Optimizing Thermal-Elastic Properties of C/C-SiC Composites Using a Hybrid Approach and PSO Algorithm

Area of Science:

• Materials Science
• Aerospace Engineering
• Computational Materials Science

Background:

• Carbon fiber-reinforced multi-layered pyrocarbon-silicon carbide (C/C-SiC) composites are crucial for aerospace applications.
• The complex microstructure and heterogeneity of C/C-SiC composites present challenges in property tailoring.
• Understanding structure-property relationships is key to optimizing composite performance.

Purpose of the Study:

• To optimize the thermal-elastic properties of unidirectional C/C-SiC composites.
• To investigate the impact of multi-layered matrix thicknesses on composite properties.
• To achieve optimal microstructures for enhanced performance in aerospace components.

Main Methods:

• A hybrid approach combining micromechanical modeling and back propagation (BP) neural networks was developed.
• The hybrid model was used to predict the thermal-elastic properties of C/C-SiC composites.
• A particle swarm optimization (PSO) algorithm was interfaced with the hybrid model for design optimization.

Main Results:

• The proposed hybrid model effectively predicts the thermal-elastic properties of C/C-SiC composites.
• The PSO algorithm successfully optimized the composite microstructure.
• The optimization minimized the coefficient of thermal expansion (CTE) while constraining the elastic modulus.

Conclusions:

• The developed hybrid micromechanical modeling and BP neural network approach is effective for predicting C/C-SiC composite properties.
• The PSO algorithm integrated with the hybrid model provides an efficient method for optimizing composite microstructures.
• This study demonstrates a pathway to tailor C/C-SiC composites for improved thermal-elastic performance in aerospace applications.