Predicting air flow in calendered paper sheets from $\mu$-CT data: combining physics with morphology

TL;DR

This paper combines physics-based simulations with morphological descriptors from micro-CT data to accurately predict air flow in porous paper sheets, addressing the challenge of structural heterogeneity.

Contribution

It introduces an integrated approach that enhances pore network model predictions by combining geometric descriptors from tomographic data.

Findings

Improved prediction accuracy of air flow using combined descriptors.

Identification of key morphological features influencing flow.

Demonstration of the non-redundancy of correlated descriptors.

Abstract

Predicting the macroscopic properties of thin fiber-based porous materials from their microscopic morphology remains challenging because of the structural heterogeneity of these materials. In this study, computational fluid dynamics simulations were performed to compute volume air flow based on tomographic image data of uncompressed and compressed paper sheets. To reduce computational demands, a pore network model was employed, allowing volume air flow to be approximated with less computational effort. To improve prediction accuracy, geometric descriptors of the pore space, such as porosity, surface area, median pore radius, and geodesic tortuosity, were combined with predictions of the pore network model. This integrated approach significantly improves the predictive power of the pore network model and indicates which aspects of the pore space morphology are not accurately represented within the pore network model. In particular, we illustrate that a high correlation among descriptors does not necessarily imply redundancy in a combined prediction.