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Numerical simulation data and FORTRAN code to compare the stress response of two transversely isotropic hyperelastic models in ABAQUS.

ABSTRACT: We present the numerical simulation data obtained by implementing a user material subroutine (UMAT) in the finite element commercial package ABAQUS. The simulation data correspond to the stress response of two transversely isotropic hyperelastic models on homogeneous and non-homogeneous deformations. First model is proposed in the co-submitted article [1] and depends on both anisotropic invariants ( I¯4 and I¯5 ) to describe the fiber reinforcement. The second model is the Holzapfel-Gaser-Ogden (HGO) model described in [2], that only depends on the anisotropic invariant I¯4 . Since the first model is not found in the ABAQUS material library, we present a FORTRAN code for a UMAT subroutine. In addition, we introduce in detail the steps to implement the material models using the attached files in ABAQUS.

SUBMITTER: Castillo-Mendez C

PROVIDER: S-EPMC8804180 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Numerical simulation data and FORTRAN code to compare the stress response of two transversely isotropic hyperelastic models in ABAQUS.

Castillo-Méndez Carlos C Ortiz Armando A

Data in brief 20220119

We present the numerical simulation data obtained by implementing a user material subroutine (UMAT) in the finite element commercial package ABAQUS. The simulation data correspond to the stress response of two transversely isotropic hyperelastic models on homogeneous and non-homogeneous deformations. First model is proposed in the co-submitted article [1] and depends on both anisotropic invariants ( I ¯ 4 and I ¯ 5 ) to describe the fiber reinforcement. The second model is the Holzapfel-Gase ...[more]

PMID: 35128007

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Project description:Given the importance of hyperelastic constitutive models in the design of engineering components, researchers have been developing the improved and new constitutive models in search of a more accurate and even universal performance. Here, a modified hyperelastic constitutive model based on the Yeoh model is proposed to improve its prediction performance for multiaxial deformation of hyperelastic polymeric materials while retaining the advantages of the original Yeoh model. The modified constitutive model has one more correction term than the original model. The specific form of the correction term is a composite function based on a power function represented by the principal stretches, which is derived from the corresponding residual strain energy when the Yeoh model predicts the equibiaxial mode of deformation. In addition, a parameter identification method based on the cyclic genetic-pattern search algorithm is introduced to accurately obtain the parameters of the constitutive model. By applying the modified model to the experimental datasets of various rubber or rubber-like materials (including natural unfilled or filled rubber, silicone rubber, extremely soft hydrogel and human brain cortex tissue), it is confirmed that the modified model not only possesses a significantly improved ability to predict multiaxial deformation, but also has a wider range of material applicability. Meanwhile, the advantages of the modified model over most existing models in the literatures are also demonstrated. For example, when characterizing human brain tissue, which is difficult for most existing models in the literature, the modified model has comparable predictive accuracy with the third-order Ogden model, while maintaining convexity in the corresponding deformation domain. Moreover, the effective prediction ability of the modified model for untested equi-biaxial deformation of different materials has also been confirmed using only the data of uniaxial tension and pure shear from various datasets. The effective prediction for the untested equibiaxial deformation makes it more suitable for the practice situation where the equibiaxial deformation of certain polymeric materials is unavailable. Finally, compared with other parameter identification methods, the introduced parameter identification method significantly improves the predicted accuracy of the constitutive models; meanwhile, the uniform convergence of introduced parameter identification method is also better.

Dataset Information

Numerical simulation data and FORTRAN code to compare the stress response of two transversely isotropic hyperelastic models in ABAQUS.

Publications

Numerical simulation data and FORTRAN code to compare the stress response of two transversely isotropic hyperelastic models in ABAQUS.

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