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Biomechanics and Modeling in Mechanobiology

, Volume 4, Issue 2–3, pp 190–199 | Cite as

Finite element implementation of a generalized Fung-elastic constitutive model for planar soft tissues

  • Wei Sun
  • Michael S. SacksEmail author
Original Paper

Abstract

Numerical simulations of the anisotropic mechanical properties of soft tissues and tissue-derived biomaterials using accurate constitutive models remain an important and challenging research area in biomechanics. While most constitutive modeling efforts have focused on the characterization of experimental data, only limited studies are available on the feasibility of utilizing those models in complex computational applications. An example is the widely utilized exponential constitutive model proposed by Fung. Although present in the biomechanics literature for several decades, implementation of this model into finite element (FE) simulations has been limited. A major reason for limited numerical implementations are problems associated with inherent numerical instability and convergence. To address this issue, we developed and applied two restrictions for a generalized Fung-elastic constitutive model necessary to achieve numerical stability. These are (1) convexity of the strain energy function, and (2) the condition number of material stiffness matrix set lower than a prescribed value. These constraints were implemented in the nonlinear regression used for constitutive model parameter estimation to the experimental biaxial mechanical data. We then implemented the generalized Fung-elastic model into a commercial FE code (ABAQUS, Pawtucket, RI, USA). Single element and multi-element planar biaxial test simulations were conducted to verify the accuracy and robustness of the implementation. Results indicated that numerical convergence and accurate FE implementation were consistently obtained. The present study thus presents an integrated framework for accurate and robust implementation of pseudo-elastic constitutive models for planar soft tissues. Moreover, since our approach is formulated within a general FE code, it can be straightforwardly adopted across multiple software platforms.

Keywords

Constitutive Model Elasticity Tensor Strain Energy Function Finite Element Implementation Fung Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Funding for this work was provided by NSF Grant BES-9978858, NIH Grant R01-HL071814-01A1 and by Edwards Lifesciences, Inc. MSS is an Established Investigator of the American Heart Association. The authors would also like to thank Dr. Michael J. Scott for his practical insights into the present work.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Engineered Tissue Mechanics Laboratory, Department of Bioengineering, McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghUSA

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