BAC: Analysis of Insect Flight Dynamics

BAC：昆虫飞行动力学分析

• 批准号: 9511681
• 负责人: Thomas Daniel
• 金额: $ 23.22万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-15 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9511681&HistoricalAwards=false
• 关键词: BAC; Analysis; Insect; Flight; Dynamics

Daniel IBN-9511681 Insect flight is a highly efficient mode of locomotion. Interest in studies of insect flight arises partially from the need to develop new design criteria for artificial robotic systems. This research project focuses on the development of computer models for flight and flight control in insects. The goal of this work is to better understand how sensory feedback from the wings and pattern-generating circuits in the central nervous system govern flight performance. The current approaches of studying any single component of insect flight (muscle mechanics, aerodynamics, or neural control) alone cannot be used as predictive models. In this work, mathematical and experimental approaches are incorporated into the models in order to understand the physics of force production by muscles and the physics of aerodynamic lift and thrust generation by wings. Computer models will solve these problems of physics simultaneously to predict flight performance in the moth Manduca sexta, the tobacco hornworm moth. This group of researchers couple these processes in developing a novel biological and engineering approach for a priori predictions of flight dynamics in response to a suite of physiological and physical parameters. By this method we can better understand the physics, physiology and control of flight dynamics.

丹尼尔·IBN-9511681昆虫飞行是一种高效的运动方式。对昆虫飞行研究的兴趣在一定程度上是因为需要为人工机器人系统开发新的设计标准。这项研究项目的重点是开发昆虫飞行和飞行控制的计算机模型。这项工作的目标是更好地了解来自机翼的感觉反馈和中枢神经系统中的模式生成电路如何控制飞行性能。目前研究昆虫飞行的任何单一组成部分(肌肉力学、空气动力学或神经控制)的方法不能单独用作预测模型。在这项工作中，数学和实验方法被结合到模型中，以了解肌肉产生的力的物理和机翼产生的气动升力和推力的物理。计算机模型将同时解决这些物理问题，以预测烟草角蛾Manduca sexta的飞行性能。这组研究人员将这些过程结合在一起，开发了一种新的生物和工程方法，用于响应一系列生理和物理参数对飞行动力学进行先验预测。通过这种方法，我们可以更好地理解飞行动力学的物理、生理和控制。