Artificial Neural Network and Support Vector Regression for Predicting Turbulent Bursting in Bluff-Body Hydrodynamics

Machine learning prediction of turbulent bursting in near- and far-wake flow zones past two horizontal cylinders was studied in the present article. Based on the bursting dataset, two predictive models were constructed using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) with stress ratios as target values for each bursting event. After analyzing a number of plots, it was observed that the ANN and SVR models achieved satisfactory estimation accuracy, with minor overfitting specifically in the case of ANN models. By using deep learning for quadrant analysis and highlighting the adaptability of machine learning methods in open-channel turbulence, the current work should strengthen the understanding of bursting occurrences in bluff-body hydrodynamics.