Experimental study on dominant vortex structures in near-wall region of turbulent boundary layer based on tomographic particle image velocimetry

Abstract

Vortex structures are very popular research objects in turbulent boundary layers (TBLs) because of their prime importance in turbulence modelling. This work performs a tomographic particle image velocimetry measurement on the near-wall region ( $y<0.1\unicode[STIX]{x1D6FF}$ ) of TBLs at three Reynolds numbers $Re_{\unicode[STIX]{x1D70F}}=1238$ , 2286 and 3081. The main attention is paid to the wall-normal evolution of the vortex geometries and topologies. The vortex is identified with swirl strength ( $\unicode[STIX]{x1D706}_{ci}$ ), and its orientation is recognized by using the real eigenvector of the velocity gradient tensor. The vortex inclination angles in the streamwise–wall-normal plane and in the streamwise–spanwise plane as functions of wall-normal positions are investigated, which provide useful information to speculate on the three-dimensional shape of the vortex tubes in a TBL. The difference between the orientations of vorticity and swirl is discussed and their inherent relationship is revealed based on the governing equation of vorticity. Linear stochastic estimation (LSE) is further deployed to directly extract three-dimensional vortex models. The LSE velocity fields for ejection events happening at different wall-normal positions shed light on the evolution of the topologies for the vortices dominating ejection events. LSE based on a centred prograde spanwise vortex provides a typical packet model, which indicates that the population density of the packets in a TBL is large enough to leave footprints in conditionally averaged flow fields. This work should help to settle the severe debate on the existence of packet structures and also lays some foundation for the TBL model theory.

Wall-attached and wall-detached eddies in wall-bounded turbulent flows

“…Near the wall, turbulence is self-sustaining through a regeneration process independent of the outer motions (Hamilton, Kim & Waleffe 1995;Waleffe 1997;Jiménez & Pinelli 1999;Schoppa & Hussain 2002). In the logarithmic layer and the outer layer, there exist large-scale structures (Adrian 2007;Marusic et al 2010;Smits, McKeon & Marusic 2011;Jiménez 2018), which are further categorized into large-scale motions, hairpin packets (Kovasznay, Kibens & Blackwelder 1970;Brown & Thomas 1977;Adrian, Meinhart & Tomkins 2000;Ganapathisubramani, Longmire & Marusic 2003;Tomkins & Adrian 2003;Deng et al 2018;Wang et al 2019), very-large-scale motions, superstructures (Kim & Adrian 1999;Guala, Hommema & Adrian 2006;Balakumar & Adrian 2007;Hutchins & Marusic 2007;Lee & Sung 2011;Wang & Zheng 2016), etc. These outer motions superimpose on top of and modulate the small-scale inner motions (Rao, Narasimha & Narayanan 1971;Bandyopadhyay & Hussain 1984;Abe, Kawamura & Choi 2004;Hutchins & Marusic 2007;Mathis, Hutchins & Marusic 2009;Chung & McKeon 2010;Ganapathisubramani et al 2012;Dogan et al 2019).…”

Vortex-to-velocity reconstruction for wall-bounded turbulence via the field-based linear stochastic estimation

“…In our previous work (Wang et al. 2019), we compared the DNS data with 3-D PIV data regarding several aspects, including the p.d.f. of the vortex orientation angles and the conditionally averaged vortex structures.…”

“…Also based on criterion, Wang et al. (2019) extended the work of Gao et al. (2011) and offered full information of vortex orientation, shapes and organizations for larger Reynolds-number range based on tomographic PIV data and DNS data.…”

“…Besides, the vorticity direction could also deviate from the vortex orientation (Wang et al. 2019). Extending application range for the vortex model to the buffer region will pose new challenges for the V2V reconstruction, which necessitates new methods in place of the Biot–Savart law.…”

Theory and applications of the vortex-surface field

“…(a) 超声速边界层转捩实验中获取灰度图像; (b) 图像尺度分解后计算小尺度结构 壁面倾角; (c) 基于壁面倾角预测转捩中的摩擦阻力增长 Figure 8 (Color online) Image-based modeling of the skin friction coefficient in transition [69] . 未 来 工 作 中 需 要 结 合 近 年 高 精 度 大 规 模 DNS [49,60] 、三维时空解析与近壁测量实验技术 [68,74,75] 及数据驱动建模算法 [76] 的进步, 系统发展完善基于涡…”