  The present work proposes an inflow turbulence generation strategy using deep
learning methods. This is achieved with the help of an autoencoder architecture
with two different types of operational layers in the latent-space: a fully
connected multi-layer perceptron and convolutional long short-term memory
layers. A wall-resolved large eddy simulation of a turbulent channel flow at a
high Reynolds number is performed to create a large database of instantaneous
snapshots of turbulent flow-fields used to train neural networks. The training
is performed with sequences of instantaneous snapshots so as to learn a
snapshot at the next time instant. For the present work, velocity and pressure
fluctuations are used for training to produce the same parameters at the next
time instant. Within the autoencoders, different types of methods like average
pooling and strided convolutions are tested for the reduction of spatial
dimensions. For convolutional neural networks, the physical boundary conditions
are implemented in the form of symmetric as well as periodic paddings. A priori
simulations are performed with the trained deep learning models to check the
accuracy of turbulence statistics produced. It is found that the use of
convolutional long short-term memory layers in the latent space improved the
quality of statistics, although issues related to stability for longer times
are observed. Though instantaneous snapshots of the target flow are required
for training, these a priori simulations suggest that the deep learning methods
for generating inflow turbulence are one of the possible alternatives to
existing methods.
