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<p>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.</p>

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Predicting Air Flow in Calendered Paper Sheets from μ-CT Data: Combining Physics with Morphology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Phillip Gräfensteiner,
  • Andoni Rodriguez,
  • Peter Leitl,
  • Ekaterina Baikova,
  • Maximilian Fuchs,
  • Eduardo Machado Charry,
  • Ulrich Hirn,
  • André Hilger,
  • Ingo Manke,
  • Robert Schennach,
  • Matthias Neumann,
  • Volker Schmidt,
  • Karin Zojer

摘要

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.