14th International Symposium on Particle Image Velocimetry – ISPIV 2021 August 1–5, 2021 Examining the Effects of Fluid Velocity Gradients on 4D Digital Holographic PIV/PTV Measurements Y. J. Xia∗, B. Sun, G. A. Ahmed, J. Soria 1 Laboratory for Turbulence Research in Aerospace & Combustion (LTRAC), Department of Mechanical and Aerospace Engineering, Monash University (Clayton Campus), Melbourne, Victoria 3800, Australia ∗ Yuan.Xia@monash.edu Abstract 4D digital holographic PIV/PTV (4D-DHPIV/PTV) methods have demonstrated theoreti- cal viability due to their relative ease of setup and high spatial resolution (Soria (2018)). This study investigates how velocity gradients related to different flow regimes and their magnitudes affect 3-component–3-dimensional (3C-3D) digital holographic PIV measure- ment uncertainty. Figure 1: Diagram of simulated digital holographic PIV/PTV setup The error introduced by velocity gradients within the interrogation volume is studied by simulating particles in a velocity field, with a given constant velocity gradient super- imposed on a uniform flow from which a time-series of hologram pairs are generated and the 3C-3D velocity fields and their errors are determined using 4D-DHPIV/PTV Sun et al. (2020). Hologram pairs are simulated by modelling the propagation and particle- diffraction of coherent laser light using the angular spectrum method (Goodman (1996)). The hologram reconstruction then involves direct reconstruction, followed by deconvo- lution, a particle position refinement and a hologram subtraction step (Sun et al. (2020)). The particle positions obtained from 4D-DHPIV/PTV are then used to resolve particle dis- placement measurements using 3D cross-correlation digital analysis with a 3D Gaussian fit to sub-pixel resolution (Soria (2006)). The effects of velocity gradients on the displacement uncertainty and bias error have been investigated by undertaking Monte Carlo simulations under a range of velocity gradient environments. Specifically, 5 common velocity gradients have been studied, which included pure strain, pure vorticity and x, y and z-directional shear. (a) Uniform mean velocity (b) Pure strain velocity (c) Pure vorticity velocity (d) Pure x shear velocity (e) Pure y shear velocity (f) Pure z shear velocity Figure 2: The 6 different flow regime displacement vector plots used in this study. All test cases are subjected to a mean velocity field (a) superimposed with a velocity gradient flow regime (b-f). Vector plots (b-f) have been scaled by a factor of 2 for visualisation purposes. All axes are in pixels. The results indicate that the novel 4D-DHPIV/PTV has poorer accuracy and precision in the z-propagation axis, resulting in larger minimum uncertainties and bias errors. The errors in the z axis are also significantly less affected by velocity gradients in the z direction when compared to the effects of x and y directional velocity gradients on x and y errors respectively. Furthermore, the rate of cross-correlation maximum and SNR decrease are approximately 1.36 times slower due to velocity gradients in the z axis than other axes. Acknowledgements The authors gratefully acknowledge the support of this project through ARC and NC- MAS (NCI, Massive). Yuan Jing Xia gratefully acknowledges the support through James McNeill Foundation and Monash University Scholarships. References Goodman J (1996) Foundations of scalar diffraction theory. in Introduction to Fourier Optics. pages 55–57. McGraw Hill, New York City, USA Soria J (2006) Particle image velocimetry - application to turbulence studies. in Lecture Notes on Turbulence and Coherent Structures in Fluids, Plasmas and Nonlinear Media. pages 307–347. World Scientific, Singapore Soria J (2018) Three-component three-dimensional (3c-3d) fluid flow velocimetry for flow turbulence investigations. in Proceedings of the 21st Australasian Fluid Mechanics Confer- ence, Adelaide, Australia, December 10-13 Sun B, Ahmed A, Atkinson C, and Soria J (2020) A novel 4d digital holographic piv/ptv (4d- dhpiv/ptv) methodology using iterative predictive inverse reconstruction. Measurement Science and Technology 31