Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning

Abstract

Unsteady flow fields over a circular cylinder are trained and predicted using four different deep learning networks: convolutional neural networks with and without consideration of conservation laws, generative adversarial networks with and without consideration of conservation laws. Flow fields at future occasions are predicted based on information of flow fields at previous occasions. Deep learning networks are trained first using flow fields at Reynolds numbers of 100, 200, 300, and 400, while flow fields at Reynolds numbers of 500 and 3000 are predicted using the trained deep learning networks. Physical loss functions are proposed to explicitly impose information of conservation of mass and momentum to deep learning networks. An adversarial training is applied to extract features of flow dynamics in an unsupervised manner. Effects of the proposed physical loss functions, adversarial training, and network sizes on the prediction accuracy are analyzed. Predicted flow fields using deep learning networks are in favorable agreement with flow fields computed by numerical simulations.

Cited by in corpus (49)

• Machine learning based spatio-temporal super resolution reconstruction of turbulent flows
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• Uncovering near-wall blood flow from sparse data with physics-informed neural networks
• Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
• Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
• Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
• Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes
• A new prediction method of unsteady wake flow by the hybrid deep neural network
• Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
• Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics
• StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction
• Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
• Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
• Graph neural networks for laminar flow prediction around random 2D shapes
• 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
• A transformer-based synthetic-inflow generator for spatially-developing turbulent boundary layers
• Super-resolution reconstruction of turbulent flow at various Reynolds numbers based on generative adversarial networks
• Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning
• Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number
• Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence
• Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows
• Single-snapshot machine learning for super-resolution of turbulence
• Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows
• Deep learning of the spanwise-averaged Navier-Stokes equations
• Predicting drag on rough surfaces by transfer learning of empirical correlations
• Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network
• Neural Network Training Using ${\ell }_1$-Regularization and Bi-fidelity Data
• Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions
• A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation
• Influence of adversarial training on super-resolution turbulence reconstruction
• Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder
• Mechanisms of a Convolutional Neural Network for Learning Three-dimensional Unsteady Wake Flow
• Reduced-order modeling of unsteady fluid flow using neural network ensembles
• A Machine Learning Approach to Classify Vortex Wakes of Energy Harvesting Oscillating Foils
• On the locality of local neural operator in learning fluid dynamics
• Physics Informed Neural Networks for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data
• Generative prediction of flow fields around an obstacle using the diffusion model
• Uncovering dynamically critical regions in near-wall turbulence using 3D Convolutional Neural Networks
• How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
• A Machine Learning Approach to Classify Kinematics and Vortex Wake Modes of Oscillating Foils
• Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection
• Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
• Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
• Model order reduction with neural networks: Application to laminar and turbulent flows
• Vortex-Induced Drag Forecast for Cylinder in Non-uniform Inflow
• Tracking the vortex motion by using Brownian fluid particles
• Hybrid Neural Interpolation of a Sequence of Wind Flows
• Inverse Nonlinearity Compensation of Hyperelastic Deformation in Dielectric Elastomer for Acoustic Actuation