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Andy Vanaerschot Department Department of Mechanical Engineering PhD defence 7 November 2014 Supervisor Prof. dr. ir. Dirk Vandepitte Co-supervisor Prof. dr. ir. Stepan Lomov Funding IWT - Vlaanderen E-mail [email protected] Multi-scale modelling of spatial variability in textile composites Uncertainty quantification based on experimental data of internal geometry Introduction / Objective The advantages of using composites for design, manufacturing and during operation are well known. Though, the introduction of composites is hampered by the relatively high cost of raw material and the uncertain quality of highperformance composite structures. An improved assessment of the quality of any composite part is achieved by identifying the irregularity in the tow reinforcement. Once the composite microstructure is identified, a reliable computational model can be established that forms a direct link between the irregularities in the reinforcement structure and the stochastic material properties. Research Methodology This dissertation provides a generic multi-scale framework for generating realistic virtual textile specimens. It is a first step towards a systematic modelling approach for textile composites where powerful simulation procedures are applied in combination with experimental data, both on the short-range (meso-scale) and long-range (macro-scale). Three main steps can be distinguished: Collection of experimental data and statistical analysis Stochastic multi-scale modelling of the reinforcement Construction of virtual specimens in the WiseTex software Results & Conclusions This framework is demonstrated for a carbon-epoxy 2/2 twill woven composite, produced by RTM, with main conclusions: Substantial differences in warp and weft direction are quantified from experiments, attributed to the manufacturing process. Stochastic multi-scale modelling of the reinforcement. The Monte-Carlo Markov Chain method is appropriate to simulate auto-correlated (along the tow) tow path parameters. Series Expansion techniques are applied to generate auto- & cross-correlated (between tows) tow path parameters. Experimental and simulated trends have good correspondence for all tow properties, and target statistics (standard deviation and correlation length) are reproduced with high accuracy. Short-range experimental data acquired from micro-CT scan. Major publication Virtual specimen of 10 by 10 unit cells. The long-range trend of the in-plane tow centrelines, as well as the shortrange variations are reproduced. A. Vanaerschot, B.N. Cox, S.V. Lomov, D. Vandepitte (2013). Stochastic Framework for quantifying the geometrical variability of laminated textile composites using micro-computed tomography. Composites Part A, 44, 122-131.