Lift enhancement by dynamically changing wingspan in forward flapping flight

Shizhao Wang; Xing Zhang; Guowei He; Tianshu Liu

doi:10.1063/1.4884130

Lift enhancement by dynamically changing wingspan in forward flapping flight

Physics of Fluids ◽

10.1063/1.4884130 ◽

2014 ◽

Vol 26 (6) ◽

pp. 061903 ◽

Cited By ~ 16

Author(s):

Shizhao Wang ◽

Xing Zhang ◽

Guowei He ◽

Tianshu Liu

Keyword(s):

Flapping Flight ◽

Lift Enhancement

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Lift enhancement of a wind/strake configuration using pneumatic blowing

10.2514/6.1994-3478 ◽

1994 ◽

Author(s):

R. Howard ◽

J. Willson ◽

C. Zgraggen

Keyword(s):

Lift Enhancement

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Gliding and Flapping Flight: an Alternative or Continuity?

Зоологический журнал ◽

10.1134/s0044513418080111 ◽

2018 ◽

Vol 97 (8) ◽

pp. 1026-1040

Author(s):

A. A. Panyutina

Keyword(s):

Flapping Flight

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Experimental Study on Forewing–Hindwing Phasing in Hovering and Forward Flapping Flight

AIAA Journal ◽

10.2514/1.j058335 ◽

2019 ◽

Vol 57 (9) ◽

pp. 3779-3790 ◽

Cited By ~ 1

Author(s):

Hiroto Nagai ◽

Koki Fujita ◽

Masahiko Murozono

Keyword(s):

Experimental Study ◽

Flapping Flight

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Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

Movement Ecology ◽

10.1186/s40462-021-00243-z ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Melinda G. Conners ◽

Théo Michelot ◽

Eleanor I. Heywood ◽

Rachael A. Orben ◽

Richard A. Phillips ◽

...

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Flapping Flight ◽

Sensor Data ◽

Accelerometer Data ◽

Fine Scale ◽

Magnetometer Data ◽

Behavioral Classification ◽

Soaring Flight

AbstractBackgroundInertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.MethodsWe deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.ResultsHMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.ConclusionsThe use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

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