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High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning

Published online by Cambridge University Press:  14 August 2024

Haokai Wu Open the ORCID record for Haokai Wu [Opens in a new window] ,
Kai Zhang Open the ORCID record for Kai Zhang [Opens in a new window] ,
Dai Zhou ,
Wen-Li Chen Open the ORCID record for Wen-Li Chen [Opens in a new window] ,
Zhaolong Han  and
Yong Cao Open the ORCID record for Yong Cao [Opens in a new window]
Haokai Wu
Affiliation:
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
Kai Zhang
Affiliation:
State Key Laboratory of Ocean Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong University, Shanghai 200240, PR China School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
Dai Zhou
Affiliation:
State Key Laboratory of Ocean Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong University, Shanghai 200240, PR China School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
Wen-Li Chen
Affiliation:
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, PR China
Zhaolong Han
Affiliation:
State Key Laboratory of Ocean Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong University, Shanghai 200240, PR China School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
Yong Cao*
Affiliation:
State Key Laboratory of Ocean Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong University, Shanghai 200240, PR China School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
*
†Email address for correspondence: [email protected]
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Abstract

This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. The DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade reconstruction framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via ‘zero-shot transfer’), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to direct numerical simulation (DNS) results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pretrained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore the potential of EC-SRGAN for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.

JFM classification

Turbulent Flows: Turbulent boundary layers Boundary Layers: Boundary layer structure
Type
JFM Rapids
Information
Journal of Fluid Mechanics , Volume 990 , 10 July 2024 , R1
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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