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. Surprisingly, the GAN
could produce fields at various Reynolds numbers without any additional
simulation based on the trained data of only three Reynolds numbers. This
indicates that the GAN could learn the universal nature of Reynolds number
effect and might reflect other simulation conditions. 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.

本文利用生成对抗网络和循环神经网络结合的方式，通过无监督学习生成了一种可以模拟各种湍流雷诺数下流动的模拟数据，并与真实数据达成了一定程度上的拟合。