Stochastic Model Modification 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 modification, in particular to a random model modification method based on an interval response surface model. Background technique [0002] The finite element model modification technology has been widely used in aerospace, civil engineering, machinery and other engineering fields. However, the traditional model modification method [1-2] It is based on the assumption of parameter certainty. Uncertainty factors in actual engineering structures are universal and inevitable, such as material dispersion, measurement errors, manufacturing errors, etc., which lead to the uncertainty of structural parameters. If deterministic theories and methods are still used to revise the finite element model, the revised results will inevitably lead to unreliable revisions, which are quite different from the actual situation. Therefore, in recent years, stochastic model revision methods that consider parame...

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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 large number of repeated calculations, which will consume a large amount of calculation costs; (3) For actual engineering structures, 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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