In this research, vehicle gravel & sand noise was studied from a view of tire design. First, the mechanism of gravel & sand noise was identified by driving on the road where sand and small particles of gravel were spread. Second, the influence of tire design parameter on vehicle gravel noise was studied by controlling tire tread pattern and viscoelastic characteristics of tire tread rubber and then the influence on braking and rolling resistance performance was also studied. It was found that high frequency gravel noise was mainly recognized in the rear seat of SUV and decreases as road surface temperature increases. In addition, the directional tread pattern with symmetry was found to amplify gravel noise. Finally, it was found that gravel noise has a relationship with tanδ characteristics in low temperature and braking distance. In particular, gravel noise decreases as braking distance decreases.

Tire qualification for an OE program consists of several rounds of submissions by the tire manufacturer for evaluation by the vehicle manufacturer. Tires are evaluated both subjectively where the tire performance is rated by an expert driver, and objectively where sensors and testing instruments are used to measure the tire performance. At the end of each round of testing the evaluation results are shared and requirements for performance improvement for the next round are communicated with the tire manufacturer. As building and testing is both expensive and time consuming predictive modeling and simulation analysis that can be applied to the performance of the tire is of great interest and value.

This paper presents an application of FEA modeling along with experimental verification to solve tire noise objections at certain frequencies raised by an OEM account. Coupled structural-acoustic analysis method was used to find modal characteristics of the tire at the objectionable frequencies. Sensitivity calculations were then carried out to evaluate the strength of contribution from each tire component to the identified modes. Based on these findings changes to the construction were proposed and implemented that addressed the noise issue.

Tire parameter identification (PI) for multi-body-system (MBS) tire models is a time-consuming task. Since MBS tire simulations implement highly abstract, effective models, the tire model parameters cannot be interpreted as physical (material) properties directly, they must be seen as macroscopic entities in an integral sense. In a set of typically 30 and more parameters with varying orders of magnitude, optimal values are to be found that make various tire simulation results fit best to experimental data in some sense.

Interpreted as an optimization program, some numerical schemes can be applied to reduced (sub)-sets of programs - but this approach always includes making an initial guess based on physical tire properties or experience. Also, repeated manual interactions take place during a typical program - monitoring the current progress by visual inspection and iterating over this process until a certain optimum is reached. Due to the variety of parameter combinations and the relatively long simulation time, the global optimum for all available measurements usually cannot be found with acceptable effort (if at all); instead a subspace strategy is typically applied.

Hence the definition of “optimum” often lies in the eye of the beholder, i.e. identical PI tasks done by different experts generally lead to different optima. Real test data are acquired by static experiments (e.g. vertical/longitudinal stiffness), stationary experiments (longitudinal/lateral slip) and dynamic experiments (e.g. cleat runs). The way of comparing experimental and simulation results also influences the detected optimum and leads to the problem of defining suitable error criteria (i.e. what is “good” PI).

This paper focuses on a new programmatic approach developed at Fraunhofer LBF relying on a rule based expert system. The new PI-Tool makes PI faster, more standardized, as automatable as possible and the results at least as good as achieved by current processes. Special focus was put on developing error criteria which try to mimic the human skill while comparing measurement and simulation results - a multi-layered and complex process. Various local signal properties and signal processing algorithms are derived to simulate the experiment-conform, visual human inspection.

Together with the presentation of this new PI method, results achieved with our new tool will be shown using the tire model CDTire for an already parameterized tire.

Physically noise could be analyzed with respect to energy distribution in frequency range and its level. But it is not the best way to fix the tire noise sometimes if the objective analysis result is not well correlated with human perception. Therefore many studies have been done to make the good objective parameters in noise analysis and we call them sound quality matrix. On the contrary noise subjective evaluator should be studied not only by own experience but also by the intended training program. However we don’t have any formal education course for subjective noise evaluation skill upgrade until now. Triadic comparison tests are done by the best skilled subjective evaluators who have over 15 years experience. From these noise preferences verbal technical words are unified and they are used for training. Also we made timbral training simulator to improve tire noise evaluation skill. This simulator is tested for several beginners and it shows a good result. We combine two processes and make total training method for improving tire noise evaluation ability.

Tire non-uniformity may affect vehicle ride comfort quality especially while vehicles are running at highway speeds. Spindle force variations in the radial, tangential and lateral directions are normally used as parameters evaluating tire high speed uniformity performance. An interesting physical phenomenon – directionality of tangential force variation has been observed for most tires. The directionality of tangential force variation means that tangential force variation is different when a tire is measured in the clockwise and counter-clockwise directions. In this paper, a theory is developed to explain the interesting physical phenomenon. Simulation and analysis are conducted using a dynamic tire model.  It is found that the directionality of tangential force variation is caused by the interaction of different types of non-uniformities in tires such as mass and geometry variations.