A Stochastic Model Correction Method Based on Quadratic Response Surface Inversion

A response surface and inversion technology, applied in special data processing applications, instruments, calculations, etc., can solve problems such as inaccurate simulation of complex connections, limited applications, and scattered design and manufacturing of materials, so as to avoid ill-conditioned sensitivity matrix problems and improve calculations. Efficiency, the effect of simplifying problem analysis

Active Publication Date: 2019-03-19
BEIHANG UNIV
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Problems solved by technology

In practical problems, uncertainties are ubiquitous, such as insufficient sensor configuration or signal processing; material dispersion or design and manufacturing errors; inaccurate simulation of complex connections, etc.
The deterministic model correction method strives to find the accurate nominal value of the parameter, ignoring the objective uncertainty, which greatly limits its application in practice, and the reliability is greatly reduced

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  • A Stochastic Model Correction Method Based on Quadratic Response Surface Inversion
  • A Stochastic Model Correction Method Based on Quadratic Response Surface Inversion
  • A Stochastic Model Correction Method Based on Quadratic Response Surface Inversion

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Embodiment Construction

[0022] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0023] like figure 1 As shown, the present invention is a stochastic model correction method based on quadratic response surface inversion, comprising the following steps:

[0024] Step 1: Establish a preliminary finite element model based on the structural design drawings, and determine all parameters with uncertainties in the structure. Combined with the experimental model, the optimization objective function is constructed based on the structural response output (frequency, displacement, etc.), and the optimization correction is carried out under the initial interval of the uncertain parameters to obtain the nominal value of the uncertain parameters and the preliminary correction model with the highest matching degree with the experimental model;

[0025] Step 2: Centering on the nominal values ​​of uncertain parameters obtained in Step 1, ...

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Abstract

The invention provides a method for random model correction based on secondary response surface inversion. According to the method, against the problem of uncertainty in engineering system parameter identification or model correction, through single-point successive inversion iteration, errors caused by uncertainty analysis and sensitivity matrix solving in a conventional random model correction method are avoided effectively. It shows through example verification and method contrast that according to the method, uncertainty parameter on a structure having obvious uncertainty can be performed effectively, and the identification efficiency and accuracy are very high.

Description

technical field [0001] The invention relates to the field of uncertainty model correction and system parameter identification, in particular to a stochastic model correction method based on quadratic response surface inversion. Background technique [0002] The current structural design tends to be refined and large-scale, and CAE analysis technology based on finite element analysis plays an increasingly important role in structural product design. Accurate and effective finite element models are essential for structural mechanical performance analysis, failure warning, and damage identification. However, due to various reasons such as the complexity of the structure itself and the inappropriate simplification strategy adopted in the modeling process, the finite element model established according to the design drawings cannot reflect the mechanical properties of the real structure. In order to ensure the consistency of the static and dynamic responses of the finite element...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
Inventor 丁和武邱志平吕峥朱静静孙佳丽
Owner BEIHANG UNIV
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