Numerical Approach for On-the-Fly Active Flow Control via Flow Map Learning Method

TL;DR

This paper introduces a data-driven flow map learning method for real-time active flow control that effectively reduces drag in two-dimensional flow past a cylinder without requiring flow field simulations during control optimization.

Contribution

It develops a flow map learning framework using neural networks for on-the-fly active flow control, integrating with existing control strategies for real-time applications.

Findings

Achieves over 20% drag reduction in simulations.

Enables real-time control without flow field simulations.

Compatible with reinforcement learning and model predictive control.

Abstract

We present a data-driven numerical approach for on-the-fly active flow control and demonstrate its effectiveness for drag reduction in two-dimensional incompressible flow past a cylinder. The method is based on flow map learning (FML), a recently developed framework for modeling unknown dynamical systems that is particularly effective for partially observed systems. For active flow control, we construct an FML dynamical model for the quantities of interest (QoIs), namely the drag and lift forces. During offline learning, training data are generated for the responses of drag and lift to the control variable, and a deep neural network (DNN)-based FML model is constructed. The learned FML model enables online optimal flow control without requiring simulations of the flow field. We demonstrate that the FML-based approach can be integrated with existing optimal control strategies, including deep reinforcement learning (DRL) and model predictive control (MPC). Numerical results show that the proposed approach enables on-the-fly flow control and achieves more than $20\%$ drag reduction. By eliminating the need for forward simulations during control optimization, the approach offers the potential for real-time optimal control in other systems.