Calibration and Validation of Multiscale Model for Ultimate Strength Prediction of Composite Laminates Under Uncertainty

Abstract

A methodology to account for the effect of epistemic uncertainty (regarding model parameters) on the strength prediction of carbon fiber reinforced polymer (CFRP) composite laminates is presented. A three-dimensional concurrent multiscale physics modeling framework is considered. A continuum damage mechanics-based constitutive relation is used for multiscale analysis. The parameters for the constitutive model are unknown and need to be calibrated. A least squares-based approach is employed for the calibration of model parameters and a model discrepancy term. The calibrated constitutive model is validated quantitatively using experimental data for both unnotched and open-hole specimens with different composite layups. The quantitative validation results are used to indicate further steps for model improvement.

A comparative study on stochastic multiscale analysis of composite laminates

“…To further evaluate the effectiveness of each approach, this work needs to be performed for laminates of different configuration, backed up by experimental data. It is to be noted that most of the studies regarding uncertainty quantification of mechanical analysis of CFRP composites involve either a forward problem or an inverse problem with assumption of deterministic elastic constituent parameters (as in [15]). To properly quantify epistemic uncertainty research needs to performed by model parameter calibration with the assumption of input dependent discrepancy term.…”

“…The prediction on the structural performance unidirectional laminates due to the microscale spatial variability have also been studied [11,12]. The progress on the uncertainty analysis of the composites has been reported by researchers [13,14,15,16]. The second approach considers manufacturing defects to model the material variability.…”

Meshfree simulation of progressive damage in composite laminates using discrete element analysis

“…Therefore, future research should incorporate the variations of model input parameters based on the given spread of properties obtained from experimental measurements. [61][62][63] Limitation II -Computational cost…”