Advancing super-resolution of turbulent velocity fields: An artificial intelligence approach

This study presents a novel deep learning framework aimed at achieving super-resolution of velocity fields within turbulent channel flows across various wall-normal positions. The model excels at reconstructing high-resolution flow fields from low-resolution data, with an emphasis on accurately capturing spatial structures and spectral energy distributions. Input data are generated through fine-grid large eddy simulations, employing a data-driven approach. The model's efficacy is evaluated using standard image quality metrics, including peak signal-to-noise ratio, structural similarity index measure, root mean square error, mean absolute error, good pixel percentage, as well as spectral analyses to encapsulate the complex dynamics of turbulent flow physics. The findings demonstrate substantial correlations between model performance and wall-normal location. Specifically, the model performs superior in regions distal from the channel wall but faces challenges in accurately recovering small-scale turbulent structures near the boundary layer.