Fuzzy intelligent multiple extreme response surface method for calculating blade life

A multi-extremum, response surface method technology, applied in computing, computer-aided design, design optimization/simulation, etc., can solve problems such as FSVR rarely integrates ERSM

Inactive Publication Date: 2017-07-25
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Currently, FSVR rarely incorporates ERSM and applies

Method used

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  • Fuzzy intelligent multiple extreme response surface method for calculating blade life
  • Fuzzy intelligent multiple extreme response surface method for calculating blade life
  • Fuzzy intelligent multiple extreme response surface method for calculating blade life

Examples

Experimental program
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Effect test

Embodiment 1

[0068] A fuzzy intelligent multiple extreme value response surface method for calculating blade life, comprising the following steps:

[0069] a. Establish a finite element model of the structure;

[0070] b. Select the input random variable;

[0071] c. Perform thermal-structural coupling deterministic analysis on the blade structure, use Latin hypercube sampling technology to extract a certain number of sample points and calculate the corresponding extreme value output response;

[0072] d. Use the KFCM clustering algorithm to calculate the membership degree of the sample, normalize the data, and finally obtain the training sample of the fuzzy support vector regression machine FSVR;

[0073] e. Use the genetic algorithm to optimize the parameters of the FSVR model and construct the FSVR mathematical model;

[0074] f. Based on the basic idea of ​​the multiple extreme value response surface method, use the FSVR model to complete the construction of the mathematical model of...

Embodiment 2

[0078] According to the fuzzy intelligent multi-extreme value response surface method for calculating blade life described in Embodiment 1, in the step a, the blade structure is divided into tetrahedral network by using ANSYS software.

Embodiment 3

[0080] According to the fuzzy intelligent multiple extreme value response surface method for calculating blade life described in Embodiment 1, in the step b, the material attribute parameters, mechanical load and temperature load related parameters for the reliability analysis of blade fatigue life are selected.

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Abstract

The invention discloses a fuzzy intelligent multiple extreme response surface method (FIMERSM) for calculating blade life. The method comprises the following specific steps of building a finite element model of a structure; selecting and inputting random variables; performing heat-structure coupling analysis on the blade structure, and extracting a certain quantity of sample points by adopting a Latin hypercube sampling technology and calculating corresponding extreme output responses; calculating a membership degree of a sample by adopting a KFCM clustering algorithm, performing normalization processing on data, and finally obtaining a training sample of a fuzzy support vector regression (FSVR) machine; performing parameter optimization on an FSVR model by adopting a genetic algorithm, and building an FSVR mathematical model; based on a basic thought of a multiple extreme response surface method, building an FIMERSM mathematical model by applying the FSVR model; performing coupling failure reliability analysis on a blade by adopting the FIMERSM; and performing validity verification on the FIMERSM. According to the method, a new way is opened up for reliability analysis of a multi-failure mode structure.

Description

technical field [0001] The invention relates to a reliability analysis method for calculating the fatigue life of an aeroengine blade, in particular to a fuzzy intelligent multiple extreme value response surface method for calculating the blade life. Background technique [0002] The aeroengine is the heart of the aircraft, and its reliability affects the performance of the aircraft. As an important part of aero-engines, turbine blades are often subjected to mechanical loads such as high temperature and high speed, and experience complex failure modes such as stress, strain, and low-cycle fatigue. Therefore, it is of great significance to establish a reasonable and efficient blade reliability analysis method. [0003] Traditional probabilistic reliability analysis methods have been extensively studied, among which Response surface method (Response surface method, RSM) and extreme value response surface method (Extremum response surface method, ERSM) are used to solve the re...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/15G06F30/23G06F2111/08G06F2119/04
Inventor 张春宜孙田刘宝升王爱华景慧哲
Owner HARBIN UNIV OF SCI & TECH
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