High-Reynolds-number turbulent-boundary-layer wall-pressure fluctuations with dilute polymer solutions

2010 ◽  
Vol 22 (8) ◽  
pp. 085104 ◽  
Author(s):  
Brian R. Elbing ◽  
Eric S. Winkel ◽  
Steven L. Ceccio ◽  
Marc Perlin ◽  
David R. Dowling
2004 ◽  
Author(s):  
Brendan F. Perkins

In order to better understand boundary layer turbulence at high Reynolds number, the fluctuating wall pressure was measured within the turbulent boundary layer that forms over the salt playa of Utah’s west desert. Pressure measurements simultaneously acquired from an array of nine microphones were analyzed and interpreted. The wall pressure intensity was computed and compared with low Reynolds number data. This analysis indicated that the variance in wall pressure increases logarithmically with Reynolds number. Computed autocorrelations provide evidence for a hierarchy of surface pressure producing scales. Space-time correlations are used to compute broadband convection velocities. The convection velocity data indicate an increasing value for larger sensor separations. To the author’s knowledge, the pressure measurements are the highest Reynolds number, well resolved measurements of fluctuating surface pressure to date.


Author(s):  
Peter A. Chang ◽  
Meng Wang ◽  
Jonathan Gershfeld

ATTACHED, wall-bounded flows impose computational requirements on LES that increase drastically with Reynolds number. For that reason, even simple geometries, such as airfoils at small angles of attack, with spanwise uniform section shape, challenge the bounds of LES as chord-based Reynolds numbers increase much above 1 million. Of particular concern is the ability of LES to predict the occurrence, and strength of, weak vortex shedding from the airfoil trailing edge (by weak vortex shedding we mean that the acoustic vortex shedding signature may rise only a few decibels above that for the broadband turbulent boundary layer acoustic sources). Correct prediction of weak vortex shedding may depend on accurately predicting the flow over the entire airfoil that includes the attached, turbulent upstream flow, adverse pressure gradient and separated flow regions and finally, the turbulent wake. This paper compares results of two full-LES and two LES with wall-stress model for the flow about a modified NACA 0016 airfoil with a 41° trailing edge apex angle and a slightly convex pressure side. Comparisons of vortex shedding, as measured by the power spectral density (PSD) of wall pressure fluctuations (WPF) on the pressure side of the TE and the PSD of the vertical velocity fluctuations in the wake are made. The results indicate that vortex shedding predictions are dependent upon the stream-wise and spanwise grid resolution. In order to reduce the large computational times required for simulating the high-Reynolds number flows with fully-resolved LES, a wall-stress model that solves the turbulent boundary layer equations in the near-wall region is applied. Compared with the fully-resolved LES, the LES with wall-stress simulations require about 20 percent the number of grid points and require about 10 percent of the computational time. However, the LES with wall stress model results under-predict the vortex shedding peak in the wake and are not able to predict the vortex shedding signature in TE wall pressure spectra. These results indicate that near-wall turbulence structures need to be resolved in order to correctly predict the occurence and strength of vortex shedding.


Author(s):  
Frank J. Aldrich

A physics-based approach is employed and a new prediction tool is developed to predict the wavevector-frequency spectrum of the turbulent boundary layer wall pressure fluctuations for subsonic airfoils under the influence of adverse pressure gradients. The prediction tool uses an explicit relationship developed by D. M. Chase, which is based on a fit to zero pressure gradient data. The tool takes into account the boundary layer edge velocity distribution and geometry of the airfoil, including the blade chord and thickness. Comparison to experimental adverse pressure gradient data shows a need for an update to the modeling constants of the Chase model. To optimize the correlation between the predicted turbulent boundary layer wall pressure spectrum and the experimental data, an optimization code (iSIGHT) is employed. This optimization module is used to minimize the absolute value of the difference (in dB) between the predicted values and those measured across the analysis frequency range. An optimized set of modeling constants is derived that provides reasonable agreement with the measurements.


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