Data-Driven Prediction of Turbulent Flow Statistics Past Bridge Piers in Large-Scale Rivers Using Convolutional Neural Networks

Prediction of statistical properties of the turbulent flow in large-scale rivers is essential for river flow analysis. The large-eddy simulation (LES) provides a powerful tool for such predictions; however, it requires a very long sampling time and demands significant computing power to calculate the turbulence statistics of riverine flows. In this study, we developed encoder-decoder convolutional neural networks (CNNs) to predict the first- and second-order turbulence statistics of the turbulent flow of large-scale meandering rivers using instantaneous LES results. We train the CNNs using a data set obtained from LES of the flood flow in a large-scale river with three bridge piers—a training testbed. Subsequently, we employed the trained CNNs to predict the turbulence statistics of the flood flow in two different meandering rivers and bridge pier arrangements—validation testbed rivers. The CNN predictions for the validation testbed river flow were compared with the simulation results of a separately done LES to evaluate the performance of the developed CNNs. We show that the trained CNNs can successfully produce turbulence statistics of the flood flow in the large-scale rivers, that is, the validation testbeds.