Damping Loss Factor Estimation for Test-Based Vehicle SEA Modeling

2001 ◽  
Author(s):  
Jae-Hak Woo ◽  
Xiandi Zeng
Keyword(s):  
Loss Factor ◽  
Bias Error ◽  
Reverberation Time ◽  
Frequency Range ◽  
Air Cavity ◽  
Damping Loss Factor ◽  
Factor Estimation

Abstract In the test-based SEA models, the major parameters are measured or estimated from measured quantities. One of the parameters is Damping Loss Factor (DLF) of the air (passenger) cavity of a vehicle. In the SEA model, the air cavity is divided into several sub-cavities. The required DLF for each sub-cavity can be calculated from the reverberation time (T60) measured in that sub-cavity in the vehicle. However, if nothing is done to separate one sub-cavity from other sub-cavities in the T60 measurement in the vehicle, the measured T60 for that sub-cavity is the T60 of the whole air cavity. When the resulted DLF is used in SEA model of that sub-cavity, it is the DLF of the whole air cavity that is used for a sub-cavity, which will result in an over/under-damped. Thus, the prediction from such a SEA model will have bias error especially in the higher frequency range. This has been seen in the results of a vehicle SEA model. In this paper, a method is proposed to estimate the DLF of each sub-cavity based on the T60 of the whole air cavity. When these estimated DLF’s are used in the SEA model for each sub-cavity, the correlation in SEA model was improved by 2.5∼3 dB above 1kHz.

A New Methodology for the Accurate and Consistent Identification of Modal Parameters Using Multi-FRF Analysis

1995 ◽  
Author(s):  
R. M. Lin ◽  
S.-F. Ling
Keyword(s):  
Eigenvalue Problem ◽  
Loss Factor ◽  
Frequency Response Function ◽  
Natural Frequencies ◽  
Identification Problem ◽  
Modal Parameters ◽  
Frequency Range ◽  
Case Examples ◽  
Measured Frequency Response ◽  
Damping Loss Factor

Abstract A new method for the estimation of modal parameters is presented in this paper. Unlike the majority of the existing methods which involve complicated curve fitting and interpolative procedures, the proposed method calculates the modal parameters by solving eigenvalue problem of an equivalent eigensystem derived from measured frequency response function (FRF) data. It is developed based on the practical assumption that only one incomplete column of the FRF matrix of the test structure has been measured in a frequency range of interest. All the measured FRFs are used simultaneously to construct the equivalent eigensystem matrices from which natural frequencies, damping loss factor and modeshape vectors of interest can be directly solved. Since the identification problem is reduced to an eigenvalue problem of an equivalent system, natural frequencies and damping loss factors identified are consistent. Further procedures for normalizing the identified eigenvectors so that they become mass-normalized are developed. Numerical case examples are given to demonstrate the practicality of the proposed method and results obtained are indeed very promising. It is believed that with the availability of such identification method, modal analysts’ dream of intelligent and full automatic modal analysis will become a reality.

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