Exploring Immersed FEM, Material Design, and Biological Tissue Material Modeling

This thesis utilizes the Immersed Interface Finite Element Method (IIFEM) for shape optimization and material design, while also investigating the modeling and parameterization of lung tissue for Diver Underwater Explosion (UNDEX) simulations. In the first part, a shape optimization scheme utilizing a four-noded rectangular immersed-interface element is presented. This method eliminates the need for interface fitted mesh or mesh morphing, reducing computational costs while maintaining solution accuracy. Analytical design sensitivity analysis is performed to obtain gradients for the optimization formulation, and various parametrization techniques are explored. The effectiveness of the approach is demonstrated through verification and case studies. For material design, the study combines topological shape optimization with IIFEM, providing a computational approach for architecting materials with desired effective properties. Numerical homogenization evaluates effective properties, and level set-based topology optimization evolves boundaries within the unit cell to generate optimal periodic microstructures. The design space is parameterized using radial basis functions, facilitating a gradient-based optimization algorithm for optimal coefficients. The method produces geometries with smooth boundaries and distinct interfaces, demonstrated through numerical examples. The thesis then delves into modeling the mechanical response of lung tissues, particularly focusing on hyperelastic and hyperviscoelastic models. The research adopts a phased approach, emphasizing hyperelastic model parametrization while reserving hyperviscoelastic model parametrization for future studies. Alternative methods are used for parametrization, circumventing direct experimental tests on biological materials. Representative material properties are sourced from literature or refit from existing experimental data, incorporating both empirically derived data and practical values suitable for simulations. Damage parameter quantification relies on asserted quantitative relationships between injury levels and the regions or percentages of affected lung tissue.