论文标题
在湍流旋流喷气机中噪声驱动的全球不稳定的随机建模
Stochastic modelling of a noise driven global instability in a turbulent swirling jet
论文作者
论文摘要
开发了一种方法来估算从极限周期振荡的测量数据中湍流中全局流体动力不稳定的特性。为此,流动动力学在代表全局模式的确定性贡献和代表内在湍流强迫的随机贡献的确定性贡献中分开。开发了随机模型,以说明两者之间的相互作用并允许从固定数据确定流量的动态特性。确定性的贡献是通过振幅方程来建模的,该方程描述了不稳定性的振荡动力学,并以均值场模型的第二种方法来建模,该模型还捕获了不稳定性与平均流校正之间的相互作用。随机贡献被认为是有色噪声强迫,代表了随机湍流扰动的光谱特征。该方法将其应用于具有主导全局模式的湍流旋转喷气机。进行PIV测量是为了确保模式是最优势的相干结构,进一步的压力测量为模型校准提供了长时间序列。超临界的HOPF分叉是从全局模式的线性成长中确定的,并且测量统计和估计统计数据之间的出色一致性表明该模型捕获了相关的动力学。这项工作表明,极限周期振荡的唯一观察不足以确定湍流的稳定性,因为随机扰动掩盖了实际分叉点。但是,在动态模型中提出的确定性和随机贡献的分离允许从固定测量中识别流程。
A method is developed to estimate the properties of a global hydrodynamic instability in turbulent flows from measurement data of the limit-cycle oscillations. For this purpose, the flow dynamics are separated in deterministic contributions representing the global mode and a stochastic contribution representing the intrinsic turbulent forcing. Stochastic models are developed that account for the interaction between the two and that allow determining the dynamic properties of the flow from stationary data. The deterministic contributions are modelled by an amplitude equation, which describes the oscillatory dynamics of the instability, and in a second approach by a mean-field model, which additionally captures the interaction between the instability and the mean-flow corrections. The stochastic contributions are considered as coloured noise forcing, representing the spectral characteristics of the stochastic turbulent perturbations. The methodology is applied to a turbulent swirling jet with a dominant global mode. PIV measurements are conducted to ensure that the mode is the most dominant coherent structure and further pressure measurements provide long time series for the model calibration. The supercritical Hopf bifurcation is identified from the linear growthrate of the global mode and the excellent agreement between measured and estimated statistics suggest that the model captures the relevant dynamics. This work demonstrates that the sole observation of limit-cycle oscillations is not sufficient to determine the stability of turbulent flows since the stochastic perturbations obscure the actual bifurcation point. However, the proposed separation of deterministic and stochastic contributions in the dynamical model allows identifying the flow sate from stationary measurements.