Uncertainty Quantification of Motorcycle Racing Upstream Flow Conditions

The upstream flow conditions in front of any vehicle are the first barrier a vehicle must overcome beside the acceleration itself. Both together, the vehicle speed and the upstream flow conditions, result in the oncoming wind vector experienced by the moving vehicle. The aim of the present work is to show a new approach to consider the chaotic and random behavior of surrounding flow conditions and their influence on driving performance. Special interest was put on a description of the flow conditions with respect to well know turbulent flow field parameters like the turbulence length scale or the turbulence intensity. Depending on where the flow conditions are measured, stationary in the earth reference frame, or on a moving vehicle, it is quite difficult to get a robust description of the previously mentioned flow field parameters. These parameters are used together with the Reynolds number to predict the aerodynamic behavior by correlation functions or maps. A lot of aerodynamic characteristics for road vehicles are deter-mined in wind tunnels or from numerical flow simulations for specific flow conditions. Detailed studies of Reynolds dependency are well known for shapes like the sphere, but not for more complex shapes like cars or motorbikes. As the determination of all these dependencies is difficult for complex shapes the presented approach uses stochastic sensitivity analysis to quantify the influence of the upstream flow condition uncertainties. The test case for this is a generic multi-body model of a motorbike which is accelerating and driving through different environmental flow conditions. The time and the speed at defined observation points are used to quantify the influence of the upstream flow conditions.