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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Quantification and evaluation of uncertainty in the mathematical modelling of a suspension strut using Bayesian model validation approach
 Barthorpe, R. ; Society for Experimental Mechanics SEM, Bethel: 35th IMAC, a Conference and Exposition on Structural Dynamics 2017. Proceedings. Vol.3: Model validation and uncertainty quantification : Garden Grove, California, January 30February 2, 2017 Cham: Springer International Publishing, 2017 (Conference proceedings of the Society for Experimental Mechanics series) ISBN: 9783319548579 (Print) ISBN: 9783319548586 (Online) pp.113124 
 Conference and Exposition on Structural Dynamics <35, 2017, Garden Grove/Calif.> 

 English 
 Conference Paper 
 Fraunhofer LBF () 
Abstract
Mathematical models of a suspension strut such as an aircraft landing gear are utilized by engineers in order to predict its dynamic response under different boundary conditions. The prediction of the dynamic response, for example the external loads, the stress and the strength as well as the maximum compression in the springdamper component aids engineers in early decision making to ensure its structural reliability under various operational conditions. However, the prediction of the dynamic response is influenced by model uncertainty. As far as the model uncertainty is concerned, the prediction of the dynamic behavior via different mathematical models depends upon various factors such as the model’s complexity in terms of the degrees of freedom, material and geometrical assumptions, their boundary conditions and the governing functional relations between the model input and output parameters. The latter can be linear or nonlinear, axiomatic or empiric, time variant or timeinvariant. Hence, the uncertainty that arises in the prediction of the dynamic response of the resulting different mathematical models needs to be quantified with suitable validation metrics, especially when the system is under structural risk and failure assessment. In this contribution, the authors utilize the Bayes factor as a validation metric to quantify the model uncertainty of a suspension strut system with similar specifications as actual suspension struts in automotive or aerospace applications for decision making in early design stage.