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A wide range of research problems in physics and engineering involve the acquisition of high-resolution data. Recently, deep learning has proved to be a prospective technique for super-resolution (SR) reconstruction of fluid flows. General deep learning methods develop temporal multi-branch networks to improve SR accuracy while ignoring computational efficiency. Further, the generalization ability of the deep learning model in different fluid flow scenarios is still an unstudied issue. In this article, we propose an efficient multi-scale integration network called FlowSRNet to reconstruct the high-resolution flow fields. Specifically, we elaborately design a lightweight multi-scale aggregation block (LMAB) to capture multi-scale features of fluid data, which contains a parallel cascading architecture (PCA) and feature aggregation module (FAM). The residual backbone of FlowSRNet is built by cascading the lightweight multi-scale aggregation blocks (LMABs) (cascaded blocks number N=8) in a serial manner. Also, we present a small architecture LiteFlowSRNet (cascaded blocks number N=2) for comparison. In addition, a corresponding SR dataset is constructed to train and test the proposed model, which contains different kinds of fluid flows. Finally, extensive experiments are performed on different fluid data to evaluate the performance of the proposed model. The results demonstrate that our approach achieve state-of-the-art SR performance on various fluid flow fields. Notably, our method enjoys merit of lightweight, which facilitates the development of the complicated calculation in computational fluid dynamics.

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This study analyzes the kinematic characteristics and flow field information of zebrafish in straight and steering acceleration states using time-resolved particle image velocimetry to explore how vortices are generated and controlled to achieve the desired motion. The important role of the high- and low-pressure zones in the wake of zebrafish wavy propulsion is also presented by quantifying the pressure field around the zebrafish. With their precise body and motion control, fish have a movement advantage that cannot be achieved by artificial machinery. Exploring the evolutionary process of the fish structure and surrounding flow field during zebrafish autonomous propulsion is helpful for understanding the active control means and propulsion mechanism of fish. Zebrafish were constrained in a transparent water tank, and laser and image acquisitions systems were used to capture their spontaneous movement behavior. The results show that the pull provided by the low-pressure area and the thrust provided by the high-pressure area together provide the forward power of the zebrafish. The findings from this analysis of the bending control and propulsion mechanism of the zebrafish body can facilitate the optimal design of underwater vehicle propulsion methods, such as the propulsion efficiency and maneuverability of a bionic propeller.

Motion fields estimated from image data has been widely used in physics and engineering. Time-resolved particle image velocimetry (TR-PIV) is considered as an advanced flow visualization technique that measures multi-frame velocity fields from successive images. Contrary to conventional PIV, TR-PIV essentially estimates a velocity field video that provides both temporal and spatial information. However, performing TR-PIV with high computational efficiency and high computational accuracy is still a challenge for current algorithms. To solve these problems, we put forward a novel deep learning network named Deep-TRPIV in this study, to effectively estimate fluid motions from multi-frame particle images in an end-to-end manner. First, based on particle image data, we modify the optical flow model known as recurrent all-pairs field transforms (RAFT) that iteratively updates flow fields through a Conv-GRU recurrent unit. Second, we specifically design a temporal recurrent network architecture based on this optical flow model by conveying features and flow information from previous frame. When fed N successive images, the network can efficiently estimate N-1 motion fields. Moreover, we generate a dataset containing multi-frame particle images and true fluid motions to supervised train the network. Eventually, we conduct extensive experiments on synthetic and experimental data to evaluate the performance of the proposed model. Experimental evaluation results demonstrate that our proposed approach achieves high accuracy and computational efficiency, compared with classical approaches and related deep learning models.