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The Three-Dimensional Flow Structure and Forces of Flapping-Wing Hovering from Experiments

The Three-Dimensional Flow Structure and Forces of Flapping-Wing Hovering from Experiments
扑翼悬停的三维流动结构和受力实验
批准号:
1336548
负责人:
Matthew Ringuette
金额:
$28.86万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-01 至 2017-11-30

项目摘要

项目成果

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中文摘要
翻译
本课题的研究目的是了解扑翼在悬停过程中产生的非定常三维流动结构及其与力的关系,以及冲程幅度等关键参数的影响。在自然界中,扑翼具有低展弦比(ARs),并且由于大攻角和低雷诺数,会发生流动分离和卷升成涡。众所周知,悬停时的翅膀会产生高度3D的、相互连接的旋涡循环,这些旋涡循环会在每次半划时脱落,并产生一个整体向下的射流,以维持身体的重量。先前使用动物或机械模型进行的实验,加上流动可视化或二维数字粒子图像测速(DPIV),已经揭示了环结构,但三维涡旋拓扑及其与升力的关系仍然是一个开放的主题。主要关注动物形态的计算研究提供了更多的细节和洞察力。然而,缺乏实验的三分量3D DPIV数据,并且半冲程幅度、AR和运动程序等参数对流动结构的影响尚不清楚。一个关键的问题是:为什么悬停的动物拍动的幅度是3-5个和弦长度?该方法采用简化机翼几何形状和运动的2自由度扑翼模型进行实验。目标是了解三维涡旋拓扑结构,其随半冲程振幅、AR和速度程序的变化,以及它与力的关系。令人感兴趣的是不同情况下涡结构的变化。诊断包括染料可视化,立体DPIV (SDPIV)的三分量速度场,和力的测量。更广泛的影响:这项研究将为高机动性生物微型飞行器(MAVs)的设计提供有价值的见解。它们可以在复杂的环境中收集科学信息,例如城市环境、洞穴和地震后倒塌的建筑物等灾难现场,而传统的无人机无法在这些环境中操作。此外,它们可以高效地成群行动,例如追踪有毒气体羽流的扩散。所提出的研究结果将有助于更好地理解非定常旋涡以及运动学和AR如何影响扑翼飞行器的最大升力和屈服设计策略。教育和推广部分侧重于通过生物动力推进的实践研究在多个层面上吸引和指导学生,这将激励他们在STEM领域接受高等教育和职业。这解决了对杰出的科学家和工程师的国家劳动力的需求。
英文摘要
Ringuette, Matthew 1336548 The research objective of this proposal is to understand the unsteady, 3D flow structure produced by a flapping wing in hover, how it relates to the forces, and the effect of key parameters such as stroke amplitude. In nature, flapping wings have low aspect ratios (ARs), and due to the high angles of attack and low Reynolds numbers flow separation and roll-up into vortices occurs. Intellectual MeritWings in hover are known to generate highly 3D, interconnected vortex loops, which shed each half-stroke and create an overall downward jet-like flow to sustain the body weight. Prior experiments using animals or mechanical models, coupled with flow visualization or 2D digital particle image velocimetry (DPIV), have shed light on the loop structure, but the 3D vortex topology and its relationship to the lift force remain open topics. Computational studies focusing primarily on animal configurations provide greater detail and insight. However, there is a lack of experimental, 3-component 3D DPIV data, and the effects of parameters such as half-stroke amplitude, AR, and motion program on the flow structure are not well understood. A key question is: why do hovering animals flap with amplitudes of 3-5 chord lengths? The approach is to perform experiments using a 2-degree-of-freedom flapping-wing model with simplified wing geometries and motions. The goals are to understand the 3D vortex topology, its variation with half-stroke amplitude, AR, and velocity program, and how it relates to the forces. Of interest are changes in the vortex structure for different cases. Diagnostics include dye visualization, stereoscopic DPIV (SDPIV) for 3-component velocity fields, and force measurements.Broader Impacts:This research will provide valuable insight into the design of highly-maneuverable bio-inspired micro air vehicles (MAVs). They can collect scientific information in complex environments, e.g. urban settings, caves, and disaster sites such as collapsed buildings after an earthquake, where conventional drones cannot operate. Moreover, they could operate efficiently in swarms to track the spread of a toxic gas plume, for example. The results of the proposed research will yield a greater understanding of the unsteady vortical flow and how kinematics and AR affect maximum lift, yielding design strategies for flapping-wing MAVs. The educational and outreach component, which focuses on engaging and mentoring students at many levels through hands-on research in bio-inspired propulsion, will motivate them to pursue higher education and careers in STEM. This addresses the need for a national workforce of exceptional scientists and engineers.
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会议论文
MRI: Acquisition of a High-Speed 3D Velocimetry System to Study Complex Flows
  • 批准号:
    1828544
  • 项目类别:
    Standard Grant
  • 资助金额:
    $41.31万
  • 财政年份:
    2018
  • 负责人:
    Matthew Ringuette
  • 依托单位:
Lift Management for Unsteady Translating Wings Using a Rotating, Swept Wing Tip
  • 批准号:
    1706453
  • 项目类别:
    Standard Grant
  • 资助金额:
    $29.97万
  • 财政年份:
    2017
  • 负责人:
    Matthew Ringuette
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis