CN114491405A - Flutter stability parameter acquisition method and device - Google Patents
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Abstract
The application provides a flutter stability parameter obtaining method and device. Firstly, generating b training sample vectors by an orthogonal design method according to the mean value and the standard deviation corresponding to a uncertainty parameters; then, according to the b training sample vectors and one or more deterministic parameters, acquiring flutter stability coefficients corresponding to the b training sample vectors through a stability algorithm; b training sample vectors are used as independent variables, flutter stability coefficients corresponding to the b training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm; and finally, acquiring flutter stability parameters according to N randomly generated to-be-detected sample vectors subject to joint probability distribution, the mapping relation expression and a preset discrimination function. The method obtains the flutter stability parameters through the preset discrimination function, and can obtain the flutter stability parameters more accurately.
Description
Technical Field
The application relates to the field of wind tunnel tests, in particular to a flutter stability parameter obtaining method and device, electronic equipment and a storage medium.
Background
In the flutter test of the wind tunnel test, in order to obtain the flutter test data, a subcritical continuous variable speed pressure flutter analysis method is adopted at present. However, this method is a sectional calculation when power calculation is performed, and does not fully consider the characteristic that the flutter test data has time-varying characteristics, and the calculation result is unreliable. Therefore, it is an urgent problem to be solved by those skilled in the art to provide a data processing method capable of accurately obtaining flutter test data.
Disclosure of Invention
An embodiment of the present application provides a method and an apparatus for acquiring a flutter stability parameter, an electronic device, and a storage medium, so as to solve the above technical problem.
The invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides a flutter stability parameter obtaining method, including: according toaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA vector of training samples, each of said vector of training samples being derived from saidaTest data corresponding to each uncertainty parameter is formed, wherein,aandbis a non-zero natural number, and the number of the non-zero natural number,bmaximum value of andapresenting an exponential relationship; according to the abovebA training sample vector and one or more deterministic parameters, said training sample vector being obtained by a stability algorithmbThe flutter stability coefficients corresponding to the training sample vectors respectively; with the saidbThe training sample vectors are independent variables, the flutter stability coefficients corresponding to the training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm; according to random generationNSample vector to be tested subject to joint probability distribution and mapping relation expressionObtaining flutter stability parameters according to a formula and a preset discriminant function, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter is formed.
Compared with the prior art, the flutter stability parameter obtaining method provided by the embodiment of the invention comprises the steps of firstly, generating b training sample vectors through an orthogonal design method according to the mean value and the standard deviation corresponding to a uncertainty parameters; then, according to the b training sample vectors and one or more deterministic parameters, acquiring flutter stability coefficients corresponding to the b training sample vectors through a stability algorithm; b training sample vectors are used as independent variables, flutter stability coefficients corresponding to the b training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm; and finally, acquiring flutter stability parameters according to N randomly generated sample vectors to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function. The method obtains the flutter stability parameters through the preset discrimination function, and can obtain the flutter stability parameters more accurately.
With reference to the technical solution provided by the first aspect, in some possible implementations, the method is based onaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA training sample vector comprising: according toaGenerating at least one group of first training sample vectors by an orthogonal design method according to the mean value and standard deviation of each uncertainty parameter, wherein the mean value of each uncertainty parameter is
Standard deviation of
And 3 levels corresponding to each uncertainty parameter in each of the first training sample vectors are:
or is of
Wherein
The number of the first training sample vectors in each group is
，jIs numbered for a group, there is
(ii) a Combining the first training sample vector into a training sample vector, wherein the number of samples of the training sample vector isb，
Or
Wherein, in the step (A),
is the number of sets of the first training sample vector.
With reference to the technical solution provided by the first aspect, in some possible implementations, the method further includesbThe training sample vector is an independent variable, the flutter stability coefficient corresponding to each training sample vector is used as a dependent variable to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm, wherein the mapping relation expression comprises the following steps: according to the abovebTraining sample vectors, flutter stability coefficients corresponding to the training sample vectors and preset rules are obtained, and optimal offset and the flutter stability coefficients are obtainedbThe optimal Lagrange dual corresponding to each training sample vector; according to the optimal offset and thebAnd obtaining the mapping relation expression by the training sample vectors and the optimal Lagrange dual corresponding to the training sample vectors.
In combination with the first aspect mentioned aboveIn some possible implementations, the method further includes generating the reference signal according to a random basisNObtaining flutter stability parameters by the sample vector to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter comprises: according to random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNThe flutter stability coefficients corresponding to the sample vectors to be measured are calculatedNThe mean value and the standard deviation of the flutter stability coefficients corresponding to the sample vectors to be detected respectively, wherein each sample vector to be detected is formed by the flutter stability coefficientsaRandom data corresponding to each uncertainty parameter; according to the aboveNThe flutter stability coefficients corresponding to the sample vectors to be measured and the preset discriminant function are respectively calculatedNFuzzy membership values corresponding to the sample vectors to be detected respectively; calculating the saidNThe sum of the fuzzy membership value corresponding to each sample vector to be testedNAnd taking the ratio as the failure probability; and calculating the reliability index according to the failure probability and a preset rule.
With reference to the technical solution provided by the first aspect, in some possible implementations, the method is performed according to the abovebTraining sample vectors, flutter stability coefficients corresponding to the training sample vectors and preset rules are obtained, and optimal offset and the flutter stability coefficients are obtainedbBefore the optimal lagrangian dual corresponding to each training sample vector, the method further includes: to the abovebCarrying out normalization processing on test data in each training sample vector; in said according to random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNBefore the flutter stability coefficients corresponding to the sample vectors to be detected, the method further comprises: for the random generationNSubject to joint probabilityAnd carrying out normalization processing on the distributed sample vectors to be detected.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the stability algorithm includes: any one of a limit balance method, a finite element method, or a finite difference method.
With reference to the technical solution provided by the first aspect, in some possible implementations, the deterministic parameters include a wing bending stiffness, a torsion stiffness, a wing center of gravity, and an air density.
In a second aspect, an embodiment of the present application provides a flutter stability parameter obtaining apparatus, including: a generation module for generatingaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA vector of training samples, each of said vector of training samples being derived from saidaTest data corresponding to each uncertainty parameter is formed, wherein,aandbis a non-zero natural number, and the number of the non-zero natural number,bmaximum value of andapresenting an exponential relationship; a first obtaining module for obtaining the data according tobA training sample vector and one or more deterministic parameters, said training sample vector being obtained by a stability algorithmbThe flutter stability coefficients corresponding to the training sample vectors respectively; a second obtaining module for obtaining the databThe training sample vectors are independent variables, the flutter stability coefficients corresponding to the training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm; a third obtaining module for obtaining the data according to random generationNObtaining flutter stability parameters by the sample vector to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter is formed.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory, the processor and the memory connected; the memory is used for storing programs; the processor is configured to invoke a program stored in the memory to perform a method as provided in the above-described first aspect embodiment and/or in combination with some possible implementations of the above-described first aspect embodiment.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, performs the method as set forth in the above first aspect embodiment and/or in combination with some possible implementations of the above first aspect embodiment.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating steps of a method for obtaining a flutter stability parameter according to an embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating steps of another method for obtaining flutter stability parameters according to an embodiment of the present disclosure.
Fig. 4 is a block diagram of a flutter stability parameter obtaining device according to an embodiment of the present application.
An icon: 100-an electronic device; 110-a processor; 120-a memory; 300-a flutter stability parameter acquisition device; 310-a generation module; 320-a first obtaining module; 330-a second obtaining module; 340-third obtaining module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, a schematic block diagram of an electronic device 100 applying a method and an apparatus for acquiring a flutter stability parameter according to an embodiment of the present disclosure is provided. In the embodiment of the present application, the electronic Device 100 may be, but is not limited to, a Personal Computer (PC), a smart phone, a tablet computer, a Personal Digital Assistant (PDA), an Internet access Device (aobe), and the like. Structurally, electronic device 100 may include a processor 110 and a memory 120.
The processor 110 and the memory 120 are electrically connected directly or indirectly to enable data transmission or interaction, for example, the components may be electrically connected to each other via one or more communication buses or signal lines. The flutter stability parameter acquiring means includes at least one software module which may be stored in the form of software or firmware (firmware) in the memory 120 or solidified in an Operating System (OS) of the electronic device 100. The processor 110 is configured to execute executable modules stored in the memory 120, such as software functional modules and computer programs included in the flutter stability parameter acquiring device, so as to implement the flutter stability parameter acquiring method. The processor 110 may execute the computer program upon receiving the execution instruction.
The processor 110 may be an integrated circuit chip having signal processing capabilities. The Processor 110 may also be a general-purpose Processor, for example, a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a discrete gate or transistor logic device, or a discrete hardware component, which may implement or execute the methods, steps, and logic blocks disclosed in the embodiments of the present Application. Further, a general purpose processor may be a microprocessor or any conventional processor or the like.
The memory 120 may be, but is not limited to, a random Access memory (RAa), a Read Only memory (ROa), a programmable Read Only memory (PROa), an Erasable programmable Read Only memory (EPROa), and an electrically Erasable programmable Read Only memory (EEPROa). The memory 120 is used for storing a program, and the processor 110 executes the program after receiving the execution instruction.
It should be noted that the structure shown in fig. 1 is only an illustration, and the electronic device 100 provided in the embodiment of the present application may also have fewer or more components than those shown in fig. 1, or have a different configuration than that shown in fig. 1. Further, the components shown in fig. 1 may be implemented by software, hardware, or a combination thereof.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a method for obtaining a flutter stability parameter according to an embodiment of the present disclosure, where the method is applied to the electronic apparatus 100 shown in fig. 1. It should be noted that the flutter stability parameter obtaining method provided in the embodiment of the present application is not limited by the sequence shown in fig. 2 and the following, and the method includes: step S101-step S104.
Step S101: according toaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA vector of training samples, each of said vector of training samples being derived from saidaTest data corresponding to each uncertainty parameter is formed, wherein,aandbis a non-zero natural number, and is,bmaximum value of andain an exponential relationship.
Wherein, step S101 may specifically include: according toaGenerating at least one group of first training sample vectors by an orthogonal design method according to the mean value and standard deviation of each uncertainty parameter, wherein the mean value of each uncertainty parameter is
Standard deviation of
And 3 levels corresponding to each uncertainty parameter in each of the first training sample vectors are:
or is of
In which
The number of the first training sample vectors in each group is
，jIs numbered as a group, having
(ii) a Combining the first training sample vector into a training sample vector, wherein the number of samples of the training sample vector isb，
Or
Wherein, in the process,
is the number of sets of the first training sample vector.
Step S102: according to the abovebA training sample vector and one or more deterministic parameters, said training sample vector being obtained by a stability algorithmbAnd the flutter stability coefficients of the training sample vectors correspond to the training sample vectors respectively.
Wherein the stability algorithm comprises: any one of a finite-balance method, a finite-element method, or a finite-difference method.
The deterministic parameters comprise wing bending rigidity, torsional rigidity, wing gravity center and air density.
Step S103: with the saidbAnd (3) taking the training sample vector as an independent variable and the flutter stability coefficient corresponding to the training sample vector as a dependent variable to form a mapping relation, and obtaining a mapping relation expression through an SVM algorithm.
Referring to fig. 3, step S103 may specifically include: step S201-step S202.
Step S201: according to the abovebTraining sample vector and its respective flutter stabilityDetermining the coefficient, and presetting the rule to obtain the optimal offset andband the optimal Lagrangian dual corresponding to each training sample vector.
Step S202: according to the optimal offset and thebAnd obtaining the mapping relation expression by the training sample vectors and the optimal Lagrange dual corresponding to the training sample vectors.
Optionally, before step S201, the method for obtaining the flutter stability parameter provided in the embodiment of the present application further includes: to the abovebCarrying out normalization processing on test data in each training sample vector; at said random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNBefore the flutter stability coefficients corresponding to the sample vectors to be detected, the method further comprises: for the random generationNAnd carrying out normalization processing on the sample vector to be detected subjected to the joint probability distribution.
Step S104: according to random generationNObtaining flutter stability parameters by the sample vector to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter is formed.
Wherein, the step S104 may specifically include: according to random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNThe flutter stability coefficients corresponding to the sample vectors to be measured are calculatedNThe mean value and the standard deviation of the flutter stability coefficients corresponding to the sample vectors to be detected respectively, wherein each sample vector to be detected is formed by the flutter stability coefficientsaRandom data corresponding to each uncertainty parameter; according to the aboveNThe flutter stability coefficients corresponding to the sample vectors to be measured and the preset discriminant function are respectively calculatedNFuzzy membership values corresponding to the sample vectors to be detected respectively; calculate theNThe accumulated sum and of the fuzzy membership value corresponding to each sample vector to be measuredNAnd taking the ratio as the failure probability; and calculating the reliability index according to the failure probability and a preset rule.
In summary, compared with the prior art, in the method for acquiring flutter stability parameters provided in the embodiments of the present invention, firstly, b training sample vectors are generated by an orthogonal design method according to the respective mean values and standard deviations corresponding to a uncertainty parameters; then, according to the b training sample vectors and one or more deterministic parameters, acquiring flutter stability coefficients corresponding to the b training sample vectors through a stability algorithm; b training sample vectors are used as independent variables, flutter stability coefficients corresponding to the b training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm; and finally, acquiring flutter stability parameters according to N randomly generated sample vectors to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function. The method obtains the flutter stability parameters through the preset discrimination function, and can obtain the flutter stability parameters more accurately.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present application further provides a flutter stability parameter obtaining apparatus 300, including:
a generating module 310 for generatingaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA vector of training samples, each of said vector of training samples being derived from saidaTest data corresponding to each uncertainty parameter is formed, wherein,aand withbIs a non-zero natural number, and the number of the non-zero natural number,bmaximum value of andain an exponential relationship.
A first obtaining module 320 for obtaining a first parameter according to the abovebA training sample vector and one or more deterministic parameters, said training sample vector being obtained by a stability algorithmbAnd the flutter stability coefficients of the training sample vectors correspond to the training sample vectors respectively.
A second obtaining module 330 for obtaining the databEach training sample vector is used as an independent variable, and the flutter stability coefficient corresponding to each training sample vector is used as an independent variableAnd (4) forming a mapping relation by the dependent variable, and acquiring a mapping relation expression through an SVM algorithm.
A third obtaining module 340 for obtaining the data according to random generationNObtaining flutter stability parameters by the sample vector to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter is formed.
Optionally, the generating module 310 is specifically configured toaGenerating at least one group of first training sample vectors by an orthogonal design method according to the mean value and standard deviation of each uncertainty parameter, wherein the mean value of each uncertainty parameter is
Standard deviation of
And 3 levels corresponding to each uncertainty parameter in each of the first training sample vectors are:
or is of
Wherein
The number of the first training sample vectors in each group is
，jIs numbered as a group, having
(ii) a Combining the first training sample vector into a training sample vectorThe number of samples of the training sample vector isb，
Or
Wherein, in the step (A),
is the number of sets of the first training sample vector.
Optionally, the second obtaining module 330 is specifically configured to obtain the information according to the above descriptionbTraining sample vectors, flutter stability coefficients corresponding to the training sample vectors and preset rules are obtained, and optimal offset and the flutter stability coefficients are obtainedbThe optimal Lagrange dual corresponding to each training sample vector; according to the optimal offset and thebAnd obtaining the mapping relation expression by the training sample vectors and the optimal Lagrange dual corresponding to the training sample vectors.
Optionally, the third obtaining module 340 is specifically configured to generate according to randomNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNThe flutter stability coefficients corresponding to the sample vectors to be measured are calculatedNThe mean value and the standard deviation of the flutter stability coefficients corresponding to the sample vectors to be detected respectively, wherein each sample vector to be detected is formed by the flutter stability coefficientsaRandom data corresponding to each uncertainty parameter; according to the aboveNThe flutter stability coefficients corresponding to the sample vectors to be measured and the preset discriminant function are respectively calculatedNFuzzy membership values corresponding to the sample vectors to be detected respectively; calculating the saidNThe accumulated sum and of the fuzzy membership value corresponding to each sample vector to be measuredNAnd taking the ratio as the failure probability; and calculating the reliability index according to the failure probability and a preset rule.
Optionally, the apparatus further comprises a normalization module. The normalization module is specifically configured for said method according tobTraining sample vectors, their respective flutter stability coefficients, and presetsRules, obtaining optimal offsets and thebBefore the optimal Lagrange dual corresponding to each training sample vector, the training sample vectors are pairedbCarrying out normalization processing on test data in each training sample vector; in said according to random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNBefore the flutter stability coefficients corresponding to the sample vectors to be detected, the method further comprises: for the random generationNAnd carrying out normalization processing on the sample vector to be detected subjected to the joint probability distribution.
It should be noted that, as those skilled in the art can clearly understand, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Based on the same inventive concept, embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the computer program performs the methods provided in the above embodiments.
The storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A flutter stability parameter acquisition method, comprising:
according toaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA vector of training samples, each of said vector of training samples being derived from saidaTest data corresponding to each uncertainty parameter is formed, wherein,aandbis a non-zero natural number, and the number of the non-zero natural number,bmaximum value of andapresenting an exponential relationship;
according to the abovebA training sample vector and one or more deterministic parameters, said training sample vector being obtained by a stability algorithmbEach training sample vector corresponding toA flutter stability factor;
with the saidbThe training sample vectors are independent variables, the flutter stability coefficients corresponding to the training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm;
according to random generationNObtaining flutter stability parameters by the sample vector to be detected subject to joint probability distribution, the mapping relation expression and a preset discrimination function, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter is formed.
2. The flutter stability parameter obtaining method according to claim 1, wherein the reference is based onaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA training sample vector comprising:
according toaGenerating at least one group of first training sample vectors by an orthogonal design method according to the mean value and standard deviation of each uncertainty parameter, wherein the mean value of each uncertainty parameter is
Standard deviation of
And 3 levels corresponding to each uncertainty parameter in each of the first training sample vectors are:
or is of
Wherein
The number of the first training sample vectors in each group is
，jIs numbered for a group, there is
；
Combining the first training sample vector into a training sample vector, wherein the number of samples of the training sample vector isb，
Or
Wherein, in the step (A),
is the number of sets of the first training sample vector.
3. The flutter stability parameter obtaining method according to claim 1, wherein the flutter stability parameter is obtained bybThe training sample vector is an independent variable, the flutter stability coefficient corresponding to the training sample vector is a dependent variable, a mapping relation is formed, and a mapping relation expression is obtained through an SVM algorithm, and the method comprises the following steps:
according to the abovebTraining sample vectors, flutter stability coefficients corresponding to the training sample vectors and preset rules are obtained, and optimal offset and the flutter stability coefficients are obtainedbThe optimal Lagrange dual corresponding to each training sample vector;
according to the optimal offset and thebAnd obtaining the mapping relation expression by the training sample vectors and the optimal Lagrange dual corresponding to the training sample vectors.
4. The flutter stability parameter acquisition method according to claim 3, wherein the method is according to randomly generatedNSubject to federationCombining the probability distribution of the vector of the sample to be detected, the mapping relation expression and a preset discrimination function to obtain the flutter stability parameters, wherein the flutter stability parameters compriseNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter comprises:
according to random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNThe flutter stability coefficients corresponding to the sample vectors to be measured are calculatedNThe mean value and the standard deviation of the flutter stability coefficients corresponding to the sample vectors to be detected respectively, wherein each sample vector to be detected is formed by the flutter stability coefficientsaRandom data corresponding to each uncertainty parameter;
according to the aboveNThe flutter stability coefficients corresponding to the sample vectors to be measured and the preset discriminant function are respectively calculatedNFuzzy membership values corresponding to the sample vectors to be detected respectively;
calculating the saidNThe accumulated sum and of the fuzzy membership value corresponding to each sample vector to be measuredNAnd taking the ratio as the failure probability;
and calculating the reliability index according to the failure probability and a preset rule.
5. The flutter stability parameter acquiring method according to claim 4, wherein the flutter stability parameter acquiring method is based on thebTraining sample vectors, flutter stability coefficients corresponding to the training sample vectors and preset rules are obtained, and optimal offset and the flutter stability coefficients are obtainedbBefore the optimal lagrangian dual corresponding to each training sample vector, the method further comprises:
to the abovebCarrying out normalization processing on test data in each training sample vector;
in said according to random generationNObtaining a sample vector to be measured subject to joint probability distribution and the mapping relation expressionNBefore the flutter stability coefficients corresponding to the sample vectors to be detected, the method further comprises:
for the random generationNAnd carrying out normalization processing on the sample vector to be detected subjected to the joint probability distribution.
6. The flutter stability parameter acquiring method according to claim 1, wherein the stability algorithm comprises: any one of a limit balance method, a finite element method, or a finite difference method.
7. The method of obtaining flutter stability parameters according to claim 1, wherein the deterministic parameters comprise wing bending stiffness, torsional stiffness, wing center of gravity and air density.
8. A flutter stability parameter obtaining device, comprising:
a generation module for generatingaThe mean value and standard deviation corresponding to each uncertainty parameter are generated by an orthogonal design methodbA vector of training samples, each of said vector of training samples being derived from saidaTest data corresponding to each uncertainty parameter is formed, wherein,aandbis a non-zero natural number, and the number of the non-zero natural number,bmaximum value of andapresenting an exponential relationship;
a first obtaining module for obtaining the data according tobA training sample vector and one or more deterministic parameters, said training sample vector being obtained by a stability algorithmbThe flutter stability coefficients corresponding to the training sample vectors respectively;
a second obtaining module for obtainingbThe training sample vectors are independent variables, the flutter stability coefficients corresponding to the training sample vectors are used as dependent variables to form a mapping relation, and a mapping relation expression is obtained through an SVM algorithm;
a third obtaining module for obtaining the data according to random generationNObtaining a flutter stability parameter by a to-be-detected sample vector subject to joint probability distribution, the mapping relation expression and a preset discrimination function, wherein the flutter stability parameterIncludedNThe mean value and standard deviation, failure probability and reliability index of flutter stability coefficient corresponding to each sample vector to be detected, wherein each sample vector to be detected is composed ofaRandom data corresponding to each uncertainty parameter is formed.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107229768A (en) * | 2017-04-12 | 2017-10-03 | 中国地质大学（武汉） | Slopereliability parameter acquiring method and device based on fuzzy classification technology |
| CN109086501A (en) * | 2018-07-20 | 2018-12-25 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of flutter prediction technique |
| US10482348B1 (en) * | 2012-01-22 | 2019-11-19 | Sr2 Group, Llc | System and method for tracking coherently structured feature dynamically defined with migratory medium |
| CN110470450A (en) * | 2019-08-27 | 2019-11-19 | 中国空气动力研究与发展中心高速空气动力研究所 | Wind tunnel test flutter stability parameter prediction method and device |
| CN110657939A (en) * | 2019-08-30 | 2020-01-07 | 中国空气动力研究与发展中心高速空气动力研究所 | Flutter critical prediction method and device |
| CN114169036A (en) * | 2021-09-06 | 2022-03-11 | 东南大学 | Wind vibration response early warning system and method for large-span bridge |
-
2022
- 2022-04-02 CN CN202210340461.8A patent/CN114491405A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10482348B1 (en) * | 2012-01-22 | 2019-11-19 | Sr2 Group, Llc | System and method for tracking coherently structured feature dynamically defined with migratory medium |
| CN107229768A (en) * | 2017-04-12 | 2017-10-03 | 中国地质大学（武汉） | Slopereliability parameter acquiring method and device based on fuzzy classification technology |
| CN109086501A (en) * | 2018-07-20 | 2018-12-25 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of flutter prediction technique |
| CN110470450A (en) * | 2019-08-27 | 2019-11-19 | 中国空气动力研究与发展中心高速空气动力研究所 | Wind tunnel test flutter stability parameter prediction method and device |
| CN110657939A (en) * | 2019-08-30 | 2020-01-07 | 中国空气动力研究与发展中心高速空气动力研究所 | Flutter critical prediction method and device |
| CN114169036A (en) * | 2021-09-06 | 2022-03-11 | 东南大学 | Wind vibration response early warning system and method for large-span bridge |
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