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

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.

arXiv comment23 pages, 14 figures. arXiv admin note: text overlap with arXiv:2109.04250

References in corpus (19)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Conditional Generative Adversarial Nets
• Machine Learning for Fluid Mechanics
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Turbulence Modeling in the Age of Data
• Super-resolution reconstruction of turbulent flows with machine learning
• The relativistic discriminator: a key element missing from standard GAN
• Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control
• Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning
• Machine learning based spatio-temporal super resolution reconstruction of turbulent flows
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• Searching for turbulence models by artificial neural network
• Residual Dense Network for Image Super-Resolution
• From coarse wall measurements to turbulent velocity fields through deep learning
• High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network
• Experimental velocity data estimation for imperfect particle images using machine learning

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