This paper investigates the impact behavior between water drops with different velocities and cylindrical superhydrophobic surfaces with various diameters and presents two possible outcomes of drop impact, which are asymmetric rebound and stretched breakup. Due to the special cylindrical topology of the surface, drops undergo an asymmetric spreading and retracting process in the azimuthal and the axial direction, which results in three types of asymmetric rebound, including jug-like rebound, wing-like rebound, and rebound breakup. The stretched breakup is observed in the collision of drops with higher impact velocities and smaller cylinder diameters. The diameter ratio D* and Weber number We are found to be the determinants of the bouncing patterns. With the decrease in the diameter ratio D* or the increase in the Weber number We, the bouncing patterns transformed from jug-like rebound through wing-like rebound and finally to stretched breakup. We put forward a modification form of the Weber number (α = We/D*) affected by the diameter ratio D*, indicating the ratio between the inertia force and the surface tension, as the criterion to distinguish the upward rebound from the downward stretch, which helps obtain the linear relation of critical We_cr and D*_cr. Furthermore, asymmetric rebound and stretched breakup could effectively shorten the contact time between drops and substrates. The contact time is found to be mainly determined by the dimensionless parameter α. The correlation between the dimensionless contact time and the dimensionless parameter α is demonstrated to be τ_c ∝ αⁿ.

In many fluid experiments, we can only obtain low-spatial high-temporal resolution flow images and high-spatial low-temporal resolution flow images due to the limitation of high-speed imaging systems. To solve this problem, we proposed a degradation and super-resolution attention model (D-SRA) using unsupervised machine learning to super-resolution reconstruct high resolution (HR) time-resolved fluid images from coarse data. Unlike the prior research to increase the resolution of coarse data artificially generated by simple bilinear down-sampling, our model that consists of a degradation neural network and a super-resolution neural network aims to learn the mappings between experimental low-resolution data and corresponding HR data. What is more, channel and spatial attention modules are also adopted in D-SRA to facilitate the restoration of abundant and critical details of flow fields. The proposed model is validated by two high-speed schlieren experiments of under-expanded impinging supersonic jets. The comprehensive capability of D-SRA is statistically analyzed based on the synthetic unpaired schlieren images. The spatial-resolution of coarse images can be successfully augmented by $4^2$ times and $8^2$ times with most physical details recovered perfectly, which outperforms the existing method. The D-SRA also exhibits considerable generalization and robustness against unknown-degenerated schlieren images. Moreover, the practicability of the proposed method is also further explored on real unpaired jets schlieren images. It is convincingly demonstrated that the present study successfully surpasses the performance limitations of high-speed cameras and has significant applications in various fluid experiments to obtain flow images with high spatial and temporal resolution.