Neural network-based prediction of actively controlled sweep events in a fully turbulent channel flow

This study investigates the use of a feed-forward neural network (FNN) and a hybrid approach combining an FNN with proper orthogonal decomposition (POD) to estimate the effect of control on sweep events within an opposition control framework. Conditionally averaged sweep events (CASEs) were experimentally sampled in a fully turbulent channel flow at a friction Reynolds number of 350. The two architectures were then trained to reconstruct the controlled CASEs using the control parameters (frequency, voltage amplitude, and delay time) as input. The results indicate that both approaches are able to reproduce the control signature across different actuation parameters with comparable accuracy. At the same time, the hybrid POD–FNN architecture requires over two orders of magnitude fewer trainable parameters. Finally, both local and global sensitivity analyses were performed, leveraging the differentiability of the trained models and their rapid inference times. The Jacobian matrix and Sobol indices were computed to conduct a local and a global sensitivity analysis, respectively. The findings revealed a high sensitivity to the actuation frequency and delay time parameters, whereas a lower sensitivity was observed with respect to the voltage amplitude.