Application of mesh generation technology in bone mechanics research

Yanan Qin1

1Binzhou University, Binzhou, China

1Corresponding author

Journal of Complexity in Health Sciences, Vol. 2, Issue 1, 2019, p. 23-28. https://doi.org/10.21595/chs.2019.20498
Received 3 January 2019; received in revised form 11 April 2019; accepted 30 April 2019; published 30 June 2019

Copyright © 2019 Yanan Qin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract.

High quality mesh is the basis of bone mechanics research and the precondition to ensure the accuracy of FEA (Finite Element Analysis) calculation. In order to obtain high accuracy and low distortion mesh of bone, the CFD (Computational Fluid Dynamics) meshing method is applied in this paper. The main process of bone finite element model construction consists of three parts, including CT (Computed Tomography) imaging, three-dimensional reverse modeling and meshing. By mesh optimization and boundary conditions imposition, the stress distribution of the occipital-atlantoaxial complex model and the relative motion angle between the Occipital Atlas and Atlantoaxial are obtained. According to the rotation angle checks, it is known that all the simulation results are within the range of cadaveric test data. The mesh generation technology provides a good idea and method for the study of bone mechanics.

Graphical Abstract

Highlights
  • CFD (Computational Fluid Dynamics) meshing method is applied in the process of bone finite element model construction.
  • Stress distribution of the occipital-atlantoaxial complex model and the relative motion angle between the Occipital Atlas and Atlantoaxial are obtained.
  • High quality mesh is provided for bone mechanics research and the precondition to ensure the accuracy of FEA (Finite Element Analysis) calculation.

Keywords: mesh, bone mechanics, FEA, stress, CFD.

Acknowledgements

The paper is supported by the Youth Talent Innovation Project (BZXYQNLG201703).

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