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Closed-loop supersonic flow control with a high-speed experimental deep reinforcement learning framework

Published online by Cambridge University Press:  11 April 2025

Haohua Zong Open the ORCID record for Haohua Zong [Opens in a new window] ,
Yun Wu ,
Jinping Li ,
Zhi Su  and
Hua Liang
Haohua Zong*
Affiliation:
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China
Yun Wu
Affiliation:
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China
Jinping Li
Affiliation:
National Key Lab of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi’an 710038, PR China
Zhi Su
Affiliation:
National Key Lab of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi’an 710038, PR China
Hua Liang
Affiliation:
National Key Lab of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi’an 710038, PR China
*
Corresponding author: Haohua Zong, haohua_zong@163.com
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Abstract

Although active flow control based on deep reinforcement learning (DRL) has been demonstrated extensively in numerical environments, practical implementation of real-time DRL control in experiments remains challenging, largely because of the critical time requirement imposed on data acquisition and neural-network computation. In this study, a high-speed field-programmable gate array (FPGA) -based experimental DRL (FeDRL) control framework is developed, capable of achieving a control frequency of 1–10 kHz, two orders higher than that of the existing CPU-based framework (10 Hz). The feasibility of the FeDRL framework is tested in a rather challenging case of supersonic backward-facing step flow at Mach 2, with an array of plasma synthetic jets and a hot-wire acting as the actuator and sensor, respectively. The closed-loop control law is represented by a radial basis function network and optimised by a classical value-based algorithm (i.e. deep Q-network). Results show that, with only ten seconds of training, the agent is able to find a satisfying control law that increases the mixing in the shear layer by 21.2 %. Such a high training efficiency has never been reported in previous experiments (typical time cost: hours).

JFM classification

Compressible Flows: Supersonic flow Flow Control: Mixing enhancement Wakes/Jets: Jets

Information

Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 1009 , 25 April 2025 , A3
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

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