Numerical Multiphysics CFD Modelling of Porosity Evolution in Thermoset Prepreg Microstructures

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🔬研究者:For researchers in composites manufacturing, this provides a novel numerical tool for void evolution prediction in realistic microstructures.

🏢実務担当者:Manufacturing engineers in aerospace can use the model to optimize cure cycles and reduce porosity, improving production efficiency and material performance.

Carbon Fiber Reinforced Polymers (CFRPs) are essential to the aerospace industry, offering superior strength-to-weight ratios. Currently, the manufacturing of primary structures via standard autoclave curing is a robust, mastered process that successfully minimizes defects, keeping porosity levels below critical thresholds (typically < 1 %). Consequently, porosity is generally not considered as an issue in standard, optimized production lines.However, this stability may be affected by emerging industrial paradigms aimed at increasing production rates and reducing costs. The shift toward accelerated manufacturing – characterized by rapid heating rates, shortened cure cycles and by new manufacturing processes – and the introduction of complex material architectures risk re-introducing significant porosity. In parallel, there is currently no numerical model capable of accurately predicting porosity formation and evolution under these complex conditions. Existing simulation approaches are typically macroscopic and rely on homogenized porous media assumptions, failing to capture the essential micro-scale interactions between bubbles and fibres.To address this gap, this study presents an extended, custom multi-physics Computational Fluid Dynamics (CFD) solver built upon an existing OpenFOAM framework. The goal is to provide the first predictive tool for void evolution within realistic microstructures. The numerical framework couples a two-phase compressible flow model with the complete thermo-chemo-rheological physics of thermoset curing.The solver is applied to 2D Representative Volume Elements (RVEs) of a prepreg ply. Simulations of a standard autoclave cycle demonstrated the solver's ability to capture micro-scale dynamics, showing how voids are compressed and transported during the resin viscosity drop before being frozen at gelation. A parametric study comparing 3-bars and 7-bars pressures confirmed the model's physical ability in predicting void volume reduction.While currently focused on mechanical compression, the tool is designed to support the development of future manufacturing cycles. Future work will incorporate moisture diffusion physics and includes experimental validation via X-ray micro-tomography and in-situ synchrotron monitoring.

• semanticscholar https://doi.org/10.4028/p-5relxgfirst seen 2026-05-06 00:09:42