Super-resolution reconstruction of turbulent flow fields at various Reynolds numbers based on generative adversarial networks

Yousif, Mustafa Z.; Yu, Linqi; Lim, Hee-Chang

doi:10.1063/5.0074724

Cited by 46 publications

(17 citation statements)

References 31 publications

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“…More details for MSP can be found in Yousif et al. (2021, 2022 b ). The outputs of the three submodels are summed and passed through a final convolutional layer to generate HR artificial data .…”

Section: Methodsmentioning

confidence: 99%

“…(Goodfellow et al 2014) have shown great success in image transformation and super-resolution problems (Mirza & Osindero 2014;Ledig et al 2017;Zhu et al 2017;Wang et al 2018). Generative adversarial network-based models have also shown promising results in reconstructing HR turbulent flow fields from coarse data (Fukami et al 2019a;Fukami, Fukagata & Taira 2021;Güemes et al 2021;Kim et al 2021;Yousif et al , 2022bYu et al 2022). In a GAN model that is used for image generation, two adversarial neural networks called the generator (G) and the discriminator (D) compete with each other.…”

Section: Masked Multiheadmentioning

confidence: 99%

“…Deep learning is a subset of machine learning, where deep neural networks are used for classification, prediction and feature extraction (LeCun, Bengio & Hinton 2015). Recently, several models have shown great potential in solving different problems in the field of turbulence, such as turbulence modelling (Wang, Wu & Xiao 2017;Duraisamy, Iaccarino & Xiao 2019), turbulent flow prediction (Lee & You 2019;Srinivasan et al 2019), reduced-order modelling (Nakamura et al 2021, flow control (Rabault et al 2019;Fan et al 2020;Park & Choi 2020;, non-intrusive sensing (Guastoni et al 2021;Güemes et al 2021) and turbulent flow reconstruction (Deng et al 2019;Fukami, Fukagata & Taira 2019a;Kim et al 2021;Yousif, Yu & Lim 2021, 2022bEivazi et al 2022;Yu et al 2022) .…”

Section: Introductionmentioning

confidence: 99%

“…2019; Fukami, Fukagata & Taira 2019 a ; Kim et al. 2021; Yousif, Yu & Lim 2021, 2022 b ; Eivazi et al. 2022; Yu et al.…”

Section: Introductionmentioning

confidence: 99%

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A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

Yousif

Zhang

et al. 2023

J. Fluid Mech.

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This study proposes a newly developed deep-learning-based method to generate turbulent inflow conditions for spatially developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced super-resolution generative adversarial network is utilised to predict velocity fields of a spatially developing TBL at various planes normal to the streamwise direction. Datasets of direct numerical simulation (DNS) of flat plate flow spanning a momentum thickness-based Reynolds number, $Re_\theta = 661.5\unicode{x2013}1502.0$ , are used to train and test the model. The model shows a remarkable ability to predict the instantaneous velocity fields with detailed fluctuations and reproduce the turbulence statistics as well as spatial and temporal spectra with commendable accuracy as compared with the DNS results. The proposed model also exhibits a reasonable accuracy for predicting velocity fields at Reynolds numbers that are not used in the training process. With the aid of transfer learning, the computational cost of the proposed model is considered to be effectively low. Furthermore, applying the generated turbulent inflow conditions to an inflow–outflow simulation reveals a negligible development distance for the TBL to reach the target statistics. The results demonstrate for the first time that transformer-based models can be efficient in predicting the dynamics of turbulent flows. They also show that combining these models with generative adversarial networks-based models can be useful in tackling various turbulence-related problems, including the development of efficient synthetic-turbulent inflow generators.

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Section: Methodsmentioning

confidence: 99%

Section: Masked Multiheadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…2019; Fukami, Fukagata & Taira 2019 a ; Kim et al. 2021; Yousif, Yu & Lim 2021, 2022 b ; Eivazi et al. 2022; Yu et al.…”

Section: Introductionmentioning

confidence: 99%

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A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

Yousif

Zhang

et al. 2023

J. Fluid Mech.

Self Cite

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“…The MSP, which consists of three parallel 3D convolutional sub-models with different kernel sizes, is applied to the data features extracted by the RRDBs. More details regarding MSP can be found in Yousif et al 29 , 30 . The outputs of the three sub-models are summed and passed through a final 3D convolutional layer to generate an artificial 3D data ( ).…”

Section: Methodsmentioning

confidence: 99%

A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data

Yousif

Hoyas

et al. 2023

Sci Rep

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Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is currently not possible. This motivates utilising deep learning on subsets of the available data to reduce the required cost of reconstructing the full flow in such full-scale applications. Here, we develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data represented by a cross-plane of unpaired two-dimensional velocity observations. The model could successfully reconstruct the flow fields with accurate flow structures, statistics and spectra. The results indicate that our model can be successfully utilised for reconstructing three-dimensional flows from two-dimensional experimental measurements. Consequently, a remarkable reduction in the complexity of the experimental setup and the storage cost can be achieved.

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Super-resolution analysis via machine learning: a survey for fluid flows

Fukami

Fukagata

Taira

2023

Theor. Comput. Fluid Dyn.

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This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data. Graphical abstract

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Super-resolution reconstruction of turbulent flow fields at various Reynolds numbers based on generative adversarial networks

Cited by 46 publications

References 31 publications

A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers

A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data

Super-resolution analysis via machine learning: a survey for fluid flows

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