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Publication

Experimental Acoustic Modal Analysis of Automotive Cabin

Journal Contribution - Journal Article

In the automotive industry, one of the most important requirements in designing a high quality vehicle is to avoid or minimize the noise in the passenger compartment in order to improve operator comfort. Therefore, an ever increasing interest exists to predict the interior acoustic behavior by means of accurate simulation models both to improve the vehicle NVH performances and to reduce the development cycle for new products. Prediction of intrinsic dynamic characteristics is the main factor to understand the modeling challenges, so experimental methods in which an acoustic characterization is performed based on measurements play an important role in validation of the numerical model and, more in general, in comprehension of the physical behaviour. In this paper, an interior acoustic modal analysis carried out for a fully trimmed sedan car, the equipment requirements and the testing procedure will be discussed. Due to specific acoustic modal analysis challenges, such as the high modal damping ratios and the use of a large number of references spread around the cabin to get a sufficient excitation of the modes, the modal parameter (i.e. resonance frequency, damping ratio, mode shape&) extraction is often a non-trivial task. Here the modal parameters will be extracted by the ML-MM estimator, a multivariable frequency-domain maximum likelihood estimator based on a modal model formulation, which had been presented by one of the authors and whose performance will be compared to other classical modal parameter estimation methods. The results of this testing will be reported and different investigations on the cabin interior acoustic field will be carried out and examined.
Journal: Sound and Vibration
ISSN: 1541-0161
Issue: 5
Volume: 49
Pages: 10 - 18
Publication year:2015
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:0.1
CSS-citation score:1
Authors:International
Authors from:Private, Higher Education
Accessibility:Open