Optimal parallelization strategies for active flow control in deep reinforcement learning-based computational fluid dynamics

The present study applies a Deep Reinforcement Learning (DRL) algorithm to Active Flow Control (AFC) of a two-dimensional flow around a confined square cylinder. Specifically, the Soft Actor-Critic (SAC) algorithm is employed to modulate the flow of a pair of synthetic jets placed on the upper and lower surfaces of the confined squared cylinder in flow configurations characterized by Re of 100, 200, 300, and 400. The investigation starts with an analysis of the baseline flow in the absence of active control. It is observed that at Re = 100 and Re = 200, the vortex shedding exhibits mono-frequency characteristics. Conversely, at Re = 300 and Re = 400, the vortex shedding is dominated by multiple frequencies, which is indicative of more complex flow features. With the application of the SAC algorithm, we demonstrate the capability of DRL-based control in effectively suppressing vortex shedding, while significantly diminishing drag and fluctuations in lift. Quantitatively, the data-driven active control strategy results in a drag reduction of approximately 14.4%, 26.4%, 38.9%, and 47.0% for Re = 100, 200, 300, and 400, respectively. To understand the underlying control mechanism, we also present detailed flow field comparisons, which showcase the adaptability of DRL in devising distinct control strategies tailored to the dynamic conditions at varying Re. These findings substantiate the ability of DRL to control chaotic, multi-frequency dominated vortex shedding phenomena, underscoring the robustness of DRL in complex AFC problems.

Robust and adaptive deep reinforcement learning for enhancing flow control around a square cylinder with varying Reynolds numbers

Jia,

2024

Deep reinforcement learning-based active flow control of an elliptical cylinder: Transitioning from an elliptical cylinder to a circular cylinder and a flat plate

Jia,

2024

We study the adaptability of deep reinforcement learning (DRL)-based active flow control (AFC) technology for bluff body flows with complex geometries. It is extended from a cylinder with an aspect ratio Ar = 1 to a flat elliptical cylinder with Ar = 2, slender elliptical cylinders with Ar less than 1, and a flat plate with Ar = 0. We utilize the Proximal Policy Optimization (PPO) algorithm to precisely control the mass flow rates of synthetic jets located on the upper and lower surfaces of a cylinder to achieve reduction in drag, minimization of lift, and suppression of vortex shedding. Our research findings indicate that, for elliptical cylinders with Ar between 1.75 and 0.75, the reduction in drag coefficient ranges from 0.9% to 15.7%, and the reduction in lift coefficient ranges from 95.2% to 99.7%. The DRL-based control strategy not only significantly reduces lift and drag, but also completely suppresses vortex shedding while using less than 1% of external excitation energy, demonstrating its efficiency and energy-saving capabilities. Additionally, for Ar from 0.5 to 0, the reduction in drag coefficient ranges from 26.9% to 43.6%, and the reduction in lift coefficient from 50.2% to 68.0%. This reflects the control strategy's significant reduction in both drag and lift coefficients, while also alleviating vortex shedding. The interaction and nonlinear development of vortices in the wake of elliptical cylinders lead to complex flow instability, and DRL-based AFC technology shows adaptability and potential in addressing flow control problems for this type of bluff body flow.

Effect of synthetic jets actuator parameters on deep reinforcement learning-based flow control performance in a square cylinder

Jia,

2024

We conduct an active flow control study on the mass flow rate of synthetic jets on the upper and lower surfaces of a square cylinder using a deep reinforcement learning algorithm, with a focus on investigating the influence of the position and width of the synthetic jets on the flow control performance. At Reynolds numbers (Re) of 100 and 500, it is found that our proposed method significantly reduced the lift and drag coefficients of the square cylinder and completely suppressed vortex shedding in the wake. In particular, at Re = 100, placing the synthetic jets near the tail corner was beneficial for reducing drag, with a maximum drag reduction rate of 14.4%. When Re = 500, positioning the synthetic jets near the leading edge corner resulted in a maximum optimal drag reduction effect of 65.5%. This indicates that locating the synthetic jet at the main flow separation point can achieve optimal control. Furthermore, we notice that when the synthetic jets are positioned near the tail corner, vortex shedding can be completely suppressed. Additionally, a narrower width of the synthetic jets can enhance flow instability and increase the cost of flow control.