Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements

Abstract

Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.

References in corpus (8)

• Enhancing Computational Fluid Dynamics with Machine Learning
• Machine learning based spatio-temporal super resolution reconstruction of turbulent flows
• From coarse wall measurements to turbulent velocity fields through deep learning
• Synchronization of turbulence in channel flow
• Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network
• Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions
• Generative Adversarial Networks to infer velocity components in rotating turbulent flows
• Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks

Cited by in corpus (4)

• Single-snapshot machine learning for super-resolution of turbulence
• Some effects of limited wall-sensor availability on flow estimation with 3D-GANs
• Reconstruction of three-dimensional turbulent flows from sparse and noisy planar measurements: A weight-sharing neural network approach
• Mapping surface height dynamics to subsurface flow physics in free-surface turbulent flow using a shallow recurrent decoder