Xie Luyin¹, Guan Zhengrong², Tian Hongliang³, Guan Pengfei³, Liu Xuan¹

1.Power China Northwest Engineering Corporation Limited; 2.School of Mechanical Engineering, Xi'an Shiyou University; 3.PowerChina Geothermal Development Co.,Ltd.

Abstract：

Fiber-reinforced thermal insulation composites represent a critical class of materials for extreme environment applications, where optimal balance between thermal performance and mechanical integrity is paramount. This comprehensive review examines the transformative role of Computational Fluid Dynamics (CFD) in advancing the analysis, design, and optimization of these multifunctional composites. Through multiscale modeling frameworks that integrate representative volume elements (RVEs), finite element method (FEM) simulations, and data-driven approaches, researchers can now effectively unravel the complex thermo-mechanical interactions within these heterogeneous materials. The paper highlights how CFD-driven methodologies have enabled the synergistic optimization of otherwise competing properties—thermal conductivity, elastic modulus, and thermal expansion coefficient—leading to groundbreaking material configurations with tailored performance characteristics. Recent advances in machine learning integration, particularly artificial neural network (ANN) surrogates combined with genetic algorithms, have dramatically accelerated the discovery of optimal microstructural parameters. The review also explores applications across aerospace, energy, and electronic systems, while addressing persistent challenges and future directions in this rapidly evolving field. 

Key Words：

Computational Fluid Dynamics (CFD); fiber-reinforced thermal insulation composites; mechanical analysis