## 3 Results  
In the Figures 5 and 6, the temperature trends of all the tire layers of the thermodynamic model are illustrated (it must be highlighted that in the above figures the temperature values are dimensionless because of confidentiality agreements with industrial partners). It has to be clarified that the measured data in Figure 5 in red (acquired from external IR sensors) report the temperature of fixed points along the lateral direction of the tire tread, while the simulated ones, in blue, are related to each one of the 15 ribs available, in any moment of the run. \begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Quantity** & **Description** \\ \hline T\({}_{\text{treadSurf}}\) & Tread surface temperature \\ T\({}_{\text{treadCore}}\) & Tread core temperature \\ T\({}_{\text{treadBase}}\) & Tread base temperature \\ T\({}_{\text{innerLiner}}\) & Inner liner temperature \\ T\({}_{\text{innerAir}}\) & Internal air temperature \\ P\({}_{\text{innerAir}}\) & Internal air pressure \\ W\({}_{\text{ext}Conrection}\) & Ambient air convection \\ W\({}_{\text{int}Conrection}\) & Chamber air convection \\ W\({}_{\text{ext}Conduction}\) & Road pavement conduction \\ W\({}_{\text{long}}\)Friction & Longitudinal friction \\ W\({}_{\text{lat}Friction}\) & Lateral friction \\ W\({}_{\text{sel}}\) & Strain Energy Loss \\ \hline \hline \end{tabular}
\end{table} 
Table 2: thermoRIDE model outputs. The difference in the thermal shapes of the external layers is due to their position inside the tire structure: the tread layers, especially the surface one, are subjected to the instant thermal powers generated by the tire/road interaction and convective flows; meanwhile a slow temperature trend induced concurrently by the rolling fatigue effect and by the convective heat exchanges characterizes the internal layers' dynamics. That is why, the internal tire layers seem to have a slow temperature ascent during the rolling motion of the wheel, while the tread surface is characterized by an oscillating profile. The ability to predict the interior temperature distribution, and thus the grip behavior of the tire, is fundamental in terms of the vehicle handling improvement and of the asset optimization according to highly variable outdoor testing conditions. Vehicle optimal setup is deeply linked with the correct compound working conditions, as highlighted in Figure 7 for a reference automotive tire, not directly linked to the described motorcycle thermal model, but able to show expected physical trends. Such optimization is achievable with a  
Figure 5: Comparison between the tread external temperatures obtained by means of the 16-ribs’ configuration model (in blue) and the acquired ones (in red). Figure 6: Tire temperatures of all the tire layers obtained by means of the thermoRIDE model: tread surface (in blue), tread core (in red), tread base (in black) and innerliner (in green). proper suspension layout in terms of stiffness, compliance, and of suitable wheel alignment geometry. In particular, owing to the availability of information on the time by time tread core temperature, plotted on the x-axis, it becomes clear that the tire shows an optimal grip in a specific thermal range that results to be the variable for each different compound, useful to be known in order to set an optimal configuration of the vehicle in any condition. Differences in terms of interaction characteristics (Figure 8), in which a considerable stiffness decrease because of a higher tire temperature is clearly appreciable, are shown both for longitudinal and lateral tire interaction curves. The influence of a large amount of parameters can be evaluated, such as, inclination angle, inflation pressure, different track and weather conditions, and the influence of the manufacturer's vehicle settings.