The Influence of Surface Roughness on Supersonic High Reynolds Number Turbulent Boundary Layer Flow PDF Download

Author: Robert M. Latin
Publisher:
ISBN: 9781423562139
Category : Surface roughness
Languages : en
Pages : 362

Get Book Here

Book Description
A comprehensive study of rough-wall high speed (M=2.9) high Reynolds number (Re/m = 1.9. 10 to the 7th power) turbulent boundary layer flow was performed consisting of experimental, analytical, and numerical methods. Six wall topologies consisting of a smooth and five rough surfaces (two and three dimensional machined roughness plates; and 80, 36, and 20 grit sand-grain roughened plates) were studied. A confocal laser scan microscope was used to measure the topography of the sand grain roughnesses. The experimental measurement techniques included a convention Pitot pressure probe, laser Doppler velocimetry, hot wire anemometry; color schlieren and laser sheet Mie scattering images. Mean measurements included velocity, Mach number, density, and mass flux. Turbulent measurements included velocity and mass flux turbulence intensities, kinematic Reynolds shear stress, compressible Reynolds shear stress in two planes, and the traverse apparent mass flux. Kinematic turbulent flow statistical properties were found to scale by local mean quantities and displayed a weak dependence on surface roughness. Turbulent flow statistical properties with the explicit appearance of density did not scale by local mean quantities, and had a strong linear dependence on roughness. Surface roughness also had a significant effect on the flow structure size, angles, and energy spectra. A theoretical analysis was performed and a new integral method for the estimation of skin friction was developed. The skin friction estimates were within 4% of compressible semi-empirical relations. A numerical study was performed which used a parabolized Navier Stokes solver with two algebraic turbulence models and the Rotta model for surface roughness. A new method for the estimation of momentum loss improved the numerical flow predictability.