| In part I, the response of turbulence subjected to planar straining and de-straining is studied experimentally, and the impact of the applied distortions on the energy transfer across different length scales is quantified. The data are obtained using Planar Particle Image Velocimetry (PIV) in a water tank, in which high Reynolds number turbulence with very low mean velocity is generated by an array of spinning grids. Planar straining and de-straining mean flows are produced by pushing and pulling a rectangular piston towards, and away from, the bottom wall of the tank. The data are processed to yield the time evolution of Reynolds stresses, anisotropy tensors, turbulent kinetic energy production, and mean subgrid-scale (SGS) dissipation rate at various scales. During straining, the production rises rapidly. After the relaxation period the small-scale SGS stresses recover to isotropy, but the Reynolds stresses still display significant anisotropy. Thus, when destraining is applied, a strong negative production (mean backscatter) is observed where turbulence fluctuations return kinetic energy to the mean flow.; In part II, a new PIV cross-correlation analysis algorithm is introduced. The Peak-locking effect causes mean bias in most of the existing cross-correlation based algorithms for PIV data analysis. This phenomenon is inherent to the smooth curve-fitting through discrete correlation values, which are used to obtain the sub-pixel part of the displacement. Almost all of the existing effective methods to solve this problem require iterations. In this thesis we introduce a new technique for obtaining sub-pixel accuracy, which bypasses the sub-pixel curve fitting, and thus eliminates the peak-locking effect, but does not require iterations. The principles of the "Correlation Mapping Method" are based on the following logic: If one uses a bi-cubic interpolation to express the second image based on the first one and the unknown displacement, the correlation between them becomes a polynomial of the displacement, whose coefficients depend on the first image. Matching this polynomial with the measured correlation provides an equation for the displacement for each point of the correlation map. (Abstract shortened by UMI.)... |
