Recognition of a quasi-static region in a granular bed impacted with a sphere

Various characteristics such as stationary, laminar, and turbulent flow exist in the granular flow and it is critical to recognize the flow features based on the full-field velocity measurement to reveal the complex motion of granular flow. We propose a Dilated Convolution UNet++ (DC-UNet++) framework modeling of flow field images, which can recognize the features within the region in sphere impact on granular beds without using Particle Image Velocimetry (PIV). When compared with the results of the thresholding method, the accuracy of feature recognition using this model is improved by 8.3%. The accuracy of feature recognition reaches 73.44% for the flow field of transparent glass beads, which is difficult to analyze using PIV. This suggests that DC-UNet++ has a wide range of applications for granular materials that are difficult to handle using PIV and is a new tool for developing a feature recognition model for granular flow.