Light reflections detection and correction for Robotic Volumetric PIV

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Abstract

Laser light reflection mitigation in Particle Image Velocimetry (PIV) is crucial for accurate flow
field measurements. While numerous methods exist for planar PIV, fewer have been developed for volumetric PIV systems, especially for coaxial setups like Robotic PIV. Light reflections in volumetric PIV experiments result in high-intensity regions that corrupt particle detection and analysis.

This study presents three novel approaches for treating light reflections in Robotic PIV experiments. The first and second methods use image filtering and masking techniques in the
wavenumber space to separate particle images from reflection regions. The first technique called Spatial Fourier Filter involves decomposing the image signal into low- and high-wavenumber components using the 2D discrete Fourier transform (DFT). A high-pass filter is then applied to attenuate the intensity of reflection regions. Then, the second methodology Spatial Fourier Filter + Mask takes the resulting image from the first method and performs a step of automated adaptive masking to remove residual reflection areas that the filtering approach is not able to eliminate. The third methodology named 3D-based Particle Concentration Mask acts in a later stage of the processing pipeline, creating a 3D mask on the instantaneous processed Shake-the-Box data by analysing the particle concentration distribution over the flow domain.

The proposed methods are tested on experimental data obtained from experiments performed with Robotic PIV on three different geometries: a side-view mirror, Formula 1 car and a propeller. The tests were conducted at one of TU Delft Aerospace Engineering Faculty’s facilities, the W-tunnel in the High-Speed Laboratory (HSL). Comparison between raw and pre-processed images, as well as particle tracking results, is presented.

The results from this data comparison show unsatisfactory outcomes from both Spatial
Fourier Filter and 3D-based Particle Concentration Mask, which fail to fully remove the spurious regions. Nevertheless, the results confirm the successful removal of reflection-induced artifacts in instantaneous images by using the spatial Fourier filter automated masking approach. The developed image pre-processing strategy effectively removes reflection regions in Robotic PIV images, preventing the appearance of spurious particle tracks. The method shows promising results mitigating unsteady light reflections in Robotic PIV, improving the accuracy of flow field measurements. Additional attention is required in the PIV sequence creation step to ensure an adequate level of overlap between measurement volumes. This facilitates addressing the spatial gaps introduced by the masking procedure, that have been proven to robustly be filled in by the multi-view advantage offered by Robotic PIV.