Optimization of vehicle compartment low frequency noise based on Radial Basis Function Neuro-Network Approximation Model

Abstract

Abstract. For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function) neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel's thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver's head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively .The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly. 1.INTRODUCTIONIn addition to focus on the stability and safety of driving, customers pay more attention to the acoustic characteristics of vehicle compartment. One of the main reasons that affect the acoustic performance of the cab is the prominent low frequency noise which is difficult to be eliminated [1][2][3]. Previous investigation shows that the thin plates' structure radiation noise which is generated by external excitation in the frequency range of 0Hz~200Hz contributes significantly to the low frequency noise of the cab [1]. In order to reduce the noise in cab and improve vehicle acoustical comfort, the body structure that contributes tremendously to the acoustic should be optimized. The finite element calculation method for optimization is time consuming in view of the model size. Comparatively, the approximation model optimization method is of high efficiency [1][2][3]. In paper [3], the thickness of plate was designated as the design variable. The minimum pressure at the driver's right ear and minimum body mass was defined as the optimization objective. The approximation model was established by response surface method to optimize the acoustic performance. In paper [4], the second response surface approximation model was established for the optimization of acoustic and vibration. The method of using response surface method to establish the approximation model has the characteristics of simplicity expression and high computational efficiency. However, it fails to guarantee the accuracy of the model while the number of samples is limited.This paper is organized as follows. In section 2, considering the low frequency noise of the cab, the optimal Latin Hypercube Sampling (LHS) method was used to collect sample data and the RBF neural network  Corresponding author:haqiankaka@163.com method was implemented to establish the ap...