Modeling the Wavevector-Frequency Spectrum of Boundary-Layer Wall Pressure During Transition on a Flat Plate

1990 ◽  
Vol 112 (4) ◽  
pp. 523-534 ◽  
Author(s):  
M. A. Josserand ◽  
G. C. Lauchle

A spectral model for the wall-pressure fluctuations induced on a zero pressure gradient, flat, rigid surface by a transitioning boundary layer at low Mach number is developed in this paper. The central assumption used in this modeling is that the space-time statistics associated with the formation, convection, and interaction of turbulent spots in a naturally occurring boundary-layer transition are independent of the space-time statistics of the wall-pressure fluctuations that are induced by the turbulence in the individual spots. Space-time correlations for the spots were determined experimentally and semi-empirical formulae are developed to predict these correlations. Previously published statistical descriptions of turbulence-induced wall-pressure fluctuations are coupled with the spot statistics to arrive at the model for the wavevector-frequency spectrum of the transition region. The basic result suggests that the wall-pressure wavevector-frequency spectrum of a transitioning boundary layer is approximately that produced by a fully-turbulent layer weighted by the intermittency factor.

2011 ◽  
Vol 671 ◽  
pp. 288-312 ◽  
Author(s):  
MATTEO BERNARDINI ◽  
SERGIO PIROZZOLI ◽  
FRANCESCO GRASSO

The structure of wall pressure fluctuations beneath a turbulent boundary layer interacting with a normal shock wave at Mach number M∞ = 1.3 is studied exploiting a direct numerical simulation database. Upstream of the interaction, in the zero-pressure-gradient region, pressure statistics compare well with canonical low-speed boundary layers in terms of fluctuation intensities, space–time correlations, convection velocities and frequency spectra. Across the interaction zone, the root-mean-square wall pressure fluctuations attain very large values (in excess of 162 dB), with a maximum increase of about 7 dB from the upstream level. The two-point wall pressure correlations become more elongated in the spanwise direction, indicating an increase of the pressure-integral length scales, and the convection velocities (determined from space–time correlations) are reduced. The interaction qualitatively modifies the shape of the frequency spectra, causing enhancement of the low-frequency Fourier modes and inhibition of the higher ones. In the recovery region past the interaction, the pressure spectra collapse very accurately when scaled with either the free-stream dynamic pressure or the maximum Reynolds shear stress, and exhibit distinct power-law regions with exponent −7/3 at intermediate frequencies and −5 at high frequencies. An analysis of the pressure sources in the Lighthill's equation for the instantaneous pressure has been performed to understand their contributions to the wall pressure signature. Upstream of the interaction the sources are mainly located in the proximity of the wall, whereas past the shock, important contributions to low-frequency pressure fluctuations are associated with long-lived eddies developing far from the wall.


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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