A Swin-Transformer-based Model for Efficient Compression of Turbulent Flow Data

TL;DR

This paper introduces a Swin-Transformer-based deep learning model that efficiently compresses turbulent flow data while preserving physical properties, outperforming CNN auto-encoders in accuracy and robustness at various compression ratios.

Contribution

The study presents a novel Swin-Transformer model with a physics-based loss function for turbulent flow data compression and demonstrates its superiority over traditional CNN auto-encoders.

Findings

Swin-Transformer model achieves higher accuracy than CNN auto-encoders.

Model maintains low error even at high compression ratios.

Transfer learning reduces training time by 64%.

Abstract

This study proposes a novel deep-learning-based method for generating reduced representations of turbulent flows that ensures efficient storage and transfer while maintaining high accuracy during decompression. A Swin-Transformer network combined with a physical constraints-based loss function is utilized to compress the turbulent flows with high compression ratios and then restore the data with the underlying physical properties. The forced isotropic turbulent flow is used to demonstrate the ability of the Swin-Transformer-based (ST) model, where the instantaneous and statistical results show the excellent ability of the model to recover the flow data with remarkable accuracy. Furthermore, the capability of the ST model is compared with a typical Convolutional Neural Network-based auto-encoder (CNN-AE) by using the turbulent channel flow at two friction Reynolds numbers $Re_\tau$ = 180 and 550. The results generated by the ST model are significantly more consistent with the DNS data than those recovered by the CNN-AE, indicating the superior ability of the ST model to compress and restore the turbulent flow. This study also compares the compression performance of the ST model at different compression ratios (CR) and finds that the model has low enough error even at very high CR. Additionally, the effect of transfer learning (TL) is investigated, showing that TL reduces the training time by 64\% while maintaining high accuracy. The results illustrate for the first time that the Swin-Transformer-based model incorporating a physically constrained loss function can compress and restore turbulent flows with the correct physics.