High Spatial Resolution 2C-2D PIV Measurements Using A 47 MPx Sensor Of High Reynolds Number Turbulent Boundary Layer Flow
B. Sun (1), M. Shehzad (1), C. Willert (2), J.-M. Foucaut (3), C. Cuvier (3), Y. Ostovan (3), C. Atkinson (1), J. Soria (1)
(1) Laboratory for Turbulence Research in Aerospace & Combustion (LTRAC), Department of Mechanical and Aerospace Engineering, Monash University (Clayton Campus), VIC 3800, Australia
(2) German Aerospace Center (DLR), Institute of Propulsion Technology, Köln, Germany
(3) Univ. Lille, CNRS, ONERA, Arts et Métiers Institute of Technology, Centrale Lille, UMR 9014 - LMFL - Laboratoire de Mécanique des Fluides de Lille (LMFL) - Kampé de Fériet, F-59000, Lille, France
In the past decade, advances in electronics technology have made larger imaging sensors available to the experimental fluid mechanics community. These advancements have enabled the measurement of 2-component 2-dimensional (2C-2D) velocity fields using particle image velocimetry (PIV) with much higher spatial resolution than previously possible using a single camera. Although previously reported experiments have incorporated multiple-camera arrays to acquire high spatial resolution PIV, using a single large camera can greatly reduce the complexity of the experimental setup as well as the error introduced by the calibration between the cameras. In this paper, the ability of a single large sensor for high spatial resolution PIV is demonstrated by performing the measurement of a zero-pressure-gradient turbulent boundary layer (ZPG-TBL). In post-processing the PIV images, the lens distortion error is of particular importance, as the lens distortion error increases with the size of the imaging sensor. The third-order polynomial functions are used to model the lens distortion in this study, and the correction is performed on the PIV vectors to save computational cost. The first- and second-order statistics are calculated and compared with the profiles captured by small camera arrays, and the result shows that the corrected profiles agree well with the previously acquired data, therefore, the lens distortion error can be corrected.