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Dynamic substructuring using experimental-analytical state-space models of automotive components

Axel Bylin
Göteborg : Chalmers tekniska högskola, 2018. Master's thesis - Department of Mechanics and Maritime Sciences; 2018:69, 2018.
[Examensarbete på avancerad nivå]

Even though simulation models are getting better and better, thanks to both increased knowledge and computational powers, they are sometimes not perfect or not even good. Experimental models also have their drawbacks as they are often expensive and hard to obtain, to name a few. Within dynamic substructuring models of different components are coupled to get the dynamic response of the coupled system. Simulation models are typically finite element (FE) models and are denoted analytical models. The most common methods for coupling are best suited for either coupling of purely experimental, typically by frequency based substructuring, or purely analytical models, typically by component mode synthesis. A coupling method based on state space models is however specifically developed to couple experimental models with analytical ditto. A successful implementation of the method, deeper understanding and highlighting of its advantages and drawbacks can thereby reduce the need for experimental models. This report will describe the procedure of coupling an experimental model of a Volvo XC90 body-in-white with an analytical model of a rear subframe. Experimental modal analysis is performed to retrieve frequency response functions of the body-in-white. By system identification a state space model based on these are then coupled to a state space model of the subframe, based on FE data. The resulting hybrid model, based on both experimental and analytical models, is then compared to both an experimental and an analytical model of the coupled system. Results are good, but to achieve good results the method put high demands on the used models. Keywords: Substructuring, structural dynamics, system identification, modal analysis



Publikationen registrerades 2018-12-10. Den ändrades senast 2018-12-10

CPL ID: 256384

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