Chen, Hailong


Peridynamics is a nonlocal reformulation of classical continuum mechanics theory, which allows spatial discontinuity, such as cracks, to exist in the solution domain. Governed by the integro (spatial)-differential (temporal) equations, material points interact with neighboring material points within finite distance. Among three different types of peridynamic models, the correspondence material model has the greatest potential in modeling arbitrary materials failure within the peridynamics framework. However, there are some practical issues prevent from utilizing the correspondence model, mainly the formulation instability. The current research focus is to develop validated stabilization scheme and apply the developed model to study thermomechanical failure of various nuclear fuel types.


Collaborators (non-UK)

Dr. Spencer, Idaho National Laboratory

Dr. Unal, Los Alamos National Laboratory


Student

WaiLam Chan (PhD candidate)

Jiayi Li

Donglai Liu

Di Liu, Added 01/10/2022

WaiLam Chan, Graduate, Added on MCC resources on 09/01/2022 


Software

MOOSE framework (we are the developers for the peridynamics module)


Lattice particle method for mesoscale materials failure modeling and simulation

The lattice particle method is a nonlocal meshfree method targeted at modeling the material failure at mesoscale. It directly uses the material pixel/voxel (for isotropic material) and underlying lattice structure (for polycrystalline material) data in the modeling process. Hence, the problematic mesh generation process in mesh-based methods is avoided in lattice particle method. The current research is to develop advanced modeling capabilities of lattice particle method, including fatigue, crystal plasticity, and multiscale modeling with higher and lower scale models.


Collaborators (non-UK)

Prof. Yongming Liu (Arizona State University)

Prof. Yang Jiao (Arizona State University)


Student

Md Mehedi Hasan (PhD student)


Software

In-house codes developed using FORTRAN and C++


Grants:


Publications:


Patents:


Center for Computational Sciences