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Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks

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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 \(Re_D = 300\). 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.

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Availability of Data and Materials

Upon reasonable request.

Code Availability

Available at https://github.com/MitsuMatsu/2D-3D-CNN

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Acknowledgements

This work was supported by the Japan Society for the Promotion of Science (KAKENHI grant number: 18H03758 and 21H05007). We are also grateful to Professor Shinnosuke Obi, Professor Keita Ando, and Professor Linyu Peng, Professor Takuya Kawata for fruitful discussions. The authors thank Mr. Hikaru Murakami for sharing his DNS code.

Funding

Funded by the Japan Society for the Promotion of Science (KAKENHI grant number: 18H03758 and 21H05007).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Keio University, Yokohama, 223-8522, Japan

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

  2. Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA, 90095, USA

    Kai Fukami

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  1. Mitsuaki Matsuo
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  2. Kai Fukami
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  4. Masaki Morimoto
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Contributions

MM: software, validation, investigation, writing—original draft, visualization. TN: software, investigation, writing—original draft. MM: software, investigation, writing—original draft. KaF: conceptualization, software, writing—review and editing, supervision. KoF: conceptualization, resources, writing—review and editing, supervision, project administration, funding acquisition.

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Correspondence to Koji Fukagata.

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Matsuo, M., Fukami, K., Nakamura, T. et al. Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks. SN COMPUT. SCI. 5, 306 (2024). https://doi.org/10.1007/s42979-024-02602-0

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