000013215 001__ 13215
000013215 005__ 20161114160332.0
000013215 04107 $$aeng
000013215 046__ $$k2009-06-22
000013215 100__ $$aGiagopoulos, D.
000013215 24500 $$aFinite element model updating of an experimental vehicle model using measured modal characteristics

000013215 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013215 260__ $$bNational Technical University of Athens, 2009
000013215 506__ $$arestricted
000013215 520__ $$2eng$$aMethods for modal identification and structural model updating are employed to develop high fidelity finite element models of an experimental vehicle model using acceleration measurements. The identification of modal characteristics of the vehicle is based on acceleration time histories obtained from impulse hammer tests. An available modal identification software is used to obtain the modal characteristics from the analysis of the various sets of vibration measurements. A high modal density modal model is obtained. The modal characteristics are then used to update an increasingly complex set of finite element models of the vehicle. A multi-objective structural identification method is used for estimating the parameters of the finite element structural models based on minimizing the modal residuals. The method results in multiple Pareto optimal structural models that are consistent with the measured modal data and the modal residuals used to measure the discrepancies between the measured modal values and the modal values predicted by the model. Single objective structural identification methods are also evaluated as special cases of the proposed multiobjective identification method. The multi-objective framework and the corresponding computational tools provide the whole spectrum of optimal models and can thus be viewed as a generalization of the available conventional methods. The results indicate that there is wide variety of Pareto optimal structural models that trade off the fit in various measured quantities. These Pareto optimal models are due to uncertainties arising from model and measurement errors. The size of the observed variations depends on the information contained in the measured data, as well as the size of model and measurement errors. The effectiveness of the updated models and the predictive capabilities of the Pareto vehicle models are assessed.

000013215 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013215 653__ $$aModal Identification, Model Updating, Structural Identification, Multi-Objective Optimization, Structural Dynamics. Abstract. Methods for modal identification and structural model updating are employed to develop high fidelity finite element models of an experimental vehicle model using acceleration measurements. The identification of modal characteristics of the vehicle is based on acceleration time histories obtained from impulse hammer tests. An available modal identification software is used to obtain the modal characteristics from the analysis of the various sets of vibration measurements. A high modal density modal model is obtained. The modal characteristics are then used to update an increasingly complex set of finite element models of the vehicle. A multi-objective structural identification method is used for estimating the parameters of the finite element structural models based on minimizing the modal residuals. The method results in multiple Pareto optimal structural models that are consistent with the measured modal data and the modal residuals used to measure the discrepancies between the measured modal values and the modal values predicted by the model. Single objective structural identification methods are also evaluated as special cases of the proposed multiobjective identification method. The multi-objective framework and the corresponding computational tools provide the whole spectrum of optimal models and can thus be viewed as a generalization of the available conventional methods. The results indicate that there is wide variety of Pareto optimal structural models that trade off the fit in various measured quantities. These Pareto optimal models are due to uncertainties arising from model and measurement errors. The size of the observed variations depends on the information contained in the measured data, as well as the size of model and measurement errors. The effectiveness of the updated models and the predictive capabilities of the Pareto vehicle models are assessed.

000013215 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013215 720__ $$aGiagopoulos, D.$$iNtotsios, E.$$iPapadimitriou, C.$$iNatsiavas, S.
000013215 8560_ $$ffischerc@itam.cas.cz
000013215 8564_ $$s659818$$uhttps://invenio.itam.cas.cz/record/13215/files/CD300.pdf$$yOriginal version of the author's contribution as presented on CD, section: Identification methods in structural dynamics - iii - (MS).
000013215 962__ $$r13074
000013215 980__ $$aPAPER