Functionally gradient material and structure parameter statistics recognition method

Abstract

The invention relates to a functionally gradient material and structure parameter statistics recognition method. The method is used for statistics recognition based on functionally gradient material component material physical property parameter and component distribution corrected by a functionally gradient structure random model. On one hand, the method for adjusting the search range for each step iteration is adopted for increasing the recognition speed; on the other hand, propagation from the material recognition parameter randomness to the structure modal parameter randomness is achievedby building the corresponding relation between first-order partial derivatives of relative recognition parameters of functionally gradient structure modal parameters and first-order partial derivatives of relative recognition parameters of functionally gradient material effective physical property parameters, and misconvergence caused by accumulative errors in the iteration recognition process isavoided.

Description

technical field [0001] The invention relates to a parameter statistical identification technology of heterogeneous materials and structures, more specifically, a method for parameter statistical identification of functionally graded materials and structures. It is used for the statistical identification of the physical parameters and component distribution of functionally graded materials and structures in the fields of mechanical engineering and aerospace. Background technique [0002] Functionally graded materials have attracted much attention in mechanical engineering, aerospace and other fields because of their excellent high temperature resistance, high mechanical strength and thermal shock resistance. Due to the complexity and diversity of the preparation process of functionally graded materials, there are still many difficulties in meeting the physical and mechanical properties of functionally graded materials in a pre-designed ideal gradient change. It is almost impo...

AI Technical Summary

Problems solved by technology

The major defect of these techniques when used in heterogeneous functionally graded materials and structures is that the trust method will affect the recognition speed, and due to the complex relationship between the effective physical parameters of functionally graded materials and the volume component index, the difference method will lead to There is a large error in the random propagation, and the error accumulation of each iteration will cause the iterative process to fail to converge, and eventually lead to unsuccessful recognition

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