Wavelet analysis of the coherent structures in airfoil leading-edge separation control by bionic coverts

Abstract

We experimentally investigate leading-edge separation control effect by bionic coverts with various materials and sawteeth shapes in wind tunnel tests. The artificial flexible coverts, bio-inspired by bird covert feathers on upper wings, are hinged at the trailing-edge of a NACA 0018 airfoil at a constant high angle-of-attack of 15°. The chord-based Reynolds number is 1.0 × 105 in the generic range of bird flight in nature and low-speed fixed-wing unmanned aerial vehicles. The velocity profiles in the wake flow are measured by multi-channel hot-wire anemometer. By comparing the mean velocity profiles and root-mean-square velocities, we find the trailing-edge coverts reduced the thickness of the shear layers by 0.05 chord length. The turbulence intensity of the trailing- and leading-edge shear layers are reduced 34% and 5%, respectively. Further wavelet analysis reveals that the large sizes of vortices are considerably suppressed in the time-frequency spectrum. Based on the hot-wire datasets, we develop a novel multi-dimensional genetic algorithm to analyze the featured ordered structures in the shear layers and quantitatively characterize the amplitude modulation between the large- and small-scale flow structures. As a result, we find that the coverts-generated perturbations induce an increase in the high-frequency ( f = 91.2 Hz) coherence between the leading- and trailing-edge shear layers from 40% to 70%, leading to a reduction of the flow separation bubble on the upper wing. The present work reveals that the artificial bionic coverts have leading-edge flow separation control effectiveness and shows the engineering potential for aircrafts and unmanned aerial vehicles.

A Spatial Correlation Identification Model for Coherent Structure Extraction and Three-Dimensional Visualization

“…They coexist in the water column [11,12], but extracting these structures and analyzing their properties are not trivial. Previous research has developed several approaches to detect these structures, including the proper orthogonal decomposition method [13][14][15], wavelet approach [16][17][18], Fourier analysis [19,20], Eulerian methods [21,22], and Lagrangian method [23][24][25]. The main limitation of these methods is the relatively arbitrary selection of a threshold.…”

“…Considering the above limitation, we propose a spatial correlation identification (SCI) model for a pair of shear data measured in an orthogonal pattern to identify coherent structures and provide a three-dimensional visualization based on the wavelet transform and time-dependent intrinsic correlation (TDIC) method. The wavelet transform provides the advantage of decomposing turbulence shear signal scale by scale since the turbulent fluid is composed of multi-scale coherent structures and because energy and other quantities tend to cascade from large to smaller events [17,[45][46][47]. Extensive research has been conducted to evaluate the local energy density, such as studies by Lucia and Gassmann [46], Gong et al [17], and Protzko et al [47], providing a potential for the extraction and characterization of multi-scale structures.…”

“…The wavelet transform provides the advantage of decomposing turbulence shear signal scale by scale since the turbulent fluid is composed of multi-scale coherent structures and because energy and other quantities tend to cascade from large to smaller events [17,[45][46][47]. Extensive research has been conducted to evaluate the local energy density, such as studies by Lucia and Gassmann [46], Gong et al [17], and Protzko et al [47], providing a potential for the extraction and characterization of multi-scale structures. Meanwhile, TDIC offers a major advance in evaluating the spatial correlation variation between a pair of turbulence shear signals since the spatial information is highly self-similar in a coherent structure.…”