Micro-Ramp Flow Dynamics

Abstract

Micro-ramps are passive flow control devices used to delay flow separation. Their use is widespread due to their reduced drag and structural robustness. We reproduce with Direct Numerical Simulations (DNS) recent Particle Image Velocimetry (PIV) experiments of the micro-ramp flow performed at TU Delft to study the wake of a micro-ramp immersed in a laminar and incompressible boundary layer. The micro-ramp is a vortex generator which induces a pair of streamwise counter-rotating vortices. The current literature identifies this structure as the main flow feature contributing to the increase of the near-wall momentum. The micro-ramp is also a surface roughness element which can trigger laminar-turbulent transition. The action of the induced vortices introduces a strong detached shear layer into the flow field, susceptible to Kelvin-Helmholtz (K-H) instability. We analyse the micro-ramp flow dynamics and the transitional mechanisms which develop in the micro-ramp wake. Furthermore, we intend to contribute to the discussion on the micro-ramp working principle, which has been put into question by other authors. We show the importance of the transitional perturbation development in the micro-ramp functionality. %In the past decade, numerous computational and experimental studies performed inquired into the topology of the flow behind a micro-ramp and its relation to the functionality of the device.

Downstream-travelling streamwise vortices and transitional disturbances serve to the same purpose of increasing the momentum close to the surface. To examine their relative contribution in this regard, we numerically decompose the micro-ramp flow field into a laminar steady state and a time-dependant perturbation field. To achieve that, we apply Selective Frequency Damping (SFD), a numerical technique used to compute the steady solutions of globally unstable dynamical systems. SFD is a popular method nowadays and the preferred approach for aerospace applications. However, it has two case-dependant model parameters which are key to the method's effectivity and efficiency, and whose selection remains a challenge in the literature. Not every combination of the model parameters guarantees the success of SFD, and even if so, the required computational time may be so large that the approach is impractical. We provide the first rigorous analysis of the influence of these parameters to the functionality of SFD, leading to simple expressions and procedures for choosing them optimally. Furthermore, we prove that, under certain conditions, SFD is always able to stabilise a globally unstable flow configuration.