Image-based approach to optimize the tyre pitch sequence for a reduction in the air-pumping noise based on a genetic algorithm

Abstract

The paper presents a novel approach to solving problems involved in the application of a genetic algorithm to determine the optimal tyre pitch sequence to reduce the tyre air-pumping noise which is generated by the repeated compression and expansion of the air cavity between the tyre pitch and the road surface. The genetic algorithm was used to determine the optimal tyre pitch sequence with a low level of tyre air-pumping noise using the image-based air-pumping model. In the genetic algorithm used in previous studies, there are a number of problems related to the encoding structure and the selection of an objective function. This paper proposes a single encoding element with five integers, a divergent objective function based on an evolutionary process, and the optimal evolutionary rate based on the Shannon entropy in an attempt to solve the problems. The results of the proposed genetic algorithm with an evolutionary process are compared with those of a randomized algorithm. The randomized algorithm is a traditional method used to obtain the tyre pitch sequence. It was confirmed that the genetic algorithm more effectively reduces the peak value of the predicted tyre air-pumping noise. The consistency and cohesion of the obtained simulation results are also improved.

Keywords

Tyre air-pumping noise image-based noise prediction genetic algorithm Shannon entropy

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