Fuzzy random reliability evaluation method for pipeline with crack defect based on R6-FAD and BP-MCS

A BP-MCS, R6-FAD technology, applied in CAD numerical modeling, probabilistic CAD, stochastic CAD, etc., can solve the problem of not giving a unified value of safety margin, and achieve the effect of simplifying the randomization process

Inactive Publication Date: 2021-06-11
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

However, the R6 specification does not give a uniform value for the safety margin

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  • Fuzzy random reliability evaluation method for pipeline with crack defect based on R6-FAD and BP-MCS

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Embodiment Construction

[0008] The present invention will be further described below in conjunction with the accompanying drawings.

[0009] Such as figure 1 As shown, the present invention is based on R6-FAD and BP-MCS fuzzy random reliability evaluation method for pipelines containing crack defects, detailed steps: Step S1: input pipeline internal pressure and other actual working conditions external load, crack defect size and The probability distribution type and parameters of material properties and the maximum number of simulations N, establish the fuzzy limit state equation; step S2: data preprocessing, determine the basic parameters, and construct the initial BP neural network; step S3: train the neural network, when it can better approximate the fuzzy For the limit state equation, execute step S4, otherwise, return to step S2 to re-adjust the network; step S4: calculate j=1, M=0, generate [0,1] uniformly distributed random number F(x ij ), from these random numbers from each given probabili...

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Abstract

The invention discloses an R6-FAD and BP-MCS-based fuzzy random reliability evaluation method for a pipeline with crack defects, and the method combines a BP neural network and Monte Carlo simulation based on a failure evaluation diagram of an R6 specification, and is applied to the fuzzy random reliability evaluation of the pipeline with the crack defects. The method mainly comprises the following steps: step 1, inputting probability distribution types and parameters of actual working condition load, crack defect size and material performance and maximum simulation times N, and establishing a fuzzy limit state equation; 2, determining basic parameters, and constructing an initialized BP neural network; 3, training a neural network to ensure that the neural network can better approach a fuzzy limit state equation; and 4, random sampling being carried out, and a Monte Carlo principle being used to calculate a failure probability and a reliability index. According to the method, the fuzzy reliability randomization process can be simplified, and a numerical solution with relatively high simulation precision can be obtained; meanwhile, the fuzzy random coupling uncertainty of the structure is considered, the method is better in line with engineering practice, and the method has guiding significance for safety assessment of the structure.

Description

technical field [0001] The invention relates to a method for evaluating fuzzy random reliability of a pipeline with crack defects. Background technique [0002] The expansion of crack defects in oil and gas pipelines will lead to fracture and damage, which directly affects the safe operation of pipelines. Therefore, it is of great significance to analyze the reliability of pipelines with crack defects. Since the 1970s, reliability engineering has been gradually applied to pipeline evaluation technology. At home and abroad also began to establish and gradually improve the corresponding assessment norms. After continuous improvement and development, the most commonly used ones are R6, BS7910 and SINTAP. Probabilistic fracture analysis only considers the randomness of parameters, and treats some variables as random variables that obey certain statistical laws. In actual engineering design, variables usually have both random and fuzzy uncertain factors, so fuzzy mathematics ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06F111/08G06F111/10G06F113/14G06F119/02
CPCG06F30/27G06N3/084G06F2113/14G06F2119/02G06F2111/10G06F2111/08G06N3/047
Inventor 吴韬略张鹏伍颖侯向秦黄云飞刘思铭
Owner SOUTHWEST PETROLEUM UNIV
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