Deep learning based prediction of fibrous microstructure permeability
B. Caglar (TU Delft - Aerospace Manufacturing Technologies)
Guillaume Broggi (École Polytechnique Fédérale de Lausanne)
Muhammad A. Ali (Khalifa University)
L. Orgéas (Université Grenoble Alpes)
V. Michaud (École Polytechnique Fédérale de Lausanne)
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Abstract
Knowledge of permeability of fibrous microstructures is crucial for predicting the mold fill times and resin flow path in composite manufacturing. Herein we report a method to rapidly predict the permeability of 3D fibrous microstructures. Our method relies on predicting the permeability of 2D cross-sections via deep neural networks and extending this capability to 3D microstructures via circuit analogy as a means of reduced order modeling. Approximately 50% of the permeability predictions of 2D cross-sections have 10% or less deviation from the permeability results obtained via flow simulations in Geodict. Computational time required for predicting the permeability of 3D microstructures is reduced from hours to less than 10 seconds. This framework enables fast and accurate prediction of micro-permeability and serves as the first building block towards prediction of fabric mesostructures’ permeability via deep learning based methods.
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