Global sensitivity analysis in the identification of cohesive models using full-field kinematic data
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Mechanical Engineering Program
Composite and Heterogeneous Material Analysis and Simulation Laboratory (COHMAS)
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AbstractFailure of adhesive bonded structures often occurs concurrent with the formation of a non-negligible fracture process zone in front of a macroscopic crack. For this reason, the analysis of damage and fracture is effectively carried out using the cohesive zone model (CZM). The crucial aspect of the CZM approach is the precise determination of the traction-separation relation. Yet it is usually determined empirically, by using calibration procedures combining experimental data, such as load-displacement or crack length data, with finite element simulation of fracture. Thanks to the recent progress in image processing, and the availability of low-cost CCD cameras, it is nowadays relatively easy to access surface displacements across the fracture process zone using for instance Digital Image Correlation (DIC). The rich information provided by correlation techniques prompted the development of versatile inverse parameter identification procedures combining finite element (FE) simulations and full field kinematic data. The focus of the present paper is to assess the effectiveness of these methods in the identification of cohesive zone models. In particular, the analysis is developed in the framework of the variance based global sensitivity analysis. The sensitivity of kinematic data to the sought cohesive properties is explored through the computation of the so-called Sobol sensitivity indexes. The results show that the global sensitivity analysis can help to ascertain the most influential cohesive parameters which need to be incorporated in the identification process. In addition, it is shown that suitable displacement sampling in time and space can lead to optimized measurements for identification purposes.
SponsorsThe authors wish to thank King Abdullah University of Science and Technology (KAUST) for supporting this research. M.A. gratefully acknowledges the financial support from University of Calabria (ex MURST 60%), and the support received from University of Illinois during his visit at the Department of Civil and Environmental Engineering in 2013.