Abstract
This paper presents a data-driven solver (DDS) for the growth-induced deformation problems of soft materials. Contrary to the over-reliance of traditional numerical methods on material constitutive models, the DDS requires only a discrete material dataset of stress–strain pairs to describe the stress–strain relationship. With the multiplicative decomposition of the gradient tensor, the growth effects are introduced. The growth-induced deformation problems are then solved as the pre-strain problems. To overcome the difficulty in determining the growth-induced pre-stress without an explicit constitutive model, a strain-driven searching scheme is thus proposed. Several numerical examples demonstrate the robustness and its good performance of the proposed DDS. In particular, the reason for choosing the strain-driven searching scheme is also illustrated according to the numerical example results.
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The supports from the National Key R&D Program of China (No. 2022YFB4201200) and the National Natural Science Foundation of China (Nos. 12072062, 12072061 and 11972108) are gratefully acknowledged.
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Appendix 1 The strain–displacement matrix in the nonlinear elasticity
Appendix 1 The strain–displacement matrix in the nonlinear elasticity
In the nonlinear elasticity, the strain–displacement matrix concludes linear and nonlinear parts. The strain–displacement matrix for the 2D plane-strain problems have been formulated in the work of Nguyen [38]. In this work, the strain–displacement matrix for the case of 3D continuum body is further rpovided. Discretizing the 3D continuum body with the eight-node brick elements, and the displacement \(\mathbf{u}={\left\{\begin{array}{ccc}{u}_{x}& {u}_{y}& {u}_{z}\end{array}\right\}}^{\mathrm{T}}\) of any point in element \(e\) can be expressed in the interpolation form of node displacement \({\mathbf{u}}^{e}={\left\{\begin{array}{ccc}\begin{array}{ccc}{{u}_{x}^{e}}_{1}& {{u}_{y}^{e}}_{1}& {{u}_{z}^{e}}_{1}\end{array}& \cdots & \begin{array}{ccc}{{u}_{x}^{e}}_{8}& {{u}_{y}^{e}}_{8}& {{u}_{y}^{e}}_{8}\end{array}\end{array}\right\}}^{\mathrm{T}}\) as
Writing the Green–Lagrange strain tensor \(\mathbf{E}\) in the Voigt representation (the vector dimension is \(6\times 1\)), and the components of \(\mathbf{E}\) can be expressed as
in which the \({\mathbf{Q}}_{n}\) and \({\mathbf{H}}_{n}\) \((n=1,\cdots ,6)\) are the combinatorial operations of the directional derivatives of shape functions. \({\mathbf{Q}}_{n}\) can be written as
in which,
Then, the linear part of the strain–displacement matrix can be expressed as.
As for the nonlinear part, \({\mathbf{H}}_{n}\) can be written as
where
Thus, the nonlinear part of the strain–displacement matrix can be expressed as
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Zheng, Z., Qiu, Y., Ye, H. et al. Data-driven computational method for growth-induced deformation problems of soft materials. Acta Mech 235, 441–466 (2024). https://doi.org/10.1007/s00707-023-03742-9
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DOI: https://doi.org/10.1007/s00707-023-03742-9