The non-affine fiber network solver: A multiscale fiber network material model for finite-element analysis

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Multiscale mechanical models in biomaterials research have largely relied on simplifying the microstructure in order to make large-scale simulations tractable. The microscale simplifications often rely on approximations of the constituent distributions and assumptions on the deformation of the constituents. Of particular interest in biomechanics are fiber embedded materials, where simplified fiber distributions and assumed affinity in the fiber deformation greatly influence the mechanical behavior. The consequences of these assumptions are problematic when dealing with microscale mechanical phenomena such as cellular mechanotransduction in growth and remodeling, and fiber-level failure events during tissue failure. In this work, we propose a technique for coupling non-affine network models to finite element solvers, allowing for simulation of discrete microstructural phenomena within macroscopically complex geometries. The developed plugin is readily available as an open-source library for use with the bio-focused finite element software FEBio, and the description of the implementation allows for the adaptation to other finite element solvers.

Original languageEnglish (US)
Article number105967
JournalJournal of the Mechanical Behavior of Biomedical Materials
Volume144
DOIs
StatePublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Fiber network composites
  • Finite-element constitutive model
  • Multiscale computational biomechanics

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't

Fingerprint

Dive into the research topics of 'The non-affine fiber network solver: A multiscale fiber network material model for finite-element analysis'. Together they form a unique fingerprint.

Cite this