On the critical importance of physical modelling for the analysis of coastdown data

Novel experimental and analytical methods were developed, with the objective of improving the reliability and repeatability of aerodynamic and road-load results derived from coastdown tests. The new methods were applied in the context of a coastdown test campaign conducted using a SUV and a tractor-trailer. Both vehicles had been tested in the NRC 9m Wind Tunnel, providing aerodynamic comparison-points. The rationale behind the novel techniques was to minimize the number of unknowns in the equation of motion by measuring directly the following parameters: axle mechanical resistance, rolling resistance at low speed and wheel-axle moments of inertia. This was achieved, respectively, by means of an axle-deceleration apparatus, a low-speed hauling facility and a wheel-oscillator developed for this study. In addition, the speed-dependence of tire rolling resistance was measured at a private laboratory, via rotating-drum tests, and modeled using a previously-validated exponential formulation. Since the test wind conditions were characterized by minimal yaw angles, the yaw-angle dependence of the aerodynamic drag could be neglected. The only remaining unknowns were the zero-speed rolling-resistance coefficient and the zero-yaw drag coefficient, which were determined by regression analysis. The resulting drag coefficient was closer to its wind-tunnel value, and the predicted low-speed road load was closer to the value derived from the low-speed hauling tests, than when the previous, conventional methods were used. There remains uncertainty in the applicability of the exponential rolling-resistance model due to the roughness and unevenness of test track’s pavement, which induced large fluctuations in vehicle speed. Therefore, a second data analysis was conducted, whereby the exponent governing the speed-dependence of the rolling resistance was treated as a third unknown. However, this resulted in unrealistically low values of the drag coefficient, emphasizing the critical importance of proper physical modelling with a reasonable number of unknown parameters.