Study of a stall cell using stereo particle image velocimetry

Abstract

The structure of Stall Cells (SCs) on wings is analyzed on the basis of stereo particle image velocimetry measurements. All experiments regard a Reynolds number 0.87 × 106 flow around a rectangular wing with endplates and an aspect ratio of 2.0. The inherently unstable stall cell is stabilized by means of a localized spanwise disturbance. Velocity, vorticity, and Reynolds stress data above the wing and in the wake are presented and discussed, also in combination with Computational Fluid Dynamics data. The present study completes and clarifies the previously suggested models regarding the SC structure. The SC emerges in between the separation and trailing edge shear layer where three different types of vortices are identified: (a) the stall cell vortices that start normal to the wing surface and continue downstream aligned with the free stream, (b) the separation line vortex, and (c) the trailing edge line vortex that both run parallel to the wing trailing edge and grow significantly at the center of the stall cell. Analysis of the Reynolds stress data reveals high anisotropy. Concentration of high streamwise shear stress values is connected to the two shear layers and high cross shear Reynolds stresses are connected to vortex stretching. High normal Reynolds stress values are observed (a) in the separation but not in the trailing edge shear layer indicating the flapping of the former and (b) along the stall cell vortices indicating their wandering motion. The eddy viscosity based Reynolds averaged Navier-Stokes simulations are found in good qualitative agreement with the experiments in terms of the type and position of the identified vortex structures, an agreement which is linked to the correct trend in the predicted shear Reynolds stresses distributions. Quantitative deviations of the numerical results from the measurements are attributed to the isotropic definition of the turbulence model. Therefore, use of large eddy simulation is suggested for better prediction of the flow.