On the Critical Importance of Physical Modelling for the Analysis of Coast-Down Data

Novel experimental and analytical methods were developed with the objective of improving the reliability and repeatability of coast-down test results. The methods were applied to coast-down tests of a SUV and a tractor-trailer combination, for which aerodynamic wind-tunnel data were available for comparison. The rationale was to minimize the number of unknowns in the equation of motion by measuring rolling and mechanical resistances and wheel-axle moments of inertia, which was achieved using novel experimental techniques and conventional rotating-drum tests. This led to new modelling functions for the rolling and mechanical resistances in the equation of motion, which was solved by regression analysis. The resulting aerodynamic drag coefficient was closer to its wind-tunnel counterpart, and the predicted low-speed road load was closer to direct measurements, than the results obtained using conventional methods. It is anticipated that applying the novel techniques to characterize the rolling and mechanical resistances and wheel inertia of a large number of vehicles and tires may lead to more accurate, generalized models for the terms in the equation of motion. Uncertainty remains in the applicability of the rolling-resistance model to the current data set, due to a discrepancy between the surface texture of the test drum used to characterize the speed dependence and the structure of the rough, uneven track pavement. A re-evaluation of the coast-down data, whereby a parameter governing the speed-dependence of the rolling resistance was treated as a third unknown, resulted in aberrant values of the drag coefficient. It is hypothesized that the function used to model rolling resistance was not fully representative of the pavement conditions and that an additional term would be required to capture the physical interaction between the rugged track and the test vehicles. This emphasized the critical importance of proper physical modelling with a minimal number of unknown parameters.