Developing a Statistical Model to Predict Tire Noise at Different Speeds

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WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Tire noise is caused due to the complex interactions between the rotating tire and the road surface at the tire/road interface. It is usually caused due to a combination of individual noise generation mechanisms, which can either be structural or air-borne. The influence of each of these noise generation mechanism may vary, depending on various conditions such as tire design, road surface and operating conditions. Due to the many variables that affect the noise generation mechanisms in tires, it is usually a very complex task to isolate and categorize those that are present in the overall tire/road noise spectrum. Various approaches are used to categorize noise generation mechanisms in tires. In this paper, a statistical model based on the assumption that the tire noise acoustic pressure at a specific frequency band is related to the vehicle speed, is used, in order to study tire noise at different speeds. Consequently, the variation of the speed coefficients with frequency bands is studied for a tire with two transverse slots cut into it, and an effort is presented here, to isolate the different noise generation mechanisms as structural or air-borne. While the goal of the statistical model is to indirectly isolate different noise generation mechanisms, the experimental studies presented here, directly attempt to isolate air-borne and structure-borne noise using a foam. Overall, the current study, tries to establish a trend for the variation of tire noise with tire speed, and also serves to reinforce the notion that statistical models can be used to isolate the different tire noise generation mechanisms.
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DOI
https://doi.org/10.4271/2017-01-1507
Pages
6
Citation
Gautam, P., Azizi, Y., and Chandy, A., "Developing a Statistical Model to Predict Tire Noise at Different Speeds," Vehicle Dynamics, Stability, and NVH 1(2):198-203, 2017, https://doi.org/10.4271/2017-01-1507.
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Publisher
Published
Mar 28, 2017
Product Code
2017-01-1507
Content Type
Journal Article
Language
English