Super-resolution reconstruction of propeller wake based on deep learning

Abstract

This paper proposes a super-resolution (SR) reconstruction method based on deep learning, which efficiently reconstructs the global high-resolution wake flow field from the low-resolution (LR) wake data of a propeller. The extensive wake data for the propeller under various operating conditions are generated using numerical simulations based on a delayed detached eddy simulation model. The proposed approach, propeller super-resolution convolutional neural networks (PSCNN), uses a dilated convolutional module to capture multi-scale spatial characteristics of wake flow fields. The performance of the proposed SR method is evaluated by improving the resolution of the wake flow field under different scaling factors, and its superiority is demonstrated by comparing the reconstruction accuracy with that of two other typical SR reconstruction methods. The results indicate that PSCNN can effectively improve the resolution of the propeller wake flow field by 32 times, with an overall mean relative error of the three velocity components being less than 4.0%, and the reconstructed global SR wake flow field agrees well with the ground truth in spatial distribution variation. Furthermore, PSCNN can reconstruct the SR wake flow field with reasonable accuracy under unseen operating conditions, further proving the generalizability of the proposed SR model in capturing spatial relationships of the propeller wake. Overall, the proposed SR reconstruction method has significant applications in obtaining high-resolution flow snapshots in fluid experiments.