Abstract
Racing tires and motorcycle tires present individual segments of the tire market. For instance, while the average life of car and truck tires is 50 000 miles, the average life of race tires is 100 miles. Because tires play a critical role in a race, technical demands to assure safety and performance are growing. Similarly, tires have a large influence on safety, handling/grip, and performance of the rapidly growing world fleet of motorcycles, due to the fact of only two wheels being in contact with the ground. Thus, the common feature of both market segments is that the typical tire compromise of wear, rolling resistance, and traction is strongly weighted toward traction. Most of the recent efforts of rubber scientists have been directed toward lowering rolling resistance of the tread compounds, which left a certain void in the science of compounding for racing and motorcycle treads. Particularly, the industrial assortment of polymers and fillers used for motorcycle treads is commonly different from that used for car or truck treads, but it is not known how the filler properties affect the hysteresis–stiffness compromise. The objective of this study is to evaluate the effects of the carbon black characteristics on the important properties of a typical racing and motorcycle tire tread compound. More than 50 individual carbon blacks were mixed in a SBR formulation. The acquired data were statistically analyzed, and a linear multiple regression model was developed to relate rubber properties (responses), such as static modulus, complex dynamic modulus, hysteresis, and viscosity to the key carbon black characteristics (variables) of surface area, structure, aggregate size distribution, and surface activity. Prediction profiles created from the model demonstrate rubber performance limits for the range of carbon blacks tested, and indicate the niches to provide required combinations of the rubber properties.
Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance
