Tracking stall cell dynamics at high Reynolds numbers

TL;DR

The paper investigates stall-cell dynamics and spanwise organization on a moderately thick airfoil at high Reynolds numbers ($Re$ up to $3.4\times10^6$) using time-resolved surface-pressure measurements across chords and transverse lines. The authors connect local pressure fluctuations to a global stall-cell footprint defined by an intermittent-separation line and apply Proper Orthogonal Decomposition (POD) to identify two dominant spanwise modes that capture the majority of the variance, revealing a low-frequency stall-cell sweep ($St\sim O(10^{-3})$) with a convection speed of ~$U_c\sim 0.1U$. Importantly, the stall cell is globally coherent, enabling tracking of global dynamics from local measurements on a single transverse line, which has implications for flow estimation and control in high-Re aerodynamics and wind-energy applications. The findings highlight the Reynolds-number dependence of stall-cell fluctuations, the potential splitting of the stall cell at lower $Re$, and provide a practical framework for sparse-sensor flow estimation and real-time stall monitoring.

Abstract

The spanwise organization of the flow over a thick airfoil is investigated using surface pressure measurements for a range of angles of attack around maximum lift and high Reynolds numbers (1 Million). Locally strong pressure fluctuations, which are not detected in the global lift coefficient, are shown to be associated with the presence of a stall cell. The stall cell width is of the order of the chord length and increases linearly with the angle of attack, with a weak dependence on the Reynolds number. Its dynamics at Reynolds numbers larger than 1 Million is dominated by a coherent motion in the spanwise direction with a characteristic velocity of order tenth of the freestream velocity. The motion can be decomposed into a large-scale, low-frequency sweep with a Strouhal number equal to 0.001 combined with faster, smaller-scale oscillations. The coherence of the stall cell makes it possible to track global dynamics from local measurements.

Figures (21)

• Figure 1: Aerodynamic test section of the CSTB wind tunnel.
• Figure 2: Pressure tap positions - top view.
• Figure 3: Pressure tap positions along the chords.
• Figure 4: Time-averaged (black points) and standard deviation (red points) of $C_L$ from the balance (green) and the integration of pressure taps around mid-chord (dash-blue and dash-red lines), $Y_M$ and $Y_P$ for $Re=3.4 \times 10^6$.
• Figure 5: Mean pressure distribution along the chord on the suction side of the airfoil $Y_M$ at $AoA=15^\circ$ and $Re=3.4 \times 10^6$. The colormap based on $C_{P_{min}}$ and $C_{P_{S}}$ where is defined in the text.