Model identification in computational stochastic dynamics using experimental modal data

Abstract : This paper deals with the identification of a stochastic computational model using experimental eigenfrequencies and mode shapes. In presence of randomness, it is difficult to construct a one-to-one correspondence between the results provided by the stochastic computational model and the experimental data because of the random modes crossing and veering phenomena that may occurs from one realization to another one. In this paper, this correspondence is constructed by introducing an adapted transformation for the computed modal quantities. Then the transformed computed modal quantities can be compared with the experimental data in order to identify the parameters of the stochastic computational model. The methodology is applied to a booster pump of thermal units for which experimental modal data have been measured on several sites.
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Submitted on : Monday, May 12, 2014 - 10:44:28 AM
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Anas Batou, Christian Soize, S. Audebert. Model identification in computational stochastic dynamics using experimental modal data. Mechanical Systems and Signal Processing, Elsevier, 2014, 50-51 (-), pp.307-322. ⟨10.1016/j.ymssp.2014.05.010⟩. ⟨hal-00989208⟩

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