The research in resolving the smaller scales of turbulence has gained significant attention in recent years, owing to the advancements in the measurement techniques. High resolution experimental studies are required to investigate high Reynolds number flows where the scales of tu
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The research in resolving the smaller scales of turbulence has gained significant attention in recent years, owing to the advancements in the measurement techniques. High resolution experimental studies are required to investigate high Reynolds number flows where the scales of turbulence are very small. This is of particular importance in addressing the theoretical issues in studying high Reynolds number wall turbulence which finds applications in transport, energy, and aerospace industries. Hot-Wire Anemometry (HWA) is often employed to reach very high spatial resolution. Early research in Particle Image Velocimetry (PIV), a qualitative flow visualization technique, was not able to achieve a higher resolution as comparable with that of HWA. However, recent developments in PIV allow high resolution measurements, using information on the time averaged ensemble correlation.
The ensemble correlation can be used to reduce the size of the interrogation windows employed in a PIV analysis, without compromising for the particle image density in an interrogation window. This is achieved by using a large number of image pairs. In this thesis, ensemble correlation is used to study the turbulence in two specific cases: a turbulent jet and high Reynolds number pipe flow. The ensemble correlation, though being a time averaged quantity, contains information on the velocity Joint Probability Distribution Functions (JPDFs) which can be used to estimate the turbulence statistics. This information can be retrieved from the shape of the ensemble correlation. It is assumed that the ensemble correlation is the convolution of the auto-correlation and the velocity JPDFs. The velocity JPDFs are retrieved by estimating the second moments of the ensemble correlation by fitting a Gaussian profile, and further using a correction for auto-correlation. The second moments can be used to estimate the Reynolds stresses in the flow.
The proposed method is validated by implementing it to study the turbulent jet. For this purpose, 9000 image pairs are acquired. The results show that the retrieved moments predict the Reynolds stresses accurately. Further, high Reynolds number pipe flow experiments are performed in the Alpha Loop facility at Deltares. The bulk Reynolds number is varied from 0.3 to 0.6 million, acquiring 20,000 image pairs per measurement series. Also, the pressure drop is measured across the pipe along with the PIV measurements to estimate the roughness of the pipe. The results from the PIV measurements show that the mean velocity profile follows a similar trend as observed in the literature. A small bias is observed between the mean velocity obtained from the ensemble correlation and that obtained by averaging the instantaneous velocity vectors, in regions of high velocity gradients. The Reynolds shear stress estimated from the shape of the ensemble correlation is underestimated. However, the streamwise turbulent fluctuations estimated follow a trend, similar to that observed in the literature.