Page Comparison

...

Hui Yang

I am a postdoctoral fellow in the Department of Mechanical Engineering at Tufts University, working with Dean Qu of the School of Engineering. My research interest lies in developing and applying theoretical and computational models to study the mechanical behaviors of nanostructured materials across different length and time scales, with the combination of in-situ experimental characterizations. Particular emphasis is placed on translating atomistic insights into the coarse-grained continuum models to understand material behaviors and processes at the macroscopic level. The ultimate goal of my research is to guide experiments and rational designs of new materials with improved performance and reliability through predictive numerical modeling. My current research primarily involves chemo-mechanical modelings of lithium-ion battery materials for energy storage applications. My investigations on a broad range of high-capacity energy materials have provided us the opportunities to obtain atomistic understanding of degradation mechanisms in nanoelectrodes, which will pave the way toward the development of failure-resistant high-performance electrodes for the next-generation rechargeable batteries. The HPC cluster at Tufts provides a reliable computational platform that enables me to run multiple jobs across different length scales simultaneously, i.e., the molecular dynamic simulation at the atomic level, phase-field simulation at microscale, and finite element modeling at macroscale. With the HPC cluster, I can run the computationally expensive simulations with multiple CPUs, which are totally impossible for me to run on my office desktop, and thus the efficiency of my simulation work is highly promoted. In addition, with the various installed software and compilers on the cluster, I can also run some in-house codes and user subroutines (i.e., in C or Fortran) to meet my research requirements.  Finally, the service and support from the HPC center has been very helpful for software installation, debugging.

Eliyar Asgarieh 

 My Civil Eng.  Ph.D research identifies robust models for real-world civil structures, which heavily relied upon optimization and deterministic/stochastic simulations. In the initial stages of my Ph.D studies, I needed to apply various optimization methods using MATLAB toolboxes, which also required running a structural analysis software, OpenSees, for structural simulation. Each optimization could take more than three days, and hundreds of models needed to be designed and optimized (calibrated). Tufts HPC Cluster helped me to run many optimizations/simulations simultaneously, which would never be possible on regular desktops. The final part of my research was on identifying probabilistic models of structures in Bayesian framework using an advanced version of Markov chain Monte Carlo (MCMC) method called TMCMC (transitional MCMC). Each probabilistic model identification case required submission of 1000 jobs in several consecutive steps, which would add up to approximately 20000 to 30000 jobs in each case! To be computationally feasible the jobs needed to be run in parallel in each step. The cluster made  the impossible possible for me, and I could run millions of jobs to finish my research on probabilistic model identification.      

...