# A Fast Approach to Optimize Trad Pattern Shape for Tire Noise Reduction
## 6 Conclusions  
This paper aimed to contribute to reducing tire pattern impact noise. The tire pattern noise prediction method was introduced and validated through indoor anechoic chambers and pass-by noise experiments. Then, the pattern shape optimization method was proposed, and the pattern shape was generated using the basis vector method. Finally, the potential of leading edge and pattern shape optimization to reduce pattern noise was discussed. The main conclusions of this work are summarized below:  
Figure 16: Results of leading-edge optimization at different times and at a velocity of 60 km/h. Figure 17: The optimized shape of the leading edges at a velocity of 60 km/h. 1. A predictive model for tire-pavement interaction noise was developed, and noise experiments were conducted in a semi-anechoic chamber by measuring pass-by noise. The prediction results are in good agreement with the experiment results above 800 Hz. 2. The basis vector method was applied to generate complex tire pattern structures. The new pattern shape was synthesized with linear weighting parameters among these base shapes, and it was found that the optimization parameters can be reduced by this method. 3. The novel multi-objective function that was proposed aims to minimize the impact noise generated by the tire pattern. The optimization parameters were obtained using a genetic algorithm. This method can be used to improve the design scheme at the pattern design stage. 4. Noise can be reduced through optimization of the pattern shape or leading edge, but changing the pattern may be a better choice when considering other performance factors. In summary, the tire noise prediction and optimization method proposed in this paper offers the potential to effectively reduce noise, minimize trial and error costs in the early stage of pattern design, and significantly enhance development efficiency. However, low noise is a tire design criterion that may conflict with other objectives [61], such as wet traction, handling, hydroplaning, or safety. For example, when considering the wet skid performance of a tire, the water flow characteristics around a tire are largely influenced by lateral grooves [62]. The model proposed in [63] can be used to determine the parameters of the tread pattern, such as the width and angle of the lateral groove, for better wet skid performance. By keeping these parameters with minimal changes in each base shape vector, the optimization tread pattern can combine maximum safety with the lowest noise level. In addition, compared to traditional fuel vehicles, electric vehicles (Evs) have lower engine noise and lower inlet and exhaust noise, and the tire-pavement interaction noise is the main source of vehicle noise at higher velocities [64]. The motion states of Evs (i.e., the constant speed state, the acceleration state, and the deceleration state) have a significant influence on vehicle noise [47]. When considering the optimization of Ev noise, several crucial steps must be followed. Firstly, it is essential to accurately determine the real leading edge through experiments or Finite Element Method (FEM) simulations at various motion states. Secondly, the obtained real leading edge should be used in the optimization process, as demonstrated in Figure 3. Subsequently, the objective function for reducing electric vehicle noise should be established by Equations (15) and (16) at each motion state. Finally, the optimization process of Ev noise can be carried out by employing the method proposed in this research. Methodology, B.Z. ; data curation, D.H. and F.L. ; software, B.Z. and J.C.; writing--review and editing, B.Z., B.S. and J.W. ; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (52075119) and the Major Program of the National Natural Science Foundation of China (51790502). Not applicable.