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Li Research Group
Mechanics and physics of complex material systems
Computational mechanics for complex material/structure systems in harsh environments
We seek to develop state-of-the-art computational mechanics techniques for modeling the nonlinear behaviors of complex material/structure systems involving multi-body contact, friction, and fracture encountered in harsh environments (e.g., strong excitation in earthquakes, high-pressure compaction in manufacturing, etc.). Granular media is a model example in this regard. These techniques allow us to access every unit's kinetics and kinematics information, which is essential to developing a simple yet effective method for large-scale applications. Current interests lie in incorporating tribological physics and dynamic fracture mechanics into our in-house simulation engine. Tools we use for developments include but are not limited to, variational phase-field approach to fracture, contact mechanics, and CPU/GPU parallel computing. Lastly, we support the constitutive modeling component through our advanced material characterization and testing techniques (see below).
Multi-modal characterization of dynamic brittle fracture in natural and engineering materials
Brittle materials such as ceramic and glass find applications in many industry sectors such as construction, energy, and consumer electronics. Characterizing and understanding these brittle materials' fracture properties is essential for safety-critical applications. However, their high-stiffness and low-toughness properties make investigations using conventional image-based techniques (e.g., Digital Image Correlation) challenging, especially when going beyond quasistatic scenarios. We seek to overcome this challenge by adapting and developing high-speed optical sensing techniques that are compact in space yet effective with high spatial-temporal resolutions. Current research lies in integrating Shack-Hartmann wavefront sensing (SHWFS) with X-ray phase contrast imaging (XPCI) to realize time-resolved in-situ multi-modal fracture characterization for a wide range of brittle materials from glass, ceramics, to polymers. In the long term, we wish to expand our research effort to additively manufactured materials/parts, providing fundamental insights into defect-modulated fracture processes.
Information decomposition and extraction across scales for granular rheology
Heterogeneous granular flows are ubiquitous in nature and industry, from landslides to pharmaceutical engineering, and even additive manufacturing. At the continuum scale, it is well known that they exhibit collisional, dense, and creep regimes, which can coexist in space. How to accurately predict and control such complex phenomena has many applications in both mitigating natural hazards and optimizing industrial processes. However, it still remains a challenge to establish a robust granular rheology model with a unified microphysical foundation, due to the lack of understanding of the internal structure variation across different regimes and its interaction with the boundary. To address this challenge, we combine our in-house simulation engine with knowledge from network theory and machine learning to identify the minimal set of particle-scale ingredients that are most relevant to spatial phase transition observed at the macroscopic scale (TLDR: what to keep and what to throw away?). Our long-term goal is to build a predictive and practical theory with a unified microscopic interpretation.
Granular materials/structures by design for novel engineering applications
Granular material/structure by design, or architected granular material/structure, are promising candidates for potential applications from wearable robotics to impact-resistant structures. Unlike conventional architected materials, architected granular materials introduce an additional dimension of discreteness to the design space. This extra dimension creates great potential for realizing multifunctional materials and structures. Current research interests lie in studying the static and dynamic properties of topologically interlocked granular solids and wave manipulations in mechanically disordered granular crystals. Our research in this direction is mostly computational, taking advantage of our in-house simulation engine; however, we actively collaborate with colleagues from advanced manufacturing and experimental mechanics communities for model validation and research prototyping.
Physics-based design of mesoscale material heterogeneity toward fracture-resistant composites
Brittle solids are often toughened by adding a second-phase material. This practice often results in composites with material heterogeneities on the meso scale: large compared to the scale of the fracture process zone but small compared to that of the application. The specific configuration (both geometrical and mechanical) of this mesoscale heterogeneity is generally recognized as important in determining crack propagation, and, subsequently, the (effective) toughness of the composites. We seek to understand the fundamental physics associated with material heterogeneity-modulated crack propagation in brittle solids. We wish to harness such an understanding to help realize the rational design of functional composites that optimally balances the tradeoff between strength and toughness. In the long term, we also wish to integrate such composite design strategy to assemble in a modular way functional structures that are simultaneously lightweight, reconfigurable, and damage-tolerant. Current research is mostly computational in nature (via phase-field modeling), and we plan to conduct desktop-scale experiments for model validation and prototyping.
Machine learning-aided design of mechanically disordered granular crystals
Granular crystals are popular candidates for designing energy-absorption and protective systems. The nonlinear nature of contacts, together with the wide range of available particle shapes and materials, give rise to a huge design space that has remained largely unexplored. Taking advantage of our in-house simulation engine and through collaborations, we are accelerating the exploration of the design space using graph neural networks. Our current focus is on the quasi-static properties of these novel granular crystals, and we plan to extend to studying their time-dependent (or dynamic) properties in the near future.
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