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

Fukami, Kai; Fukagata, Koji; Taira, Kunihiko

doi:10.1017/jfm.2020.948

Cited by 207 publications

(99 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…2019 c ; Fukami, Fukagata & Taira 2020 a ; Fukami et al. 2021 b ; Omata & Shirayama 2019; Morimoto, Fukami & Fukagata 2020 a ; Fukami, Fukagata & Taira 2021 a ; Matsuo et al. 2021; Morimoto et al.…”

Section: Methodsmentioning

confidence: 99%

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

et al. 2021

Self Cite

View full text Add to dashboard Cite

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the thresholded least square algorithm and the adaptive least absolute shrinkage and selection operator which show reasonable ability with a wide range of sparsity constant in our preliminary tests, to a two-dimensional single cylinder wake at $Re_D=100$ , its transient process and a wake of two-parallel cylinders, as examples of high-dimensional fluid data. To handle these high-dimensional data with SINDy whose library matrix is suitable for low-dimensional variable combinations, a convolutional neural network-based autoencoder (CNN-AE) is utilized. The CNN-AE is employed to map a high-dimensional dynamics into a low-dimensional latent space. The SINDy then seeks a governing equation of the mapped low-dimensional latent vector. Temporal evolution of high-dimensional dynamics can be provided by combining the predicted latent vector by SINDy with the CNN decoder which can remap the low-dimensional latent vector to the original dimension. The SINDy can provide a stable solution as the governing equation of the latent dynamics and the CNN-SINDy-based modelling can reproduce high-dimensional flow fields successfully, although more terms are required to represent the transient flow and the two-parallel cylinder wake than the periodic shedding. A nine-equation turbulent shear flow model is finally considered to examine the applicability of SINDy to turbulence, although without using CNN-AE. The present results suggest that the proposed scheme with an appropriate parameter choice enables us to analyse high-dimensional nonlinear dynamics with interpretable low-dimensional manifolds.

show abstract

Section: Methodsmentioning

confidence: 99%

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, machine learning methods have exemplified their great potential in fluid dynamics, e.g., turbulence modeling [17][18][19][20][21][22][23][24][25], and spatio-temporal data estimation [26][27][28][29][30][31][32][33][34][35][36][37]. Reduced order modeling (ROM) is no exception, referred to as machine-learning-based ROM (ML-ROM).…”

Section: Introductionmentioning

confidence: 99%

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

et al. 2021

Self Cite

View full text Add to dashboard Cite

We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical fluid flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, and (4) a cross-sectional field of turbulent channel flow, in terms of a number of latent modes, the choice of nonlinear activation functions, and the number of weights contained in the AE model. We find that the AE models are sensitive to the choice of the aforementioned parameters depending on the target flows. Finally, we foresee the extensional applications and perspectives of machine learning based order reduction for numerical and experimental studies in the fluid dynamics community.

show abstract

“…Murata et al [44], Fukami et al [45] used convolutional neural networks (CNN) for data compression and reconstruction of high dimensional flow fields. These models require a large amount of data for training, which also increases the computational expense [44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil

Farooq

Saeed

Akhtar

et al. 2021

Fluids

View full text Add to dashboard Cite

In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack. The proper orthogonal decomposition (POD) of the flow field data is employed to obtain pressure modes and the temporal coefficients. These temporal pressure coefficients are used to train the ANN using data from three different angles of attack. The trained network then takes the value of angle of attack (AOA) and past POD coefficients as an input and predicts the future temporal coefficients. We also decompose the surface pressure modes into lift and drag components. These surface pressure modes are then employed to calculate the pressure component of lift CLp and drag CDp coefficients. The train model is then tested on the in-sample data and out-of-sample data. The results show good agreement with the true numerical data, thus validating the neural network based model.

show abstract

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

Abstract: Abstract

Cited by 207 publications

References 44 publications

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil

Contact Info

Product

Resources

About