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• W4220888575 abstract "<div class=section abstract><div class=htmlview paragraph>We describe experiments and numerical modeling of snow surface contamination on two simplified automotive bluff bodies: The Ahmed body and a wedge. The purpose was twofold: 1) To obtain well defined experimental results of snow contamination on simple geometries; 2) To propose a numerical modeling approach for snow contamination. The experiments were performed in a climatic wind tunnel using a snow cannon at −15 °C and the results show that the snow accumulation depends on the aerodynamics of the studied bluff bodies. Snow accumulates on surfaces in proximity to the aerodynamic wakes of the bodies and characteristic snow patterns are obtained on side surfaces. The numerical modeling approach consisted of an aerodynamic setup coupled with Lagrangian particle tracking. Particles were determined to adhere or rebound depending on an adhesion model combined with a resuspension criterion. The adhesion model was based on adhesive-elastic contact theory and the resuspension criterion is derived from the balance between the aerodynamic forces acting on a particle and the critical force for onset of resuspension. The results show that the numerical method can predict certain characteristic snow patterns obtained from the experiments and we also highlight deviations obtained between experimental and simulation results. The simulation results show that the snow accumulation patterns on a bluff body will depend on the smallest ice particles in a snow sample which implies that samples with larger ice particle (for example natural snow) could produce different snow patterns than the fine machine-made snow used in this study.</div></div>" @default.
• W4220888575 created "2022-04-03" @default.
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• W4220888575 date "2022-03-29" @default.
• W4220888575 modified "2023-10-18" @default.
• W4220888575 title "Snow Contamination of Simplified Automotive Bluff Bodies: A Comparison Between Wind Tunnel Experiments and Numerical Modeling" @default.
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• W4220888575 doi "https://doi.org/10.4271/2022-01-0901" @default.
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