Micro Air Vehicles (MAVs) have a growing role in a broad range of civil and military operations. However, it is still challenging for them to fly robustly in natural or human-constructed environments, where they routinely encounter various forms of unsteady ambient flow or wind gusts. Flying animals such as insects and birds can attenuate the adverse effects of unsteady ambient flow with much greater success, due to the inherent aerodynamic capability of their flapping wings. Therefore, this project aims to establish the fundamental science underlying the fluid dynamics of flapping wings interacting with unsteady flow structures. This project will quantify the flow patterns and force production of flapping wings in interaction with various wind gusts to understand the best behavior for flapping wings to overcome the ambient flow disturbances. The results of this research will provide a foundation of scientific knowledge rather than mere intuition to develop better MAVs that can quickly recover from atmospheric gusts. Because of the cross-disciplinary and collaborative efforts in experimental fluid dynamics and robotics, this award will also expose undergraduate students to graduate-level research, thereby inspiring them to pursue advanced degrees or careers in STEM majors. Moreover, through K-12 education and outreach activities, this project will increase the public awareness and underrepresented groups engagements in science and engineering.
The goal of this project is to investigate the performance and the flow physics of flapping wings in interactions with unsteady ambient flow structures. This will be enabled by 1) parsing the complex wing-gust interaction problem into the interactions between four canonical types of gust structures and flapping wings in four kinematic configurations; and 2) coalescing tools in experimental and theoretical fluid dynamics with reinforcement learning methods. This research will first use parametric experimental studies to provide a comprehensive quantification of the gust-mitigation performance of flapping wings. Then, it will develop fundamental understandings of the flow physics pertinent to the interactions between wing-generated and ambient flow structures, in terms of vortex shedding pattern, vorticity transport dynamics, and aerodynamic force production. Effects of three-dimensionality and stability of the leading edge vortex (LEV) on gust-mitigation and vorticity dynamics will be studied. Finally, this research will use reinforcement learning methods and hardware-in-the-loop optimization to optimize the gust-mitigation performance of bioinspired robotic flapping wings.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
