Skip to Main content Skip to Navigation
Journal articles

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.
Complete list of metadata

Cited literature [50 references]  Display  Hide  Download
Contributor : Christian Soize Connect in order to contact the contributor
Submitted on : Monday, May 12, 2014 - 10:44:28 AM
Last modification on : Saturday, January 15, 2022 - 4:12:38 AM
Long-term archiving on: : Tuesday, August 12, 2014 - 11:00:34 AM


Files produced by the author(s)




Anas Batou, Christian Soize, S. Audebert. Model identification in computational stochastic dynamics using experimental modal data. Mechanical Systems and Signal Processing, Elsevier, 2015, 50-51 (-), pp.307-322. ⟨10.1016/j.ymssp.2014.05.010⟩. ⟨hal-00989208⟩



Record views


Files downloads