MOLECULAR DYNAMICS STUDIES OF SIMPLE MODEL FLUIDS AND WATER CONFINED IN CARBON NANOTUBE

Molecular Dynamics (MD) simulation is one of the most important computational techniques with broad applications in physics, chemistry, chemical engineering, materials design and biological science. Traditional computational chemistry refers to quantum calculations based on solving Schrodinger equations. Later developed Density Functional Theory (DFT) based on solving Kohn-Sham equations became the more popular ab initio calculation technique which could deal with ~1000 atoms by explicitly considering electron interactions. In contrast, MD simulation based on solving classical mechanics equations of motion is a totally different technique in the field of computational chemistry. Electron interactions were implicitly included in the empirical atom-based potential functions and the system size to be investigated can be extended to ~106 atoms. The thermodynamic properties of model fluids are mainly determined by macroscopic quantities, like temperature, pressure, density. The quantum effects on thermodynamic properties like melting point, surface tension are not dominant. In this work, we mainly investigated the melting point, surface tension (liquid-vapor and liquid-solid) of model fluids including Lennard-Jones model, Stockmayer model and a couple of water models (TIP4P/Ew, TIP5P/Ew) by means of MD simulation. In addition, some new structures of water confined in carbon nanotube were discovered and transport behaviors of water and ions through nano-channels were also revealed.

On Modular Design of Field Robotic Systems

Robots are needed to perform important field tasks such as hazardous material clean-up, nuclear site inspection, and space exploration. Unfortunately their use is not widespread due to their long development times and high costs. To make them practical, a modular design approach is proposed. Prefabricated modules are rapidly assembled to give a low-cost system for a specific task. This paper described the modular design problem for field robots and the application of a hierarchical selection process to solve this problem. Theoretical analysis and an example case study are presented. The theoretical analysis of the modular design problem revealed the large size of the search space. It showed the advantages of approaching the design on various levels. The hierarchical selection process applies physical rules to reduce the search space to a computationally feasible size and a genetic algorithm performs the final search in a greatly reduced space. This process is based on the observation that simple physically based rules can eliminate large sections of the design space to greatly simplify the search. The design process is applied to a duct inspection task. Five candidate robots were developed. Two of these robots are evaluated using detailed physical simulation. It is shown that the more obvious solution is not able to complete the task, while the non-obvious asymmetric design develop by the process is successful.