Spatiotemporal prediction of bluff-body hydrodynamics using the graph neural network

A predictive model has been developed based on experimental data obtained from open channel flow past two horizontal cylinders, one above the other, using a graph neural network framework. Upon investigating the error plots, it is evident that the decreasing mean square error indicates the model's capability to predict near accurate data. Furthermore, second- and third-order structure functions have been illustrated to validate Kolmogorov's 2/3 and 4/5 laws for both original and predicted data, which were found to be a little inconsistent near the two cylinders in the near-wake zone. To investigate this inconsistency further, scaling exponents from extended self-similarity have been calculated and plotted with the conventional Kolmogorov 1941 theory in order to visualize the deviation. It is found that strong anisotropic flow occurs in the vicinity of the rough bed and two cylinders, which resulted in substantial intermittency. This article bears a significant contribution to the understanding of turbulence intermittency and deep learning implementation in turbulent open channel flow.