Deep Learning models for turbulent shear flow

Abstract: Deep neural networks trained with spatio-temporal evolution of a dynamical system may be regarded as an empirical alternative to conventional models using differential equations. In this thesis, such deep learning models are constructed for the problem of turbulent shear flow. However, as a first step, this modeling is restricted to a simplified low-dimensional representation of turbulence physics. The training datasets for the neural networks are obtained from a 9-dimensional model using Fourier modes proposed by Moehlis, Faisst, and Eckhardt [29] for sinusoidal shear flow. These modes were appropriately chosen to capture the turbulent structures in the near-wall region. The time series of the amplitudes of these modes fully describe the evolution of flow. Trained deep learning models are employed to predict these time series based on a short input seed. Two fundamentally different neural network architectures, namely multilayer perceptrons (MLP) and long short-term memory (LSTM) networks are quantitatively compared in this work. The assessment of these architectures is based on (i) the goodness of fit of their predictions to that of the 9-dimensional model, (ii) the ability of the predictions to capture the near-wall turbulence structures, and (iii) the statistical consistency of the predictions with the test data. LSTMs are observed to make predictions with an error that is around 4 orders of magnitude lower than that of the MLP. Furthermore, the flow fields constructed from the LSTM predictions are remarkably accurate in their statistical behavior. In particular, deviations of 0:45 % and 2:49 % between the true data and the LSTM predictions were obtained for the mean flow and the streamwise velocity fluctuations, respectively.