1. Random uncertainties model in dynamic substructuring using a nonparametric probabilistic model
- Author
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Hamid Chebli, Christian Soize, Laboratoire de Mécanique (LaM), Université Paris-Est Marne-la-Vallée (UPEM), ONERA - The French Aerospace Lab [Châtillon], and ONERA-Université Paris Saclay (COmUE)
- Subjects
Dynamic substructuring ,Mathematical optimization ,model uncertainties ,uncertainty quantification ,random uncertainties ,02 engineering and technology ,01 natural sciences ,0203 mechanical engineering ,[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,Applied mathematics ,dynamic substructuring ,0101 mathematics ,Uncertainty quantification ,Mathematics ,Parametric statistics ,[PHYS.MECA.VIBR]Physics [physics]/Mechanics [physics]/Vibrations [physics.class-ph] ,Stochastic process ,Mechanical Engineering ,Nonparametric statistics ,random matrix ,Statistical model ,[SPI.MECA]Engineering Sciences [physics]/Mechanics [physics.med-ph] ,structural dynamics ,modeling errors ,Semiparametric model ,010101 applied mathematics ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,020303 mechanical engineering & transports ,Mechanics of Materials ,random vibrations ,nonparametric probabilistic method ,Random matrix - Abstract
International audience; This paper presents a new approach, called a nonparametric approach, for constructing a model of random uncertainties in dynamic substructuring in order to predict the matrix-valued frequency response functions of complex structures. Such an approach allows nonhomogeneous uncertainties to be modeled with the nonparametric approach. The Craig-Bampton dynamic substructuring method is used. For each substructure, a nonparametric model of random uncertainties is introduced. This nonparametric model does not require identifying uncertain parameters in the reduced matrix model of each substructure as is usually done for the parametric approach. This nonparametric model of random uncertainties is based on the use of a probability model for symmetric positive-definite real random matrices using the entropy optimization principle. The theory and a numerical example are presented in the context of the finite-element method. The numerical results obtained show the efficiency of the model proposed.
- Published
- 2003
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