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Technical Paper

Interior Noise Analysis of a Commercial Vehicle Cab by Using Finite Element Method and Boundary Element Methods

2016-09-27
2016-01-8051
In order to predict the interior noise of a commercial vehicle cab, a finite element model of a heavy commercial vehicle cab was established. An acoustic-structure coupling model of the cab was built based on experimentally validated structure model and acoustic model of a commercial vehicle cab. Moreover, based on the platform of Virtual. Lab, the acoustic field modes of the acoustic model of the commercial vehicle cab and the coupled modes of the acoustic-structure coupling model were analyzed by using the acoustic-structure coupling analysis technique. The excitation of the vehicle cab was tested at an average speed on an asphalt road. Then, the interior noise of the heavy commercial vehicle cab was predicted based on FEM-FEM method and FEM-BEM method with all the parameters and excitation. Furthermore, the predicted interior noise of the commercial vehicle cab was compared with the tested interior noise.
Journal Article

Objective Evaluation of Interior Sound Quality in Passenger Cars Using Artificial Neural Networks

2013-04-08
2013-01-1704
In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.
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