Missing compensation of flow field in pump jet based on U-shaped attention-driven deformable generative adversarial network

The pump-jet propulsor (PJP), as a new type of propulsion system for underwater vehicles characterized by high efficiency, low noise, and high critical speed, plays a significant role in advancing marine engineering. Obtaining complete internal flow field data with high spatial and temporal resolution is crucial for revealing the mechanisms underlying mechanical-acoustic coupling evolution. During experiments, when using particle image velocimetry (PIV) to measure the internal flow fields of pump jets, factors such as complex structural occlusions, wall reflections, and uneven particle distribution can lead to random missing flow field information. Therefore, we developed a U-shaped attention-driven deformable generative adversarial network (UAD-GAN) framework based on data-driven techniques and deep learning. First, we obtained datasets of internal pump-jet flow fields at various advanced coefficients using computational fluid dynamics (CFD), which serves as the sample information for network training. Next, our framework employs an attention transfer network to establish a multi-scale feature fusion mechanism that facilitates dynamic interaction between shallow and deep flow features. By integrating deformable convolutions, our approach captures the complex and irregular boundary flow characteristics of pump-jet components, achieving high-precision end-to-end reconstruction of missing flow fields. To evaluate the robustness and generalization capability of our model, we systematically assess its performance in compensating for missing flow fields under four different missing modes. Additionally, we conduct a comprehensive comparison of UAD-GAN, deep convolutional neural networks (DCNNs), and autoencoder-based CNNs (CNN-AEs) from multiple perspectives, including compensation error, single-frame inference speed, and model training duration. The final results demonstrate that the UAD-GAN framework maintains a root mean square error below 5% when compensating for missing flow fields in pump jets, effectively achieving compensation for complex structures.