Vehicle interior noise transfer function predictions applying semi-analytical, finite element and boundary element approaches

2011 ◽  
Vol 7 (2) ◽  
pp. 91 ◽  
Author(s):  
Wael Elwali ◽  
Mingfeng Li ◽  
Teik C. Lim
Keyword(s):  
Finite Element ◽  
Transfer Function ◽  
Boundary Element ◽  
Interior Noise ◽  
Vehicle Interior ◽  
Noise Transfer Function ◽  
Vehicle Interior Noise

A Coupled Structural-Acoustic Finite Element Model for Vehicle Interior Noise Analysis

1984 ◽  
Vol 106 (2) ◽  
pp. 314-318 ◽  
Author(s):  
S. H. Sung ◽  
D. J. Nefske
Keyword(s):  
Finite Element ◽  
Noise Analysis ◽  
Coupled Model ◽  
Element Model ◽  
Interior Noise ◽  
Vehicle Interior ◽  
Noise Sources ◽  
Acoustic Cavity ◽  
Vehicle Interior Noise ◽  
Vehicle Structure

An analytical method is developed for predicting vehicle interior noise and identifying noise sources. In this method, the finite element models representing the vehicle structure and its enclosed acoustic cavity are coupled mathematically. A modal formulation is employed to solve for the interior acoustic response, and an analysis is developed to identify the structural and acoustic modal participation as well as the boundary panel participation in producing the response. As an example application, a coupled model of an automotive vehicle is presented and experimentally evaluated. The modal and panel participations are identified from the results.

Plane Wave Resonance in the Tire Air Cavity as a Vehicle Interior Noise Source

1995 ◽  
Vol 23 (1) ◽  
pp. 2-10 ◽  
Author(s):  
J. K. Thompson
Keyword(s):  
Closed Form Solution ◽  
Form Solution ◽  
Interior Noise ◽  
Cavity Resonance ◽  
Test Results ◽  
Vehicle Interior ◽  
Air Cavity ◽  
Vehicle Interior Noise ◽  
Wave Resonance ◽  
Resonance Frequencies

Abstract Vehicle interior noise is the result of numerous sources of excitation. One source involving tire pavement interaction is the tire air cavity resonance and the forcing it provides to the vehicle spindle: This paper applies fundamental principles combined with experimental verification to describe the tire cavity resonance. A closed form solution is developed to predict the resonance frequencies from geometric data. Tire test results are used to examine the accuracy of predictions of undeflected and deflected tire resonances. Errors in predicted and actual frequencies are shown to be less than 2%. The nature of the forcing this resonance as it applies to the vehicle spindle is also examined.

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

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