نوع مقاله : مقاله پژوهشی
نویسندگان
1 هوافضا، امیرکبیر، تهران، ایران
2 دانشکده هوافضا، دانشگاه امیرکبیر، تهران، ایران
3 دانشکده مهندسی هوافضا، دانشگاه صنعتی امیرکبیر، تهران، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
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.
کلیدواژه‌ها [English]
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