Random model updating method based on interval response surface model

Abstract

The invention relates to random model updating method based on an interval response surface model. The method is characterized by including the steps of firstly, building a second-order polynomial response surface model without cross terms according to experiment design and regression analysis; secondly, using a square completing method to convert a polynomial response surface expression into perfect square; thirdly, substituting interval parameters into the response surface expression to allow the definite response surface model to be changed into the interval response surface model; fourthly, performing interval calculation on the interval response surface model to obtain predicted structural response intervals, and combining the predicted structural response intervals with actual response intervals to build a target function; fifthly, building a optimization inversion problem to identify interval distribution of parameters. By the method, the expansion problem of interval calculation is avoided, fast calculation of structural response intervals is considered, finite element analyzing calculation and sensitivity matrix building during (interval) random model updating are avoided, a large amount of calculation time and cost is saved, and ill-conditioned optimization is avoided as much as possible.

Description

technical field [0001] The invention relates to the technical field of finite element model correction, in particular to a random model correction method based on an interval response surface model. Background technique [0002] Finite element model correction technology has been widely used in aerospace, civil, mechanical and other engineering fields. However, traditional model correction methods [1-2] It is based on the assumption of parameter determinism. Uncertainty factors in actual engineering structures are ubiquitous and unavoidable, such as material discreteness, measurement errors, manufacturing errors, etc., which lead to uncertainties in structural parameters. If the deterministic theory and method are still used to correct the finite element model, it will inevitably lead to unreliable correction results, which are quite different from the actual situation. Therefore, the stochastic model correction method that considers the parameter uncertainty has begun to ...

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Problems solved by technology

The above process is actually a multiple deterministic model modification process, which has certain limitations: (1) This method requires an optimization algorithm with global search capabilities, and this type of algorithm is relatively complex, and the optimization result is likely to fall into a local optimum. The obtained structural parameter intervals are not necessarily accurate; (2) When the statistical data obtained from the test is large, this method requires a lot of repeated calculations, which will consume a lot of computing costs; (3) For the actual engineering structure, researchers often use more Focus on the maximum and minimum values ​​of the structural response, that is, the upper and lower bounds of the response interval, so this method is likely to fail when only the response interval is known

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