Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers

Understand

A realistic inflow boundary condition is essential for successful simulation of the developing turbulent boundary layer or channel flows.

• Recent advances in artificial intelligence (AI) have enabled the development of an inflow generator that performs better than the synthetic methods based on intuitions.
• 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.