Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder
A neural network technique that extracts underlying flow features from the original flow field data is newly proposed. The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder. Unlike the typical convolutional neural n...
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| Veröffentlicht in: | AIP advances Jg. 11; H. 10; S. 105211 - 105211-6 |
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American Institute of Physics
01.10.2021
AIP Publishing LLC |
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| Abstract | A neural network technique that extracts underlying flow features from the original flow field data is newly proposed. The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder. Unlike the typical convolutional neural network (CNN) that changes the size of the data itself in the intermediate layers, flow field data size is not changed in the learning process of this method and only the numbers of channels are changed in each layer. Different but the same size of the data as the input are obtained by convolution with multiple spatially overlapping flow field data under the assumption of sparsity. When data restoration is realized in this autoencoder system, the channel numbers of data in the intermediate layers turn out to contain different flow characteristics of the original flow field. The proposed method is applied to the low Reynolds number flows over a circular cylinder. The high-fidelity unsteady flow data obtained by solving two-dimensional compressible Navier–Stokes equations with a high-resolution numerical scheme are used as a test case. In the proposed method, sparsity introduced in the middle-hidden layer is essential for the successful separation of the original data. The results presented in the example seem to correspond to positive and negative magnitudes of the original data, but future studies will reveal other features of the method. The present method shows flow features different from those of proper orthogonal decomposition in each mode, which is probably due to nonlinear decomposition in the CNN process. |
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| AbstractList | A neural network technique that extracts underlying flow features from the original flow field data is newly proposed. The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder. Unlike the typical convolutional neural network (CNN) that changes the size of the data itself in the intermediate layers, flow field data size is not changed in the learning process of this method and only the numbers of channels are changed in each layer. Different but the same size of the data as the input are obtained by convolution with multiple spatially overlapping flow field data under the assumption of sparsity. When data restoration is realized in this autoencoder system, the channel numbers of data in the intermediate layers turn out to contain different flow characteristics of the original flow field. The proposed method is applied to the low Reynolds number flows over a circular cylinder. The high-fidelity unsteady flow data obtained by solving two-dimensional compressible Navier–Stokes equations with a high-resolution numerical scheme are used as a test case. In the proposed method, sparsity introduced in the middle-hidden layer is essential for the successful separation of the original data. The results presented in the example seem to correspond to positive and negative magnitudes of the original data, but future studies will reveal other features of the method. The present method shows flow features different from those of proper orthogonal decomposition in each mode, which is probably due to nonlinear decomposition in the CNN process. |
| Author | Aono, Hikaru Fujii, Kozo Obayashi, Wataru Tatsukawa, Tomoaki |
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| Cites_doi | 10.1126/science.1127647 10.1016/0021-9991(92)90324-r 10.1017/jfm.2019.238 10.1063/1.4913500 10.1007/s42979-021-00867-3 10.1145/3065386 10.1063/1.4816650 10.2514/1.j056060 10.1017/s0022112010001217 10.2514/3.45890 10.1063/1.4935357 10.2514/1.11672 10.1063/1.5067313 10.1016/j.jcp.2013.12.019 10.2514/1.j058462 10.1017/jfm.2019.822 |
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| References | Fujii, Obayashi (c14) 1989; 26 Krizhevsky, Sutskever, Hinton (c13) 2012; 60 Sato, Nonomura, Okada, Asada, Aono, Yakeno, Abe, Fujii (c17) 2015; 27 Omata, Shirayama (c12) 2019; 9 Kawai, Fujii (c15) 2005; 43 Lee, Kawai, Nonomura, Anyoji, Aono, Oyama, Asai, Fujii (c18) 2015; 27 Lele (c19) 1992; 103 Taira, Brunton, Dawson, Rowley, Colonius, McKeon, Schmidt, Gordeyev, Theofilis, Ukeiley (c3) 2017; 55 Abe, Nonomura, Iizuka, Fujii (c20) 2014; 260 Taira, Hemati, Brunton, Sun, Duraisamy, Bagheri, Dawson, Yeh (c4) 2020; 58 Hinton, Salakhutdinov (c5) 2006; 313 Fukami, Hasegawa, Nakamura, Morimoto, Fukagata (c11) 2021; 2 Fukami, Fukagata, Taira (c9) 2019; 870 Schmid (c2) 2010; 656 Murata, Fukami, Fukagata (c10) 2020; 882 Li, Nonomura, Fujii (c16) 2013; 25 (2024031516523375500_c15) 2005; 43 (2024031516523375500_c10) 2020; 882 (2024031516523375500_c18) 2015; 27 (2024031516523375500_c20) 2014; 260 (2024031516523375500_c9) 2019; 870 (2024031516523375500_c16) 2013; 25 (2024031516523375500_c8) 1995 (2024031516523375500_c4) 2020; 58 (2024031516523375500_c14) 1989; 26 (2024031516523375500_c19) 1992; 103 (2024031516523375500_c13) 2012; 60 (2024031516523375500_c21) 1998 (2024031516523375500_c1) 1967 (2024031516523375500_c5) 2006; 313 2024031516523375500_c6 (2024031516523375500_c2) 2010; 656 (2024031516523375500_c11) 2021; 2 (2024031516523375500_c17) 2015; 27 (2024031516523375500_c7) 2011 (2024031516523375500_c12) 2019; 9 (2024031516523375500_c3) 2017; 55 |
| References_xml | – volume: 27 start-page: 117101 year: 2015 ident: c17 article-title: Mechanisms for laminar separated-flow control using dielectric-barrier-discharge plasma actuator at low Reynolds number publication-title: Phys. Fluids – volume: 103 start-page: 16 year: 1992 ident: c19 article-title: Compact finite difference schemes with spectral-like resolution publication-title: J. Comput. Phys. – volume: 58 start-page: 998 year: 2020 ident: c4 article-title: Modal analysis of fluid flows: Applications and outlook publication-title: AIAA J. – volume: 25 start-page: 086101 year: 2013 ident: c16 article-title: Mechanism of controlling supersonic cavity oscillations using upstream mass injection publication-title: Phys. Fluids – volume: 55 start-page: 4013 year: 2017 ident: c3 article-title: Modal analysis of fluid flows: An overview publication-title: AIAA J. – volume: 43 start-page: 953 year: 2005 ident: c15 article-title: Analysis and prediction of thin-airfoil stall phenomena with hybrid turbulence methodology publication-title: AIAA J. – volume: 870 start-page: 106 year: 2019 ident: c9 article-title: Super-resolution reconstruction of turbulent flows with machine learning publication-title: J. Fluid Mech. – volume: 26 start-page: 1123 year: 1989 ident: c14 article-title: High-resolution upwind scheme for vortical-flow simulations publication-title: J. Aircr. – volume: 27 start-page: 023602 year: 2015 ident: c18 article-title: Mechanisms of surface pressure distribution within a laminar separation bubble at different Reynolds publication-title: Phys. Fluids – volume: 2 start-page: 467 year: 2021 ident: c11 article-title: Model order reduction with neural networks: Application to laminar and turbulent flows publication-title: SN Comput. Sci. – volume: 882 start-page: A13 year: 2020 ident: c10 article-title: Nonlinear mode decomposition with convolutional neural networks for fluid dynamics publication-title: J. Fluid Mech. – volume: 9 start-page: 015006 year: 2019 ident: c12 article-title: A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder publication-title: AIP Adv. – volume: 656 start-page: 5 year: 2010 ident: c2 article-title: Dynamic mode decomposition of numerical and experimental data publication-title: J. Fluid Mech. – volume: 60 start-page: 84 year: 2012 ident: c13 article-title: ImageNet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 260 start-page: 163 year: 2014 ident: c20 article-title: Geometric interpretations and spatial symmetry property of metrics in the conservative form for high-order finite-difference schemes on moving and deforming grids publication-title: J. Comput. Phys. – volume: 313 start-page: 504 year: 2006 ident: c5 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 313 start-page: 504 year: 2006 ident: 2024031516523375500_c5 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 103 start-page: 16 year: 1992 ident: 2024031516523375500_c19 article-title: Compact finite difference schemes with spectral-like resolution publication-title: J. Comput. Phys. doi: 10.1016/0021-9991(92)90324-r – start-page: 1 year: 1998 ident: 2024031516523375500_c21 article-title: Simple ideas for the accuracy and efficiency improvement of compressible flow simulation methods – volume: 870 start-page: 106 year: 2019 ident: 2024031516523375500_c9 article-title: Super-resolution reconstruction of turbulent flows with machine learning publication-title: J. Fluid Mech. doi: 10.1017/jfm.2019.238 – start-page: 1 volume-title: CS294A Lecture notes year: 2011 ident: 2024031516523375500_c7 article-title: Sparse autoencoder – volume: 27 start-page: 023602 year: 2015 ident: 2024031516523375500_c18 article-title: Mechanisms of surface pressure distribution within a laminar separation bubble at different Reynolds publication-title: Phys. Fluids doi: 10.1063/1.4913500 – volume: 2 start-page: 467 year: 2021 ident: 2024031516523375500_c11 article-title: Model order reduction with neural networks: Application to laminar and turbulent flows publication-title: SN Comput. Sci. doi: 10.1007/s42979-021-00867-3 – volume: 60 start-page: 84 year: 2012 ident: 2024031516523375500_c13 article-title: ImageNet classification with deep convolutional neural networks publication-title: Adv. Neural Inf. Process. Syst. doi: 10.1145/3065386 – volume: 25 start-page: 086101 year: 2013 ident: 2024031516523375500_c16 article-title: Mechanism of controlling supersonic cavity oscillations using upstream mass injection publication-title: Phys. Fluids doi: 10.1063/1.4816650 – volume: 55 start-page: 4013 year: 2017 ident: 2024031516523375500_c3 article-title: Modal analysis of fluid flows: An overview publication-title: AIAA J. doi: 10.2514/1.j056060 – ident: 2024031516523375500_c6 – start-page: 166 volume-title: Atmospheric Turbulence and Radio Wave Propagation year: 1967 ident: 2024031516523375500_c1 article-title: The structure of inhomogeneous turbulent flows – volume: 656 start-page: 5 year: 2010 ident: 2024031516523375500_c2 article-title: Dynamic mode decomposition of numerical and experimental data publication-title: J. Fluid Mech. doi: 10.1017/s0022112010001217 – volume: 26 start-page: 1123 year: 1989 ident: 2024031516523375500_c14 article-title: High-resolution upwind scheme for vortical-flow simulations publication-title: J. Aircr. doi: 10.2514/3.45890 – volume: 27 start-page: 117101 year: 2015 ident: 2024031516523375500_c17 article-title: Mechanisms for laminar separated-flow control using dielectric-barrier-discharge plasma actuator at low Reynolds number publication-title: Phys. Fluids doi: 10.1063/1.4935357 – volume: 43 start-page: 953 year: 2005 ident: 2024031516523375500_c15 article-title: Analysis and prediction of thin-airfoil stall phenomena with hybrid turbulence methodology publication-title: AIAA J. doi: 10.2514/1.11672 – start-page: 1 volume-title: The Handbook of Brain Theory and Neural Networks year: 1995 ident: 2024031516523375500_c8 article-title: Convolutional networks for images, speech, and time-series – volume: 9 start-page: 015006 year: 2019 ident: 2024031516523375500_c12 article-title: A novel method of low-dimensional representation for temporal behavior of flow fields using deep autoencoder publication-title: AIP Adv. doi: 10.1063/1.5067313 – volume: 260 start-page: 163 year: 2014 ident: 2024031516523375500_c20 article-title: Geometric interpretations and spatial symmetry property of metrics in the conservative form for high-order finite-difference schemes on moving and deforming grids publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2013.12.019 – volume: 58 start-page: 998 year: 2020 ident: 2024031516523375500_c4 article-title: Modal analysis of fluid flows: Applications and outlook publication-title: AIAA J. doi: 10.2514/1.j058462 – volume: 882 start-page: A13 year: 2020 ident: 2024031516523375500_c10 article-title: Nonlinear mode decomposition with convolutional neural networks for fluid dynamics publication-title: J. Fluid Mech. doi: 10.1017/jfm.2019.822 |
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| SubjectTerms | Algorithms Artificial neural networks Circular cylinders Compressibility Feature extraction Flow characteristics Flow control Fluid dynamics Fluid flow Low Reynolds number flow Machine learning Neural networks Proper Orthogonal Decomposition Reynolds number Sparsity Unsteady flow |
| Title | Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder |
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