Optimal Closed-Loop Control of Step Flow Separation Using Genetic Programming

Document Type : Original Article

Authors

Siroos Kasmaiee ¹

Mehran Tadjfar ²

saman kasmaiee ³

¹ Department of Aerospace Engineering, Amirkabir University of Technology, Tehran, Iran.

² Aerospace engineering department, AmirKabir university. Tehran. Iran

³ Department of Aerospace Engineering, Amirkabir University of Technology, Tehran, Iran

10.22067/jacsm.2024.88143.1261

Abstract

In this research, novel model-free method based on machine learning for closed-loop control of flow separation behind a step is introduced. The primary goal of this study is to reduce the recirculation zone behind the step at a Reynolds number of 1350, which is achieved by a jet slot. In this study, the flow was simulated as steady and two-dimensional based on finite volume discretization. Feedback control rules have been optimized based on a cost function that includes the area of the recirculation flow and the costs associated with the injection. This optimization process was carried out using genetic programming algorithms. A tree-based genetic programming was used to construct various injection model functions and create a closed-loop control system. After evolving through 8 generations with 500 samples in each generation, the algorithm arrives at a feedback rule capable of reducing the recirculation flow area by up to 60%. This machine learning-based control system was compared with the best open-loop jet based on Kelvin-Helmholtz frequency pulsation.

Keywords

Flow control

Genetic programming

Numerical simulation

Machine learning

Computational fluid mechanics

Subjects

• Aerodynamics

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