Genetic-algorithm-based artificial intelligence control of a turbulent boundary layer
基于遗传算法的湍流边界层人工智能控制
乱流境界層の遺伝的アルゴリズムに基づく人工知能制御
난류 경계층의 유전자 알고리즘 기반 인공 지능 제어
Control de inteligencia artificial basado en algoritmos genéticos de una capa límite turbulenta
Contrôle par intelligence artificielle basée sur un algorithme génétique d'une couche limite turbulente
Искусственный интеллект на основе генетических алгоритмов управления турбулентным пограничным слоем
Jianing Yu ¹, Dewei Fan 范德威 ¹, Bernd. R. Noack ¹ ², Yu Zhou 周裕 ¹
¹ Center for Turbulence Control, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
中国 深圳 哈尔滨工业大学（深圳）湍流控制研究所
² School of Mechanical Engineering and Automation, HarbinInstitute of Technology (Shenzhen), Shenzhen 518055, China
中国 深圳 哈尔滨工业大学（深圳）机电工程与自动化学院
An artificial intelligence (AI) open-loop control system is developed to manipulate a turbulent boundary layer (TBL) over a flat plate, with a view to reducing friction drag. The system comprises six synthetic jets, two wall-wire sensors, and genetic algorithm (GA) for the unsupervised learning of optimal control law. Each of the synthetic jets through rectangular streamwise slits can be independently controlled in terms of its exit velocity, frequency and actuation phase.
Experiments are conducted at a momentum-thickness-based Reynolds number Reθ of 1450. The local drag reduction downstream of the synthetic jets may reach 48% under conventional open-loop control (COC). This local drag reduction rises to 60%, with an extended effective drag reduction area, under the AI control that finds optimized non-uniform forcing. The results point to the significant potential of AI in the control of a TBL given distributed actuation.
