CN107729658A - A kind of fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design - Google Patents
A kind of fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design Download PDFInfo
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Abstract
The present invention discloses a kind of fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design.Its detailed process is as follows：The output under failure mode is each corresponded to by Thermo-structure experiment deterministic parsing acquisition turbine disk to respond；The fuzzy composition decomposition extreme value Response Face Function of structure（FIDCERSF）;Dynamic reliability sensitivity analysis is carried out with MCM;Establish fuzzy composition decomposition reliability optimal mathematical model（FDCRBDO）.The present invention has very strong advantage in terms of more components, multi-invalidation mode structure global reliability optimization design.
Description
Technical field
The present invention relates to a kind of more components, multi-invalidation mode structure global reliability Optimization Design, more particularly to
A kind of fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design.
Background technology
Mechanical structure is all the structural system being assembled by several components in many cases, such as turning for aero-engine
Substructure System, exactly it is assembled by components such as main shaft, wheel disc and blades.For more components, multi-invalidation mode structural system
Reliability design only can not be separately carried out to each component, and structure global reliability must be carried out to all components and set
Meter, could obtain superior technique economic effect.
More components, the design of multi-invalidation mode structural optimization based on reliability need solve following both sides problem：First be as
Failure correlation problem in what processing structure between each component and in component between each failure mode, is mainly considered as two kinds of phases
Guan Xing, first, failure cause is related（Such as：The deformation of member caused by vibration fails）, second, failure mechanism coupling failure（Such as：Fatigue
Intercoupled between creep failure pattern）.The process problem of failure correlation is always the focus of structural reliability design research,
But the various methods engineering practicality proposed at present are poor, and failure correlation processing method is still that structural optimization based on reliability is set
The major obstacle of meter.Second is how to establish more components, multi-invalidation mode structure global reliability Optimized model.
The content of the invention
The purpose of the present invention is：In more components, multi-invalidation mode structural optimization based on reliability design process is carried out, if directly
Connect the global reliability mathematical optimization models for establishing structure, then the model established is one and is related to more materials, multidisciplinary, more things
The structural model of reason response coupled field, carry out a global analysis and solve amount of calculation just very greatly, if to carry out massive iterative
Solve, computational efficiency is especially prominent.Proposition enables computational accuracy and efficiency reaches more components of engineering requirements, fail mould more
Formula structure global reliability optimization design is significant.
The invention provides a kind of fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design, its specific mistake
Journey is as follows：
A. the output under failure mode is each corresponded to by thermal-structure coupled deterministic parsing acquisition blade-wheel disc to respond;
B. fuzzy composition decomposition extreme value Response Face Function is built（FIDCERSF）;
C. dynamic reliability sensitivity analysis is carried out with MCM;
D. fuzzy composition decomposition reliability optimal mathematical model is established（FDCRBDO）.
The fuzzy intelligence composition decomposition extreme value response phase method of described reliability Optimum Design, in described step a, with
The density of blade-wheel disc, temperature, Pneumatic pressure, modulus of elasticity, thermal coefficient of expansion are used as input stochastic variable, pass through certainty
Analyze the maximum stress point for finding blade, maximum creep strain point, least life point, maximum crackle J points and wheel disc
Maximum stress point, maximum creep strain point, least life point, and combine aero-engine GH4133B alloy fatigues-creep impairment
The strong parametric synthesis equation of relational expression, creep rupture heat, the linear progressive damage rules of Miner obtain blade-wheel disc creep impairment,
Fatigue damage, creep life.
The fuzzy intelligence composition decomposition extreme value response phase method of described reliability Optimum Design, in described step b, examine
The ambiguity and randomness of data are considered, using above-mentioned input variable as input stochastic variable, with Latin Hypercube Sampling technology
（LHS）Input stochastic variable sample is extracted, to each sample solving finite element fundamental equation, obtains corresponding stress, creep is answered
The output of change, low-cycle fatigue life, crackle J integrations, creep impairment, fatigue damage, creep life in analysis time domain rings
Should, using dynamical output response analysis time domain in whole maximums and its it is corresponding input stochastic variable as sample point, adopt
With possibility Fuzzy C-Means Clustering Algorithm（Possibilistic fuzzy c-means clustering,PFCM）Calculate sample
This degree of membership, data normalization is handled into the training sample as FV-SVR models, with artificial fish-swarm algorithm（AFSA）It is right
FV-SVR models carry out parameter optimization, construct fuzzy intelligence composition decomposition extreme value Response Face Function（FIDCERSF）, and determine
FIDCERSF coefficients.
The fuzzy intelligence composition decomposition extreme value response phase method of described reliability Optimum Design, in described step c, use
FIDCERSF models replace the limit state equation of blade-wheeling disk structure, with Monte Carlo Method（MCM）To FIDCERSF models
In high volume linkage sampling is carried out, obtains the reliability and sensitivity index of blade-wheeling disk structure, while calculates the random change of each input
The sensitivity index of amount.
The fuzzy intelligence composition decomposition extreme value response phase method of described reliability Optimum Design, in described step d, with
The rotating speed of blade-wheel disc, temperature, density are design variable, using stress, creep strain, fatigue damage, creep impairment as target letter
Number, constraint function is used as using reliability and other constraintss, establishes FDCRBDO models, carry out decoupling Coordinating And Iterating Methods solution, it is complete
Into the reliability Optimum Design of blade-wheel disc.
The present invention has the advantages that compared with prior art：
1. the correlation of failure mode and the fuzzy quality of constraint boundary condition are considered, by fuzzy V- regression machines（FV-SVR）Go out
The anti-noise ability of color and composition decomposition response phase method（DCERSM）Simplification computing capability be combined, improve more components, lose more
Imitate the computational accuracy and computational efficiency of mode configuration global reliability optimization design.
2. can effectively solve the problem that problem described in background technology, this method is by 3 kinds of intelligent algorithms：Fuzzy V- supporting vectors are returned
Return machine（FV-SVR）, artificial fish-swarm algorithm（AFSA）, possibility Fuzzy C-Means Cluster Algorithm (PFCM）It is combined, utilizes
Respective algorithms are realized in MATLAB intelligent algorithms tool box, facilitate actual analysis to calculate.
Brief description of the drawings
Fig. 1 is fuzzy intelligence composition decomposition extreme value response phase method reliability Optimum Design flow chart.
Embodiment
Embodiment 1
A kind of fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design, comprises the following steps：
A. the output under failure mode is each corresponded to by thermal-structure coupled deterministic parsing acquisition blade-wheel disc to respond;
B. fuzzy composition decomposition extreme value Response Face Function is built（FIDCERSF）;
C. dynamic reliability sensitivity analysis is carried out with MCM;
D. fuzzy composition decomposition reliability optimal mathematical model is established（FDCRBDO）.
Embodiment 2
The fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to embodiment 1, described step a
In, using the density of blade-wheel disc, temperature, Pneumatic pressure, modulus of elasticity, thermal coefficient of expansion as input stochastic variable, by true
The maximum stress point of blade, maximum creep strain point, least life point, maximum crackle J points and wheel are found in qualitative analysis
The maximum stress point of disk, maximum creep strain point, least life point, and combine aero-engine GH4133B alloy fatigues-creep
Injuring relation formula, the strong parametric synthesis equation of creep rupture heat, the linear progressive damage rules of Miner obtain the creep of blade-wheel disc
Damage, fatigue damage, creep life.
Embodiment 3
The fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to embodiment 1, described step b
In, the ambiguity and randomness of data are considered, using above-mentioned input variable as input stochastic variable, with Latin Hypercube Sampling
Technology（LHS）Input stochastic variable sample is extracted, to each sample solving finite element fundamental equation, obtains corresponding stress, compacted
Change strain, low-cycle fatigue life, crackle J integrations, creep impairment, fatigue damage, creep life are defeated in analysis time domain
Go out response, using dynamical output response analysis time domain in whole maximums and its it is corresponding input stochastic variable as sample
Point, using possibility Fuzzy C-Means Clustering Algorithm（Possibilistic fuzzy c-means clustering,PFCM）
The degree of membership of sample is calculated, data normalization is handled into the training sample as FV-SVR models, with artificial fish-swarm algorithm
（AFSA）Parameter optimization is carried out to FV-SVR models, constructs fuzzy intelligence composition decomposition extreme value Response Face Function (FIDCERSF),
And determine FIDCERSF coefficients.
Embodiment 4
The fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to embodiment 1, described step c
In, the limit state equation of blade-wheeling disk structure is replaced with FIDCERSF models, with Monte Carlo Method（MCM）It is right
FIDCERSF models carry out high-volume linkage sampling, obtain the reliability and sensitivity index of blade-wheeling disk structure, calculate simultaneously
The sensitivity index of each input stochastic variable.
Embodiment 5
The fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to embodiment 1, described step d
In, using the rotating speed of blade-wheel disc, temperature, density as design variable, using stress, creep strain, fatigue damage, creep impairment as
Object function, using reliability and other constraintss as constraint function, FDCRBDO models are established, carry out decoupling Coordinating And Iterating Methods
Solve, complete the reliability Optimum Design of blade-wheel disc.
Claims (5)
1. the fuzzy intelligence composition decomposition extreme value response phase method of a kind of reliability Optimum Design, it is characterised in that including following step
Suddenly：
A. the output under failure mode is each corresponded to by thermal-structure coupled deterministic parsing acquisition blade-wheel disc to respond;
B. fuzzy composition decomposition extreme value Response Face Function is built（FIDCERSF）;
C. dynamic reliability sensitivity analysis is carried out with MCM;
D. fuzzy composition decomposition reliability optimal mathematical model is established（FDCRBDO）.
2. the fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to claim 1, its feature
It is, it is random using the density of blade-wheel disc, temperature, Pneumatic pressure, modulus of elasticity, thermal coefficient of expansion as input in step a
Variable, maximum stress point, maximum creep strain point, least life point, the maximum crackle J that blade is found by deterministic parsing are accumulated
The maximum stress point of branch and wheel disc, maximum creep strain point, least life point, and combine aero-engine GH4133B and close
The strong parametric synthesis equation of golden spleen tissue extracts injuring relation formula, creep rupture heat, the linear progressive damage rules of Miner obtain blade-
Creep impairment, fatigue damage, the creep life of wheel disc.
3. the fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to claim 1, its feature
It is, in step b, considers the ambiguity and randomness of data, using above-mentioned input variable as input stochastic variable, with Latin
Hypercube sampling techniques（LHS）Input stochastic variable sample is extracted, to each sample solving finite element fundamental equation, is obtained correspondingly
Stress, creep strain, low-cycle fatigue life, crackle J integrations, creep impairment, fatigue damage, creep life analysis when
Output response in domain, whole maximums and its corresponding input stochastic variable work in time domain are being analyzed into dynamical output response
For sample point, using possibility Fuzzy C-Means Clustering Algorithm（Possibilistic fuzzy c-means clustering,
PFCM）The degree of membership of sample is calculated, data normalization is handled into the training sample as FV-SVR models, calculated with artificial fish-swarm
Method（AFSA）Parameter optimization is carried out to FV-SVR models, constructs fuzzy intelligence composition decomposition extreme value Response Face Function
（FIDCERSF）, and determine FIDCERSF coefficients.
4. the fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to claim 1, its feature
It is, in step c, the limit state equation of blade-wheeling disk structure is replaced with FIDCERSF models, with Monte Carlo Method
（MCM）High-volume linkage sampling is carried out to FIDCERSF models, obtains the reliability and sensitivity index of blade-wheeling disk structure,
The sensitivity index of each input stochastic variable is calculated simultaneously.
5. the fuzzy intelligence composition decomposition extreme value response phase method of reliability Optimum Design according to claim 1, its feature
Be, in step d, using the rotating speed of blade-wheel disc, temperature, density as design variable, with stress, creep strain, fatigue damage,
Creep impairment is object function, using reliability and other constraintss as constraint function, establishes FDCRBDO models, is solved
Coupling Coordinating And Iterating Methods solve, and complete the reliability Optimum Design of blade-wheel disc.
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| CN109388884A (en) * | 2018-10-09 | 2019-02-26 | 哈尔滨理工大学 | A kind of generalized regression extreme value response phase method calculating coupling leaf dish fatigue life |
| CN109885965A (en) * | 2019-03-11 | 2019-06-14 | 哈尔滨理工大学 | A kind of random multiple extreme response phase method of flexible member fail-safe analysis |
| CN110083916A (en) * | 2019-04-22 | 2019-08-02 | 湖南工业大学 | Based on the mechanical structure Fuzzy fatigue reliability optimization method from structure membership function |
| CN110532723A (en) * | 2019-09-06 | 2019-12-03 | 北京航空航天大学 | A kind of turbine disk multi-invalidation mode reliability optimization method based on EGRA |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108875233A (en) * | 2018-06-28 | 2018-11-23 | 电子科技大学 | Based on the structural reliability method of changeable weight response surface under Hybrid parameter matrix |
| CN108875233B (en) * | 2018-06-28 | 2021-04-27 | 电子科技大学 | Structure reliability method based on dynamic weight response surface under mixed uncertainty |
| CN109388884A (en) * | 2018-10-09 | 2019-02-26 | 哈尔滨理工大学 | A kind of generalized regression extreme value response phase method calculating coupling leaf dish fatigue life |
| CN109885965A (en) * | 2019-03-11 | 2019-06-14 | 哈尔滨理工大学 | A kind of random multiple extreme response phase method of flexible member fail-safe analysis |
| CN110083916A (en) * | 2019-04-22 | 2019-08-02 | 湖南工业大学 | Based on the mechanical structure Fuzzy fatigue reliability optimization method from structure membership function |
| CN110532723A (en) * | 2019-09-06 | 2019-12-03 | 北京航空航天大学 | A kind of turbine disk multi-invalidation mode reliability optimization method based on EGRA |
| CN110532723B (en) * | 2019-09-06 | 2021-06-22 | 北京航空航天大学 | EGRA-based turbine disk multi-failure-mode reliability optimization method |
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Application publication date: 20180223 |