identified optimal forcing, the wake frequency is altered much faster than with a standard sinusoidal actuation. Furthermore, the amplitude-penalized forcing achieves $20\%$ suppression of the lift coefficient fluctuation compared to the optimal forcing without amplitude penalty. The current amplitude-penalized technique may offer an efficient path for fast flow modification without causing detrimental fluctuations in periodic aerodynamic and aeroelastic systems with fluid-structure interactions.

发表机构

• Tohoku University（东北大学）
• Johns Hopkins University（约翰斯·霍普金斯大学）
• Institute of Science Tokyo（东京科学研究所）

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