Aerodynamics of Heavy Vehicles

Choi, Haecheon; Lee, Jung-Il; Park, Hyungmin

doi:10.1146/annurev-fluid-011212-140616

Cited by 152 publications

(78 citation statements)

References 113 publications

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“…The second term includes a longitudinal velocity deficit contribution, a vortexinduced drag contribution and a Reynolds stress contribution. The last term represents the rate of change 2 ) comprises only velocity fluctuations, but due to the quadratic nature of its components has both a mean and fluctuating part. Reducing the size of this term reduces D. That is, reducing velocity fluctuations normal to the streamwise direction, or increasing velocity fluctuations in the streamwise direction, results in a time-averaged drag reduction.…”

Section: The Relationship Between Mean Drag and Flow Fluctuationsmentioning

confidence: 99%

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Reducing the pressure drag of a D-shaped bluff body using linear feedback control

Longa

Morgans

Dahan

2017

Theor. Comput. Fluid Dyn.

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The pressure drag of blunt bluff bodies is highly relevant in many practical applications, including to the aerodynamic drag of road vehicles. This paper presents theory revealing that a mean drag reduction can be achieved by manipulating wake flow fluctuations. A linear feedback control strategy then exploits this idea, targeting attenuation of the spatially integrated base (back face) pressure fluctuations. Large-eddy simulations of the flow over a D-shaped blunt bluff body are used as a test-bed for this control strategy. The flow response to synthetic jet actuation is characterised using system identification, and controller design is via shaping of the frequency response to achieve fluctuation attenuation. The designed controller successfully attenuates integrated base pressure fluctuations, increasing the time-averaged pressure on the body base by 38%. The effect on the flow field is to push the roll-up of vortices further downstream and increase the extent of the recirculation bubble. This control approach uses only body-mounted sensing/actuation and input-output model identification, meaning that it could be applied experimentally.

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Section: The Relationship Between Mean Drag and Flow Fluctuationsmentioning

confidence: 99%

“…The issue of how to best capture the (v (t) 2 2 ) term using body-mounted pressure sensing is an open question. Reducing the above bracketed term reduces the mean drag, D, and also tends to reduce drag fluctuations, D (t).…”

Section: Linear Feedback Control For Fluctuation Attenuationmentioning

confidence: 99%

Reducing the pressure drag of a D-shaped bluff body using linear feedback control

Longa

Morgans

Dahan

2017

Theor. Comput. Fluid Dyn.

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“…Passive control has been widely applied on bluff bodies. The use of base cavities and boat tails is considered to be one of the most effective devices for base drag reduction (Choi et al 2014). Such passive approaches, however, are restricted by design and practical considerations and cannot be 'turned off' when not needed.…”

Section: Introductionmentioning

confidence: 99%

Drag reduction of a car model by linear genetic programming control

Noack

Cordier

et al. 2017

Exp Fluids

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We investigate open-and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at Re H ≈ 3 × 10 5 based on body height. The actuation is performed with pulsed jets at all trailing edges combined with a Coanda deflection surface. The flow is monitored with pressure sensors distributed at the rear side. We apply a model-free control strategy building on Dracopoulos & Kent (1997) and Gautier et al. (2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combination thereof. Key enabler is linear genetic programming as simple and efficient framework for multiple inputs (actuators) and multiple outputs (sensors). The proposed linear genetic programming control can select the best open-or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing by selecting one pressure sensor in the optimal control law. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.

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“…At motorway speeds, aerodynamic drag arising from vortex shedding over the bluff rear end of the vehicle is responsible for up to two thirds of this energy consumption [2,3]. Initial numerical studies of prototype bluff body flows have shown the potential for feedback control to suppress vortex shedding, thus leading to an overall increase in base mean pressure.…”

Section: Introductionmentioning

confidence: 99%

Dynamically correct formulations of the linearised Navier–Stokes equations

Dellar

Jones

2017

Numerical Methods in Fluids

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SUMMARYMotivated by the need to efficiently obtain low-order models of fluid flows around complex geometries for the purpose of feedback control system design, this paper considers the effect on system dynamics of basing plant models on different formulations of the linearised Navier-Stokes equations. We consider the dynamics of a single computational node formed by spatial discretisation of the governing equations in both primitive variables (momentum equation & continuity equation) and pressure Poisson equation (PPE) formulations. This reveals fundamental numerical differences at the nodal level, whose effects on the system dynamics at the full system level are exemplified by considering the corresponding formulations of a two-dimensional (2D) channel flow, subjected to a variety of different boundary conditions.

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Aerodynamics of Heavy Vehicles

Cited by 152 publications

References 113 publications

Reducing the pressure drag of a D-shaped bluff body using linear feedback control

Reducing the pressure drag of a D-shaped bluff body using linear feedback control

Drag reduction of a car model by linear genetic programming control

Dynamically correct formulations of the linearised Navier–Stokes equations

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