Flowfield and Force Evolution for a Symmetric Hovering Flat-Plate Wing

Krishna, Swathi; Green, Melissa A.; Mülleners, Karen

doi:10.2514/1.j056468

Cited by 28 publications

(34 citation statements)

References 38 publications

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“…In general, the lift coefficient primarily followed the sinusoidal stroke motion. This general evolution of the lift coefficient for simulated flapping wing hovering flight has been reported in the past by various experimental and numerical studies [19,27,29,31].…”

Section: Resultssupporting

confidence: 75%

“…with ν the kinematic viscosity of the fluid and U = 2φ f R 2 the stroke average velocity at the second moment of area. The model wing shown in Figure 2 is similar to the one used by Krishna et al [31]. The associated Reynolds number and reduced frequency are in the range of flying insects of the size of bees (Table 1).…”

Section: Wing Model and Kinematicsmentioning

confidence: 97%

“…Honeybee [3] Hawkmoth [3] Hoverfly [32] Model [31] Wing The stroke motion of many birds and insects in hovering flight closely resembles a harmonic function [19,28]. The pitching angle profile tends to be more complex and has a stronger impact on the aerodynamic performance.…”

Section: Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Genetic Algorithm Based Optimization of Wing Rotation in Hover

Gehrke

Guyon-Crozier

Mülleners

2018

Fluids

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The pitching kinematics of an experimental hovering flapping wing setup are optimized by means of a genetic algorithm. The pitching kinematics of the setup are parameterized with seven degrees of freedom to allow for complex non-linear and non-harmonic pitching motions. Two optimization objectives are considered. The first objective is maximum stroke average efficiency, and the second objective is maximum stroke average lift. The solutions for both optimization scenarios converge within less than 30 generations based on the evaluation of their fitness. The pitching kinematics of the best individual of the initial and final population closely resemble each other for both optimization scenarios, but the optimal kinematics differ substantially between the two scenarios. The most efficient pitching motion is smoother and closer to a sinusoidal pitching motion, whereas the highest lift-generating pitching motion has sharper edges and is closer to a trapezoidal motion. In both solutions, the rotation or pitching motion is advanced with respect to the sinusoidal stroke motion. Velocity field measurements at selected phases during the flapping motions highlight why the obtained solutions are optimal for the two different optimization objectives. The most efficient pitching motion is characterized by a nearly constant and relatively low effective angle of attack at the start of the half stroke, which supports the formation of a leading edge vortex close to the airfoil surface, which remains bound for most of the half stroke. The highest lift-generating pitching motion has a larger effective angle of attack, which leads to the generation of a stronger leading edge vortex and higher lift coefficient than in the efficiency optimized scenario.

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Section: Resultssupporting

confidence: 75%

Section: Wing Model and Kinematicsmentioning

confidence: 97%

See 1 more Smart Citation

Genetic Algorithm Based Optimization of Wing Rotation in Hover

Gehrke

Guyon-Crozier

Mülleners

2018

Fluids

Self Cite

View full text Add to dashboard Cite

show abstract

“…Before that, the flow field is reconstructed using the POD method with 90 % of the total energy, so that the small-scale structures and noise can be filtered out. Now FTLE is a convenient method to identify the structure boundary with the ridges corresponding to the Lagrangian coherent structures (LCS) (Haller 2002; Huang & Green 2015; Krishna, Green & Mulleners 2018). According to previous studies (Shadden, Dabiri & Marsden 2006; Haller 2015; Huang & Green 2015; He et al.…”

Section: Resultsmentioning

confidence: 99%

“…2016; Krishna et al. 2018), the calculation of FTLE is based on the fluid particle trajectories which can be derived from the velocity field. If is defined as the flow map for fluid particles from the location at the initial time to the location after a time interval , the FTLE can be calculated from where is the maximum eigenvalue of the Cauchy–Green strain tensor (the item within the braces), and the time interval can be either positive or negative, with the ridges donating the repelling or attracting LCS, respectively.…”

Section: Resultsmentioning

confidence: 99%

Effect of excitation frequency on flow characteristics around a square cylinder with a synthetic jet positioned at front surface

Wang

Feng

et al. 2019

J. Fluid Mech.

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The flow over a square cylinder controlled by a slot synthetic jet positioned at the front surface is investigated experimentally at different excitation frequencies. The Reynolds number based on the free-stream velocity and the side length of the square cylinder is 1000. The flow visualization was conducted using the laser-induced fluorescence technique. The velocity fields upstream and downstream of the square cylinder were measured synchronously with the two-dimensional time-resolved particle image velocimetry technique. Both the evolution of vortex structures and the characteristic frequencies of upstream and downstream flow fields are presented. The flow dynamics vary significantly with the excitation frequency at a fixed stroke length. During one excitation cycle, the synthetic jet vortex pair deflects to one side and later swings to the other side at a quite small excitation frequency of $f_{e}/f_{0}=0.6$, while it only deflects toward one side and does not turn to the other side at $f_{e}/f_{0}=1.0$. Compared with the natural case, the wake characteristics for the above two cases are not changed much by the synthetic jet adopted. At a moderate excitation frequency of $f_{e}/f_{0}=2.0$, the synthetic jet deflects upwards and downwards alternatively. The upstream flow field has a dominant frequency identical to half of the excitation frequency. Under the perturbations of the synthetic jet, two wake vortex pairs are formed per shedding cycle with a shedding frequency equal to that of the square cylinder without control. At a higher excitation frequency of $f_{e}/f_{0}=3.4$, the synthetic jet keeps deflecting to one side, and the upstream flow field is governed by the excitation frequency. The flow separation on the deflected side is suppressed effectively, and no periodic vortex shedding can be observed in the wake. Statistically, the velocity profiles also change with control. The recirculation bubble length in the wake is shortened, and the time-averaged velocity fluctuation is weakened remarkably. The control effects of the synthetic jet and the continuous jet are compared in this paper when placed at the front surface of a square cylinder.

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Prediction of leading-edge-vortex initiation using criticality of the boundary layer

Ramanathan

Gopalarathnam

2023

Theor. Comput. Fluid Dyn.

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The initiation of leading-edge-vortex formation in unsteady airfoil flows is governed by flow criticality at the leading edge. While earlier works demonstrated the promise of criticality of leading-edge suction in governing LEV shedding, this criterion is airfoil and Reynolds number dependent. In this work, by examining results from Navier-Stokes computations for a large set of pitching airfoil cases at laminar flow conditions, we show that the onset of flow reversal at the leading edge always corresponds to the boundary-layer shape factor reaching the same critical value that governs laminar flow separation in steady airfoil flows. Further, we show that low-order prediction of this boundary-layer criticality is possible with an integral-boundary-layer calculation performed using potential-flow velocity distributions from an unsteady panel method. The low-order predictions agree well with the high-order computational results with a single empirical offset that is shown to work for multiple airfoils. This work shows that boundary-layer criticality governs LEV initiation, and that a low-order prediction approach is capable of predicting this boundary-layer criticality and LEV initiation.

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Flowfield and Force Evolution for a Symmetric Hovering Flat-Plate Wing

Cited by 28 publications

References 38 publications

Genetic Algorithm Based Optimization of Wing Rotation in Hover

Genetic Algorithm Based Optimization of Wing Rotation in Hover

Effect of excitation frequency on flow characteristics around a square cylinder with a synthetic jet positioned at front surface

Prediction of leading-edge-vortex initiation using criticality of the boundary layer

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