Data-driven inverse uncertainty quantification in the transverse tensile response of carbon fiber reinforced composites

Abstract Uncertainty quantification is critical for the full exploitation of composite materials’ potential. Inverse methods offer the possibility of indirectly characterizing the uncertainty of microscopic parameters by employing data sets from standard structural tests in higher scales. Two crucial requirements though, are the efficient modeling especially for the nonlinear prediction, and the measurement error availability from the tests which affects the updated scatter. This study employs effective stiffness and strength experimental data in order to quantify uncertainties of a carbon fiber UD composite in the microscale. A polynomial chaos surrogate model is trained from finite element simulations, able to efficiently predict the homogenized stiffness and strength for the uncertainty quantification procedure. The random parameters which are influential enough to be updated, are identified via a variance-based global sensitivity analysis. The inverse problem is solved with the Bayesian inference method, which updates any prior estimation of the probability models of the input parameters, based on output observations from the tests. Results show significant uncertainty reduction in comparison with typically used variance values in the literature and can be used to enrich the composite material databases. The proposed methodology is applied for the transverse tensile load case, although its non-intrusive nature allows applications for more load cases and various setups.