Prediction method of anisotropic thermal conductivity of fiber-reinforced composites based on microstructure image recognition

Abstract

The invention discloses a method for pre-estimating the anisotropic heat conduction coefficient of a fiber toughening composite material on basis of microstructural image recognition. After the method for pre-estimating the anisotropic heat conduction coefficient of the fiber toughening composite material on basis of microstructural image recognition is adopted, in terms of internal inhomogeneous and anisotropic practical distribution law of the fiber toughening composite material, the defect that it is assumed that fibers are arranged periodically in the material when the heat conduction coefficient of the composite material is predicted by means of a Rayleigh and other theoretical models, an asymptotic expansion homogenization method and unit cell direct numerical simulation can be overcome, influences of distribution randomness of the fibers in the composite material are fully considered and are led to pre-estimation of the heat conduction coefficient, and pre-estimation precision of the heat conduction coefficient of an authentic material is improved.

Description

[0001] Technical field: [0002] The invention relates to a method for estimating the anisotropic thermal conductivity of fiber toughened composite materials based on microstructure image recognition, which belongs to the technical field of engineering thermophysics. [0003] Background technique: [0004] With the development of industrial technology, various composite materials have been widely used in various industrial fields, especially in the field of aerospace. Taking ceramic matrix composites as an example, as a non-metallic material, compared with commonly used metal materials and polymer materials, it has excellent properties such as high temperature resistance, wear resistance, and corrosion resistance, so it has attracted more and more attention. . However, limited by its brittleness, ceramic materials lack sufficient strength and reliability when used on stressed structural components such as engine turbines, so they cannot be directly applied. At present, it has...

AI Technical Summary

Problems solved by technology

However, in actual situations, the arrangement of fibers in composite materials often does not have strict periodicity, but presents disorderly and random randomness, which makes the existing methods poor in applicability, and it is difficult to guarantee the prediction accuracy of thermal conductivity.

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