Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows

We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine-learning-based data reconstruction, we use the dow...

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Vydané v:	Journal of fluid mechanics Ročník 909
Hlavní autori:	Fukami, Kai, Fukagata, Koji, Taira, Kunihiko
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge, UK Cambridge University Press 25.02.2021
Predmet:	Artificial neural networks Channel flow Computational fluid dynamics Computer applications Correlation coefficient Correlation coefficients Cylinders Data Direct numerical simulation Evolution Fluid dynamics Fluid flow Isotropic turbulence JFM Papers Learning algorithms Machine learning Mathematical models Neural networks Reconstruction Resolution Robustness (mathematics) Three dimensional flow Three dimensional models Training Turbulence Turbulent flow Two dimensional models computational methods turbulence simulation
ISSN:	0022-1120, 1469-7645
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Abstract	We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine-learning-based data reconstruction, we use the downsampled skip-connection/multiscale model based on a convolutional neural network, incorporating the multiscale nature of fluid flows into its network structure. As an initial example, the model is applied to the two-dimensional cylinder wake at $Re_D = 100$. The reconstructed flow fields by the present method show great agreement with the reference data obtained by direct numerical simulation. Next, we apply the current model to a two-dimensional decaying homogeneous isotropic turbulence. The machine-learned model is able to track the decaying evolution from spatial and temporal coarse input data. The proposed concept is further applied to a complex turbulent channel flow over a three-dimensional domain at $Re_{\tau }=180$. The present model reconstructs high-resolved turbulent flows from very coarse input data in space, and also reproduces the temporal evolution for appropriately chosen time interval. The dependence on the number of training snapshots and duration between the first and last frames based on a temporal two-point correlation coefficient are also assessed to reveal the capability and robustness of spatio-temporal super resolution reconstruction. These results suggest that the present method can perform a range of flow reconstructions in support of computational and experimental efforts.
AbstractList	We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine-learning-based data reconstruction, we use the downsampled skip-connection/multiscale model based on a convolutional neural network, incorporating the multiscale nature of fluid flows into its network structure. As an initial example, the model is applied to the two-dimensional cylinder wake at $Re_D = 100$ . The reconstructed flow fields by the present method show great agreement with the reference data obtained by direct numerical simulation. Next, we apply the current model to a two-dimensional decaying homogeneous isotropic turbulence. The machine-learned model is able to track the decaying evolution from spatial and temporal coarse input data. The proposed concept is further applied to a complex turbulent channel flow over a three-dimensional domain at $Re_{\tau }=180$ . The present model reconstructs high-resolved turbulent flows from very coarse input data in space, and also reproduces the temporal evolution for appropriately chosen time interval. The dependence on the number of training snapshots and duration between the first and last frames based on a temporal two-point correlation coefficient are also assessed to reveal the capability and robustness of spatio-temporal super resolution reconstruction. These results suggest that the present method can perform a range of flow reconstructions in support of computational and experimental efforts. We present a new data reconstruction method with supervised machine learning techniques inspired by super resolution and inbetweening to recover high-resolution turbulent flows from grossly coarse flow data in space and time. For the present machine-learning-based data reconstruction, we use the downsampled skip-connection/multiscale model based on a convolutional neural network, incorporating the multiscale nature of fluid flows into its network structure. As an initial example, the model is applied to the two-dimensional cylinder wake at $Re_D = 100$. The reconstructed flow fields by the present method show great agreement with the reference data obtained by direct numerical simulation. Next, we apply the current model to a two-dimensional decaying homogeneous isotropic turbulence. The machine-learned model is able to track the decaying evolution from spatial and temporal coarse input data. The proposed concept is further applied to a complex turbulent channel flow over a three-dimensional domain at $Re_{\tau }=180$. The present model reconstructs high-resolved turbulent flows from very coarse input data in space, and also reproduces the temporal evolution for appropriately chosen time interval. The dependence on the number of training snapshots and duration between the first and last frames based on a temporal two-point correlation coefficient are also assessed to reveal the capability and robustness of spatio-temporal super resolution reconstruction. These results suggest that the present method can perform a range of flow reconstructions in support of computational and experimental efforts.
ArticleNumber	A9
Author	Taira, Kunihiko Fukagata, Koji Fukami, Kai
Author_xml	– sequence: 1 givenname: Kai orcidid: 0000-0002-1381-7322 surname: Fukami fullname: Fukami, Kai email: kfukami1@g.ucla.edu organization: 1Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095, USA – sequence: 2 givenname: Koji orcidid: 0000-0003-4805-238X surname: Fukagata fullname: Fukagata, Koji organization: 2Department of Mechanical Engineering, Keio University, Yokohama 223-8522, Japan – sequence: 3 givenname: Kunihiko orcidid: 0000-0002-3762-8075 surname: Taira fullname: Taira, Kunihiko organization: 1Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095, USA
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SubjectTerms	Artificial neural networks Channel flow Computational fluid dynamics Computer applications Correlation coefficient Correlation coefficients Cylinders Data Direct numerical simulation Evolution Fluid dynamics Fluid flow Isotropic turbulence JFM Papers Learning algorithms Machine learning Mathematical models Neural networks Reconstruction Resolution Robustness (mathematics) Three dimensional flow Three dimensional models Training Turbulence Turbulent flow Two dimensional models
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Title	Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
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