Dimension reduced turbulent flow data from deep vector quantisers. Issue 4 (4th May 2022)

• Record Type: Journal Article
• Authors: Momenifar, Mohammadreza; Diao, Enmao; Tarokh, Vahid; Bragg, Andrew D.
• Is Part Of: Journal of turbulence. Volume 23:Issue 4/5(2022)
• Page Start: 232
• Page End: 264
• Publication Date: 2022-05-04
• Subjects: Turbulence -- deep learning -- data compression -- vector qunatisation -- physics-informed machine learning; Turbulence -- Periodicals; Turbulence -- Périodiques; 532.0527
• DOI: 10.1080/14685248.2022.2060508
• Languages: English
• ISSNs: 1468-5248

Abstract

Abstract : Analysing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep Learning technique based on vector quantisation to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows. The deep learning framework is composed of convolutional layers and incorporates physical constraints on the flow, such as preserving incompressibility and global statistical characteristics of the velocity gradients. The accuracy of the model is assessed using statistical, comparison-based similarity and physics-based metrics. The training data set is produced from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow. The performance of this lossy data compression scheme is evaluated not only with unseen data from the stationary, isotropic turbulent flow, but also with data from decaying isotropic turbulence, a Taylor–Green vortex flow, and a turbulent channel flow. Defining the compression ratio (CR) as the ratio of original data size to the compressed one, the results show that our model based on vector quantisation can offer CR = 85 with a mean square error (MSE) of O ( 10 − 3 ), and predictions that faithfully reproduce the statistics of the flow, except at the very smallest scales where there is some loss. Compared to the recent study of Glaws. et al. [Deep learning for in situ data compression of large turbulent flow simulations. Phys Rev Fluids. 2020;5(11):114602], which was based on a conventional autoencoder (where compression is performed in a continuous space), our model improves the CR by more than 30%, and reduces the MSE by an order of magnitude. Our compression model is an attractive solution for situations where fast, high quality and low-overhead encoding and decoding of large data are required.