Browsing by Author "Li, Tan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Influencing Parameters on Tire–Pavement Interaction Noise: Review, Experiments, and Design Considerations
    Li, Tan (MDPI, 2018-10-18)
    Tire–pavement interaction noise (TPIN) is dominant for passenger vehicles above 40 km/h and 70 km/h for trucks. In order to reduce TPIN, numerous investigations have been conducted to reveal the influencing parameters. In this work, the influencing parameters on TPIN were reviewed and divided into five categories: driver influence parameters, tire related parameters, tread pattern parameters, pavement related parameters, and environmental parameters. The experimental setup on analyzing and insights into optimizing those parameters are given. At the end, summary tables are presented to compare all the parameters discussed, including the pertinent frequency, potential noise influence, physical mechanism, etc. As such, this review article can also serve as a reference tool for new researchers on this topic. This work covers references from 1950s till present, aiming to distribute key knowledge in both classic and recent studies.
  • Thumbnail Image
    Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)
    Li, Tan (Virginia Tech, 2017-08-11)
    Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic.