A deep encoder-decoder for surrogate modelling of liquid moulding of composites. (April 2023) - Fernández-León, J.; Keramati, K.; Miguel, C.; González, C.; Baumela, L.
Abstract: The paper proposes a surrogate model for liquid moulding of structural composites. A methodology is presented to simulate the dual-phase Darcy's flow in a heterogeneous porous medium. The approach is an encoder–decoder that receives as input a matrix of permeabilities and produces two scalar fields that represent the pressure and front flow. This model is trained with synthetic data generated with a computer fluid dynamics simulator. In this context, the lack of robustness of models trained with the popular L 2 and L 1 losses is highlighted and several enhancements to these baseline approaches are introduced. First, the study provides a piece-wise power-logarithmic loss that improves training in the presence of the bimodal distribution of error residuals produced by the dual-phase flow predictions. A non-uniform sampling strategy for the selection of time training snapshots is also included, which contributes to improve the prediction accuracy. The estimation of the front flow field is further refined with a multi-task training strategy. The introduction of these improvements in the baseline models reduce the relative error of the pressure and front flow fields by more than 50%, performing these simulations in a record time of 50 ms. The surrogate model is further evaluated as a digital twin to predict – in a real experiment – the location and spatial extent of race-tracking channels and regions with dissimilar degrees of permeability.