A novel LES-augmented machine learning algorithm for turbulent flow and bed morphodynamics prediction in large-scale environments

In erodible channel environments, changes in bed topography can lead to the structural failure of device support structures and the spread of scour or deposition features within the channel. Thus, assessing sediment transport in large-scale waterways is a crucial environmental concern. However, high-fidelity simulations of bed evolution in large-scale rivers could be computationally expensive due to the costly two-way coupling between fluid dynamics and bed morphodynamics. We propose a novel convolutional neural network autoencoder (CNNAE) algorithm to predict the time-averaged shear stress distribution and equilibrium bed topography of mobile riverbeds in large-scale meandering rivers. The proposed method is highly efficient compared to high-fidelity simulations, requiring less than two percent of the computational cost to produce high-fidelity results. This study highlights the potential of the proposed machine-learning algorithm to reduce the computational costs of coupled hydro- and morpho-dynamics modeling in large-scale rivers.