Three-dimensional realizations of the flood flow field in large-scale rivers using convolutional neural networks

We present a systematic study to develop autoencoder convolutional neural networks (CNNs) to predict statistical properties of turbulent flood flow of large-scale rivers with several bridge foundations. The training dataset to develop CNN algorithms is obtained from high-fidelity numerical simulation of the flood flow using large-eddy simulation (LES). The developed CNN algorithms are shown to predict the first- and second-order turbulent statistics of the turbulent flood flow at a small fraction of the computational cost of the high-fidelity simulations. The CNN predictions are validated using separately done LES results of different meandering large-scale river. The results show good agreement between the LES and CNN algorithms marking the promise of such artificial intelligent systems to produce efficient flood flow field in large-scale rivers.