Drag reduction mechanisms on a generic square-back vehicle using an optimised yaw-insensitive base cavity

Urquhart, Magnus; Varney, Max; Sebben, Simone; Passmore, Martin A.

doi:10.1007/s00348-021-03334-0

Cited by 15 publications

(9 citation statements)

References 65 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many experiments with either passive or active, open-and closed-loop control noted that cases with a symmetrized wake were often associated with the lowest drag (Grandemange, Gohlke & Cadot 2014;Brackston et al 2016;Evrard et al 2016;Li et al 2016;García de la Cruz, Oxlade & Morrison 2017;Li et al 2019;Lorite-Dıez et al 2020a;Urquhart et al 2020Urquhart et al , 2021Haffner et al 2021). However, although Hsu et al (2021) suppressed the wake asymmetry totally using a base suction technique, they found a significant drag increase.…”

Section: Introductionmentioning

confidence: 99%

Wake transitions and steady -instability of an Ahmed body in varying flow conditions

2022

View full text Add to dashboard Cite

The paper investigates experimentally the flow past a flat-back, taller than wide Ahmed body having rectangular base aspect ratio $H/W=1.11$ in the context of ground vehicle aerodynamics. Parametric studies at Reynolds number $2.1\times 10^5$ explore the sensitivity of the aerodynamic force and body pressure distribution with respect to varying flow conditions defined from variable ground clearance $C$ (taken at mid-distance from the front and rear axles of the body), pitch angle $\alpha$ , and yaw angle $\beta$ (equivalent to a crosswind). Two-dimensional parametric maps in the parametric spaces $(\beta,C)$ and $(\beta,\alpha )$ are obtained for parameter ranges covering real road vehicle driving conditions. The study of the base pressure gradient reveals non-trivial and sharp transitions identified as significant changes of near wake orientation in both parametric spaces. All unsteady transitions correspond to fluctuation crises of the vertical gradient component only. These transitions are summarized in phase diagram representations. Both phase diagrams in $(\beta,C)$ and $(\beta,\alpha )$ parametric spaces can be unified at large yaw in the $(\beta,C_r)$ space, where the rear clearance $C_r$ separating the lower edge of the base from the ground is a simple function of the pitch $\alpha$ and the clearance $C$ . The impacts of the wake transitions are clearly identified in the base drag, drag, lift and side force coefficients. The contribution of the steady near wake $z$ -instability (Grandemange et al., Phys. Fluids, vol. 25, 2013a, pp. 95–103) is assessed by repeating the sensitivity analysis with a rear cavity. As reported previously, the rear cavity suppresses the steady instability by symmetrizing the wake. A domain for the existence of the instability is finally proposed in the attitudes map $(\beta,\alpha )$ defined from regions where the mean lateral force coefficients are significantly decreased by the presence of the rear cavity. In addition, it is found that the steady instability forces the wake to be less unsteady for all attitudes that do not correspond to unsteady transitions.

show abstract

Section: Introductionmentioning

confidence: 99%

Wake transitions and steady -instability of an Ahmed body in varying flow conditions

2022

View full text Add to dashboard Cite

show abstract

“…In addition, the present study has been experimentally verified, promoted mainly because there is insufficient theoretical information available for the equations that govern fluid mechanics in certain new circumstances with interest in investigating new behaviors; this forces automotive aerodynamics to be a predominantly experimental science since, in this way, real-world values are measured and assessed, which provides a lot of additional value to computational simulations, which are a valuable tool in themselves. Because of this, several studies analyze aerodynamic variables such as drag coefficient [28], vortices [29], turbulence [30], boundary layer, or stability [31], through experimental testing. The preced-ing literature also proved that prototypes such as the one presented here are also interesting in the evaluation of fatigue life [32,33], and lateral stability improvement [34].…”

Section: Introductionmentioning

confidence: 99%

An Original Aerodynamic Ducting System to Improve Energy Efficiency in the Automotive Industry

et al. 2023

View full text Add to dashboard Cite

In the automotive industry, the flow of air generates high resistance in the advance of vehicles. In light of this situation, the objective of the present invention is to take advantage of the force of the air itself to help propel vehicles and thus reduce fuel consumption. A channeling system has been designed based on a deflector that collects the air that impacts against the vehicle at the front, transferring it to the rear where it is expelled, allowing the vacuum zone to be filled so that the high pressures of the channeled air are repositioned in the depression zone, significantly increasing the values of the pressures, including those that were previously negative. The deflector has been built and incorporated into a model car so that comparative experimental wind tunnel tests could be carried out to verify that the vacuum in the rear area is eliminated, and positive pressure is obtained.

show abstract

“…Flow control has attracted a great deal of research attentions since 1990's because of its potential in improving the aerodynamic performance of transport vehicles beyond a level that could be achieved through optimising the vehicles' geometry alone [1][2][3] . Passive flow control (PFC) and open-loop active flow control (AFC) methods have been extensively investigated due to their simplicity in implementation and low maintenance need [4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Active flow control using deep reinforcement learning with time delays in Markov decision process and autoregressive policy

2022

View full text Add to dashboard Cite

Classical active flow control (AFC) methods based on solving the Navier-Stocks equations are laborious and computationally intensive even with the use of reduced-order models. Data-driven methods offer a promising alternative for AFC and have been applied successfully to reduce the drag of two-dimensional bluff bodies using deep reinforcement learning (DRL) paradigms. However, the standard DRL method tends to result in large fluctuations in the unsteady forces acting on the cylinder as the Reynolds number increases. In this study, a Markov decision process (MDP) with time delays is introduced to quantify the action delays in the DRL process along with the use of a first-order autoregressive policy (ARP). This hybrid DRL method is applied to control the vortex shedding process from a two-dimensional circular cylinder with four synthetic jet actuators at a freestream Reynolds number of 400. Compared to the standard DRL method, this method is shown to reduce the magnitude of drag and lift fluctuations by approximately 90% at the same actuation frequency while achieving a similar level of drag reduction. Although both methods suppress the vortex-induced forces by extending the recirculation zone behind the circular cylinder, the hybrid method leads to a steadier and more elongated recirculation zone, and hence a weaker vortex shedding process. This study demonstrates the necessity of accurately quantifying the delay in the MDP and the benefits of introducing ARPs to achieve a smooth control of unsteady forces in AFC.

show abstract

Drag reduction mechanisms on a generic square-back vehicle using an optimised yaw-insensitive base cavity

Cited by 15 publications

References 65 publications

Wake transitions and steady -instability of an Ahmed body in varying flow conditions

Wake transitions and steady -instability of an Ahmed body in varying flow conditions

An Original Aerodynamic Ducting System to Improve Energy Efficiency in the Automotive Industry

Active flow control using deep reinforcement learning with time delays in Markov decision process and autoregressive policy

Contact Info

Product

Resources

About