 A novel approach to improve the accuracy of PTV methods by R. Theunissen , A. Stitou, M.L. Riethmuller von Karman Institute for Fluid Dynamics 72, chaussée de Waterloo, 1640 Rhode-Saint-Genèse, Belgium riethmuller@vki.ac.be * now at: ONERA – DMAE/C2A BP 4025, 2 avenue Edouard Belin, 31055 Toulouse Cedex 4, France Adel.Stitou@onecert.fr ABSTRACT Hybrid PIV-PTV approaches and pure PTV methods in general have been discussed by several authors, focusing on the way to perform the correct association between the particles images of successive given recordings. The present work is addressing the accuracy issue of such methods. An approach that correlates individual particles images is developed and it is referred as the IPC method. It aims at improving the efficiency compared to the classical approach using the estimation of the successive particle locations. This work is also addressing the redistribution of the unstructured field obtained by the PTV process towards a regular grid which is a convenient step for further data processing. As a matter of fact, it was observed the re-interpolation of the data could be advantageous to decrease the random error. Nevertheless, in some cases it is true in others not and the improvement of the IPC method can be hided. A first explanation is given by performing a Monte-Carlo simulation of the redistribution process of the AGW scheme. It is observed that the sampling error of this simple interpolator is indeed hiding the quality of the measurement. An assessment of the proposed approach is considered with a methodology based on the use of synthetic images of a reference flow field with homogeneous velocity fluctuations. The purpose of this test case is to evaluate the behavior of the SRPIV method in a turbulent-like flow field containing small fluctuations and to compare it with the response of a correlation-based PIV algorithm that integrate up-to-date processing features. The improvement of the IPC method is showed respect to the PTV associated with an estimator of the particles positions such as the gaussian fit. The artifact due to the redistribution process is also clearly highlighted. In this case, the accuracy after re-interpolation is determined and limited by the spacing of the data since both PTV approaches gives the same results. Moreover, although PTV is intrinsically less accurate than correlation-based PIV, the SRPIV performs in this case better than the correlation method. It has to be undoubtedly credited to the higher spatial sampling of the SRPIV.