Manipulation of the Aerodynamic Behavior of the DrivAer Model with Fluidic Oscillators

Wieser, Dirk; Lang, Henning; Nayeri, Christian Navid; Paschereit, Christian Oliver

doi:10.4271/2015-01-1540

Cited by 17 publications

(11 citation statements)

References 16 publications

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“…Further information on general model design can be found in the literature [9,16]. The effect of crosswinds on this DrivAer model has already been investigated in previous studies by means of force and pressure measurements [10,17,18]. The successive increase in drag was attributed to the significant change in pressure distribution at the rear of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

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Wake Structures and Surface Patterns of the DrivAer Notchback Car Model under Side Wind Conditions

2020

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The flow field topology of passenger cars considerably changes under side wind conditions. This changes the surface pressure, aerodynamic force, and drag and performance of a vehicle. In this study, the flow field of a generic passenger vehicle is investigated based on three different side wind angles. The study aimed to identify vortical structures causing changes in the rear pressure distribution. The notchback section of the DrivAer model is evaluated on a scale of 1:4. The wind tunnel tests are conducted in a closed section with a splitter plate at a Reynolds number of 3 million. The side wind angles are 0 ∘ , 5 ∘ , and 10 ∘ . The three-dimensional and time-averaged flow field downstream direction of the model is captured by a stereoscopic particle image velocimetry system performed at several measurement planes. These flow field data are complemented by surface flow visualizations performed on the entire model. The combined approaches provide a comprehensive insight into the flow field at the frontal and side wind inflows. The flow without side wind is almost symmetrical. Longitudinal vortices are evident along the downstream direction of the A-pillar, the C-pillars, the middle part of the rear window, and the base surface. In addition, there is a ring vortex downstream of the vehicle base. The side wind completely changes the flow field. The asymmetric topology is dominated by the windward C-pillar vortex, the leeward A-pillar vortex, and other base vortices. Based on the location of the vortices and the pressure distributions measured in earlier studies, it can be concluded that the vortices identified in the wake are responsible for the local minima of pressure, increasing the vehicle drag.

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Section: Introductionmentioning

confidence: 99%

“…The DrivAer model has already been evaluated using various wind tunnel tests at the Technische Universität Berlin [10,17,18]. In these studies, the fastback and the notchback versions were investigated under different inflow conditions.…”

Section: Introductionmentioning

confidence: 99%

Wake Structures and Surface Patterns of the DrivAer Notchback Car Model under Side Wind Conditions

2020

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“…For the simulations, including RANS turbulence modeling (Paper I and III-VI), turbulence intensity of 0.01 to 0.1 %, together with a turbulent viscosity ratio of 50-200, is used. In Paper V and VI, the inlet turbulence is tuned so similar turbulence intensity as seen in the measurements [65,66] is achieved in the test-section. For the LES simulation (Paper II), turbulence is generated with a spectral synthesizer and tuned such a similar turbulent intensity, as in the measurements [67,68], is obtained in the test-section.…”

Section: Domain and Boundary Conditionsmentioning

confidence: 92%

“…The data consisted of forces (drag and lift), pressure measurements, oil film visualizations, and flow field measurements of the rear wake by use of PIV. Raw images of the oil film visualization and raw pressure measurement data were obtained directly from TU Berlin, while the forces and PIV measurements are acquired from published papers [75,65,66,76].…”

Section: Experimental Datamentioning

confidence: 99%

Important Factors for Accurate Scale-Resolving Simulations of Automotive Aerodynamics

Ekman

2020

Linköping Studies in Science and Technology. Dissertations

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Road transports are responsible for almost 18 % of the greenhouse gas emission in Europe and are today the leading cause of air pollution in cities. Aerodynamic resistance has a significant effect on fuel consumption and hence the emission of vehicles. For electric vehicles, emissions are not affected by the aerodynamics as such but instead have a significant effect on the effective range of the vehicle.In 2017, a new measurement procedure was introduced, Worldwide Harmonized Light Vehicles Test Procedure (WLTP), for measuring emissions, fuel consumption, and range. This procedure includes a new test cycle with increased average driving speed compared to the former procedure, which thereby increases the importance of the aerodynamic resistance, as it drastically increases with speed. A second effect is that the exact car configuration sold to the customer needs to be certified in terms of fuel consumption and emissions. The result is that every possible combination of optional extras, which might affect the aerodynamic resistance, needs to be aerodynamically analyzed and possibly improved. From 2021, the European Commission will introduce stricter emission regulations for new passenger cars, with the fleet-wide average lowered to 95 grams CO 2 /km, which puts an even higher demand on achieving efficient aerodynamics.

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“…They link the tuft angular standard deviation to the local turbulence level which allows for the determination of separation lines. Wieser et al apply a similar methodology to a realistic car model (Wieser et al 2015) and full-scale road vehicles (Wieser et al 2016). Here, mean tuft angles are also used to perform line integral convolution (LIC).…”

Section: Introductionmentioning

confidence: 99%

Tuft deflection velocimetry: a simple method to extract quantitative flow field information

et al. 2020

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A novel method capable of assessing flow fields in a quick and relatively simple manner is introduced. In an extension to the classical qualitative flow visualization by means of cotton or polymeric tufts, digital data processing is used to extract the orientation of these tufts. This information can be related to physical quantities, in particular to time-and space-dependent velocity signals. The capability of this method is demonstrated in two test scenarios. First, it is applied to gain information on the unsteady near-wall flow along a turbulent separation bubble. Second, the two-component velocity field in the wake of a generic car model is measured, allowing for a quantification of the recirculation zone dimensions. Validation measurements with conventional techniques, e.g., particle image velocimetry, unsteady pressure measurements and hot wire anemometry, are conducted throughout the study. These generally suggest that the novel approach provides a quick and reasonably good quantitative overview of the flow configurations. However, the measurement error may be substantial in flow regions of low velocity or dominated by high-frequency oscillations.

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Manipulation of the Aerodynamic Behavior of the DrivAer Model with Fluidic Oscillators

Cited by 17 publications

References 16 publications

Wake Structures and Surface Patterns of the DrivAer Notchback Car Model under Side Wind Conditions

Wake Structures and Surface Patterns of the DrivAer Notchback Car Model under Side Wind Conditions

Important Factors for Accurate Scale-Resolving Simulations of Automotive Aerodynamics

Tuft deflection velocimetry: a simple method to extract quantitative flow field information

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