Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Matsuo, Mitsuaki; Nakamura, Taichi; Morimoto, Masaki; Fukami, Kai; Fukagata, Koji

doi:10.48550/arxiv.2103.09020

Cited by 10 publications

(12 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23,32 The local average can be used to compress HR fluid data and an SR model is then utilized to decompress the data. 75 The subsampling and local averaging are processed in four steps. First, a scale factor s is determined.…”

Section: B Data Preparationmentioning

confidence: 99%

Roto-Translation Equivariant Super-Resolution of Two-Dimensional Flows Using Convolutional Neural Networks

Yasuda¹,

Onishi²

2022

Preprint

View full text Add to dashboard Cite

Convolutional neural networks (CNNs) often process vectors as quantities having no direction like colors in images. This study investigates the effect of treating vectors as geometrical objects in terms of super-resolution of velocity on two-dimensional fluids. Vector is distinguished from scalar by the transformation law associated with a change in basis, which can be incorporated as the prior knowledge using the equivariant deep learning. We convert existing CNNs into equivariant ones by making each layer equivariant with respect to rotation and translation. The training data in the low-and high-resolution are generated with the downsampling or the spectral nudging. When the data inherit the rotational symmetry, the equivariant CNNs show comparable accuracy with the non-equivariant ones. Since the number of parameters is smaller in the equivariant CNNs, these models are trainable with a smaller size of the data. In this case, the transformation law of vector should be incorporated as the prior knowledge, where vector is explicitly treated as a quantity having direction. Two examples demonstrate that the symmetry of the data can be broken. In the first case, a downsampling method makes the correspondence between low-and high-resolution patterns dependent on the orientation. In the second case, the input data are insufficient to recognize the rotation of coordinates in the experiment with the spectral nudging. In both cases, the accuracy of the CNNs deteriorates if the equivariance is forced to be imposed, and the usage of conventional CNNs may be justified even though vector is processed as a quantity having no direction.

show abstract

Section: B Data Preparationmentioning

confidence: 99%

Roto-Translation Equivariant Super-Resolution of Two-Dimensional Flows Using Convolutional Neural Networks

Yasuda¹,

Onishi²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The filters, trainable parameters inside the CNN, are able to handle high-dimensional data efficiently and extract key features. Thanks to its unique capability in handling high-dimensional data, the use of CNN has also been spread in the fluid dynamics field in recent years [33,34,35,36,37,38,39,40,41,42].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows

Moriya,

Fukami,

Nabae

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We propose a supervised-machine-learning-based wall model for coarse-grid wall-resolved largeeddy simulation (LES). Our consideration is made on LES of turbulent channel flows with a first grid point set relatively far from the wall (∼ 10 wall units), while still resolving the near-wall region, to present a new path to save the computational cost. Convolutional neural network (CNN) is utilized to estimate a virtual wall-surface velocity from x − z sectional fields near the wall, whose training data are generated by a direct numerical simulation (DNS) at Re τ = 180. The virtual wall-surface velocity is prepared with the extrapolation of the DNS data near the wall. This idea enables us to give a proper wall condition to correct a velocity gradient near the wall. The estimation ability of the model from near wall information is first investigated as a priori test. The estimated velocity fields by the present CNN model are in statistical agreement with the reference DNS data. The model trained in a priori test is then combined with the LES as a posteriori test. We find that the LES can successfully be augmented using the present model at both the friction Reynolds number Re τ = 180 used for training and the unseen Reynolds number Re τ = 360 even when the first grid point is located at 5 wall units off the wall. We also investigate the robustness of the present model for the choice of sub-grid scale model and the possibility of transfer learning in a local domain. The observations through the paper suggest that the present model is a promising tool for recovering the accuracy of LES with a coarse grid near the wall.

show abstract

“…Although this is a laminar example, the flow at the present Reynolds number can be regarded as a good candidate to discuss the reconstructability of the present CNN model because there are complex three-dimensional structures associated with two-and three-dimensional separated shear layers (Bai and Alam, 2018), as shown in figure 2(𝑎). A direct numerical simulation (DNS) is used to prepare the training data by numerically solving the incompressible Navier-Stokes equations with a penalization term (Caltagirone, 1994;Matsuo et al, 2021;Morimoto et al, 2021a), i.e.,…”

Section: Wake Around a Square Cylindermentioning

confidence: 99%

Robust training approach of neural networks for fluid flow state estimations

Nakamura,

Fukagata

2021

Preprint

Self Cite

View full text Add to dashboard Cite

State estimation from limited sensor measurements is ubiquitously found as a common challenge in a broad range of fields including mechanics, astronomy, and geophysics. Fluid mechanics is no exception -state estimation of fluid flows is particularly important for flow control and processing of experimental data. However, strong nonlinearities and spatio-temporal high degrees of freedom of fluid flows cause difficulties in reasonable estimations. To handle these issues, neural networks (NNs) have recently been applied to the fluid flow estimation instead of conventional linear methods. The present study focuses on the capability of NNs to various fluid flow estimation problems from a practical viewpoint regarding robust training. Three types of unsteady laminar and turbulent flows are considered for the present demonstration: 1. square cylinder wake, 2. turbulent channel flow, and 3. laminar to turbulent transitional boundary layer. We utilize a convolutional neural network (CNN) to estimate velocity fields from sectional sensor measurements. To assess the practicability of the CNN models, physical quantities required for the input and robustness against lack of sensors are investigated. We also examine the effectiveness of several considerable approaches for model training to gain more robustness against the lack of sensors. The knowledge acquired through the present study in terms of effective training approaches can be transferred towards practical machine learning in fluid flow modeling.

show abstract

Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Cited by 10 publications

References 57 publications

Roto-Translation Equivariant Super-Resolution of Two-Dimensional Flows Using Convolutional Neural Networks

Roto-Translation Equivariant Super-Resolution of Two-Dimensional Flows Using Convolutional Neural Networks

Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows

Robust training approach of neural networks for fluid flow state estimations

Contact Info

Product

Resources

About