FR3D: Three-dimensional flow reconstruction and force estimation for unsteady flows around extruded bluff bodies via conformal mapping aided convolutional autoencoders
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Published version
Author(s)
Girayhan Ozbay, Ali
Laizet, Sylvain
Type
Journal Article
Abstract
In many practical fluid dynamics experiments, measuring variables such as
velocity and pressure is possible only at a limited number of sensor locations,
for a few two-dimensional planes, or for a small 3D domain in the flow. However, knowledge of the full fields is necessary to understand the dynamics
of many flows. Deep learning reconstruction of full flow fields from sparse
measurements has recently garnered significant research interest, as a way of
overcoming this limitation. This task is referred to as the flow reconstruction (FR) task. In the present study, we propose a convolutional autoencoder
based neural network model, dubbed FR3D, which enables FR to be carried
out for three-dimensional flows around extruded 3D objects with different
cross-sections. An innovative mapping approach, whereby multiple fluid domains are mapped to an annulus, enables FR3D to generalize its performance
to objects not encountered during training. We conclusively demonstrate
this generalization capability using a dataset composed of 80 training and 20
testing geometries, all randomly generated. We show that the FR3D model
reconstructs pressure and velocity components with a few percentage points
of error. Additionally, using these predictions, we accurately estimate the
Q-criterion fields as well lift and drag forces on the geometries.
velocity and pressure is possible only at a limited number of sensor locations,
for a few two-dimensional planes, or for a small 3D domain in the flow. However, knowledge of the full fields is necessary to understand the dynamics
of many flows. Deep learning reconstruction of full flow fields from sparse
measurements has recently garnered significant research interest, as a way of
overcoming this limitation. This task is referred to as the flow reconstruction (FR) task. In the present study, we propose a convolutional autoencoder
based neural network model, dubbed FR3D, which enables FR to be carried
out for three-dimensional flows around extruded 3D objects with different
cross-sections. An innovative mapping approach, whereby multiple fluid domains are mapped to an annulus, enables FR3D to generalize its performance
to objects not encountered during training. We conclusively demonstrate
this generalization capability using a dataset composed of 80 training and 20
testing geometries, all randomly generated. We show that the FR3D model
reconstructs pressure and velocity components with a few percentage points
of error. Additionally, using these predictions, we accurately estimate the
Q-criterion fields as well lift and drag forces on the geometries.
Date Issued
2023-10
Date Acceptance
2023-07-11
Citation
International Journal of Heat and Fluid Flow, 2023, 103, pp.1-15
ISSN
0142-727X
Publisher
Elsevier
Start Page
1
End Page
15
Journal / Book Title
International Journal of Heat and Fluid Flow
Volume
103
Copyright Statement
© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Identifier
https://www.sciencedirect.com/science/article/pii/S0142727X2300098X
Publication Status
Published
Article Number
109199
Date Publish Online
2023-07-27