Effect of modeling sidewalls on tire vibration and noise

Low-noise tire design demands the potent model, which regards accessible parameters in the design process before manufacturing a tire. Unlike most previous analytical models that integrated tire treadband with sidewalls with an assumption of identical properties, this research segregates them. The separation clarifies the effects of each tire part on vibration and noise individually, which has not been presented in previous publications and is noteworthy in design. The model is developed considering three connected plates, describing treadband and sidewalls, on an elastic foundation derived from vibro-acoustic coupling inside the tire. Natural frequencies are determined by the Galerkin method using modes shapes satisfying all boundary conditions. The vibration response of a tire rolling on the road is then formulated utilizing Green’s function and convolution integral. Eventually, vibrational tire noise is calculated by the boundary element method. Comparing the proposed model with the repeatedly used integrated plate model has indicated the dissimilarity of treadband and sidewall responses with a difference of 1.4 dB(A) in total noise level. Moreover, implemented parametric study based on a small central composite design has revealed their parameters’ distinct influences on generated noise. For instance, increment in treadband thickness reduces sound level, while decreasing sidewall thickness effectively leads to noise reduction. So, the proposed model is worth employing instead of the previous overused integrated model to predict and reduce tire noise.

Tire noise optimization problem: a mixed integer linear programming approach

We present a Mixed Integer Linear Programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimization Problem, dealing with an APproximation of the noise (TNOP-AP). Here we study the original non-linear problem modeling the EXact - or real - noise (TNOP-EX) and propose a new scheme to obtain a solution for the TNOP-EX. Relying on the solution for the TNOP-AP, we use a Branch&Cut framework and develop an exact algorithm to solve the TNOP-EX. We also take more industrial constraints into account. Finally, we compare our experimental results with those obtained by other methods.

A comprehensive study on statistical prediction and reduction of tire/road noise

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.

Case study: Separating source contributions of vehicle interior noise by operational transfer path analysis

The perception of vehicle interior noise is a key quality index to customers and automakers alike. By tracing noise back to key noise sources and paths, one can focus their refinement efforts. Aiming at the most efficient way to identify the primary noise sources in a vehicle cabin, this article establishes a framework of operational transfer path analysis (OTPA) for separating contributions of noise sources by operational measurements only. OTPA model design, measuring essentials and synthesis method used for separating vehicle interior noise contributions from the powertrain, tires andwindwere described in detail. To comprehend the implementation of OTPA on noise source separation, this article also addresses an exemplification study on an electric vehicle. In the case study illustrated, both spectral map and order extractions were used to validate if the OTPA synthesized results of the powertrain noise contribution agreed with the measured results. Tire noise contribution was validated using the tires driven by the dynamometer along with all other systems switched off. With well-validated OTPA model for the powertrain and tires, further individual path breakdown of the powertrain and tire noise then was investigated to identify key contributors to the interior noise. After clearly separating interior noise contributions, one therefore could design effective countermeasures to mitigate the dominant noise sources. With appropriate scheme of measurement and synthesis, the OTPA technique could therefore effectively serve target setting and refinement focus at foremost noise contributors.

Neural networks assisted computational aero-acoustic analysis of an isolated tire

The computational aero-acoustic study of an isolated passenger car tire is carried out to understand the effect of dimensions of longitudinal tire grooves and operational parameters (velocity and temperature) on tire noise. The computational fluid dynamics and acoustic models are used to obtain aero-acoustic tire noise at near-field and far-field receivers around the tire and artificial neural networks-based regression are used to study the highly non-linear and interactive causal relationships in the system. Unsteady Reynolds-Averaged Navier-Stokes based realizable k-epsilon model is used to solve the flow field in the computational domain. The Ffowcs Williams and Hawkings model is used to obtain aero-acoustic tire noise at far-field positions. Spectral analysis is used to convert the output time domain to frequency domain and to obtain A-weighted sound pressure level. Artificial neural network–based response surface regression is conducted to understand casual relationships between A-weighted sound pressure level and control variables (Groove depth, Groove width, Temperature and velocity). Maximum A-weighted sound pressure level is observed in the wake region of the tire model. The interaction study indicates that ∼10% reduction in the aero-acoustic emissions is possible by selecting appropriate combinations of groove width and groove depth. The interaction of velocity with width is found to be most significant with respect to A-weighted sound pressure level at all receivers surrounding the tire. The interaction of operational parameters, that is, velocity and temperature are found to be significant with respect to A-weighted sound pressure level at wake and front receivers. Therefore, the regional speed limits and seasonal temperatures need to be considered while designing the tire to achieve minimum aero-acoustic emissions.