Research

An offshore nuclear power plant is a small-modularized reactor (SMR) located on a floating platform at sea. 

Advantages:

• Safety
  Located offshore, floating nuclear power plants are less likely to be affected by earthquakes, tsunamis, and volcanoes. 

• Mobility
  Floating nuclear power plants can be moved to provide power to remote areas. 

• Rapid deployment
  Floating nuclear power plants can be quickly deployed to meet energy needs. 

• Clean electricity
  Floating nuclear power plants can provide clean electricity for local grids. 

Challenges: 

• Environmental impact
  Environmental groups are concerned that floating nuclear power plants may threaten marine habitats.

• Safety
  Some are concerned that floating nuclear power plants are more likely to have accidents than onshore power plants.

Computational structural dynamics is a field within engineering that utilizes computer simulations to analyze the behavior of structures under dynamic loading conditions, such as vibrations, impacts, or sudden changes in force, by modeling the structure's response to these forces using numerical methods like finite element analysis (FEA), allowing engineers to predict how a structure will react under various dynamic scenarios and optimize its design for safety and performance. 

• Dynamic loading:
  Unlike static structural analysis, which considers constant loads, computational structural dynamics focuses on analyzing structures subjected to changing loads over time, like wind gusts, earthquakes, or vehicle collisions. 

• Finite element modeling:
  Structures are typically divided into smaller elements (mesh) to represent their geometry and material properties, allowing for complex shapes and loading conditions to be analyzed. 

• Modal analysis:
  This technique is used to determine the natural frequencies and mode shapes of a structure, which are critical for understanding its dynamic behavior. 

• Time integration methods:
  Numerical techniques like Newmark-beta or Houbolt methods are used to solve the equations of motion and calculate the structural response over time. 

• Nonlinear behavior:
  Advanced computational structural dynamics can also account for non-linear material properties, geometric nonlinearities, and contact interactions, allowing for more realistic simulations of complex scenarios. 

Mechanobiology modeling refers to the use of computational (continuum) models to study how cells and tissues respond to mechanical forces, essentially simulating biological processes like growth, remodeling, and adaptation within a mechanical environment, allowing researchers to understand and predict tissue behavior under different conditions  with the goal of designing better regenerative therapies or explaining disease mechanisms. 

• Integrates mechanics and biology:
  These models combine principles of biomechanics (like stress and strain) with biological processes (like cell signaling and gene expression) to understand how mechanical forces influence cellular behavior. 

• Different scales:
  Models can be developed at various scales, from the molecular level (interactions between proteins) to the tissue level (whole organ behavior) depending on the research question. 

• Applications:

  • • Understanding tissue development: Studying how mechanical forces influence organ development and tissue patterning.

    • Designing biomaterials: Optimizing the mechanical properties of scaffolds for tissue engineering. 

    • Disease modeling: Investigating how mechanical forces contribute to disease progression, like cancer metastasis or osteoarthritis. 

Our work involves employing mathematical modeling, simulations, data analysis, and algorithm development to understand normal and disease physiological processes at the tissue and organ scales. This includes development of novel simulation-based methods for diagnosis and treatment and medical device development to improve healthcare outcomes. 

The study of how soft materials deform and respond to applied forces, characterized by their ability to undergo large deformations under moderate loads, exhibiting highly nonlinear stress-strain behavior, and often displaying complex mechanical properties like elasticity, plasticity, and viscosity, unlike traditional rigid materials; this field is particularly relevant in areas like biomaterials, soft robotics, and stretchable electronics due to the unique behaviors of soft materials like gels, polymers, and biological tissues under deformation. 

• Large deformations:
  Soft materials can experience significant changes in shape under relatively small forces, unlike hard materials which deform only slightly. 

• Nonlinear behavior:
  The relationship between stress (applied force) and strain (deformation) in soft materials is often non-linear, meaning the deformation is not proportional to the applied force. 

• Viscoelasticity:
  Many soft materials exhibit both elastic (reversible) and viscous (irreversible) properties, leading to complex behaviors like creep and stress relaxation. 

• Microstructure dependence:
  The mechanical properties of soft materials are heavily influenced by their internal microstructure, such as the network of polymer chains or the arrangement of fibers. 

• Instabilities:
  Soft materials can exhibit various instabilities like buckling, wrinkling, and creasing under certain loading conditions. 

Phase-field fracture modeling is a computational technique used to simulate crack initiation, propagation, and branching in solids without explicitly tracking the crack surface. Instead of using discrete discontinuities, like sharp cracks in classical fracture mechanics, the phase-field fracture modeling introduces a smooth scalar field (called the phase field) to represent the transition between the intact and fully broken states of solids and structures.