In this paper, a full Acoustic Modal Analysis (AMA) procedure to improve the CAE predictions of the car interior noise level is proposed. Some of the challenges that can be experienced during such an analysis are described and new solutions to face them are proposed. Particular AMA challenges range from the arrangement of the experimental setup to the post-processing analysis. Since a large number of microphones are needed, a smart localization procedure, which automatically determines the microphone three dimensional (3-D) positions and dramatically reduces the setup time, is presented herein. Furthermore, the need for a large number of sound sources spread across the cavity to assure a homogeneous sound field makes modal parameter estimation a nontrivial task. Traditional modal parameter estimators have indeed proven not to be effective in cases where many input excitation locations have to be used. Hence, a more suitable estimator, the Maximum Likelihood Modal Model-based (ML-MM) method, will be employed for such an analysis.
Experimental acoustic modal analysis of an automotive cabin: Challenges and solutions
CHIARIOTTI, PAOLO;MARTARELLI, Milena
2016-01-01
Abstract
In this paper, a full Acoustic Modal Analysis (AMA) procedure to improve the CAE predictions of the car interior noise level is proposed. Some of the challenges that can be experienced during such an analysis are described and new solutions to face them are proposed. Particular AMA challenges range from the arrangement of the experimental setup to the post-processing analysis. Since a large number of microphones are needed, a smart localization procedure, which automatically determines the microphone three dimensional (3-D) positions and dramatically reduces the setup time, is presented herein. Furthermore, the need for a large number of sound sources spread across the cavity to assure a homogeneous sound field makes modal parameter estimation a nontrivial task. Traditional modal parameter estimators have indeed proven not to be effective in cases where many input excitation locations have to be used. Hence, a more suitable estimator, the Maximum Likelihood Modal Model-based (ML-MM) method, will be employed for such an analysis.File | Dimensione | Formato | |
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