Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method.
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机译：碳纤维增强的多层焦碳-碳化硅基体（C / C-SiC）复合材料广泛用于航空航天结构。 C / C-SiC复合材料的复杂空间结构和材料异质性对定制其性能提出了挑战。因此，发现性能与微观结构之间的内在联系，并依次优化微观结构以获得性能最佳的复合材料成为实际应用的关键。这项工作的目的是通过控制多层基质厚度来优化单向C / C-SiC复合材料的热弹性性能。提出了一种基于微机械建模和BP神经网络的混合方法来预测复合材料的热弹性。然后，将粒子群优化（PSO）算法与此混合模型相接口，以实现在弹性模量约束下使复合材料的热膨胀系数（CTE）最小的最佳设计。数值算例表明了所提混合模型和优化方法的有效性。
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