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

利用完全耦合的流固相互作用：扑翼飞行中的最佳机翼异质性和有效的流动状态估计

• 批准号: 2320875
• 负责人: Andres Goza
• 金额: $ 29.95万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320875&HistoricalAwards=false
• 关键词: Exploiting; fully; coupled; fluid; structure

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.

昆虫可以以每小时35英里的速度飞行，执行令人眼花缭乱的转弯和机动，并在令人难以置信的大流量干扰中迁移一万英里。这些壮举推动了仿生机器人车辆的设计，这些车辆在灾难恢复、高效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的应用。为了实现这些应用，机器人飞行器必须变得更加灵活和强大的干扰。该研究有两个目标或问题要建立这些下一代飞行器：（i）目前的机器人机翼设计从自然飞行器中汲取灵感，但静脉，加强前缘和不对称机翼形状等特征的空气动力学效用仍然未知。如果这些特性被优化为空气动力学性能，那么会出现什么样的结构异质性特征，这些特征与昆虫中发现的特征有多少相似或不同？(ii)下一代飞行器需要改进的气流扰动感测。从飞行被动翼变形可以被利用来估计周围的流动行为，这样的估计框架产生的假设昆虫是否具有类似的估计范式？本计画将使用伴随式最佳化来决定典型扑翼飞行器的最佳机翼异质性。这种优化将使用高保真度，完全耦合的流体-结构相互作用模拟。在可能的情况下，将绘制阐明优化特性如何对关键流动结构产生有益变化的机制。最佳结果将与生物飞行器的特性进行比较，以评估它们是否有利于空气动力学性能（事先没有假设）。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状态信息。这项工作的智力价值在于确定空气动力学最佳的机翼特性和相关的流体结构机制，解释这些特性如何有利于空气动力学性能，以及从被动机翼变形的合理状态估计范例的发展。更广泛的技术影响是发展更稳定和抗干扰的微型飞行器，以及关于昆虫飞行的空气动力学的新假设。在教育方面，该计划将通过麦克奈尔学者计划，以及与UIUC芝加哥科学工程计划的合作，与来自服务不足人群的学生进行本科研究实习。在后一项合作中，来自PI共同创立的组织“航空航天少数民族”的学生将向来自代表性不足的群体及其家庭的K-12学生教授编码、控制思想和基本无人机飞行序列的实现。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。