Insect and insect-inspired aerodynamics: unsteadiness, structural mechanics and flight control

Bomphrey, Richard J.; Godoy-Diana, Ramiro

doi:10.1016/j.cois.2018.08.003

Cited by 27 publications

(20 citation statements)

References 85 publications

(97 reference statements)

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“…In terrestrial animals, proprioceptors in joints and muscles establish the timing and magnitude of important mechanical events during walking. The wings of flying animals experience inertial and aerodynamic ('aeroelastic') loads, airflow stagnation and separation, and non-linear phenomena of vortex growth and shedding, generated during gliding and flapping flight (Bomphrey and Godoy-Diana, 2018;Dickson et al 2006;Combes and Daniel 2003). The current literature on mechanical feedback during flight mostly consists of isolated reports of specific sensor types, often accompanied by proposals of their functional significance for flight control.…”

Section: Introductionmentioning

confidence: 99%

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Fabian

Siwanowicz

Uhrhan

et al. 2021

Preprint

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Fly-by-feel describes how flying animals capture aerodynamic information via their wings' sensory system to implement or enhance flight control. Traditional studies on animal flight emphasized controlling body stability via visual or inertial sensory inputs. In line with this, it has been demonstrated that wing sensory systems can provide inertial state estimation for the body. What about the state estimation of the wings themselves? Little is known about how flying animals utilize their wing sensory systems to monitor the dynamic state of their highly deformable wings. This study is a step toward a comprehensive investigation of how a flying animal senses aerodynamic and aeroelastic features of the wings relevant to flight control. Odonates: dragonflies and damselflies, are a great model for this because they have excellent flight performance and their wing structure has been extensively studied. Here, we developed a strategy to map the entire sensory system of Odonata wings via confocal microscopy. The result is the first complete map of a flying animal's wing sensory system, including both the external sensor morphologies and internal neuroanatomy. This complete search revealed over 750 sensors on each wing for one of the smallest dragonfly species and roughly half for a comparable size damselfly. We found over eight morphological classes of sensors, most of which resembled mechanosensors. Most sensors were innervated by a single neuron with an innervation pattern consistent with minimising wiring length. We further mapped the major veins of 13 Odonate species across 10 families and identified consistent sensor distribution patterns, with sensor count scaling with wing length. To explain the strain sensor density distribution along the major veins, we constructed a high-fidelity finite element model of a dragonfly wing based on micro-CT data. This flapping wing model revealed dynamic strain fields and suggested how increasing sensor count could allow encoding of different wing states. Taken together, the Odonate wing sensory system is well-equipped to implement sophisticated fly-by-feel flight control.

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Section: Introductionmentioning

confidence: 99%

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Fabian

Siwanowicz

Uhrhan

et al. 2021

Preprint

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“…Here we focus on the interaction of body structure and sensory encoding properties in the context of insect flight control, where wings provide rapid sensory feedback necessary for stable flight [10, 11, 12, 13, 14]. Although extensive previous work has examined how wing structure impacts aerodynamic performance [15, 16, 17, 18, 19, 20], the impacts of wing structure on sensing remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

Wing structure and neural encoding jointly determine sensing strategies in insect flight

Weber

Daniel

Brunton

2021

Preprint

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Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that enables stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing remain unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively over a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness inspired by the hawkmoth Manduca sexta. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, permit accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of their joint evolution.

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“…Pinpointing the factors that shape the evolution of wings and flapping kinematics is key to any in-depth understanding of flight. Within the past decades, numerous comprehensive reviews and book chapters have been published on insect flight, focusing on components such as aerodynamic mechanisms for lift enhancement [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], power requirements for wing flapping [ 12 , 13 , 14 , 15 ], wing kinematics and control [ 16 , 17 , 18 , 19 , 20 , 21 ], and the efficiency with which muscle mechanical power is turned into weight supporting lift [ 22 , 23 ]. This review is engaged in the link between three-dimensional wing structure and aerodynamics, focusing on recently published studies on the aerodynamic performance of wings in differently-sized insects.…”

Section: Introductionmentioning

confidence: 99%

Wing Design in Flies: Properties and Aerodynamic Function

Krishna

Cho

Wehmann

et al. 2020

Insects

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The shape and function of insect wings tremendously vary between insect species. This review is engaged in how wing design determines the aerodynamic mechanisms with which wings produce an air momentum for body weight support and flight control. We work out the tradeoffs associated with aerodynamic key parameters such as vortex development and lift production, and link the various components of wing structure to flight power requirements and propulsion efficiency. A comparison between rectangular, ideal-shaped and natural-shaped wings shows the benefits and detriments of various wing shapes for gliding and flapping flight. The review expands on the function of three-dimensional wing structure, on the specific role of wing corrugation for vortex trapping and lift enhancement, and on the aerodynamic significance of wing flexibility for flight and body posture control. The presented comparison is mainly concerned with wings of flies because these animals serve as model systems for both sensorimotor integration and aerial propulsion in several areas of biology and engineering.

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Insect and insect-inspired aerodynamics: unsteadiness, structural mechanics and flight control

Cited by 27 publications

References 85 publications

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Wing structure and neural encoding jointly determine sensing strategies in insect flight

Wing Design in Flies: Properties and Aerodynamic Function

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