Dr. Rui Qu Profile

Dr. Rui Qu

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Proceedings Article | 30 April 2024 Paper

3D tracer particle field reconstruction based on 3D CNN in SAPIV

Xiangju Qu, Peng Zhang, Rui Qu, Xiao Xiao, Yulong Qi, Xiang Liu, Chang Liu

Proceedings Volume 13156, 131561E (2024) https://doi.org/10.1117/12.3018841

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The three-dimensional particle image velocimetry (3D PIV) technique, as a non-intrusive method for three-dimensional full-field velocity measurement, has garnered extensive utilization across diverse domains including biomimetic dynamics, combustion diagnostics, and the structural design of aerospace equipment. Synthetic Aperture Particle Image Velocimetry (SAPIV), based on camera arrays, digitally merges images obtained from different perspectives to simulate the imaging effects of large-aperture cameras. This approach allows for large-scale, high-resolution flow field measurements. In this study, the three-dimensional intensity characteristics of particles within sequences of refocused images are investigated. Leveraging the spatial distribution patterns of grayscale information for individual focused particles, we designed a three-dimensional convolutional neural network (3DCNN) capable of extracting focused particle positions. Throughout the particle extraction procedure, this three-dimensional CNN network systematically analyzes the sequence of refocused images and subsequently derives both particle positions and grayscale information for focused particles based on their distinctive characteristics. The tracer particle field in simulated experiments were reconstructed and the reconstruction quality was evaluated. The results demonstrate the high precision of our proposed method in reconstructing three-dimensional tracer particle information in SAPIV.

Proceedings Article | 30 April 2024 Paper

Spray droplet field reconstruction based on synthetic aperture imaging

Xiangju Qu, Hao Liu, Rui Qu, Yulong Qi, Chang Liu, Xiang Liu

Proceedings Volume 13156, 1315611 (2024) https://doi.org/10.1117/12.3017960

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Quantitative analysis of spray droplet fields plays a pivotal role in various domains, encompassing internal combustion engine combustion diagnostics, equipment spray coating and corrosion prevention, and unmanned aerial vehicle-based agricultural pesticide dispersion. Precise measurement of the spatial distribution of spray droplet fields facilitates accurate control and orientation of spraying, thereby propelling the intelligent evolution of both industrial and agricultural sectors. In light of the substantial dimensions of spray fields, achieving focused imaging of all droplets on the camera imaging plane during reconstruction proves unattainable. Addressing this challenge, this study suggests employing a four-camera array configuration. According to the characteristics of the defocusing blur of spray droplets, the cameras on the array capture images of the droplets from diverse perspectives. Subsequently, these images are merged through a refocusing process. This method offers accurate extraction of out-of-focus droplet centers. Employing three-dimensional cross-correlation analysis, the motion trajectories of the spray droplet field can be inferred with precision.

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