Predictive Modeling of Architected Solids across Scales

Citation

Zhou, Ziran (2025) Predictive Modeling of Architected Solids across Scales. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/cn82-z804. https://resolver.caltech.edu/CaltechTHESIS:02242025-045741904

Abstract

Architected solids, comprising discrete or continuous materials and structures, are purposefully designed to achieve specific functional objectives, such as tailored mechanical properties or enhanced performance. The integration of architectural features and material science has revolutionized design and functionality across multiple length scales. However, experimental exploration of architected solids is often constrained by physical, financial, or technological limitations. To address these challenges, this study leverages computational models as powerful tools for validating and probing the behaviors of architected solids through three distinct case studies spanning different length scales.

The first case study focuses on capturing the seismic performance of multiblock concrete structures at CERN for radiation shielding. The Level Set Discrete Element Method (LS-DEM), combined with Monte Carlo sampling of material properties, is employed to benchmark the displacement profiles of four concrete configurations against experimental data. In the second case study, a bonded LS-DEM model is utilized to investigate the bending response of a woven topological interlocking material (TIM). After validation against experimental results, the model is employed to explore how friction and contact area influence the bending resistance of the TIM system. The third case study introduces a 3D translational tensegrity structure modeled using the Finite Element Method (FEM). This model captures the deformation responses of single cells, monolayers, and multicellular spheroids under various loading conditions. Additionally, a data-driven (DD) framework with multiscale analysis is implemented, offering accurate results with enhanced computational efficiency. Through these three case studies, this research illustrates the evolution of computational models from tools for validating known behaviors to frameworks for exploring new phenomena.