Mining Truck’s Cabin Interior Low-frequency Noise Analysis And Panel Optimization

Mining truck, as a construction machinery used for transportation in large open mining area, is always running on the complex road, making its vibration and noise problem much worse. On particular, the low frequency noise, which ranges from 20 Hz to 200 Hz, is more obvious than expected, and it would do harm to driver’s physical and mental health when working under such harsh environment for a very long time. Therefore, improving interior acoustic environment of driver’s cab of mining truck to reduce structural noise and enhance riding comfort will boost development of heavy vehicle industry and working efficiency.This paper takes a self-developed mining truck as the research object and establishes its finite element model to analyze in two cases of driving cycle. One case is that the mining truck is fully loaded running at the speed of 30km/h, and the other is no-load case. Under these two circumstances, the software of LMS Virtual.lab is used to calculate acoustic response of field points around driver’s ears and the response is analyzed to find out peak values of acoustic pressure. Then, panel contribution analysis is conducted to confirm the most influential panels and mode participation analysis is carried out on these panels to find out the most influential modes. Multi-objective topography optimization is conducted to maximize mode frequency and new structures are redesigned according to results of topography optimization. New model of driver’s cab is calculated to get the acoustic pressure level curve in contrast to the previous one to verify the effectiveness of the optimization. The main contents are as follows:(1) Cab structural finite element model and acous tic cavity finite element model are established first, and modal analysis is conducted on these two models and their coupling model to have a full understanding of cab’s interior acoustic feature. C ab’s structural dynamic response is calculated in cases of full-load and no-load driving modes at the speed of 30km/h, and the results are put into comparison with the testing data in order to prove the model’s effectiveness.(2) Cab’s structure-acoustic coupling model is imported into the software of LMS Virtual.lab and each excitation of two drivin g modes is respectively exerted on cab’s mounting position. Analyzing frequency is ranging from 20 Hz to 200 Hz to calculate acoustic response around driver’s ears and peak values of noise, which are above 80 d B, are identified with its correspondent frequency from the acoustic pressure level curve. According to cab’s structure, acoustic surface mesh is divided into several plates with damping coefficient and panel contribution analysis is carried out to get contribution coefficient of every panel at analyzing frequency. Then, panels, contributing mostly to the overall sound pressure, are picked out by comparing contribution coefficient.(3) Seek out cab’s free modes which cause large vibration of chosen panels in the frequency ranging from 20 Hz to 200 Hz, and conduct mode participation analysis on these chosen modes, presenting the results by means of bar ch arts. Find out the most influential modes by comparing modes participation coefficient and take modal frequency as optimizing objective in the later chapter.(4) In terms of chosen cab’s free modes, single objective optimization is conducted to get each optimal solution of chosen modes’ frequency and substitute these optimal values into a multi-objective optimization function created on average frequency method. Topography optimization of every chosen panel is respectively carried out when taking function’s value as optimizing objective. Taking contour of optimized results and processing technology comprehensively into consideration, redesign strengthening ribs arranged on panels’ surface and calculate acoustic response of new cab’s finite element model. Optimized sound pressure level curves are put into comparison with not-optimized ones to see the amount of reduction in noise peak values so that topography optimization can be proved to take effect in improving cab’s interior acoustic environment.