Abstract
The manufacturing of fiber composite materials involves a set of complex, interconnected processes that span across multiple physics and scales. The characterization of uncertainty in composite manufacturing predictions is a challenging task that involves high-dimensional, multiscale, multiphysics stochastic models. We demonstrate the use of a basis adaptation scheme within a polynomial chaos representation that permits the incorporation of a large number of stochastic variables in the analysis. We use the proposed PCE-based workflow to analyze the interplay of uncertainty through all the fiber composite manufacturing stages that comprise the Resin Transfer Molding (RTM) process. The proposed framework is centered on an integrated assessment of uncertainty in composite structures using probabilistic surrogate models for predefined QoI.
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