Reconstruction of three-dimensional turbulent flows from sparse and noisy planar measurements: A weight-sharing neural network approach

Abstract

This paper proposes a method for reconstructing three-dimensional turbulent flows from sparse measurements without the need for ground truth data during training. A weight-sharing network is developed to infer the full flow fields from measurements of velocity sampled at three planes and boundary pressure at one additional plane, inspired by experimental configurations. The weight-sharing network shares identical parameters along homogeneous directions, which results in efficient data utilization and reduced computational memory requirements. First, we compare the weight-sharing network to the PC-DualConvNet, adapted from prior work, by reconstructing a 3D Kolmogorov flow from noise-free measurements with a snapshot-enforced loss. Both networks accurately recover time-averaged 3D flow fields and the correct energy spectrum up to wavenumber 10. The weight-sharing network has the ability to infer flow structures distant from measurement planes. Second, we carry out reconstruction from measurements corrupted with white noise (SNR 15) using a mean-enforced loss. We show that, for the weight-sharing network, validation sensor loss on unseen data decreases with training sensor loss -- unlike PC-DualConvNet. This shows improved generalization and that training sensor loss estimates generalization error. The weight-sharing network offers good generalization, parameter efficiency, and hyperparameter robustness. The proposed method opens the possibility of three-dimensional flow reconstruction from experiments.

References in corpus (17)

• Super-resolution reconstruction of turbulent flows with machine learning
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
• Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
• Least Squares Shadowing sensitivity analysis of chaotic limit cycle oscillations
• Experimental velocity data estimation for imperfect particle images using machine learning
• Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database
• State estimation in turbulent channel flow from limited observations
• Reconstructing velocity and pressure from sparse noisy particle tracks using Physics-Informed Neural Networks
• Mean-flow Data Assimilation based on minimal correction of turbulence models: application to turbulent high-Reynolds number backward-facing step
• Gappy spectral proper orthogonal decomposition
• Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter
• Stability, sensitivity and optimisation of chaotic acoustic oscillations
• Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network
• Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements
• Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks
• Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network

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