Journal
PHYSICS OF FLUIDS
Volume 34, Issue 12, Pages -Publisher
AIP Publishing
DOI: 10.1063/5.0129203
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Funding
- Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) from the Ministry of Trade, Industry & Energy, Republic of Korea [20214000000140]
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [2019R1I1A3A01058576]
- National Supercomputing Center [KSC-2022-CRE-0282]
- ERC [2021-CoG-101043998,]
- Ministerio de Ciencia, innovacion y Universidades/FEDER [PID2021-128676OB-I00]
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This study proposes a deep-learning approach to reconstruct three-dimensional high-resolution turbulent flows from spatially limited data and demonstrates its effectiveness through experiments.
Turbulence is a complicated phenomenon because of its chaotic behavior with multiple spatiotemporal scales. Turbulence also has irregularity and diffusivity, making predicting and reconstructing turbulence more challenging. This study proposes a deep-learning approach to reconstruct three-dimensional (3D) high-resolution turbulent flows from spatially limited data using a 3D enhanced super-resolution generative adversarial networks (3D-ESRGAN). In addition, a novel transfer-learning method based on tricubic interpolation is employed. Turbulent channel flow data at friction Reynolds numbers R e tau = 180 and R e tau = 500 were generated by direct numerical simulation (DNS) and used to estimate the performance of the deep-learning model as well as that of tricubic interpolation-based transfer learning. The results, including instantaneous velocity fields and turbulence statistics, show that the reconstructed high-resolution data agree well with the reference DNS data. The findings also indicate that the proposed 3D-ESRGAN can reconstruct 3D high-resolution turbulent flows even with limited training data.
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