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.

昆虫最多可以飞到35英里 /小时的时间，在大大的流动障碍中迁移令人眼花the乱的转弯和动作，并迁移了一万英里。这些壮举驱动了以生物风格的机器人车的设计，并在灾难恢复，高效且环保的空气套餐交付方面进行了潜在的应用，并提高了商业飞行的安全性。为了实现这些应用，机器人传单必须变得更加动摇和强大的干扰。这项研究有两个目标或问题，可以朝着这些下一代航空车发展：（i）当前的机器人翼设计从天然传单中借用灵感，但是静脉，增强的领先边缘和不对称的机翼形状等特征的空气动力学效用仍然未知。如果这些特性是针对空气动力学性能进行了优化的，那么将出现哪些结构异质特征，以及这些特征与昆虫中发现的特征有多相似或不同？ （ii）下一代飞机需要改善流动干扰的感应。可以利用飞行中的被动机翼变形以估算周围的流动行为，并且这样的估计框架是否可以假设昆虫是否具有相似的估计范例？该项目将使用基于伴随的优化来确定规范拍打传单中的最佳机翼异质性。该优化将使用高保真，完全耦合的流体结构互动模拟。在可能的情况下，将阐明优化特性如何对关键流结构产生有益的变化的机制。将最佳结果与生物传单的特性进行比较，以评估它们是否有益于空气动力学性能（而不是事先假设）。将开发出一种利用神经网络架构的状态估计范式来评估是否可以从机翼变形中获得准确的流量状态信息。这项工作的智力优点在于鉴定空气动力学最佳的机翼特性以及相关的流体结构机制，这些机制解释了这些特性如何使空气动力学性能以及从无源机翼变形的合理状态估计范式发展。技术更广泛的影响是开发更多可操纵和动荡的微型空气车，以及关于昆虫飞行空气动力学的新假设。在教育上，该计划将通过McNair Scholars计划与服务不足人群的学生一起纳入本科研究实习，并与UIUC芝加哥科学与工程计划合作。 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.