Literature review of models on tire-pavement interaction noise. (28th April 2018) - Li, Tan; Burdisso, Ricardo; Sandu, Corina
Abstract: Tire-pavement interaction noise (TPIN) becomes dominant at speeds above 40 km/h for passenger vehicles and 70 km/h for trucks. Several models have been developed to describe and predict the TPIN. However, these models do not fully reveal the physical mechanisms or predict TPIN accurately. It is well known that all the models have both strengths and weaknesses, and different models fit different investigation purposes or conditions. The numerous papers that present these models are widely scattered among thousands of journals, and it is difficult to get the complete picture of the status of research in this area. This review article aims at presenting the history and current state of TPIN models systematically, making it easier to identify and distribute the key knowledge and opinions, and providing insight into the future research trend in this field. In this work, over 2000 references related to TPIN were collected, and 74 models were reviewed from nearly 200 selected references; these were categorized into deterministic models (37), statistical models (18), and hybrid models (19). The sections explaining the models are self-contained with key principles, equations, and illustrations included. The deterministic models were divided into three sub-categories: conventional physics models, finite element and boundary element models, and computational fluid dynamics models; the statistical models were divided into three sub-categories: traditional regression models, principal component analysis models, and fuzzy curve-fitting models; the hybrid models were divided into three sub-categories: tire-pavement interface models, mechanism separation models, and noise propagation models. At the end of each category of models, a summary table is presented to compare these models with the key information extracted. Readers may refer to these tables to find models of their interest. The strengths and weaknesses of the models in different categories were then analyzed. Finally, the modeling trend and future direction in this area are given.