Simultaneous Stereo PIV and MPS3 Wall-Shear Stress Measurements in Turbulent Channel Flow

Abstract

An extended experimental method is presented in which the micro-pillar shear-stress sensor (MPS 3 ) and high-speed stereo particle-image velocimetry measurements are simultaneously performed in turbulent channel flow to conduct concurrent time-resolved measurements of the two-dimensional wall-shear stress (WSS) distribution and the velocity field in the outer flow. The extended experimental setup, which involves a modified MPS 3 measurement setup and data evaluation compared to the standard method, is presented and used to investigate the footprint of the outer, large-scale motions (LSM) onto the near-wall small-scale motions. The measurements were performed in a fully developed, turbulent channel flow at a friction Reynolds number R e τ = 969 . A separation between large and small scales of the velocity fluctuations and the WSS fluctuations was performed by two-dimensional empirical mode decomposition. A subsequent cross-correlation analysis between the large-scale velocity fluctuations and the large-scale WSS fluctuations shows that the streamwise inclination angle between the LSM in the outer layer and the large-scale footprint imposed onto the near-wall dynamics has a mean value of Θ ¯ x = 16.53 ∘ , which is consistent with the literature relying on direct numerical simulations and hot-wire anemometry data. When also considering the spatial shift in the spanwise direction, the mean inclination angle reduces to Θ ¯ x z = 13.92 ∘ .

Generalization of deep recurrent optical flow estimation for particle-image velocimetry data

“…More pronounced peaks compared to the Pas-calPIV results are detected. [27,28]. We now turn to discuss the results of internal shear flows, in particular wallnormal and wall-parallel validation measurements of a fullydeveloped TCF.…”

Machine-learning-based feedback control for drag reduction in a turbulent channel flow

“…In the present numerical study the size of the input wall variable is and with the resolution of and . In experiments, Yamagami, Suzuki & Kasagi (2005) measured along the spanwise direction ( with ), Yoshino, Suzuki & Kasagi (2008) conducted a feedback control by measuring along the spanwise direction ( with ), and Mäteling, Klaas & Schröder (2020) measured both and in the streamwise and spanwise directions ( and with and ). Therefore, the present feedback control may be realized experimentally.…”

Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

“…For instance, surface hot films [27][28][29] and wall-mounted hot-wire probes [30][31][32] can only measure temporal fluctuations at a single location, while oil-film interferometry [33][34][35] only visualizes the spatial wall-shear stress without temporal dynamics. Alternatively, emerging research focuses on developing empirical models to infer the wall-shear stress from velocity data further away from the wall [1,[36][37][38][39] or from limited sensor measurements [40,41]. However, such models are not yet used as a replacement for actual wall-shear stress data because they possess limited validity and generalizability, they typically rely on empirical coefficients, and there is no general agreement on a single model representative of all physical processes.…”