研究目的
To study the densification behavior of silicon carbide matrix composites in isothermal chemical vapor infiltration by establishing mathematical models for numerical simulation, aiming to optimize process conditions and reduce processing time without compromising material quality.
研究成果
The mathematical models effectively describe the densification behavior, showing that processing time can be reduced by 40-50% with an optimized temperature scheme (950-1000°C for first 70 hours, then 1100°C) without compromising density. Key findings include the first-order reaction nature under low temperature and pressure, and the significant impact of structural parameters like fiber radius and preform thickness on densification uniformity.
研究不足
The models assume uniform pore distribution, neglect heat from chemical reactions, and treat gases as ideal. The empirical deposition rate models may not fully capture all reaction mechanisms, and the specific surface area effects on deposition rate could lead to inaccuracies. The study is limited to numerical simulation without physical experiments.
1：Experimental Design and Method Selection:
The study involved establishing mathematical models on two scales (preform and reactor) for numerical simulation of the ICVI process, using finite element method. Theoretical models included continuity equations, dusty-gas model for mass transfer, and empirical models for deposition rate.
2：Sample Selection and Data Sources:
Preforms of continuous fiber reinforced silicon carbide ceramic matrix composites (CMC-SiC) were used, with structural parameters such as fiber radius and porosity. Experimental data from Sheldon and Besmann (1991) were referenced for validation.
3：List of Experimental Equipment and Materials:
No specific equipment or materials are listed; the focus is on numerical simulation using mathematical models.
4：Experimental Procedures and Operational Workflow:
The process involved simulating densification under various temperature and pressure conditions, comparing results with experimental data, and analyzing the influence of process parameters.
5：Data Analysis Methods:
Numerical results were compared with experimental data to validate models, and statistical differences were calculated (e.g., within about 15% error).
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