Network model for predicting structural properties of paper
Artikel i vetenskaplig tidskrift, 2022

Paper simulations that resolve the entire microscopic fiber structure are typically time-consuming and require extensive resources. Several such modeling approaches have been proposed to analyze different properties in paper. However, most use non-linear and time-dependent models resulting in high computational complexity. Resolving these computational issues would increase its usefulness in industrial applications. The model proposed in this work was developed in collaboration with companies in the papermaking industry within the Innovative Simulation of Paper (ISOP) project. A linear network model is used for efficiency, where 1-D beams represent the fibers. Similar models have been proposed in the past. However, in this work, the paper models are three-dimensional, a new dynamic bonding technique is used, and more extensive simulations are evaluated. The model is used to simulate tensile stiffness, tensile strength, and bending resistance. These simulated results are compared to experimental and theoretical counterparts and produce representable results for realistic parameters. Moreover, an off-the-shelf computer accessible to a paper developer can evaluate these models structural properties efficiently.

network model

tensile strength

tensile stiffness

bending resistance

paper simulation

Författare

Morgan Görtz

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Gustav Kettil

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Axel Målqvist

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Mats Fredlund

Stora Enso AB

Kenneth Wester

Albany International

Fredrik Edelvik

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Nordic Pulp and Paper Research Journal

0283-2631 (ISSN) 2000-0669 (eISSN)

Vol. 37 4 712-724

Ämneskategorier

Teknisk mekanik

Reglerteknik

Datavetenskap (datalogi)

DOI

10.1515/npprj-2021-0079

Mer information

Senast uppdaterat

2024-06-28