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Mentors/Projects


Prof. H. Ron Riggs
Krystian Pacskowski

Prof. Riggs and his graduate student, Krystian Pacskowski, will guide two students in research involving the effects of tsunamis on coastal structures and debris.  Go to the projects from last summer to see what their students projects.  The work will likely be a continuation of that work, using Fluent.


Prof. Marcelo Kobayashi

The students working under the guidance of Prof. Kobayashi will work on one of his biologically-inspired modelling techniques, tbd.  The projects will be chosen according to the skills and interests of the students.   Last year's projects included Hydrodynamics of  Copepods, Molecular Dynamics Simulations of Tsunami impacting a Shipping Container, and Modeling a microchannel Heat Sink with Bifurcating Geometry.


Prof. Weilin Qu

The two students working with Prof. Qu will continue the research from last year on heat transfer in thin-pin heat exchangers.  There is both an experimental and numerical component.  Review their work.


Prof. Ian Robertson
Dr. Gaur Johnson

The students mentored by Prof. Robertson and Dr. Johnson will study the effects of tsunamis on coastal structures, both through experiment and numerically.  The focus is on verification and validation of the numerical models.  They will continue the work performed by the interns last summer. 


Prof. Yi Zuo
Title: Molecular Dynamics Simulations of Biomembranes
2 Interns

Molecular dynamics (MD) is a form of computer simulation in which atoms and molecules are allowed to interact for a period of time by approximations of known physical laws [1]. Using MD simulations, we hope to understand the assemblies of molecules in terms of their molecular structure and intermolecular interactions. Therefore, MD simulations act as a bridge between microscopic length and time scales and the macroscopic world of the laboratory: we provide a guess at the interactions between molecules, and obtain "exact" predictions of bulk properties. The predictions are "exact" in the sense that they can be made as accurate as we like, subject to the limitations imposed by our computer budget. In some sense, MD is a simulation approach alternative to the Monte Carlo (MC) method. The obvious advantage of MD over MC is that it gives a route to dynamical properties of the system: transport coefficients, time-dependent responses to perturbations, rheological properties and spectra[1].


Molecular dynamic simulations of biomembranes are of great interest because they can yield molecular-level insight into the structure and dynamics of these systems on a resolution and time-scale that may not be feasible experimentally [2]. Given the rapid development of bionanotechnology, MD simulations of biomembranes are of not only biophysical interests but also biomedical and biotechnological importance. The right figure shows an example of MD simulations of permeation of fullerene through a lipid membrane [3]. The top row demonstrates monomeric fullerene permeation takes place on nanosecond time scale. Fullerene is shown in red, the lipids in cyan with blue head groups, and water is shown in yellow. Second and third rows demonstrate penetration of a cluster of ten fullerenes in to the lipid bilayer, taking place on a time scale that is about one order of magnitude slower compared to the single-fullerene permeation. Water is not shown for clarity. These MD simulations provide important implications for the study of nanoparticle-based drug delivery and the potential cytotoxicity of nanoparticles.

 

References:

1. M.P. Allen, Introduction to Molecular Dynamics Simulation, http://www.fz-juelich.de/nic-series/volume23.

2. H.L. Scott, Modeling the lipid component of membranes, Current Opinion in Structural Biology 12 (2002) 495.

3. J. Wong-ekkabut, S. Baoukina, W. Triampo, I. M. Tang, D. P. Tieleman and L. Monticelli, Computer simulation study of fullerene translocation through lipid membranes, Nat. Nanotechnol. 3 (2008) 363.