A Framework for Design Allowables Accounting for Paucity of Data and Errors in Complex Models

Abstract

This work introduces a novel framework for the prediction of design allowables of composite laminates with reduced experimental cost. Building on high-fidelity simulations, polynomial chaos expansions (PCE) are first used to build probabilistic models for the material properties (input parameters). The coefficients of the models are themselves randomized by perturbing them with new stochastic degrees of freedom, thus accounting for uncertainty on the uncertainty and generating a family of distributions for these parameters. High-fidelity simulations are used to generate samples of the quantity of interest (QoI) which is then represented by a new PCE in terms of the previously described stochastic degrees of freedom. With this construction, the distribution of the QoI can be cheaply estimated and updated with experimental observations. The framework is applied to a hybrid carbon-glass fiber composite laminate under 3-point bending. We demonstrate how the updated distribution of the QoI can be used to predict the A-basis design allowable.