Exploiting fully coupled fluid-structure interaction: optimal wing heterogeneity and efficient flow state estimation in flapping flight

昆虫可以以每小时35英里的速度飞行，进行令人眼花缭乱的转弯和机动，并在令人难以置信的巨大流动扰动中迁徙超过1万英里。这些壮举推动了仿生机器人飞行器的设计，在灾难恢复、高效和环保的航空包裹递送以及提高商业飞行安全性方面具有潜在应用。为了实现这些应用，机器人飞行器必须变得更具机动性和抗干扰性。这项研究对这些下一代飞行器有两个目标或问题：(I)当前的机器人机翼设计借鉴了自然飞行者的灵感，但纹理、强化前缘和不对称机翼形状等特征的空气动力学效用仍不清楚。如果这些特性针对空气动力学性能进行了优化，那么会出现哪些结构不同的特征，这些特征与昆虫中发现的特征有多相似或不同？(2)下一代飞行器需要改进对气流扰动的感知。能否利用飞行中被动的机翼变形来估计周围的流动行为，以及这样的估计框架是否可以产生关于昆虫是否具有类似的估计范例的假设？本项目将使用基于伴随的优化方法来确定正则扑翼飞行器的最佳机翼不均匀性。这一优化将使用高保真、完全耦合的流固耦合模拟。在可能的情况下，将绘制澄清优化属性如何对关键流动结构产生有益变化的机制。最优结果将与生物飞行器的性能进行比较，以评估它们是否有利于空气动力学性能(无需事先假设)。将开发一种利用神经网络结构的状态估计范例，以评估是否可以从机翼变形中获得准确的流动状态信息。这项工作的学术价值在于确定了气动最优机翼特性和相关的流体结构机制，解释了这些特性如何有助于气动性能，以及根据被动机翼变形开发合理的状态估计范例。技术上更广泛的影响是更具机动性和抗干扰性的微型飞行器的开发，以及关于昆虫飞行空气动力学的新假设。在教育方面，该计划将通过McNair学者计划以及与UIUC芝加哥科学与工程计划的合作，整合到针对服务不足人群的本科生研究实习中。在后一次合作中，来自航空航天的少数民族学生将向来自代表不足的群体及其家庭的K-12学生传授编码、控制思想和基本无人机飞行序列的实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Insects can fly up to 35 mph, execute dizzying turns and maneuvers, and migrate over ten thousand miles amid incredibly large flow disturbances. These feats have driven the design of bio-inspired robotic vehicles, with potential applications in disaster recovery, efficient and environmentally friendly air package delivery, and improved safety in commercial flight. To realize these applications, robotic flyers must become more maneuverable and robust to disturbances. The research has two goals or questions to build towards these next-generation aerial vehicles: (i) Current robotic wing designs borrow inspiration from natural flyers, but the aerodynamic utility of features such as veins, reinforced leading edges, and asymmetric wing shapes remain unknown. If these properties were optimized for aerodynamic performance, what structurally heterogeneous features would arise and how similar or different are these from those found in insects? (ii) Next-generation aerial vehicles require improved sensing of flow disturbances. Can the passive wing deformations from flight be leveraged to estimate the surrounding flow behavior, and could such an estimation framework yield hypotheses about whether insects possess similar estimation paradigms? This project will use adjoint-based optimization to determine optimal wing heterogeneity in canonical flapping flyers. This optimization will use high-fidelity, fully coupled fluid-structure interaction simulations. Where possible, mechanisms that clarify how the optimized properties yield beneficial changes to key flow structures will be drawn. Optimal results will be compared to properties of biological flyers to assess whether they benefit aerodynamic performance (without assuming so beforehand). A state estimation paradigm that leverages neural-network architectures will be developed to assess whether accurate flow state information can be obtained from wing deformations. The intellectual merit of this work lies in the identification of aerodynamically optimal wing properties and the associated fluid-structure mechanisms that explain how these properties benefit aerodynamic performance, as well as the development of plausible state estimation paradigms from passive wing deformations. The technical broader impacts are the development of more maneuverable and disturbance-robust micro-air vehicles, as well as new hypotheses about the aerodynamics of insect flight. Educationally, this program will be integrated into an undergraduate research internship with students from under-served populations via the McNair Scholars Program, as well as a collaboration with the UIUC Chicago Science & Engineering Program. In this latter collaboration, students from Minorities in Aerospace, an organization co-founded by the PI, will teach K-12 students from under-represented groups and their families the coding, control ideas, and implementation of a basic drone flight sequence.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.