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.

现实的流入边界条件对于成功模拟正在发展的湍流边界层或通道流至关重要。在当前的工作中，我们应用了生成对抗网络（GANs）（无监督学习的代表）来生成湍流通道入口边界条件。在使用从湍流通道流动的直接数值模拟（DNS）获得的数据获悉湍流的二维空间结构后，GAN可以生成统计上与DNS相似的瞬时流场。在仅学习三个雷诺数的数据后，GAN可以在一定范围内产生各种雷诺数的场，而无需额外的模拟。最终，通过GAN和递归神经网络（RNN）的组合，我们开发了一个新颖的模型（RNN-GAN），该模型可以长时间产生随时间变化的完全展开的流。生成流的时空相关性与DNS的时空相关性很好。这证明了无监督学习在合成湍流场产生中的有用性。