Data-Driven Constitutive Modeling via Conjugate Pairs and Response Functions
Abstract
1. Introduction
2. Kinematics
3. Constitutive Equations
3.1. Constitutive Equation via Invariants of the Right Cauchy–Green Deformation Tensor
3.2. Constitutive Equation via Laplace Stretch
3.3. Constitutive Equation via Conjugate Stress/Strain Base Pairs
4. Independence of Conjugate Stress/Strain Base Pairs in Virtual Experiments
4.1. Homogeneous Deformation
4.2. Membrane Inflation
5. Conclusions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Kinematics of a Thin Membrane
Appendix B. QR-Decomposition of Surface Deformation Gradient
References
- Holzapfel, G.A. Biomechanics of soft tissue. In The Handbook of Materials Behavior Models; Academic Press: San Diego, CA, USA, 2001; Volume 3, pp. 1049–1063. [Google Scholar]
- Payan, Y.; Ohayon, J. Biomechanics of Living Organs: Hyperelastic Constitutive Laws for Finite Element Modeling; Academic Press: San Diego, CA, USA, 2017. [Google Scholar]
- Ogden, R.W.; Saccom, I.G.; Sgura, I. Fitting hyperelastic models to experimental data. Comput. Mech. 2004, 34, 484–502. [Google Scholar] [CrossRef]
- Guan, D.; Ahmad, F.; Theobald, P.; Soe, S.; Luo, X.; Gao, H. On the AIC-based model reduction for the general Holzapfel–Ogden myocardial constitutive law. Biomech. Model. Mechanobiol. 2019, 18, 1213–1232. [Google Scholar] [CrossRef] [PubMed]
- Rivlin, R.S. Large elastic deformations of isotropic materials: IV. Further developments of the general theory. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Sci. 1948, 241, 379–397. [Google Scholar]
- Criscione, J.C.; Humphrey, J.D.; Douglas, A.S.; Hunter, W.C. An invariant basis for natural strain which yields orthogonal stress response terms in isotropic hyperelasticity. J. Mech. Phys. Solids 2000, 48, 2445–2465. [Google Scholar] [CrossRef]
- Criscione, J.C. Rivlin’s representation formula is ill-conceived for the determination of response functions via biaxial testing. In The Rational Spirit in Modern Continuum Mechanics; Springer: Dordrecht, The Netherlands, 2004; pp. 197–215. [Google Scholar]
- Srinivasa, A. On the use of the upper triangular (or QR) decomposition for developing constitutive equations for Green-elastic materials. Int. J. Eng. Sci. 2012, 60, 1–12. [Google Scholar] [CrossRef]
- Freed, A.D.; Zamani, S.; Szabó, L.; Clayton, J.D. Laplace stretch: Eulerian and Lagrangian formulations. Z. Angew. Math. Phys. 2020, 71, 1–18. [Google Scholar] [CrossRef]
- Salamatova, V.Y.; Liogky, A.A. Hyperelastic membrane modelling based on data-driven constitutive relations. Russ. J. Numer. Anal. Math. Model. 2020, 35, 163–173. [Google Scholar] [CrossRef]
- Freed, A.; Erel, V.; Moreno, M. Conjugate stress/strain base pairs for planar analysis of biological tissues. J. Mech. Mater. Struct. 2016, 12, 219–247. [Google Scholar] [CrossRef]
- Freed, A. A note on stress/strain conjugate pairs: Explicit and implicit theories of thermoelasticity for anisotropic materials. Int. J. Eng. Sci. 2017, 120, 155–171. [Google Scholar] [CrossRef]
- Erel, V.; Freed, A.D. Stress/strain basis pairs for anisotropic materials. Compos. Part B Eng. 2017, 120, 152–158. [Google Scholar] [CrossRef]
- Erel, V.; Jiang, M.; Moreno, M.R.; Freed, A.D. Anisotropic conjugate stress/strain base pair approach for laminates undergoing large deformations. Materialia 2019, 6, 100318. [Google Scholar] [CrossRef]
- Zamani, S.; Paul, S.; Kotiya, A.A.; Criscione, J.C.; Freed, A.D. Application of QR framework in modeling the constitutive behavior of porcine coronary sinus tissue. Mech. Soft Mater. 2021, 3, 7. [Google Scholar] [CrossRef]
- Paul, S.; Freed, A.D.; Szabó, L. On the use of QR kinematics in studying the Eshelby energy–momentum tensor. Int. J. Solids Struct. 2022, 254, 111854. [Google Scholar] [CrossRef]
- Lu, J.; Zhou, X.; Raghavan, M.L. Inverse method of stress analysis for cerebral aneurysms. Biomech. Model. Mechanobiol. 2008, 7, 477–486. [Google Scholar] [CrossRef]
- Roohbakhshan, F.; Duong, T.X.; Sauer, R.A. A projection method to extract biological membrane models from 3D material models. J. Mech. Behav. Biomed. Mater. 2016, 58, 90–104. [Google Scholar] [CrossRef] [PubMed]
- Kalina, K.A.; Linden, L.; Brummund, J.; Metsch, P.; Kästner, M. Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks. Comput. Mech. 2022, 69, 213–232. [Google Scholar] [CrossRef]
- Linka, K.; Hillgärtner, M.; Abdolazizi, K.P.; Aydin, R.C.; Itskov, M.; Cyron, C.J. Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning. J. Comput. Phys. 2021, 429, 110010. [Google Scholar] [CrossRef]
- Holzapfel, G.A. Nonlinear Solid Mechanics: A Continuum Approach for Engineering Science; John Wiley & Sons: Chichester, UK, 2000. [Google Scholar]
- Tepole, A.B.; Kabaria, H.; Bletzinger, K.U.; Kuhl, E. Isogeometric Kirchhoff–Love shell formulations for biological membranes. Comput. Methods Appl. Mech. Eng. 2015, 293, 328–347. [Google Scholar] [CrossRef] [PubMed]
- Budday, S.; Sommer, G.; Birkl, C.; Langkammer, C.; Haybaeck, J.; Kohnert, J.; Bauer, M.; Paulsen, F.; Steinmann, P.; Kuhl, E.; et al. Mechanical characterization of human brain tissue. Acta Biomater. 2017, 48, 319–340. [Google Scholar] [CrossRef] [PubMed]
- Salamatova, V.Y.; Liogky, A.A. Method of Hyperelastic Nodal Forces for Deformation of Nonlinear Membranes. Differ. Equ. 2020, 56, 950–958. [Google Scholar] [CrossRef]
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Salamatova, V. Data-Driven Constitutive Modeling via Conjugate Pairs and Response Functions. Mathematics 2022, 10, 4447. https://doi.org/10.3390/math10234447
Salamatova V. Data-Driven Constitutive Modeling via Conjugate Pairs and Response Functions. Mathematics. 2022; 10(23):4447. https://doi.org/10.3390/math10234447
Chicago/Turabian StyleSalamatova, Victoria. 2022. "Data-Driven Constitutive Modeling via Conjugate Pairs and Response Functions" Mathematics 10, no. 23: 4447. https://doi.org/10.3390/math10234447
APA StyleSalamatova, V. (2022). Data-Driven Constitutive Modeling via Conjugate Pairs and Response Functions. Mathematics, 10(23), 4447. https://doi.org/10.3390/math10234447