Rear axle system nonlinear strength analysis method

Abstract

The invention discloses a rear axle system nonlinear strength analysis method. The method comprises the steps that 1, a rear axle material curve is processed; 2, an error judgment value e is set; 3, an initial point counter i=1 and an ending point counter j=3 are generated; 4, initial points and ending points are connected to generate judgment lines; 5, errors e between original data, between the initial points and the ending points, and the judgment lines are calculated; 6, the calculated errors are judged, if all the errors are smaller than e, the ending point counter j is equal to j+1, an array storing the errors is reset, and the step 4 is returned to, and if at least one of the errors is greater than e, the initial point counter i is equal to j-1, the ending point counter j is equal to j+1, the array storing the errors is reset, and the step 4 is returned to; and 7, the initial point of the judgment line generated each time and the last point of material curve data processed in the step 1 are used as reserved points, other points are deleted, and optimized material data is obtained. Through the method, a material stress-strain curve and a bushing stiffness curve are reasonably shrunk, and therefore calculation efficiency is improved.

Description

technical field [0001] The invention relates to the field of automobile CAE analysis, in particular to a nonlinear strength analysis and optimization tool for a rear axle system. Background technique [0002] The automotive rear axle system has the characteristics of many connections and high structural load. In recent years, the strength analysis of automobile rear axle has gradually developed from single-component analysis to system-level analysis; from linear analysis to nonlinear analysis. Through the analysis of the entire rear axle system, the connection relationship between each component and the stress of each component can be obtained, and the performance of the rear axle system can be reasonably controlled. [0003] In the process of solving the CAE of the rear axle, a large number of nonlinear iterative algorithms are used. The nonlinear iterative algorithm mainly uses iterative methods such as Newton iteration and quasi-Newton iteration to linearize the nonlinea...

AI Technical Summary

Problems solved by technology

[0005] In the process of using the polynomial fitting reduction method, there are two steps to introduce errors with the original data: the first step is in the polynomial fitting, the order of the polynomial can generally be taken to the 5th or 6th order, and the fitting curve will still be generated The error with the original data, and the size and position of the error are not easy to calculate, and can only be identified by human eye observation; in the second step, when taking points at equal intervals, the error will be larger at positions with higher curvature of the fitting curve when taking points at equal intervals , and the error size and position are not easy to calculate, and can only be identified by human observation

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