The effects of minute vortex generator jet in a turbulent boundary layer with adverse pressure gradient

Experimental and numerical analysis of active and passive flow control is an important topic of practical value in the study of turbulent flows. This paper numerically analyzed the effects of an air microjet on an adverse pressure gradient turbulent boundary layer over a flat plane. Experimental data were employed to verify the numerical modeling. Vortex formation and development were then studied by changing the microjet to inflow velocity ratio (VR) and microjet angles. According to the results, the best values of the angles [Formula: see text] and [Formula: see text] for various velocities were found to be 30° and from 60° to 90°, respectively. Moreover, at VRs = 1, 2, and 4, the [Formula: see text] values (the distance at which the complete vortex persisted in the flow) were 0.058, 0.078, and 0.18, respectively. Compared to VR = 1, the vortex strength for VRs = 2 and 4 grew by 3.5 and 6.8 times, respectively. When the microjet was added to the flow, the highest variation in the Reynolds stress along the x-direction from VR = 1–4 was 10%. The corresponding values along the y and z- directions were 15% and 2.7 times, respectively.

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A Superposition Analysis of the Turbulent Boundary Layer in an Adverse Pressure Gradient

Journal of Applied Mechanics ◽

10.1115/1.4010226 ◽

1951 ◽

Vol 18 (1) ◽

pp. 95-100

Author(s):

Donald Ross ◽

J. M. Robertson

Keyword(s):

Boundary Layer ◽

Shear Stress ◽

Wall Shear Stress ◽

Turbulent Boundary Layer ◽

Velocity Profile ◽

Pressure Gradient ◽

Adverse Pressure Gradient ◽

Pressure Recovery ◽

Stress Coefficient ◽

Wall Shear

Abstract As an interim solution to the problem of the turbulent boundary layer in an adverse pressure gradient, a super-position method of analysis has been developed. In this method, the velocity profile is considered to be the result of two effects: the wall shear stress and the pressure recovery. These are superimposed, yielding an expression for the velocity profiles which approximate measured distributions. The theory also leads to a more reasonable expression for the wall shear-stress coefficient.

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Direct numerical simulation of thermally-stratified turbulent boundary layer subjected to adverse pressure gradient

International Journal of Heat and Fluid Flow ◽

10.1016/j.ijheatfluidflow.2016.05.020 ◽

2016 ◽

Vol 61 ◽

pp. 213-228

Author(s):

Hirofumi Hattori ◽

Amane Kono ◽

Tomoya Houra

Keyword(s):

Numerical Simulation ◽

Boundary Layer ◽

Turbulent Boundary Layer ◽

Direct Numerical Simulation ◽

Pressure Gradient ◽

Adverse Pressure Gradient

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Predicting Turbulent Boundary Layer Wall Pressure Fluctuations in Adverse Pressure Gradients

Noise Control and Acoustics ◽

10.1115/imece2005-80797 ◽

2005 ◽

Author(s):

Frank J. Aldrich

Keyword(s):

Boundary Layer ◽

Turbulent Boundary Layer ◽

Pressure Gradient ◽

Pressure Fluctuations ◽

Adverse Pressure Gradient ◽

Wall Pressure ◽

Prediction Tool ◽

Pressure Gradients ◽

Wall Pressure Fluctuations ◽

The Difference

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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