Abstract
The Nano-powder Infiltration and Transient Eutectic (NITE) process is a promising method for fabricating Fully ceramic microencapsulated (FCM) fuels at relatively low temperatures. However, when fabricating large-scale samples with high volume fractions of fuel particles, low-density or defective fuels are sometimes obtained. The same issue arises when it comes to ceramic particle-ceramic matrix fuel. To address this issue, it is crucial to properly control the relative density and internal stress of these fuels during the sintering process. This study presents a model that uses a unified approach to describe compaction and sintering simultaneously, and the model is successfully adjusted by experimental data using neural network genetic algorithms. The findings provide valuable insights into the fabrication of FCM fuels and the ceramic particle-ceramic matrix fuels. The proposed method can guide future research and development efforts in this area.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
