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Delay of swept-wing transition using a surface hump

Published online by Cambridge University Press:  07 July 2025

Alberto F. Rius-Vidales*
Affiliation:
Department of Flow Physics and Technology, Delft University of Technology, Kluyverweg 1, Delft 2629HS, The Netherlands
Luis Morais
Affiliation:
Department of Flow Physics and Technology, Delft University of Technology, Kluyverweg 1, Delft 2629HS, The Netherlands
Sven Westerbeek
Affiliation:
Department of Flow Physics and Technology, Delft University of Technology, Kluyverweg 1, Delft 2629HS, The Netherlands
Jordi Casacuberta
Affiliation:
Department of Flow Physics and Technology, Delft University of Technology, Kluyverweg 1, Delft 2629HS, The Netherlands
Mustafa Soyler
Affiliation:
Department of Energy Systems Engineering, Osmaniye Korkut Ata University, Osmaniye 80000, Türkiye
Marios Kotsonis
Affiliation:
Department of Flow Physics and Technology, Delft University of Technology, Kluyverweg 1, Delft 2629HS, The Netherlands
*
Corresponding author: Alberto F. Rius-Vidales, a.f.riusvidales@tudelft.nl

Abstract

This work explores the use of a shallow surface hump for passive control and stabilisation of stationary crossflow (CF) instabilities. Wind tunnel experiments are conducted on a spanwise-invariant swept-wing model. The influence of the hump on the boundary layer stability and laminar–turbulent transition is assessed through infrared thermography and particle image velocimetry measurements. The results reveal a strong dependence of the stabilisation effect on the amplitude of the incoming CF disturbances, which is conditioned via discrete roughness elements at the wing leading edge. At a high forcing amplitude, weakly nonlinear stationary CF vortices interact with the hump and result in an abrupt anticipation of transition, essentially tripping the flow. In contrast, at a lower forcing amplitude, CF vortices interact with the hump during linear growth. Notable stabilisation of the primary CF disturbance and considerable transition delay with respect to the reference case (i.e. without hump) is then observed. The spatial region just downstream of the hump apex is shown to be key to the stabilisation mechanism. In this region, the primary CF disturbances rapidly change spanwise orientation and shape, possibly driven by the pressure gradient change-over caused by the hump and the development of CF reversal. The amplitude and shape deformation of the primary CF instabilities are found to contribute to a long-lasting suboptimal growth downstream of the hump, eventually leading to transition delay.

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press
Figure 0

Figure 1. Experimental set-up: (a) Wing and measurement arrangement (flow direction from left to right). Surface hump (orange), the planar PIV set-up, infrared (IR) analysis region (blue shaded region, not to scale) and DRE details. (b) Measured cross-section geometry of the hump (vertical axis enlarged for visualisation, $\delta ^*_{w,h} = 485\, \unicode{x03BC} \text{m}$). Note the wing features a modified NACA 66 018 airfoil shape normal to the leading edge, for details see Serpieri (2018, pp. 28–29).

Figure 1

Figure 2. (ai,aii,bi,bii) Thermal maps (flow from left to right). Transition location ($x_t$) (aiii) at $Re_{cX} = 2.3 \times 10^6$ and $\alpha = 3^\circ$ and (biii) for varying $Re_{cX}$. Solid orange line and orange region indicate hump apex and width. Prefix A (high) and B (low) indicate the DRE forcing amplitude and solid grey lines their streamwise location. Suffix C indicates the reference case and H the hump case.

Figure 2

Figure 3. (a–d) Normalised mean and spanwise-averaged spanwise velocity ($\bar {w}_z/\bar {w}_e$) profiles; (e) displacement thickness ($\delta ^*_w$) and (f) momentum thickness ($\theta _w$) for cases B-C (black) and B-H (magenta). Solid orange line indicates the hump apex location and shaded region its width.

Figure 3

Figure 4. Contours of normalised mean spanwise velocity ($\bar {w}_R/\bar {w}_e$) for cases (i) B-C and (ii) B-H. Dashed blue constant phase isolines for $m(0,1)$. All fields are spatially filtered (i.e. $\sum _n^5m(0,n)$).

Figure 4

Figure 5. (a–d) Steady disturbance $\langle \hat {w}_R\rangle _z$ profile shape $m(0,1)$ (solid line) and $m(0,2)$ (dash–dotted line) for B-C (black) and B-H (magenta); (e) chordwise evolution of amplitude $A_T$ and (f) ratio of growth rates $\alpha _{iH}/\alpha _{iC}$ for cases B-C and B-H. Solid orange line indicates the hump apex location and shaded region its width.

Figure 5

Figure 6. Contours of steady spanwise perturbation for (i,ii) primary CFI mode $m(0,1)$ and its first (iii,iv) higher harmonic $m(0,2)$ for case (i,iii) B-C and (ii,iv) B-H. Grey solid lines correspond to $\bar w_R$ contour levels in figure 4, dashed lines corresponds to constant phase isolines for $m(0,1)$. (v) Constant phase isolines of the primary CFI mode $m(0,1)$. For visualisation purpose the constant phase isolines spanwise coordinate ($z^*/\lambda _{z,D}$) is shifted by 1 between presented chordwise locations in (v).

Figure 6

Figure 7. Contours of spanwise (i,ii) and wall-normal (iii,iv) gradients of spanwise velocity for cases (i,iii) B-C and (ii,iv) B-H. Grey solid lines correspond to the $\bar {w}_R$ contour levels in figure 4. (v) Streamwise evolution of average spanwise gradients inside the regions delimited by orange dashed lines in contours. All fields are spatially filtered (i.e. $\sum _n^5m(0,n)$).

Figure 7

Figure 8. Contours of standard deviation of temporal spanwise velocity fluctuations ($\sigma _{w_R}/\bar {w_R}$) for cases (i) B-C and (ii) B-H. Grey solid lines correspond to the $\bar {w}_R$ contour levels in figure 4. (iii) Streamwise evolution of the maximum standard deviation of temporal velocity fluctuations inside the regions delimited by orange dashed lines. All fields are spatially filtered (i.e. $\sum _n^5m(0,n)$).