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Experimental Investigation of the Bi-Stable Behavior in the Wake of a Notchback MIRA Model
Technical Paper
2019-01-0663
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper reports an experimental investigation of the wake flow behind a 1/12 scale notchback MIRA model at Re = UL/ν = 6.9×105 (where U is free-stream velocity, L the length of the model and ν viscosity). Focus is placed on the flow asymmetry over the backlight and decklid. Forty pressure taps are used to map the surface pressure distribution on the backlight and decklid, while the wake topology is investigated by means of 2D Particle Image Velocimetry. The analysis of the instantaneous pressure signals over the notch configuration clearly shows that the pressure presents a bi-stable behavior in the spanwise direction, characterized by the switches between two preferred values, which is not found in the vertical direction. Based on the barycenter of momentum deficit, the instantaneous velocity fields in the plane z/H = 0.8 (H height of the model) can be sorted into three patterns, including two bi-stable states and one switch state, corresponding to the result of the instantaneous pressure signals. The sequence of these asymmetric states is random. In order to enable a deep insight of the bi-stable states, conditional averaging on the pressure data and the unsteady wake flow based on PIV snapshots has been conducted to extract the two distinct states and switch state, and the average pressure contours of the two bi-stable states are almost reflection symmetrical and their average wakes are also nearly symmetrical, meaning this bi-stable behavior is statistically symmetric. However, this bi-stable wake dynamic is not found behind the trunk in the plane z/H = 0.44. Besides, this bi-stable phenomenon is of a long timescale with the order of 300L/U, which is hundreds of times the common vortex shedding.
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Yan, G., Xia, C., Zhou, H., Zhu, H. et al., "Experimental Investigation of the Bi-Stable Behavior in the Wake of a Notchback MIRA Model," SAE Technical Paper 2019-01-0663, 2019, https://doi.org/10.4271/2019-01-0663.Data Sets - Support Documents
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