Hybrid Machine learning based Scale Bridging Framework for Permeability Prediction of Fibrous Structures

Abstract

This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach evaluates the efficiency and accuracy of different scale-bridging methodologies combining traditional surrogate models and even integrating physics-informed neural networks (PINNs) with numerical solvers, enabling accurate permeability predictions across micro- and mesoscales. Four methodologies were evaluated: fully resolved models (FRM), numerical upscaling method (NUM), scale-bridging method using data-driven machine learning methods (SBM) and a hybrid dual-scale solver incorporating PINNs. The FRM provides the highest fidelity model by fully resolving the micro- and mesoscale structural geometries, but requires high computational effort. NUM is a fully numerical dual-scale approach that considers uniform microscale permeability but neglects the microscale structural variability. The SBM accounts for the variability through a segment-wise assigned microscale permeability, which is determined using the data-driven ML method. This method shows no significant improvements over NUM with roughly the same computational efficiency and modeling runtimes of 45 min per simulation. The newly developed hybrid dual-scale solver incorporating PINNs shows the potential to overcome the problem of data scarcity of the data-driven surrogate approaches, as well as incorporating data from both scales via the hybrid loss function. The hybrid framework advances permeability modeling by balancing computational cost and prediction reliability, laying the foundation for further applications in fibrous composite manufacturing, while its full potential awaits realization as physics-informed machine learning approaches continue to mature