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Expansion line sampling method for efficient structure failure probability function solving

A failure probability and function technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as the inability to meet the requirements of engineering structure solution accuracy and efficiency, and achieve the effect of effective design methods and high solution efficiency.

Inactive Publication Date: 2017-09-22
XIAMEN UNIV
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Problems solved by technology

However, these methods are still unable to meet the accuracy and efficiency requirements of engineering structures. Therefore, how to further improve the accuracy and efficiency of solving the failure probability function of practical engineering problems is still a subject that needs to be studied.

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  • Expansion line sampling method for efficient structure failure probability function solving
  • Expansion line sampling method for efficient structure failure probability function solving
  • Expansion line sampling method for efficient structure failure probability function solving

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Embodiment Construction

[0031] The present invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0032] Taking the problem of solving the failure probability function of the front axle of an automobile as an example, the specific implementation method of the present invention is described. The front axle is an important part of the car's load-bearing, and it is mostly designed as an I-beam structure (such as figure 1 shown), because the I-shaped section can increase the bending strength of the front axle and reduce the weight of the front axle. It is known that the maximum normal stress and shear stress of the front axle are respectively σ=M / W x and τ=T / W ρ , where M and T are bending moment and torque, and W ρ =0.8bt 2 +0.4[a 3 (h-2t) / t] are the section coefficient and polar section coefficient, respectively. The limit state function for this problem is expressed as:

[0033]

[0034] where σ s is the limit state st...

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Abstract

The invention discloses an expansion line sampling method for efficient structure failure probability function solving, and relates to structure reliability optimization design. The method comprises the steps of constructing an auxiliary sampling density function: for a design variable situation, selecting the proper auxiliary sampling density function phi(x); solving a corresponding important direction: when variables obey phi(x) distribution, solving the corresponding important direction alpha; extracting variable samples: generating sample points {x(j):j=1,2,...N} corresponding to the variables according to the variable distribution phi(x); calculating a corresponding intersection point: calculating the intersection point, defined in the specification, corresponding to a lower limit state function of line sampling through three-point quadratic interpolation; and calculating failure probability function estimation: according to an intersection point value, calculating the failure probability function estimation by reference to a formula.

Description

technical field [0001] The invention relates to structural reliability optimization design, in particular to an extended line sampling method for solving high-efficiency structural failure probability functions. Background technique [0002] In the reliability optimization design of structures, it is usually necessary to study the relationship between failure probability and design parameters. This relationship is defined as the failure probability function, that is, the probability of structural failure as a function of the parameters. However, in practical applications, it is difficult to directly obtain the explicit expression of the failure probability function of the design parameters for general structures because of the huge computational cost. Usually, this requires repeated reliability analysis under different design parameter values, and each reliability analysis needs to repeatedly calculate the value of the limit state function. The key point is that the calcula...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/15G06F30/17
Inventor 袁修开杨济波陈斌
Owner XIAMEN UNIV
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