Stochastic modelling of 3D fiber structures imaged with X-ray microtomographyShow others and affiliations
2021 (English)In: Computational materials science, ISSN 0927-0256, E-ISSN 1879-0801, Vol. 194, article id 110433Article in journal (Refereed) Published
Abstract [en]
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. © 2021 The Author(s)
Place, publisher, year, edition, pages
Elsevier B.V. , 2021. Vol. 194, article id 110433
Keywords [en]
Fiber structures, Mass transport, Parameter estimation, Permeability, Stochastic modelling, X-ray microtomography, Fibers, Mass transfer, Mechanical permeability, Product design, Stochastic models, Tomography, 3D fibers, Fibre structure, Fibrous material, Parameters estimation, Production testing, Stochastic-modeling, Virtual Fiber, Virtual structures, X ray microtomography, Stochastic systems
National Category
Paper, Pulp and Fiber Technology
Identifiers
URN: urn:nbn:se:ri:diva-52961DOI: 10.1016/j.commatsci.2021.110433Scopus ID: 2-s2.0-85103693432OAI: oai:DiVA.org:ri-52961DiVA, id: diva2:1548341
Note
Funding details: Technische Universität Kaiserslautern, TU KL; Funding details: Stiftelsen för Strategisk Forskning, SSF, SSF AM13-0066; Funding details: VINNOVA, 2018–00424; Funding details: Vetenskapsrådet, VR, VR 2018-03986; Funding text 1: 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 text 2: 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.
2021-04-302021-04-302025-09-23Bibliographically approved