Article

A Statistical Approach for the Fusion of Data and Finite Element Analysis in Vibroacoustics (vor Ort)

* Presenting author
Day / Time: 24.03.2022, 10:20-10:40
Room: 7-01.
Typ: Regulärer Vortrag
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Abstract:

A problem with the finite element method (FEM) is that often it is difficult to exactly match the given measurement data of a real system. Therefore, in this paper, the statFEM, i.e. a statistical FEM is adopted to fuse sensor data with an FE model of a 2D vibroacoustics problem. The air in a cavity is excited on the boundary of the domain and the boundary function is considered as random. The resulting FEM solution is considered as a Bayesian prior and the uncertainty is quantified in form of a Gaussian Process (GP). By conditioning the prior on given data, which are created synthetically, it is shown that a posterior GP can be efficiently obtained, characterizing the behavior of the acoustic field in the domain. Thereby, the data are decomposed into three parts: FE Model, misspecification, and noise. The misspecification term is modeled as a GP, as well, whose hyperparameters are learned from the given data.By adding more sensor locations and observations, the posterior variance in the whole domain can be decreased. The dependence of the posterior variance on the positioning of the sensors is investigated and the benefits and limits of the method are explained.

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