Deep Learning of Vortex Induced Vibrations

Abstract

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics (CFD) methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier-Stokes equations coupled with the structure's dynamic motion equation. In the first case, given scattered data in space-time on the velocity field and the structure's motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure's dynamic motion. In the second case, given scattered data in space-time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.

arXiv commentarXiv admin note: text overlap with arXiv:1808.04327

References in corpus (10)

• Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
• Machine Learning of Linear Differential Equations using Gaussian Processes
• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
• Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
• Inferring solutions of differential equations using noisy multi-fidelity data
• Wavelet Scattering Regression of Quantum Chemical Energies
• Neural-net-induced Gaussian process regression for function approximation and PDE solution
• Brittleness of Bayesian Inference Under Finite Information in a Continuous World

Cited by in corpus (55)

• NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
• Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
• PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
• Physics-informed learning of governing equations from scarce data
• Physics-informed neural networks for solving Reynolds-averaged Navier$\unicode{x2013}$Stokes equations
• CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
• Efficient training of physics-informed neural networks via importance sampling
• Physics-informed deep learning for incompressible laminar flows
• Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
• PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
• DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
• A Physics-Informed Deep Learning Paradigm for Car-Following Models
• Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
• Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model
• Reinforcement-learning-based control of confined cylinder wakes with stability analyses
• Assessment of unsteady flow predictions using hybrid deep learning based reduced order models
• Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion
• Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
• Deep Learning for One-dimensional Consolidation
• Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis
• Explore missing flow dynamics by physics-informed deep learning: the parameterised governing systems
• Non-Intrusive Reduced-Order Modeling Using Uncertainty-Aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to Flood Modeling
• Latent Space Data Assimilation by using Deep Learning
• Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
• Physics-informed machine learning for Structural Health Monitoring
• Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows
• On the risks of using double precision in numerical simulations of spatio-temporal chaos
• Physics-Informed Echo State Networks
• Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks
• General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
• A Parametric and Feasibility Study for Data Sampling of the Dynamic Mode Decomposition: Spectral Insights and Further Explorations
• Learning Specialized Activation Functions for Physics-informed Neural Networks
• ModalPINN: an extension of Physics-Informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors
• Physics-Informed Neural Networks for Parametric Compressible Euler Equations
• Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)
• Predicting Opinion Dynamics via Sociologically-Informed Neural Networks
• Can Transfer Neuroevolution Tractably Solve Your Differential Equations?
• Velocity Reconstruction in Puffing Pool Fires with Physics-Informed Neural Networks
• Analysis on numerical stability and convergence of RANS turbulence models from the perspective of coupling modes
• Establishing Direct Phenomenological Connections between Fluid and Structure by the Koopman-Linearly-Time-Invariant Analysis
• The Linear-Time-Invariance Notion of the Koopman Analysis-Part 1: The Architecture, Practical Rendering on the Prism Wake, and Fluid-Structure Association
• Physics-informed neural networks for unsteady incompressible flows with time-dependent moving boundaries
• Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil
• Physics-Informed Echo State Networks for Chaotic Systems Forecasting
• The Linear-Time-Invariance Notion of the Koopman Analysis-Part 2: Physical Interpretations of Invariant Koopman Modes and Phenomenological Revelations
• PCNN: A physics-constrained neural network for multiphase flows
• Learning thermoacoustic interactions in combustors using a physics-informed neural network
• Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
• An exploratory study on machine learning to couple numerical solutions of partial differential equations
• Training Deep Physics-Informed Kolmogorov-Arnold Networks
• Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wings
• Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
• REAct: Rational Exponential Activation for Better Learning and Generalization in PINNs
• Neural inference of fluid-structure interactions from sparse off-body measurements