Reconstructing turbulence: A deep learning–enhanced interpolation approach

Abstract

High-resolution data are essential for analysing turbulent flows, yet direct numerical simulations are often computationally prohibitive. Super-resolution based on deep learning (DL) offers a promising alternative for reconstructing high-fidelity fields from coarse data. This study introduces and validates a novel hybrid framework that combines classical interpolation with a convolutional neural network (CNN) to perform super-resolution on two-dimensional turbulent flow fields. The architecture is designed to be computationally efficient and physically consistent, leveraging interpolation as a method to assist in simplifying the reconstruction task for the DL model. We evaluated the framework on three distinct turbulent flow cases—interior room airflow, sudden expansion corner flow, and turbulent channel flow—using data from implicit Large Eddy Simulations. The performance was benchmarked against both standalone interpolation methods and conventional CNN-only architectures. Results demonstrate that the hybrid approach provides superior reconstruction accuracy, as quantified by numerical measures including peak signal-to-noise ratio and the structural similarity index. Furthermore, we validate the physical consistency of the reconstructed fields by analysing their energy spectra and turbulence probability density functions, confirming that the model faithfully reproduces the essential characteristics of turbulent flow physics. This work presents a practical and effective tool for generating high-quality turbulence data at a fraction of the computational cost required by traditional methods.