CN106777696B - Flutter Design Method Based on QMU - Google Patents
Flutter Design Method Based on QMU Download PDFInfo
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- CN106777696B CN106777696B CN201611174664.5A CN201611174664A CN106777696B CN 106777696 B CN106777696 B CN 106777696B CN 201611174664 A CN201611174664 A CN 201611174664A CN 106777696 B CN106777696 B CN 106777696B
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000013461 design Methods 0.000 title claims abstract description 13
- 230000019771 cognition Effects 0.000 claims abstract description 24
- 238000013139 quantization Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 238000005516 engineering process Methods 0.000 claims abstract description 6
- 238000011156 evaluation Methods 0.000 claims abstract description 5
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 claims abstract description 3
- 239000000463 material Substances 0.000 claims description 5
- 238000010008 shearing Methods 0.000 claims description 4
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000011002 quantification Methods 0.000 abstract description 4
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- 238000000342 Monte Carlo simulation Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
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Abstract
Design Method of Flutter based on QMU is related to uncertain and nargin quantification technique.1) finite element model of structure is established;2) flutter speed of certainty condition flowering structure is calculated by commercial finite element software, and divided by certain safety coefficient as Flutter Boundaries;3) the flutter distribution under the influence of stochastic uncertainty is described by probability distribution;4) it is based on flutter speed probability distribution, quantifies the uncertain degree and nargin of flutter speed by QMU technology, and obtains confidence factor CR, as Flutter Boundaries safety evaluation foundation;5) safe range of cognition uncertain factor is solved by confidence factor, take the probabilistic two parameter sections of cognition, it is divided into the section of n × n, m sample calculation is carried out to stochastic uncertainty parameter in each section and interpolation obtains the curve of CR=1 and CR=2, marks off the uncertain factor range for ensuring flutter safety and the influence of quantization uncertainty factor to a certain extent.
Description
Technical field
The present invention relates to uncertain and nargin quantification techniques, more particularly, to a kind of Design Method of Flutter based on QMU.
Background technique
Uncertainty is widely present in actual engineering problem, is broadly divided into two classes, and one is stochastic uncertainty, is such as tied
The parameter uncertainties such as the material load attribute during structure processing and manufacturing and use, mostly use probability distribution to describe;Two
It is uncertain for cognition, it is that section distribution description is mostly used due to the uncertainty that knowledge is limited and generates.It is most of at present
Research is just in stochastic uncertainty, and in fact the uncertain influence to result of cognition is equally very important.Flutter is
The most noticeable problem of aeroelastic stability research field.Aircraft occur flutter be it is breakneck, when flying speed is super
When crossing flutter critical speed, amplitude, which swashs to increase severely, to be added, it might even be possible to quickly destroy an airplane.Aircraft is in flight envelope
Do not allow to occur any type of flutter, but Flutter Boundaries again cannot overly conservative influence flying quality, therefore according to performance and
Index is reasonably designed containing probabilistic parameter is recognized, to guarantee that Flutter Boundaries i.e. safety is not again overly conservative to Guan Chong
It wants.
QMU (quantification of margins and uncertainties, it is uncertain to quantify skill with nargin
Art) it is that the National Nuclear Security Administration of U.S. Department of Energy subordinate in 2001 combines Los Alamos, Lao Lunsi Lawrence Livermore
New method (Pilch M, Trucano T G, the Helton J C.Ideas proposed with the sub- three power laboratories in the Holy Land
underlying quantification of margins and uncertainties(QMU):a white paper[J]
.Unlimited Release SAND2006-5001,Sandia National Laboratory,Albuquerque,New
Mexico, 2006,87185:2.), for assessing the reliability and safety of inventory's nuclear weapon in the insufficient situation of experimental data
Property.QMU method is using product normal operation as research object, based on physics model of failure and margin design, it is believed that make be
System reaches required performance, it is necessary to be directed to known potential failure mode, enough design margins be reserved for system, to ensure to be
System is absolutely reliable, but when calculated performance allowance M, will receive a variety of random and cognition uncertain factor influence,
Once these uncertain integrated values are greater than performance margin M, product may generate trouble or failure.Confidence is used in QMU
The ratio of factor CR, i.e. performance margin M and not true foot U characterizes this relationship between performance margin and uncertainty, when
When CR > 1, it is believed that system is safe.This method can be used as under condition of uncertainty, the peace of flight structure flutter margin
Full property assesses foundation, and is designed based on this to cognition uncertain parameters.
Summary of the invention
The purpose of the invention is to overcome deficiencies of the prior art, a kind of flutter based on QMU is provided and is set
Meter method.
The present invention the following steps are included:
1) finite element model of structure is established;
2) flutter speed of certainty condition flowering structure is calculated by commercial finite element software, and is divided by certain safety
Number is used as Flutter Boundaries;
3) the flutter distribution under the influence of stochastic uncertainty is described by probability distribution.Consider related ginseng in finite element model
Several uncertainties, such as elasticity modulus, shearing rigidity is as stochastic uncertainty, and density of material, atmospheric density ratio is as cognition
Uncertainty, using the probability distribution of Monte Carlo methods of sampling quantization stochastic uncertainty flutter speed.
4) it is based on flutter speed probability distribution, quantifies the uncertain degree and nargin of flutter speed by QMU technology, and obtain
To confidence factor CR, as Flutter Boundaries safety evaluation foundation;
5) safe range that cognition uncertain factor is solved by confidence factor takes probabilistic two parameters of cognition
Section is divided into the section of n*n, carries out m sample calculation to stochastic uncertainty parameter in each section and interpolation obtains CR
The curve of=1 and CR=2 marks off the uncertain factor range for ensuring flutter safety and quantifies to a certain extent uncertain
The influence of sexual factor.
In step 4), uncertainty quantization and QMU appraisal procedure are as follows:
(1) stochastic uncertainty of flutter speed, uncertainty are indicated by probability distribution are as follows:
The flutter speed that Flutter Boundaries are obtained by deterministic parameters calculation is multiplied by obtained by certain safety coefficient K, it is assumed that there is also
Stochastic uncertainty is described by probability distribution, the accounting equation of uncertainty are as follows:
The then uncertainty calculation equation of whole system are as follows:
Wherein, V indicates to consider the flutter speed probability distribution of stochastic uncertainty, and VL indicates quivering multiplied by safety coefficient K
The Cumulative Distribution Function on vibration boundary, P indicate that probability, β indicate certain confidence level, generally take 0.95.
(2) nargin quantifies, safe distance of the nargin between Flutter Boundaries and the probability distribution of flutter speed, calculating side
Journey are as follows:
(3) confidence factor is solved for judging flutter margin safety, the accounting equation of confidence factor are as follows:
In QMU technology, when CR is greater than 1, it is believed that system safety, then defined Flutter Boundaries have enough nargin to cover
Uncertainty can be used as the criterion of Flutter Boundaries safety evaluation.
Under conditions of the present invention is existed simultaneously suitable for stochastic uncertainty and cognition uncertainty, to guarantee Flutter Boundaries
The premise of safety, design contain the probabilistic parameter of cognition.Consider that various mixing present in structure and flying condition are not true
It qualitatively influences, flutter safety is assessed based on QMU technology, further the safe edge of quantization cognition uncertain parameters
Boundary, and Design cognition uncertain parameters.
Compared with the prior art, the invention has the advantages that:
1) uncertain quantization is carried out based on finite element software, computational efficiency is high, easy to operate.
2) QMU considers the uncertainty of flutter speed and Flutter Boundaries simultaneously, and the safety criterion as Flutter Problem can
Reliability is high.
3) can intuitively quantify to recognize probabilistic influence, and it is not true to provide on the basis of guarantee system safety cognition
The scope of design of qualitative parameter.
Detailed description of the invention
Fig. 1 is AGARD445.6 wing figure.
Fig. 2 is the flutter speed probability distribution considered under stochastic uncertainty.
Fig. 3 is the QMU considered under stochastic uncertainty.
Fig. 4 is cognition uncertain parameters and CR relational graph.
Specific embodiment
The stochastic uncertainty of flutter speed quantifies specific implementation step
1, the finite element mould of AGARD445.6 wing structure is established using commercial finite element software Patran and Nastran
Type, for the model as shown in Figure 1, air-foil is NACA 65A004, relevant parameter is as shown in table 1, and deterministic parameter is calculated
Under flutter speed VF, and Flutter Boundaries V is calculated according to required safety coefficient KF', and assume that it has certain do not know
Property, it is described using random distribution, the VL curve on the left of Fig. 3.
Table 1
Elastic modulus E11 | Elastic modulus E22 | Shearing rigidity G12 | Poisson's ratio υ | Density of material φ | Atmospheric density ratio r |
4.16×108Pa | 3.151×109Pa | 4.392×108Pa | 0.31 | 381.98Kg/m3 | 0.3486 |
2, consider to compile using Monte Carlo nesting circulation 10000 times based on Matlab such as the stochastic uncertainty in table 2
Journey reads in parameter information from the * .bdf file of Nastran and is modified to parameter to introduce uncertainty, called commercial
Finite element software Nastran carries out FLUTTER CALCULATION;Flutter speed is read in from destination file * .f06, every circulation primary obtains one
A flutter speed finally obtains the cumulative distribution function of a flutter speed, forms the probability distribution of Fig. 2 flutter speed.
Table 2
Parameter | Distribution | Uncertain type |
Elastic modulus E11 | Normal state | At random |
Elastic modulus E22 | Normal state | At random |
Shearing rigidity G12 | Normal state | At random |
Flutter parameters design method specific implementation process based on QMU includes:
1, QMU uncertainty quantization and nargin quantization as shown in figure 3, by formula (1) (2) be calculated uncertainty and
The ratio C R of nargin and confidence factor, as CR > 1, it is believed that quivering under current level of uncertainty, safety coefficient K
Vibration boundary is safe.
2, it will divide equally in atmospheric density ratio and the density of material two probabilistic ranges of variables of cognition and obtain 10 points,
That is, a total of 100 variable combination uncertain for cognition.For each combination, other three random parameters are considered
Uncertainty under the influence of CR value.100 laggard row interpolations of CR value are being obtained, the curve of CR=1 and CR=2 are obtained, 1 <
CR < 2 are within the scope of this it is considered that current design is safety and not overly conservative, as shown in figure 4, and can therefrom determine
Property judge influence of the uncertain amount of two cognitions for CR value and flutter safety, and measured under the premise of flutter safety
The range for changing cognition uncertain parameters, to Design cognition uncertain parameters.
Claims (1)
1. the Design Method of Flutter based on QMU, it is characterised in that the following steps are included:
1) finite element model of structure is established;
2) flutter speed of certainty condition flowering structure is calculated by commercial finite element software, and is made divided by certain safety coefficient
For Flutter Boundaries;
3) the flutter distribution under the influence of stochastic uncertainty is described by probability distribution, considers relevant parameter in finite element model
Uncertainty, described uncertain uncertain including stochastic uncertainty and cognition, the stochastic uncertainty includes elastic
Modulus and shearing rigidity;The uncertain cognition includes density of material and atmospheric density ratio;
4) it is based on flutter speed probability distribution, quantifies the uncertain degree and nargin of flutter speed by QMU technology, and is set
Factor CR is believed, as Flutter Boundaries safety evaluation foundation;Uncertainty quantization and QMU appraisal procedure are as follows:
(1) stochastic uncertainty of flutter speed, uncertainty are indicated by probability distribution are as follows:
The flutter speed that Flutter Boundaries are obtained by deterministic parameters calculation is multiplied by obtained by certain safety coefficient K, it is assumed that there is also random
Uncertainty is described by probability distribution, the accounting equation of uncertainty are as follows:
The then uncertainty calculation equation of whole system are as follows:
Wherein, V indicates to consider the flutter speed probability distribution of stochastic uncertainty, and VL indicates the flutter side multiplied by safety coefficient K
The Cumulative Distribution Function on boundary, P indicate that probability, β indicate certain confidence level, take 0.95;
(2) nargin quantifies, safe distance of the nargin between Flutter Boundaries and the probability distribution of flutter speed, accounting equation
Are as follows:
(3) confidence factor is solved for judging flutter margin safety, the accounting equation of confidence factor are as follows:
In QMU technology, when CR is greater than 1, it is believed that system safety, then defined Flutter Boundaries have enough nargin to cover not really
Fixed degree, the criterion as Flutter Boundaries safety evaluation;
5) safe range that cognition uncertain factor is solved by confidence factor takes probabilistic two parameter regions of cognition
Between, it is divided into the section of n × n, m sample calculation is carried out to stochastic uncertainty parameter in each section and interpolation obtains CR=
The curve of 1 and CR=2 marks off the uncertain factor range for ensuring flutter safety and to a certain extent quantization uncertainty
The influence of factor.
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104615863A (en) * | 2015-01-14 | 2015-05-13 | 南京航空航天大学 | Flutter border prediction method for 3-dof wing with control plane |
CN104881585A (en) * | 2015-03-24 | 2015-09-02 | 南京航空航天大学 | Flutter boundary prediction method of two-degree-of-freedom wing |
CN104899471A (en) * | 2015-06-29 | 2015-09-09 | 中国航空工业集团公司西安飞机设计研究所 | Method for predicting test-flight fault load |
CN105740541A (en) * | 2016-01-29 | 2016-07-06 | 厦门大学 | Structural dynamical model modification-based prestress recognition method |
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Publication number | Priority date | Publication date | Assignee | Title |
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CN104615863A (en) * | 2015-01-14 | 2015-05-13 | 南京航空航天大学 | Flutter border prediction method for 3-dof wing with control plane |
CN104881585A (en) * | 2015-03-24 | 2015-09-02 | 南京航空航天大学 | Flutter boundary prediction method of two-degree-of-freedom wing |
CN104899471A (en) * | 2015-06-29 | 2015-09-09 | 中国航空工业集团公司西安飞机设计研究所 | Method for predicting test-flight fault load |
CN105740541A (en) * | 2016-01-29 | 2016-07-06 | 厦门大学 | Structural dynamical model modification-based prestress recognition method |
Non-Patent Citations (2)
Title |
---|
A QMU approach for characterizing the operability limits of air-breathing hypersonic vehicles;Gianluca Iaccarino 等;《Reliability Engineering and System Safety》;20111231;第1151-1160页 |
热结构不确定性动力学仿真及模型确认方法研究;张保强;《中国博士学位论文全文数据库 基础科学辑》;20140615(第6期);第A004-1页 |
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