Accurate Automotive Spinning Wheel Predictions Via Deformed Treaded Tire on a Full Vehicle Compared to Full Width Moving Belt Wind Tunnel Results

2023-01-0843

04/11/2023

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
As the automotive industry is quickly changing towards electric vehicles, we can highlight the importance of aerodynamics and its critical role in reaching extended battery ranges for electric cars. With all new smooth underbodies, a lot of attention has turned into the effects of rim designs and tires brands and the management of these tire wakes with the vehicle. Tires are one of the most challenging areas for aerodynamic drag prediction due to its unsteady behavior and rubber deformation.
With the simulation technologies evolving fast regarding modeling spinning tires for aerodynamics, this paper takes the prior work and data completed by the authors and investigates the impact on the flow fields and aerodynamic forces using the most recent developments of an Immerse Boundary Method (IBM). IBM allows us to mimic realistically a rotating and deformed tire using Lattice Boltzmann methods.
This is the third manuscript in a collection of spinning wheel investigations with a full vehicle. The work presented in this paper follows up on the next steps identified in prior publications, taking full advantage of the detailed and accurate experimental data captured at a full width single belt wind tunnel.
Different levels of complexity in wheel modeling are explored and compared to the wind tunnel data allowing to explore the effects of modeling refinement on the computed flow field and aerodynamics forces.
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DOI
https://doi.org/10.4271/2023-01-0843
Pages
14
Citation
Sbeih, K., Guzman, A., Barrera Garcia, D., Fougere, N. et al., "Accurate Automotive Spinning Wheel Predictions Via Deformed Treaded Tire on a Full Vehicle Compared to Full Width Moving Belt Wind Tunnel Results," SAE Technical Paper 2023-01-0843, 2023, https://doi.org/10.4271/2023-01-0843.
Additional Details
Publisher
Published
Apr 11, 2023
Product Code
2023-01-0843
Content Type
Technical Paper
Language
English