Machine Learning-Assisted Measurement of Three-Dimensional Particle Self-Rotation and Revolution in Swirling Flow...

Accurate characterization of particle dynamics in complex swirling flows is essential for understanding flow behavior and transport mechanisms. However, extracting three-dimensional (3D) rotational motion parameters of particles from High-Speed Motion Analyzer (HSMA) imaging experiments remains challenging due to the limitations of conventional manual frame-by-frame analysis, which suffers from low efficiency, subjective errors, and poor scalability. In this study, an intelligent imaging-based sensing system is proposed for automated 3D measurement of coupled particle self-rotation and revolution motion in hydrocyclone swirling fields. The proposed system integrates dual orthogonal high-speed cameras with a machine learning-driven processing framework, enabling an end-to-end workflow consisting of particle detection, multi-object tracking, motion-state recognition, kinematic parameter calculation. To achieve robust particle detection under different imaging conditions, You Only Look Once version 8 (YOLOv8) detectors are trained following a mixed-view strategy: the Y-view detector is trained on the Y-camera data, whereas the X-view detector is trained on the merged dual-camera data. ByteTrack is subsequently employed to associate particle detections across consecutive frames and generate continuous trajectories. For motion-state recognition, EfficientNet-B1 is adopted to classify the relative positional states of internal particle markers. Furthermore, a Self-Attention Generative Adversarial Network (SAGAN) is introduced for minority-class sample generation to alleviate class imbalance and improve classification robustness.Based on the extracted trajectories and state information, a kinematic model is integrated to automatically estimate particle revolution and self-rotation velocities. Experimental results demonstrate that the proposed YOLOv8 detectors achieve reliable detection performance, with mean average precision at an Intersection over Union (IoU) threshold of 0.5 (mAP@0.5) values of 79.84% and 71.62% for the two imaging views, respectively. The combined YOLOv8-ByteTrack framework achieves Higher Order Tracking Accuracy (HOTA) and Multiple Object Tracking Accuracy (MOTA) values of 63.791 and 90.49, respectively. After SAGAN-based data augmentation, the classification accuracy of EfficientNet-B1 increases from 98.62% to 99.54%. System-level validation using more than 60 randomly selected particle trajectories shows average measurement accuracies of 97.59% for revolution velocity and 91.8% for self-rotation velocity, while reducing the processing time from more than 600 s manually to 11.1 s per trajectory. The proposed machine learning-assisted particle 3D motion sensing system provides an efficient and reliable solution for automated extraction of particle kinematic information under complex flow imaging conditions, offering new opportunities for intelligent monitoring and mechanism analysis of multiphase flow systems.