Aerodynamic characteristics along the wing span of a dragonfly<i>Pantala Flavescens</i>

Hefler, Csaba; Qiu, Huihe; Shyy, Wei

doi:10.1242/jeb.171199

Cited by 20 publications

(41 citation statements)

References 51 publications

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“…Since significant ecological and climatic differences arise across biomes, the range of zones dominated by a species can provide us with information linked to dispersal capacity and environmental tolerance (Renner et al, 2019). In some cases, this distribution pattern may be related to morphological traits of these species, such as body size, or even the shape, size, and venation of the wings (Hefler et al, 2018; Wootton, 2020). In fact, morphology has been considered an important element to understand the biological mechanisms of the group.…”

Section: Discussionmentioning

confidence: 99%

Amazonian Odonata Trait Bank

Ferreira

Resende

Bastos

et al. 2023

Ecology and Evolution

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Discussion regarding the gaps of knowledge on Odonata is common in the literature.Such gaps are even greater when dealing with basic biological data for biodiverse environments like the Amazon Rainforest. Therefore, studies that address, classify, and standardize functional traits allow the elaboration of a wide range of ecological and evolutionary hypotheses. Moreover, such endeavors aid conservation and management planning by providing a better understanding of which functional traits are filtered or favored under environmental changes. Here, our main goal was to produce a database with 68 functional traits of 218 Odonata species that occur in the Brazilian Amazon. We extracted data on behavior, habit/habitat (larvae and adults), thermoregulation, and geographic distribution from 419 literature sources classified into different research areas. Moreover, we measured 22 morphological traits of approximately 2500 adults and categorized species distributions based on approximately 40,000 geographic records for the Americas. As a result, we provided a functional matrix and identified different functional patterns for the Odonata suborders, as well as a strong relationship between the different trait categories. For this reason, we recommend the selection of key traits that represent a set of functional variables, reducing the sampling effort. In conclusion, we detect and discuss gaps in the literature and suggest research to be developed with the present Amazonian Odonata Trait Bank

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Section: Discussionmentioning

confidence: 99%

Amazonian Odonata Trait Bank

Ferreira

Resende

Bastos

et al. 2023

Ecology and Evolution

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“…Aeroelastic loading of the wing during flight causes complex deformation of the architecture (e.g., Wootton et al, 1998;Norris et al, 2010;Lu et al, 2010, Jongerius andLentink, 2010). The flow patterns which have been observed on the wings of dragonflies during flight are similarly complex ( (e.g., Thomas et al, 2004;Wang & Sun, 2005;Bomphrey et al, 2016;Li and Dong, 2017;Bode-Oke at al., 2017;Bode-Oke et al, 2018;Hefler et al, 2018;Shumway et al, 2020). We expect the local placement and overall density of wing sensors is critical for capturing relevant information.…”

Section: A Complete Sensor Map Of Odonata Wingsmentioning

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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“…In contrast, Bomphrey et al 29 used a particle image velocimetry (PIV) experiment to study the flow pattern generated by a freeflying dragonfly. The recent studies on the free-flying dragonfly [30][31][32] explore different interaction mechanisms of the forewing and hindwing and its impact on lift and thrust generation along the span-wise by using CFD and PIV techniques. It has been shown that the hindwing thrust is extremely sensitive towards the forewing-hindwing interaction, and the magnitude of this force spreads equally at the central span of the wing.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of wing–wing interaction and its effect on the aerodynamic force of a hovering dragonfly

Swain

Dora

Battula

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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The present research focuses on the timing of wing–wing interaction that benefits the aerodynamic force of a dragonfly in hovering flight at Reynolds number 1350. A 3-D numerical simulation method, called the system coupling, was utilised by implementing a two-way coupling between the transient structural and flow analysis. We further explore the aerodynamic forces produced at different phase angles on the forewing and hindwing during the hovering flight condition of a dragonfly. A pair of dragonfly wings is simulated to obtain the force generated during flapping at a 60° inclination stroke plane angle with respect to the horizontal. The hovering flight is simulated by varying the phase angle and the inter-distance between the two wings. We observe a significant enhancement in the lift (16%) of the hindwing when it flaps in-phase with the forewing and closer to the forewing, maintaining an inter-wing distance of 1.2 cm (where centimetre is the mean chord length). However, for the same condition, the lift of the hindwing reduces by 9% when the wings are out of phase/counterstroke flapping. These benefits and drawbacks are dependent on the timing of the interactions between the forewing and hindwing. The time of interaction of wake capture, wing–wing interaction, dipole structure and development of root vortex are examined by 2-D vorticity of the flow field and isosurface of the 3-D model dragonfly. From the isosurface, we found that the root vortex elicited at the root of the hindwing in counter-flapping creates an obstacle for the shedding of wake vortices, which results in reduction of vertical lift during the upstroke of flapping. Hence, at the supination stage, a dragonfly uses a high rotation angle for the hovering flight mode. It is observed that the system coupling method was found to be more efficient and exhibited better performance. The present numerical methodology shows a very close match to the previously reported results.

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Aerodynamic characteristics along the wing span of a dragonflyPantala Flavescens

Cited by 20 publications

References 51 publications

Amazonian Odonata Trait Bank

Amazonian Odonata Trait Bank

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Numerical investigation of wing–wing interaction and its effect on the aerodynamic force of a hovering dragonfly

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