To prepare Carbon/Carbon (C/C) composites with advanced performance, the thermal gradient chemical vapor infiltration (TCVI) process has been optimized by simulation. A 2D axisymmetric unstable model was built, which ...To prepare Carbon/Carbon (C/C) composites with advanced performance, the thermal gradient chemical vapor infiltration (TCVI) process has been optimized by simulation. A 2D axisymmetric unstable model was built, which included convection, conduction, diffusion, densification reactions in the pores and the evolution of the porous medium. The multi-physical field coupling model was solved by finite element method (FEM) and iterative calculation. The time evolution of the fluid, temperature and preform density field were obtained by the calculation. It is indicated that convection strongly affects the temperature field. For the preform of carbon/carbon composites infiltrated for 100 h by TCVI, the radial average densities from simulation agrees well with those from experiment. The model is validated to be reliable and the simulation has capability of forecasting the process.展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 90716024)
文摘To prepare Carbon/Carbon (C/C) composites with advanced performance, the thermal gradient chemical vapor infiltration (TCVI) process has been optimized by simulation. A 2D axisymmetric unstable model was built, which included convection, conduction, diffusion, densification reactions in the pores and the evolution of the porous medium. The multi-physical field coupling model was solved by finite element method (FEM) and iterative calculation. The time evolution of the fluid, temperature and preform density field were obtained by the calculation. It is indicated that convection strongly affects the temperature field. For the preform of carbon/carbon composites infiltrated for 100 h by TCVI, the radial average densities from simulation agrees well with those from experiment. The model is validated to be reliable and the simulation has capability of forecasting the process.
