Abstract
The main purpose of this thesis is to set up a multi-scale simulation method which stems from computational quantum mechanics (CQM) to molecular dynamics (MD) to calculate material properties and ionic transport phenomenon. Furthermore, it is integrated with the computational fluid dynamics (CFD) technique to evaluate heat/mass transfer and electrochemical performance of thermal batteries. The simulation results have been compared with experimental data from non-confidential literatures. This is for verifying the correctness and accuracy of the simulation results. The next objective is to establish a multi-scale simulator to design the future novel thermal battery for defense purpose. Thermal batteries are also named as thermally activated batteries, which employ eutectic salts as their electrolytes, so they are also called molten-salt batteries. At first, we construct a nano-scale model of binary molten-salt electrolytes to perform computational quantum mechanics (CQM) calculations. Secondly, we use molecular dynamics (MD) technique to calculate the ionic conductivity, thermal conductivity, specific heat, and melting point of the material. The CFD technique is then employed to predict the temperature distribution and concentration field in a macro-scale model. Finally, the heat transfer and thermo-electrochemical performance prediction of this battery is carried out. In addition, we also use this package to do the failure analysis (due to thermal runaway and short circuit) of various designs of thermal batteries. A novel thermal battery, which employs LiCl-LiBr-based ternary and quaternary systems, has been designed using this multiscale simulation package. The results reveal that both the ternary and quaternary materials will enhance the battery performance, also the quaternary ones can reduce the operating temperature that implies quicker start up and longer working life. The multiscale simulation technique provides a low cost alternative to expensive experiments and is able to optimize the battery design under realistic operating conditions for future R&D.