Stochastic model updating and validation of the GARTEUR structure based on random sampling and distance discrimination

A stochastic model updating (SMU) method using distance discrimination analysis and random sampling technique is proposed and subsequently applied to the updating process of the GARTEUR benchmark structure. In contrast to the traditional deterministic model updating procedure in which parameters are calibrated by sensitivity and optimization analysis, the proposed SMU method takes into consideration uncertainties which are general in the modeling as well as test processes. Uncertainty propagation is performed by Monte Carlo sampling method in which a large scale stochastic sampling process is proposed to describe uncertainties from parameters to features. Distance discrimination analysis is presented to quantify the degree of similarity and dissimilarity between analytical and test data. Input parameters are calibrated to the test data through an iterative procedure integrating the above uncertainty propagation and quantification methods. In order to reduce calculation cost, a metamodel is constructed using radial basis function with an acceptable precision. The relative PCL program of MSC.Patran is employed to submit multiple finite element (FE) analyses and to extract information for subsequent analysis. An application is performed on the GARTEUR structure and the updating results are assessed by the widely accepted 3-steps validation criteria. The updating and validation results show the proposed SMU method is valid and effective in engineering application.