Wake flow analysis and control on a 47° slant angle Ahmed body

Abstract

Purpose The purpose of this paper is to present numerical investigations of the flow dynamic characteristics of a 47° Ahmed Body to identify wake flow control strategy leading to drag coefficient reduction, which could be tested later on sport utility vehicles. Design/methodology/approach This study begins with a mean flow topology description owing to dynamic and spectral analysis of the aerodynamic tensor. Then, the sparse promoting dynamic modal decomposition method is discussed and compared to other modal approaches. This method is then applied on the wall and wake pressure to determine frequencies of the highest energy pressure modes and their transfers to other frequency modes. This analysis is then used to design appropriated feedback flow control strategies. Findings This dynamic modal decomposition highlights a reduced number of modes at low frequency which drive the flow dynamics. The authors especially notice that the pressure mode at a Strouhal number of 0.22, based on the width between feet, induces aerodynamic losses close to the rear end. Strategy of the proposed control loop enables to dampen the energy of this mode, but it has been transferred to lower frequency mode outside of the selected region of interest. Originality/value This analysis and methodology of feedback control shows potential drag reduction with appropriated modal energy transfer management.

Aerodynamical characteristics of a reduced scale ground vehicle according to yaw angle variations

“…Besides wind tunnel measurements, fluid dynamics computations have been performed on a reduced scale model representing a sport utility vehicle (SUV). It should be outlined that experimental data dealing with standard deviation and average rear pressure maps have been previously presented in Edwige et al (2018), where the pressure over the tailgate was optimised with flow control actuations, and Edwige et al (2020) deal with the numerical simulation over the same geometrical model with a large eddy simulation (LES) solver. Main characteristics of the geometrical and the numerical model (Edwige et al, 2018;Edwige, 2019) can be summarized as follows: the reduced SUV model is placed in a wind tunnel of 1.62 m 2 with a ground clearance that has been selected after a DOE leading to the best experimental Cd value of 0.36.…”

“…It should be outlined that experimental data dealing with standard deviation and average rear pressure maps have been previously presented in Edwige et al (2018), where the pressure over the tailgate was optimised with flow control actuations, and Edwige et al (2020) deal with the numerical simulation over the same geometrical model with a large eddy simulation (LES) solver. Main characteristics of the geometrical and the numerical model (Edwige et al, 2018;Edwige, 2019) can be summarized as follows: the reduced SUV model is placed in a wind tunnel of 1.62 m 2 with a ground clearance that has been selected after a DOE leading to the best experimental Cd value of 0.36. Special care has been taken meshing the geometry, including refinements in the underbody, the near wake and in the wall boundary layers of the mock-up that leads to a numerical model of 300 million elements.…”

“…According to the Cd value, the pressure coefficient Cp over the tailgate (Edwige et al, 2018), the velocity and turbulent intensity profiles in the boundary layer and the two cut planes presented in Figure 1 in the near wake, it seems reasonable to use the 3 D average velocity field for streamlines computations. Once validated, the location of the pressure sensors is selected according to the streamline direction, starting in the front of the vehicle mock-up and ending in the wake recirculation zone, as presented in Figure 3.…”

“…According to the streamline direction highlighted in Section 2, side pressure sensors have been added on the two different paths described above to the rear pressure sensors already used on this mock-up for a previous study dealing with flow control (Edwige et al, 2018). These additional sensors enable to look at phase correlations between side and rear pressure frequencies, along these assume convective paths (Figure 4).…”

“…The experimental setup described in Edwige et al (2018) is used to look at synchronized pressure measurements sampled at 2,000 Hz on the side and rear sensors (Figures 4 and 5) of the mock-up. Reference pressure is taken at the Pitot tube located at X = À1.3 m and Z = 1.08 m (Figure 5) from the coordinate system placed on the ground at the maximum X-value of the rear bumper to compute the pressure coefficient Cp, its average and standard deviation, according to the following definition: Parallel to these wall pressure measurements, the instantaneous aerodynamic stress tensor is measured at different yaw angles at the same sampling frequency of 2,000 Hz (Figure 5).…”