PermXCT: A novel framework for imaging-based virtual permeability prediction

PermXCT is an open-source computational framework designed to predict virtual permeability in fiber-reinforced polymer composites based on data extracted from X-ray computed tomography (XCT). It provides an automated and reproducible workflow that connects imaging based geometry extraction, mesh generation, and numerical flow simulation for permeability estimation. The framework integrates both mesoscale and microscale morphological characteristics, such as intra and inter-yarn porosity and fiber orientation, to capture realistic flow pathways within complex composite geometries. PermXCT utilises a combination of established open-source tools, including DREAM3D for mesh creation, OpenFOAM for fluid flow simulation, and Python and MATLAB for data processing and automation. Computational efficiency is achieved through optimized meshing strategies and domain scaling, enabling large XCT datasets to be analyzed with reduced computational cost. Validation against experimental permeability measurements demonstrates strong agreement, confirming the reliability and physical accuracy of the imaging based predictions. By minimizing uncertainties and repeatability issues associated with experimental permeability testing, PermXCT provides a robust foundation for XCT-informed virtual permeability characterization.