Sound Radiation Modes of a Tire on a Reflecting Surface

Wave number decomposition of a tire\u27s radial vibration can be used to reveal the wave propagation characteristics of tires. In this paper, the relationship between the structural wave propagation characteristics of a tire excited at one point and its sound radiation is considered. The sound radiation resulting from structural vibration of a tire in contact with the ground was investigated by using boundary element analysis. In particular, the orthogonal radiation modes of a tire in the presence of a reflecting surface, along with their radiation efficiency characteristics, were calculated by applying an eigenvector analysis to the tire\u27s radiation resistance matrix. The latter analysis made use of acoustic transfer vectors and a recovery surface appropriate for a pass‐by noise test. The radiation mode results reveal that it is the vibration in the region close to the contact patch that primarily controls sound radiation. In particular, to reduce pass‐by noise levels, it is necessary to mismatch the tire\u27s structural ring mode and the radiation modes with high radiation efficiencies. It has also been found that the radiation from a tire is controlled by a relatively small number of radiation modes (although the number of contributing modes increases with frequency)

Sound Radiation Control Resulting from Tire Structural Vibration

The objective here was to study the control of sound radiation resulting from the structural vibration of a tire excited at one point. First, the tire was modeled as an orthotropic shell by using finite elements and the effect of various tire material parameters on structural wave propagation and the associated sound radiation was estimated. The parameters that were effective at controlling structural wave propagation were then identified. In addition, the radiation field characteristics in the space surrounding a tire placed on a rigid ground were analyzed by using radiation mode analysis. Based on these analyses, a strategy for reducing the radiated sound levels by modifying the tire parameters from a base set was determined. An improved set of material parameters was identified that resulted in reduced sound radiation within a specified target frequency region. That reduction was achieved by an increase of treadband circumferential stiffness that was found to move the onset of longitudinal wave motion within the treadband into a higher frequency region. Secondly, flexural wave propagation was found to be mainly controlled by inflation pressure and cross-sectional treadband stiffness. By appropriate adjustment of these three parameters, it was found possible to substantially reduce sound radiation in a mid-frequency region

Influence of Tire Size and Shape on Sound Radiation from a Tire in the Mid-Frequency Region

In this research, the influence of tire size and shape on sound radiation in the mid-frequency region was studied. First, the relationship between the structural wave propagation characteristics of a tire excited at one point and its sound radiation was identified by using FE and BE analyses. Then, by using that relationship, the effect of modifying a tire’s aspect ratio, width and wheel diameter on its sound radiation between 300 Hz and 800 Hz was investigated. Finally, an optimization of the sound radiation was performed by modification of the tire structure and shape. It was found that most of a tire’s structural vibration does not contribute to sound radiation. In particular, the effective radiation was found to occur at the frequencies where low wave number components of the longitudinal wave and the flexural wave first appear. In addition, when the tire size and shape were modified, it was found that the flexural wave motion was controlled primarily by the tire cross-sectional length while the longitudinal wave motion was mainly affected by the treadband overall diameter

Control of structural-acoustic radiation from tires by structural modification

The objective of this research was to reduce sound radiation resulting from a tire\u27s structural vibration by modification of its orthotropic material parameters and tire shape. First, the structural wave propagation characteristics on a treadband were studied by using orthotropic shell theory and wave number decomposition and then tire surface vibration was investigated empirically and analytically. The effect of various tire material parameters on structural wave propagation and the associated sound radiation was estimated. Second, the sound radiation resulting from the structural vibration of a tire in contact with the ground was investigated by using BE analysis and experiment. In particular, the orthogonal radiation modes of a tire in the presence of a reflecting surface were calculated by applying an eigenvector analysis to the tire\u27s radiation resistance matrix. Based on these analyses, the relationship between the structural wave propagation characteristics of a tire and its sound radiation was estimated. In addition, the effect of actual porous pavements and tire shape on sound radiation was studied. A strategy for reducing the radiated sound levels by modifying the tire parameters from a base set was determined. Finally an optimized set of tire parameters and tire shape that reduced noise emission was suggested. It was found that all structural vibration does not contribute to the sound radiation from a tire. The significance of the fast, longitudinal wave mode propagating through the treadband was confirmed by the large contribution of the modified ring radiation mode to the radiated sound power at the tire\u27s ring frequency. The third radiation mode above 800 Hz is principally responsible for the horn effect in the presence of reflecting surface. The reduction of radiated sound below 800 Hz was achieved by an increase of treadband circumferential stiffness that was found to move the onset of longitudinal wave motion within the treadband into a higher frequency region. Secondly, flexural wave propagation was found to be mainly controlled by inflation pressure, cross-sectional treadband stiffness and cross-sectional length. By appropriate adjustment of these four parameters, it was found possible to substantially reduce sound radiation in a mid-frequency region

Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals

This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on road surface classification has not yet been conducted. If road surface classification and recognition are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, road surface classification is essential. To achieve this, tire-pavement interaction noise (TPIN) is adopted as a data source for road surface classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimensional image through time–frequency analysis. Converted images were used together with a CNN architecture to examine the feasibility of the road surface classification system

Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals

This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on road surface classification has not yet been conducted. If road surface classification and recognition are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, road surface classification is essential. To achieve this, tire-pavement interaction noise (TPIN) is adopted as a data source for road surface classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimensional image through time–frequency analysis. Converted images were used together with a CNN architecture to examine the feasibility of the road surface classification system