A realistic inflow boundary condition is essential for successful simulation of the developing turbulent boundary layer or channel flows. In the present work, we applied generative adversarial networks (GANs), a representative of unsupervised learning, to generate an inlet boundary condition of turbulent channel flow. Upon learning the two-dimensional spatial structure of turbulence using data obtained from direct numerical simulation (DNS) of turbulent channel flow, the GAN could generate instantaneous flow fields that are statistically similar to those of DNS. After learning data at only three Reynolds numbers, the GAN could produce fields at various Reynolds numbers within a certain range without additional simulation. Eventually, through a combination of the GAN and a recurrent neural network (RNN), we developed a novel model (RNN-GAN) that could generate time-varying fully developed flow for a long time. The spatiotemporal correlations of the generated flow are in good agreement with those of the DNS. This proves the usefulness of unsupervised learning in the generation of synthetic turbulence fields.
|Journal||Journal of Computational Physics|
|Publication status||Published - 2020 Apr 1|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2017R1E1A1A03070282).
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) ( 2017R1E1A1A03070282 ). Appendix A
© 2020 Elsevier Inc.
All Science Journal Classification (ASJC) codes
- Numerical Analysis
- Modelling and Simulation
- Physics and Astronomy (miscellaneous)
- Physics and Astronomy(all)
- Computer Science Applications
- Computational Mathematics
- Applied Mathematics