Shaqarin Tamir, Jiang Zhutao, Wang Tianyu, Hou Chang, Cornejo Maceda Guy Y, Deng Nan, Gao Nan, Noack Bernd R
Department of Mechanical Engineering, Tafila Technical University, Tafila, 66110, Jordan.
Chair of Artificial Intelligence and Aerodynamics, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, People's Republic of China.
Sci Rep. 2024 Oct 29;14(1):25952. doi: 10.1038/s41598-024-75688-4.
Jet mixing is a critical factor in various engineering applications, influencing pollutant dispersion, chemical processes, medical treatments, and combustion enhancement. Hitherto, jet mixing has typically been optimized by either passive or active control techniques. In this experimental study, we combine simultaneous optimization of active control with 12 inward-pointing minijets and a tuneable nozzle exit shape commanded by 12 stepper motors. Jet mixing is monitored at the end of the potential core with an array of 7 × 7 Pitot tubes. This high-dimensional actuation space is conquered with Particle Swarm Optimization through Targeted, Position-Mutated Elitism. Our results underscore the significant impact of combining control techniques, illustrating the complex interactions of both passive and active control on jet flow dynamics. The mixing area of the combined control optimization is 4.5 times larger than the area of the unforced state. This mixing increase significantly outperforms the effect of shape optimization of the nozzle alone. Our study points at the potential of optimization in high-dimensional design spaces for shapes as well as passive and active control-leveraging the rapid development of flow control hardware and the increasingly powerful tools of artificial intelligence for optimization.
射流混合是各种工程应用中的一个关键因素，影响着污染物扩散、化学过程、医学治疗和燃烧增强。迄今为止，射流混合通常通过被动或主动控制技术进行优化。在本实验研究中，我们将主动控制的同步优化与12个向内的微型射流以及由12个步进电机控制的可调喷嘴出口形状相结合。在势核末端用7×7皮托管阵列监测射流混合。通过有针对性的位置变异精英粒子群优化算法攻克了这个高维驱动空间。我们的结果强调了组合控制技术的显著影响，说明了被动和主动控制对射流流动动力学的复杂相互作用。组合控制优化的混合面积比无外力状态下的面积大4.5倍。这种混合增加显著优于仅喷嘴形状优化的效果。我们的研究指出了在高维设计空间中对形状以及被动和主动控制进行优化的潜力——利用流动控制硬件的快速发展和日益强大的人工智能优化工具。
