Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks

Abstract

The recent development of high-performance computing enables us to generate spatio-temporal high-resolution data of nonlinear dynamical systems and to analyze them for a deeper understanding of their complex nature. This trend can be found in a wide range of science and engineering, which suggests that detailed investigations on efficient data handling in physical science must be required in the future. This study considers the use of convolutional neural networks (CNNs) to achieve efficient data storage and estimation of scientific big data derived from nonlinear dynamical systems. The CNN is used to reconstruct three-dimensional data from a few numbers of two-dimensional sections in a computationally friendly manner. The present model is a combination of two- and three-dimensional CNNs, which allows users to save only some of the two-dimensional sections to reconstruct the volumetric data. As examples, we consider a flow around a square cylinder at the diameter-based Reynolds number $R{e}_D=300$. We demonstrate that volumetric fluid flow data can be reconstructed with the present method from as few as five sections. Furthermore, we propose a combination of the present CNN-based reconstruction with an adaptive sampling-based super-resolution analysis to augment the data compressibility. Our report can serve as a bridge toward practical data handling for not only fluid mechanics but also a broad range of physical sciences.

arXiv commentTo appear in SN Computer Science

References in corpus (36)

• Machine Learning for Fluid Mechanics
• Deep learning for universal linear embeddings of nonlinear dynamics
• Data-driven discovery of coordinates and governing equations
• Super-resolution reconstruction of turbulent flows with machine learning
• PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
• Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
• Generating 3D structures from a 2D slice with GAN-based dimensionality expansion
• Machine learning based spatio-temporal super resolution reconstruction of turbulent flows
• A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries
• Unsupervised deep learning for super-resolution reconstruction of turbulence
• Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
• Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
• Assessment of supervised machine learning methods for fluid flows
• 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
• Improving aircraft performance using machine learning: a review
• Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics
• Probabilistic neural networks for fluid flow surrogate modeling and data recovery
• Synthetic turbulent inflow generator using machine learning
• Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
• Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
• Time-series learning of latent-space dynamics for reduced-order model closure
• 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
• Generalization techniques of neural networks for fluid flow estimation
• 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
• Graph Convolutional Neural Networks for Body Force Prediction
• Leveraging reduced-order models for state estimation using deep learning
• Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
• Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions
• Real-time thermoacoustic data assimilation
• Robust training approach of neural networks for fluid flow state estimations
• Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers

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