Kunwei Lin

Gas–liquid flow in millimeter-scale tubes exhibits transient dynamics at both bulk and individual-bubble scales, which are difficult to measure simultaneously under low-frame-rate imaging. This study develops a computer-vision framework for synchronized measurement of bulk flow parameters and Lagrangian bubble motion in bubbly and slug flows using single-view 30 fps backlit image sequences. Attention-UNet segmentation is combined with a kinematically constrained matching strategy based on foreground-normalized mask overlap and an inertial continuity constraint. The framework extracts a relative image-derived gas-occupancy proxy, global bubble velocity Uglobal, individual-bubble trajectories, projected-area variation, image-derived apparent near-wall clearance, and in-plane nose curvature from the same video stream. Cross-validation yielded a segmentation Dice coefficient of 0.9736. Synthetic-displacement tests gave a mean relative offset error of 0.95%, and the representative type-B uncertainty in individual-bubble velocity was 0.47%. The global bubble-velocity signal contained a dominant low-frequency component whose frequency increased nearly linearly with U¯global (R2=0.984); the fitted slope corresponded to an apparent advective length of 1.52 m, close to the apparatus length and supporting a facility-scale interpretation. At the bubble scale, the higher Weber-number end-member group exhibited lower mean in-plane nose curvature. The framework therefore enables synchronized characterization of millimeter-scale gas–liquid flow at global and bubble-resolved scales using a single low-frame-rate image sequence.

This study proposes a hybrid flow-field modeling framework that integrates physics-based simulation, parameter-space interpolation, and deep learning for rapid and high-accuracy prediction of wind farm wakes under uniform yaw conditions. First, a steady-state CFD yaw-wake simulation is developed to produce physically consistent and high-quality dataset. Second, this paper rigorously proves the Lipschitz continuity of steady yaw wake solutions and their image representations with respect to the input parameters. Based on this theoretical basis, we propose the Parameter-Space Intermediate Flow Estimation method (PS-IFE) for steady wake image augmentation, which recasts video frame interpolation into the parameter space to generate intermediate-condition steady wakes. Finally, a lightweight deep neural network (DNN) is trained on the augmented dataset for data-driven uniform-yaw wake modeling. In a 55 wind farm with 7D spacing using the NREL 5 MW turbine, PS-IFE achieves augmentation errors as low as RMSE: 0.063 m/s and MAPE: 0.584%, outperforming conventional augmentation algorithms with tailored superiority. Sensitivity analysis further recommends m/s for reliable interpolation path design. The resulting data-driven yaw wake model attains RMSE: 0.127 m/s and MAPE: 1.041%, about 75% lower than the Bastankhah model, while maintaining millisecond-level inference time. The proposed framework maintains physical consistency while achieving both high accuracy and high efficiency, with strong scalability to other complex flow-field modeling scenarios.