Introducing a procedure for predicting and reducing tire/road noise using a fast-computing hybrid model.

Area of Science:

• Automotive Engineering
• Acoustics
• Mechanical Engineering

Background:

• Tire noise is a significant concern for designers, requiring a scientific understanding of its complex origins.
• Achieving low-noise tire designs necessitates a deep dive into the underlying acoustic phenomena.
• Existing models may not fully capture the multifaceted nature of tire-generated sound.

Purpose of the Study:

• To develop a knowledge-based hybrid model for predicting tire noise.
• To identify and quantify the contributions of various sound generation mechanisms.
• To provide a scientific basis for low-noise tire design and noise reduction strategies.

Main Methods:

• A hybrid model was developed, combining incoherent summation of sounds from texture impact, tread impact, air-pumping, and resonances (Helmholtz, pipe, air cavity).
• Experimental data was collected from C1 radial tires in a semi-anechoic chamber.
• A fast-computing statistical model using Taguchi design and machine learning (Support Vector Machine) was developed to replace the initial model.

Main Results:

• The hybrid model achieved approximately 1.7 dB(A) error in total noise prediction.
• The model successfully identified the contribution of each sound generation mechanism to overall tire noise.
• Analysis of 21 tire parameters revealed the critical role of tread pattern characteristics, particularly groove angle, in noise generation.

Conclusions:

• The developed fast-computing hybrid model offers a scientifically grounded approach for tire noise reduction.
• Understanding the specific mechanisms of sound generation allows for targeted design modifications.
• Tread pattern design, especially groove angle optimization, is a key factor in achieving quieter tires.