Application of Real-World Wind Conditions for Assessing Aerodynamic Drag for On-Road Range Prediction 2015-01-1551
Aerodynamic evaluation of vehicles using static yaw angle changes in wind tunnel testing and numerical simulation has been used as standard practice for evaluating vehicle performance under a range of wind conditions. However, this approach does not consider dynamic wind effects coming from changing wind conditions, passing other vehicles and roadside obstacles, and transient non-uniform wind conditions coming from environmental turbulence. In previous work by the authors, computational fluid dynamics (CFD) simulation methodology for considering dynamic wind conditions and on-road turbulence was demonstrated, showing the important effects of the wind conditions on the vehicle aerodynamics. The technique allows the vehicle to be tested under a range of transient gust conditions, also accounting for wind turbulence coming from upstream vehicles and natural environmental wind fluctuations. These wind fluctuations are represented using the spectrum, intensity, and length scale of turbulent velocity fluctuations, which are then used as a transient boundary condition in the numerical simulation.
In this paper, this method is utilized for determining the aerodynamic drag over a representative drive cycle representing highway driving through typical wind conditions on a California highway in order to support prediction of electric vehicle on-road range under a single battery charge. A long transient simulation was used with wind conditions varying using a prescribed cycle representing prevailing wind changes, wind gusts, and on-road turbulence. To understand the important effects of wind variations on vehicle aerodynamics, the results are shown separately for each wind condition, as well as using a weighted average representing a prescribed wind cycle.
Citation: D'Hooge, A., Rebbeck, L., Palin, R., Murphy, Q. et al., "Application of Real-World Wind Conditions for Assessing Aerodynamic Drag for On-Road Range Prediction," SAE Technical Paper 2015-01-1551, 2015, https://doi.org/10.4271/2015-01-1551. Download Citation
Andrew D'Hooge, Luke Rebbeck, Robert Palin, Quinn Murphy, Joaquin Gargoloff, Bradley Duncan