https://hal.archives-ouvertes.fr/hal-01158275Batou, AnasAnasBatouMSME - Laboratoire de Modélisation et Simulation Multi Echelle - UPEM - Université Paris-Est Marne-la-Vallée - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - CNRS - Centre National de la Recherche ScientifiqueSoize, ChristianChristianSoizeMSME - Laboratoire de Modélisation et Simulation Multi Echelle - UPEM - Université Paris-Est Marne-la-Vallée - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - CNRS - Centre National de la Recherche ScientifiqueAudebert, S.S.AudebertEDF R&D AMA - Analyses Mécaniques et Acoustiques - EDF R&D - EDF R&D - EDF - EDFExperimental identification of a stochastic computational dynamical model using modal data measured for a family of built-up structuresHAL CCSD2015Structural dynamicsModel identificationComputational stochastic dynamicsMode crossingExperimental modal analysisUncertainty Quantification[SPI.MECA] Engineering Sciences [physics]/Mechanics [physics.med-ph][MATH.MATH-PR] Mathematics [math]/Probability [math.PR][MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]Soize, Christian2015-05-30 20:29:022022-09-29 14:21:152015-06-01 09:28:36enConference papersapplication/pdf1This research is focused on the construction and the identification of a stochastic computational model (SCM), using experimental eigenfrequencies and mode shapes measured for a family of real structures exhibiting slight differences that induce variability in the measured quantities. The statistical properties of the SCM are controlled by a set of hyperparameters such as mean values, coefficients of variation, etc. The hyperparameters are identified using the first experimental natural frequencies, and the associated experimental mass-normalized mode shapes measured for the family of real structures. The methodology proposed introduces a random transformation of the computational modal quantities (computational eigenfrequencies and computational mode shapes) in order to make them almost surely in correspondence with the experimental modal data of each measured real structure. Thus this methodology automatically takes into account the mode crossings and mode veerings which can take place between the experimental configurations and the computational realizations of the SCM. Then the hyperparameters are identified using the maximum likelihood method. The proposed methodology is applied to a booster pump of thermal units for which experimental modal data have been measured on several sites.