Random defect mode superposition method based on response surface method
A technology of response surface method and superposition method, applied in the field of random defect modal superposition, which can solve problems such as poor operability
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Embodiment 1
[0112] For the case where the number of random variables is 3, the fitting results of different polynomials and the fitting results of different collocation points are analyzed.
[0113] (1) Comparison of different polynomial fitting results
[0114] Using the CCD collocation method, the stochastic modal superposition method is used for analysis to obtain the critical load variance, mean value and other factors as well as the consumed CPU time. At the same time, the numerical model is calculated using the Monte Carlo method, and the number of samples is 10000. The calculation results and relative errors of the two analysis methods are shown in Table 1.
[0115] Table 1 Calculation results of different fitting polynomials in Example 1 when the number of random variables is 3 (CCD, n=3)
[0116]
[0117] Table 1 shows that in the response surface method (CCD), different fitting polynomials are used, according to and the critical unstable load p calculated from the minimum...
Embodiment 2
[0126] (1) The random variable is 4, and the results of different polynomial fittings are compared, and the obtained data are shown in Table 3.
[0127] Table 3 Calculation results of different fitting polynomials in Example 1 when the number of random variables is 4 (CCD, n=4)
[0128]
[0129] The critical load value p calculated by ccd second-order polynomial fitting * The fitting results of ccd linear polynomials are closer to the critical load under the MCS method, and their errors with MCS are: 0.39%, 0.39%, and 0.66% respectively;
[0130] The same as when the random variable is 3, when the random variable is 4, in the process of regression analysis and optimization of the response surface function containing the cross term in the ccd method, the influence of the cross term in the quadratic polynomial is filtered; the ccd second order polynomial fitting And the program that considers the intersection item requires the least cpu time, only 3m7s, which is much smaller...
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