Super-resolution reconstruction of turbulent flow at various Reynolds numbers based on generative adversarial networks

TL;DR

This paper introduces a deep learning framework using GANs to reconstruct high-resolution turbulent flow fields from low-resolution data across various Reynolds numbers, demonstrating accurate and generalizable results.

Contribution

The study develops a multiscale super-resolution GAN model capable of reconstructing turbulent velocity fields at unseen Reynolds numbers from low-resolution data.

Findings

Accurately reconstructs high-resolution velocity fields from low-resolution data.

Reconstructs velocity fields at Reynolds numbers not included in training.

Demonstrates robustness across different Reynolds numbers.

Abstract

This study presents a deep learning-based framework to reconstruct high-resolution turbulent velocity fields from extremely low-resolution data at various Reynolds numbers using the concept of generative adversarial networks (GANs). A multiscale enhanced super-resolution generative adversarial network (MS-ESRGAN) is applied as a model to reconstruct the high-resolution velocity fields, and direct numerical simulation (DNS) data of turbulent channel flow with large longitudinal ribs at various Reynolds numbers are used to evaluate the performance of the model. The model is found to have the capacity to accurately reproduce high-resolution velocity fields from data at two different low-resolution levels in terms of the quantities of velocity fields and turbulent statistics. The results further reveal that the model is able to reconstruct velocity fields at Reynolds numbers that are not used in the training process.