Stochastic modelling of 3D fiber structures imaged with X-ray microtomography

Research output: Contribution to journalArticle

Abstract

Many products incorporate into their design fibrous material with particular levels of permeability as a way to control the retention and flow of liquid. The production and experimental testing of these materials can be expensive and time consuming, particularly if it needs to be optimised to a desired level of absorbency. We consider a parametric virtual fiber model as a replacement for the real material to facilitate studying the relationship between structure and properties in a cheaper and more convenient manner. 3D image data sets of a sample fibrous material are obtained using X-ray microtomography and the individual fibers isolated. The segmented fibers are used to estimate the parameters of a 3D stochastic model for generating softcore virtual fiber structures. We use several spatial measures to show the consistency between the real and virtual structures, and demonstrate with lattice Boltzmann simulations that our virtual structure has good agreement with respect to the permeability of the physical material.

Details

Authors
  • Philip Townsend
  • Emanuel Larsson
  • Tomas Karlson
  • Stephen A. Hall
  • Malin Lundman
  • Per Bergström
  • Charlotta Hanson
  • Niklas Lorén
  • Tobias Gebäck
  • Aila Särkkä
  • Magnus Röding
Organisations
External organisations
  • Essity Hygiene and Health AB
  • Research Institutes of Sweden (RISE)
  • Chalmers University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Paper, Pulp and Fiber Technology

Keywords

  • Fiber structures, Mass transport, Parameter estimation, Permeability, Stochastic modelling, X-ray microtomography
Original languageEnglish
Article number110433
JournalComputational Materials Science
Volume194
Publication statusPublished - 2021 Jun 15
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

Funding Information: This work was financially supported by Vinnova (project number 2018–00424) as part of the CoSiMa project, as well as by the Swedish Research Council (VR 2018-03986) and by the Swedish Foundation for Strategic Research (SSF AM13-0066). The authors would like to thank Claudia Redenbach and Katja Schladitz of the Technical University of Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics for their hospitality and informative discussions during a research visit to Kaiserslautern. We are also very grateful for the valuable comments given by the anonymous reviewers. Funding Information: This work was financially supported by Vinnova (project number 2018?00424) as part of the CoSiMa project, as well as by the Swedish Research Council (VR 2018-03986) and by the Swedish Foundation for Strategic Research (SSF AM13-0066). The authors would like to thank Claudia Redenbach and Katja Schladitz of the Technical University of Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics for their hospitality and informative discussions during a research visit to Kaiserslautern. We are also very grateful for the valuable comments given by the anonymous reviewers. Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.