A field programmable gate array-based deep reinforcement learning framework for experimental active flow control and its application in airfoil flow separation elimination

Although deep reinforcement learning (DRL) has gained increasing popularity in numerical studies of active flow control, practical implementations of this technique in experiments remain challenging, particularly for fast air flows. In this study, we proposed a field programmable gate array (FPGA)-based DRL framework for high-speed experimental active flow control. By splitting the training and execution process of artificial neural network and implementing them effectively in central processing unit (CPU) and FPGA, experimental DRL control with an interaction frequency up to 10–100 kHz can be realized, which is two orders higher than that of the traditional CPU-based DRL framework. Such a new framework is tested in the classical problem of airfoil leading flow separation control at Rec = 2.13 × 105, with a plasma actuator and a hotwire serving as the flow disturber and the state sensor, respectively. As a result, experimental DRL control is rather attractive in terms of the time cost, able to find a converged closed-loop control strategy in only one run of 5 min, eliminating the tedious parameter tuning process in open-loop control (time cost: dozens of minutes to hours). Moreover, the magnitude of lift increment in the case of optimal DRL control is 3.2% higher than that of the best open-loop periodical control strategy.