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基于二维经验模态分解的湍流槽道流中附着涡的识别

Identity of attached eddies in turbulent channel flows with bidimensional empirical mode decomposition.

作者信息

Cheng Cheng, Li Weipeng, Lozano-Durán Adrián, Liu Hong

机构信息

School of Aeronautics and Astronautics, Shanghai JiaoTong University, Shanghai 200240, PR China.

Center for Turbulence Research, Stanford University, CA 94305, USA.

出版信息

J Fluid Mech. 2019 Jul 10;870:1037-1071. doi: 10.1017/jfm.2019.272.

Abstract

Bidimensional empirical mode decomposition (BEMD) is used to identify attached eddies in turbulent channel flows and quantify their relationship with the mean skin-friction drag generation. BEMD is an adaptive, non-intrusive, data-driven method for mode decomposition of multiscale signals especially suitable for non-stationary and nonlinear processes such as those encountered in turbulent flows. In the present study, we decompose the velocity fluctuations obtained by direct numerical simulation of channel flows into BEMD modes characterized by specific length scales. Unlike previous works (e.g. Flores & Jiménez, vol. 22(7), 2010, 071704; Hwang, vol. 767, 2015, pp. 254-289), the current approach employs naturally evolving wall-bounded turbulence without modifications of the Navier-Stokes equations to maintain the inherent turbulent dynamics, and minimize artificial numerical enforcement or truncation. We show that modes identified by BEMD exhibit a self-similar behaviour, and that single attached eddies are mainly composed of streaky structures carrying intense streamwise velocity fluctuations and vortex packets permeating in all velocity components. Our findings are consistent with the existence of attached eddies in actual wall-bounded flows, and show that BEMD modes are tenable candidates to represent Townsend attached eddies. Finally, we evaluate the turbulent-drag generation from the perspective of attached eddies with the aid of the Fukagata-Iwamoto-Kasagi identity (Fukagata vol. 14(11), 2002, pp. L73-L76) by splitting the Reynolds shear stress into four different terms related to the length scale of the attached eddies.

摘要

二维经验模态分解(BEMD)用于识别湍流槽道流中的附着涡,并量化它们与平均表面摩擦阻力产生之间的关系。BEMD是一种自适应、非侵入式、数据驱动的多尺度信号模态分解方法,特别适用于非平稳和非线性过程,如湍流中遇到的那些过程。在本研究中,我们将通过槽道流直接数值模拟获得的速度波动分解为以特定长度尺度为特征的BEMD模态。与先前的工作(例如Flores和Jiménez,第22卷(7),2010年,071704;Hwang,第767卷,2015年,第254 - 289页)不同,当前方法采用自然演化的壁面边界湍流,无需修改纳维 - 斯托克斯方程以保持固有的湍流动力学,并尽量减少人为的数值强制或截断。我们表明,由BEMD识别的模态表现出自相似行为,并且单个附着涡主要由携带强烈流向速度波动的条纹结构和渗透在所有速度分量中的涡包组成。我们的发现与实际壁面边界流中附着涡的存在一致,并表明BEMD模态是表示汤森德附着涡的合理候选者。最后,我们借助深形 - 岩本 - 笠木恒等式(深形,第14卷(11),2002年,第L73 - L76页),通过将雷诺剪切应力分解为与附着涡长度尺度相关的四个不同项,从附着涡的角度评估湍流阻力的产生。

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