Flow Control on the Ahmed Body Vehicle Model Using Fluidic Oscillators

Metka, Matthew

doi:10.1115/imece2013-67343

Cited by 5 publications

(7 citation statements)

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“…A drag coefficient C d of 0.272 is measured on this simulation as shown in Figure 3. It is in good agreement with 3 per cent error compared to the experimental value of 0.28 obtained by Metka (2013) at a Reynolds number of 4.10 5 and 0.31 obtained by Dobrev and Massouh (2014) at Reynolds number of 4.7 × 10 5 . The drag force distribution also indicates that the rear is responsible for 75 per cent of aerodynamic loss.…”

Section: Uncontrolled Flowsupporting

confidence: 90%

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

Edwige

Eulalie

Gilotte

et al. 2018

HFF

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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.

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Section: Uncontrolled Flowsupporting

confidence: 90%

“…For the 47° Ahmed body, corresponding to an SUV vehicle, drag force reduction is a challenging objective for automotive industry because of the weak influence of the geometric parameters. To our knowledge, the only 47° study corresponds to the experimental work of Metka (2013) and Dobrev and Massouh (2014).…”

Section: Introductionmentioning

confidence: 99%

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

Edwige

Eulalie

Gilotte

et al. 2018

HFF

View full text Add to dashboard Cite

show abstract

“…The model is divided into high-and low-drag bodies, which correspond to 12.5°< ϕ < 30°and ϕ > 30°, respectively. The low-drag bodies (ϕ > 30°) may represent the commonly used cars such as sport utility vehicles (SUV) and multi-purpose vehicles (MPV), whose rear slant angles are usually larger than 30° (Metka 2013;Edwige et al 2018;. The associated flow is fully separated over the rear window, forming a big recirculation bubble which covers the rear window and most of the base.…”

Section: Introductionmentioning

confidence: 99%

“…The synthetic jet arrays were also placed along the two side edges of the rear window, but again no DR was achieved. Metka's (2013) attempt deploying an array of fluidic oscillators along the upper edge of the rear window of an Ahmed body with ϕ = 45°again resulted in a drag increase by 2 %. Jahanmiri & Abbaspour (2011) introduced experimentally and numerically air suction through two rows of holes near the upper edge of the rear window (ϕ = 45°), achieving a DR of 2 %.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence control of a low-drag Ahmed body using distributed jet arrays

2023

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This work proposes a machine-learning or artificial intelligence (AI) control of a low-drag Ahmed body with a rear slant angle φ = 35° with a view to finding strategies for efficient drag reduction (DR). The Reynolds number Re investigated is 1.7 × 105 based on the square root of the body cross-sectional area. The control system comprises of five independently operated arrays of steady microjets blowing along the edges of the rear window and vertical base, twenty-six pressure taps on the rear end of the body and a controller based on an ant colony algorithm for unsupervised learning of a near-optimal control law. The cost function is designed such that both DR and control power input are considered. The learning process of the AI control discovers forcing that produces a DR up to 18 %, corresponding to a drag coefficient reduction of 0.06, greatly exceeding any previously reported DR for this body. Furthermore, the discovered forcings may provide alternative solutions, i.e. a tremendously increased control efficiency given a small sacrifice in DR. Extensive flow measurements performed with and without control indicate significant alterations in the flow structure around the body, such as flow separation over the rear window, recirculation bubbles and C-pillar vortices, which are linked to the pressure rise on the window and base. The physical mechanism for DR is unveiled, along with a conceptual model for the altered flow structure under the optimum control or biggest DR. This mechanism is further compared with that under the highest control efficiency.

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“…The pulsed microjets on the car’s rear side show a negligible effect on drag and 9% reduction in lift (Aider et al , 2014). Blow-jet fluidic oscillators on rear side reduces the drag up to 20% (Metka, 2013; Phan and Nguyen, 2022). The synthetic jet near the rear windows effectively suppresses the separation and reduces drag max by 13% (Leclerc and Kourta, 2011).…”

Section: Introductionmentioning

confidence: 99%

Flow control of a vehicle using reverse flow fan

Raj Mohamed,

2023

AEAT

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Purpose This study aims to achieve optimum flow separation control for a road vehicle using a reverse flow fan on rear side. Design/methodology/approach A full-length reverse flow fan array (fan’s air speed is 50% of the car’s speed) is attached throughout the width of the vehicle at rear edge corner. Findings The reverse flow fan array positioned at rear edge of car pushes the airflow against the car’s rear window. It creates the recirculation region and alters the pressure distribution. This reduces the lift coefficient by 150%, which becomes the downforce and reduces the drag coefficient by 22%. As the car speed increases, fan speed should also be increased for effective flow control. Research limitations/implications This active flow control method for 3D Ahmed car body has been studied computationally at low speed (40 m/s). Practical implications This design increases the downforce, thus gives better cornering speed and stability, and decreases the drag which improves fuel efficiency. It can be used for effective flow control of cars (hatchback/sedan). The findings can be applied to the bluff bodies, road vehicles, UAV and helicopter fuselage for flow separation control. Originality/value The fan array is attached on car’s rear side, which blows air against the car’s rear window. It alters the pressure distribution and aerodynamics forces favorably. But the existing high-speed fan used in a sports cars sucks the air from bottom and pushes it rearward, which increases both the traction force and drag.

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Flow Control on the Ahmed Body Vehicle Model Using Fluidic Oscillators

Cited by 5 publications

References 0 publications

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

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

Artificial intelligence control of a low-drag Ahmed body using distributed jet arrays

Flow control of a vehicle using reverse flow fan

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