Constitutive modeling of sustainable materials enhanced by data-driven approaches / vorgelegt von Birte Boes. Wuppertal, 2026
Inhalt
- Introduction
- Motivation
- State-of-the-art and research relevant questions
- Anisotropic modeling of paper and paperboard
- Modeling approaches for tofu
- Machine learning in constitutive modeling
- Results and discussion of the research contribution
- Outline of the dissertation
- Article 1: A novel continuum mechanical framework for decoupled material behavior in thickness and in-plane directions
- Abstract
- Introduction
- Elastic material behavior
- Inelastic material formulation
- Co-rotated intermediate configuration
- Structural tensors in different configurations
- Derivation based on the Clausius-Duhem inequality
- Evolution equations
- Numerical Implementation
- Choice of Helmholtz free energy
- Remarks on the formulation of Helmholtz free energy functions
- Elastic material formulation - isoplanar-area-changing split
- Elastic material formulation - isochoric-volumetric split
- Inelastic material formulation
- Numerical Examples
- Single-element tests for the elastic material behavior
- Cube under compression with elastic material behavior
- Single-element tests for the inelastic material behavior
- Pinched cylinder with inelastic material behavior
- Symmetrically notched specimen with inelastic material behavior
- Conclusion
- Appendix
- The modified right Cauchy-Green tensor in the isochoric-volumetric framework
- Proof of decoupled formulation of second Piola-Kirchhoff stress formulation
- Rates of tensors
- Derivative of the Helmholtz free energy
- Comparison of the Kirchhoff stress and its conjugate force
- Calculation of invariants in different configurations
- Partial Derivatives
- Article 2: A constitutive model for describing decoupled material behavior in thickness and in-plane directions
- Abstract
- Introduction
- Decoupling of in-plane and out-of-plane material behavior
- Inelastic material formulation
- Numerical Examples
- Conclusion
- Article 3: Accounting for plasticity: An extension of inelastic constitutive artificial neural networks
- Abstract
- Introduction
- Constitutive Modeling
- Network Formulation and Architecture
- Algorithmic treatment
- Recurrent neural network architecture
- Feed-forward neural network architecture
- Automated model discovery
- Specification of network architecture
- Preprocessing
- Discovering artificially generated data
- Comparison to other neural network approaches
- Experimental data
- Discussion and limitations
- Conclusion
- Appendix
- Article 4: The mechanics and physics of tofu: understanding hydrated soft solids through feature networks
- Abstract
- Introduction
- Mechanical testing
- Physics-based model
- Neural network model
- Results
- Discussion
- Appendix
- Conclusions and Outlook
- Appendix
- List of Figures
- List of Tables
- Bibliography