Flow field reconstruction of trash rack based on generative adversarial networks

Abstract

ABSTRACT A new model - super-resolution Wasserstein Generative Adversarial Network with Gradient Penalty (SRWgan-GP) - is developed with resolution of 512×512 to reconstruct the sliced 2D high-resolution flow field from low-resolution data. To train the SRWgan-GP model, flow field data obtained from Large Eddy Simulation (LES) behind the trash racks is utilized. A sub-pixel convolution layer is incorporated in the framework to generate higher-resolution feature maps (512 × 512), which significantly reduces the network's memory requirements under the same output resolution .The performance of the proposed model is compared with that of other commonly used generative models including u-shaped architecture model (Unet) and Convolutional Neural Network (CNN). The results reveal that the SRWgan-GP model excels in reconstructing the flow field along both the x with and y axes, demonstrating the most accurate performance with minimal error achieving an MSE of 0.001, PSNR of 46.557, and SSIM of 0.994 in depicting turbulent structures and the Kįrmįn vortex street. Power Spectral Density (PSD) analysis shows that the primary shedding frequency of the vortex street is consistent with LES at approximately 10Hz for SRWgan-GP. Additionally, the SRWgan-GP exhibits proficient accuracy in computing second-order statistics of the flow field, achieving minimal error in instantaneous Reynolds shear stresses.