Model predictive and reinforcement learning methods for active flow control of an airfoil with dual-point excitation of plasma actuators Available to Purchase

This study aims to investigate the effectiveness of Model Predictive Control (MPC) and Reinforcement Learning (RL) approaches for active flow control over a NACA 4412 airfoil near the static stall condition at a Reynolds number of 4 * 10⁵. By systematically evaluating these control strategies, the research seeks to address a critical gap in optimizing excitation frequency and improving response time in flow control applications. The study contributes to a deeper understanding of the adaptability and performance of RL-based methods compared to traditional MPC in aerodynamic flow separation control.

The study employs a quantitative approach through numerical simulations of the Reynolds Averaged Navier-Stokes (RANS) equations with the Scale-Adaptive Simulation (SAS) turbulence model. Dielectric Barrier Discharge (DBD) plasma actuators, operating in dual-point excitation mode, are utilized for flow separation control. The research evaluates adaptive MPC, temporal difference reinforcement learning (TDRL) and deep Q-learning (DQL) in optimizing excitation frequency and expediting the stabilization process. Additionally, an integrated approach combining signal processing with DQL is examined to enhance control performance.

This study explores advanced control strategies for optimizing aerodynamic performance by managing flow separation using plasma actuators. We evaluate adaptive MPC, TDRL, DQL and DQL with signal processing, utilizing dual-point excitation via DBD plasma actuators. Adaptive MPC successfully achieved a target lift coefficient C_l of 1.60 using an excitation frequency of approximately 110 Hz, but struggled to reach higher target C_l values near the physical limits. RL methods effectively optimized excitation frequencies, achieving a lift coefficient of approximately 1.62 in under 2.5 s with an excitation frequency of 100 or 200 Hz.

This study presents a novel comparison of RL and MPC methods for active flow control, utilizing DBD plasma actuators to mitigate flow separation and enhance aerodynamic performance. Prior approaches have primarily focused on either MPC or RL independently, often relying on offline learning with separate training and testing phases. In contrast, our research employs an online learning framework, where RL-based techniques such as TDRL, DQL and signal processing-enhanced DQL dynamically adapt to real-time aerodynamic conditions. By simultaneously evaluating adaptive MPC and RL methods in an online learning setup, this paper provides new insights into their comparative performance in dynamic environments.