Finite Element Model Updating Method Based On Response Surface Modeling And The Improved PSO Algorithm

Platformization and modularization are employed in modern aircraft design to save research grant and shorten lead time, new spacecraft often improve the existing mature platform to meet the design requirement and the rapid expansion of the space market. The MSC/PATRAN-based model updating technology is widely used in aerospace field, and the updating process largely depends on the engineers'experiments to adjust the error location and model parameters, which decreases the updating efficiency and precision. The RSM-based model updating method doesn't need to call the FEM program in every iteration process following the parameter variation, which reduces the solving efficiency, and this method avoids the disability of FEM-based model updating method that hard to combine with the PSO algorithm. The RSM and PSO-based model updating method can effectively confirm the error location and model parameters, it can also give the quantitative correspondence of each error parameter, and various analysis abilities of the updating model are largely improved by setting multi-objective fitting function.The group-control-based improved PSO algorithm is proposed based on the analysis of the algorithm mechanism and defect of the existing PSO algorithm, which divides the particle swarm into two groups, that is, the superior group and the inferior group, the chaos search mechanism is introduced into the superior group to increase the diversity and the variation mechanism is introduced into the inferior group to break the inferior particles away from the inferior solution so as to increase the probability of finding the optimum solution. Convergence condition of the flying path and velocity of the particles are analyzed and the parameter range that makes the algorithm converge is derived, which provides guidance for the parameter selection in the improved algorithm.Model composition is the main influencing factor of the RSM, comparing the composition and calculating effect of each response surface model, the Linear-and-Gaussian combined kernel function support vector machine response surface is proposed which combines the linear fitting ability of the linear polynomial and the nonlinear fitting ability of the Gaussian polynomial with better calculation precision and widely scope of application. The weight thought is introduced into the response surface method in order to show that different parameter has different influence on the calculated result, and the weighted matrix is given by the partial derivative of the response surface to the structure parameters at different design points, which fits the explicit functions, as well as the effectiveness of structure parameters have on responses, which fits the real structures. The weighted least square support vector machine (WLS-SVM) is proposed and the construction and calculation precision are contrasted, The weighted Linear-and-Gaussian can effectively improve the analysis efficiency and precision, set the kernel factorσto be equal to the radius of the design space and then favorable fitting results are obtained.The differences between the RSM-based model updating method and the FEM-based model updating method are introduced in detail, and the combined PSO algorithm and Linear-and-Gaussian combined kernel function support vector machine response surface are used to update the multi-layered carbon fiber honeycomb sandwich panel, which shows the clear process of RSM-based model updating, the reappearance ability in the updated frequency range and prediction ability out of the updated frequency range of the updated model are tested and the updating validity is verified. Then the Linear-and-Gaussian combined kernel function support vector machine response surface and the group-control PSO algorithm are applied to the satellite model updating, the modal frequency and frequency response analysis results of the updated model are both improved, which verifies the applicability of the updating method.