CN115017963A - Flutter boundary prediction method and device, electronic equipment and storage medium - Google Patents

Flutter boundary prediction method and device, electronic equipment and storage medium Download PDF

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CN115017963A
CN115017963A CN202210943989.4A CN202210943989A CN115017963A CN 115017963 A CN115017963 A CN 115017963A CN 202210943989 A CN202210943989 A CN 202210943989A CN 115017963 A CN115017963 A CN 115017963A
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matrix
flutter
response
modal parameters
model
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郭洪涛
路波
闫昱
余立
吕彬彬
李阳
曾开春
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High Speed Aerodynamics Research Institute of China Aerodynamics Research and Development Center
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High Speed Aerodynamics Research Institute of China Aerodynamics Research and Development Center
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    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
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Abstract

The application provides a flutter boundary prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a free vibration response; extracting free attenuation response of the system from the excitation response by a random decrement method; identifying modal parameters in the free vibration response based on a matrix xPincil algorithm; and determining a flutter boundary predicted value based on the modal parameters and a preset model. In the embodiment of the application, the main modal parameters in the excitation response can be accurately identified by a random decrement method and a matrix beam method, and then the prediction of the flutter boundary is realized by the modal parameters and a preset model.

Description

Flutter boundary prediction method and device, electronic equipment and storage medium
Technical Field
The application relates to the field of wind tunnel tests, in particular to a flutter boundary prediction method and device, electronic equipment and a storage medium.
Background
Flutter is a self-excited oscillation, which is a destructive aeroelastic unstable state generated by an elastic structure under the coupling action of aerodynamic force, inertia force and elastic force. Therefore, how to provide a method for accurately acquiring the flutter boundary is a problem to be solved at present.
Disclosure of Invention
An embodiment of the present application provides a method and an apparatus for predicting a flutter boundary, an electronic device, and a storage medium, so as to accurately obtain the flutter boundary.
The invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides a flutter boundary prediction method, which is applied to an electronic device, and the method includes: acquiring a free vibration response; extracting free attenuation response of the system from the excitation response by a random decrement method; identifying modal parameters in the free vibration response based on a matrix xPincil algorithm; and determining the flutter boundary predicted value based on the modal parameters and a preset model.
In the embodiment of the application, the main modal parameters in the excitation response can be accurately identified by a random decrement method and a matrix beam method, and then the prediction of the flutter boundary is realized by the modal parameters and a preset model. Furthermore, this approach is particularly suitable for prediction of flutter boundaries at lower wind speeds.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the determining the flutter boundary prediction value based on the modal parameter and a preset model includes: inputting the modal parameters into the convolutional neural network model to obtain flutter boundary predicted values corresponding to the modal parameters; wherein the convolutional neural network model is obtained by the following steps: obtaining sample data, wherein the sample data comprises sample modal parameters and flutter boundary numerical values corresponding to the sample modal parameters; dividing the sample data into a training set and a test set; inputting the training set into a constructed initial model for training until the model converges; and checking the initial model through the test set, and when the verification accuracy exceeds a preset threshold value, taking the model as the convolutional neural network model.
In the embodiment of the present application, the preset model is a convolutional neural network model. That is, the embodiment of the application is combined with a trained neural network model to realize the prediction of the flutter boundary, so that the optimal solution can be quickly found out, and the calculation efficiency is improved. And the neural network model has strong parallelism and applicability, and can continuously perform iterative learning and continuously optimize a prediction result.
With reference to the technical solution provided by the first aspect, in some possible implementations, the obtaining the sample modal parameter includes: obtaining free vibration response of a sample; extracting a sample free attenuation response of the system from the excitation response by a random decrement method; and identifying modal parameters in the sample free attenuation response based on a Matrix Xencil algorithm.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the obtaining a free vibration response includes: obtaining a vibration response signal of a test point under the excitation of white noise; intercepting the vibration response signal based on a set interception amplitude; moving the time starting point of the intercepted vibration response signal to obtain a time-shifted random process function; determining the free-vibration response based on the stochastic process function.
With reference to the technical solution provided by the first aspect, in some possible implementations, the expression of the excitation response signal is:
Figure 100002_DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE002
representing the stimulus response signal;Mrepresenting the modal order of the system;f(t) Represents a white noise excitation;n(t) To measure noise;
Figure 100002_DEST_PATH_IMAGE003
is shown as
Figure 100002_DEST_PATH_IMAGE004
Natural frequencies of order modes;
Figure 100002_DEST_PATH_IMAGE005
is shown as
Figure 206764DEST_PATH_IMAGE004
Damping ratio of order mode.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the identifying a modal parameter in the free vibration response based on a MatrixPencil algorithm includes: expressing the free vibration response in a discrete time form to obtain a discrete time response signal; constructing a Hankel matrix based on the discrete time response signal to obtain a first matrix; singular value decomposition is carried out on the first matrix, and optimization processing is carried out to obtain a second matrix and a third matrix; constructing a matrix bundle based on the second matrix and the third matrix; and calculating the characteristic value of the matrix bundle to obtain the modal parameter.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the preset model is a flutter margin model; wherein the flutter margin model has an expression as follows:
Figure 100002_DEST_PATH_IMAGE006
；
wherein, the first and the second end of the pipe are connected with each other,FMthe flutter margin is represented by the amount of flutter,
Figure 100002_DEST_PATH_IMAGE007
and
Figure 100002_DEST_PATH_IMAGE008
natural frequencies representing different order modes;
Figure 100002_DEST_PATH_IMAGE009
and
Figure 100002_DEST_PATH_IMAGE010
representing the damping ratio of different order modes.
In a second aspect, an embodiment of the present application provides a flutter boundary prediction apparatus, which is applied to an electronic device, where the apparatus includes: the acquisition module is used for acquiring free vibration response; extracting free attenuation response of the system from the excitation response by a random decrement method; the identification module is used for identifying modal parameters in the free vibration response based on a matrix xPincil algorithm; and the determining module is used for determining the flutter boundary predicted value based on the modal parameters and a preset model.
With reference to the technical solution provided by the second aspect, in some possible implementation manners, the preset model is a convolutional neural network model, and the determining module is specifically configured to input the modal parameter into the convolutional neural network model to obtain a flutter boundary prediction value corresponding to the modal parameter.
Correspondingly, the device further comprises a training module. The training module is specifically used for acquiring sample data, wherein the sample data comprises sample modal parameters and flutter boundary numerical values corresponding to the sample modal parameters; dividing the sample data into a training set and a test set; inputting the training set into a constructed initial model for training until the model converges; and checking the initial model through the test set, and when the verification accuracy exceeds a preset threshold value, taking the model as the convolutional neural network model.
In combination with the technical solution provided by the second aspect, in some possible implementation manners, the training module is further specifically configured to obtain a free vibration response of the sample; extracting a sample free attenuation response of the system from the excitation response by a random decrement method; and identifying modal parameters in the sample free attenuation response based on a Matrix Xencil algorithm.
In some possible implementation manners, in combination with the technical solution provided by the second aspect, the obtaining module is specifically configured to obtain a vibration response signal of the test point under excitation of white noise; intercepting the vibration response signal based on a set interception amplitude; moving the time starting point of the intercepted vibration response signal to obtain a time-shifted random process function; determining the free-vibration response based on the stochastic process function.
With reference to the technical solution provided by the second aspect, in some possible implementations, the identification module is specifically configured to express the free vibration response in a discrete time form to obtain a discrete time response signal; constructing a Hankel matrix based on the discrete time response signal to obtain a first matrix; singular value decomposition is carried out on the first matrix, and optimization processing is carried out to obtain a second matrix and a third matrix; constructing a matrix bundle based on the second matrix and the third matrix; and calculating the characteristic value of the matrix bundle to obtain the modal parameter.
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, and to perform a method as provided in the foregoing first aspect embodiment and/or in conjunction with some possible implementations of the foregoing 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 predicting a flutter boundary according to an embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating steps of a training process of a convolutional neural network model according to an embodiment of the present disclosure.
Fig. 4 is a block diagram of a flutter boundary prediction apparatus according to an embodiment of the present disclosure.
Icon: 100-an electronic device; 110-a processor; 120-a memory; 200-flutter boundary prediction means; 201-an acquisition module; 202-an identification module; 203-determination 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 flutter boundary prediction method and apparatus according to an embodiment of the present application 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), a Mobile Internet Device (MID), 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 boundary predicting means includes at least one software module which may be stored in the memory 120 in the form of software or Firmware (Firmware) or solidified in an Operating System (OS) of the electronic device 100. The processor 110 is used to execute executable modules stored in the memory 120, such as software functional modules and computer programs included in the flutter boundary prediction device, so as to implement the flutter boundary prediction 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 (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), and an electrically Erasable Programmable Read-Only Memory (EEPROM). 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 predicting a flutter boundary 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 boundary prediction 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 S103.
Step S101: obtaining a free vibration response; wherein the free decay response of the system is extracted from the excitation response by a random decrement method.
Step S102: and identifying modal parameters in the free vibration response based on a Matrix Xencil algorithm.
Wherein, the MatrixPencil algorithm is a matrix bundle algorithm.
Step S103: and determining the flutter boundary predicted value based on the modal parameters and a preset model.
In the embodiment of the application, the main modal parameters in the excitation response can be accurately identified by a random decrement method and a matrix beam method, and then the prediction of the flutter boundary is realized by the modal parameters and a preset model. Furthermore, this approach is particularly suitable for prediction of flutter boundaries at lower wind speeds.
The above steps are described below with reference to specific examples.
In step S101, obtaining a free vibration response specifically includes: obtaining a vibration response signal of a test point under the excitation of white noise; intercepting the vibration response signal based on a set interception amplitude; moving the time starting point of the intercepted vibration response signal to obtain a time-shifted random process function; determining the free-vibration response based on the stochastic process function.
The above steps are described below with reference to specific equations.
For a linear structure, the expression of the vibration response signal of a test point under white noise excitation is:
Figure DEST_PATH_IMAGE012
（1）
in the formula (1), the first and second groups,
Figure DEST_PATH_IMAGE013
is at an initial displacement of 1 and an initial velocityA system free vibration response signal of 0;
Figure DEST_PATH_IMAGE014
the system free vibration response with initial displacement of 0 and initial speed of 1 is obtained;
Figure DEST_PATH_IMAGE015
and
Figure DEST_PATH_IMAGE016
respectively the initial displacement and the initial speed of the system vibration;
Figure DEST_PATH_IMAGE017
is the unit impulse response function of the system;
Figure DEST_PATH_IMAGE018
is an external stimulus.
Taking intercepted amplitude A to intercept vibration response signals of system
Figure DEST_PATH_IMAGE019
(ii) a Wherein, A is 1.5 times of the standard deviation of the input signal. Then different intersection points are obtained
Figure DEST_PATH_IMAGE020
For from
Figure DEST_PATH_IMAGE021
Response from time to time
Figure DEST_PATH_IMAGE022
By three-part linear superposition, including
Figure 786168DEST_PATH_IMAGE021
Free attenuation caused by initial displacement at a moment; by
Figure 633908DEST_PATH_IMAGE021
Free damping caused by the initial velocity at the moment and
Figure 713859DEST_PATH_IMAGE021
random excitation of time starts
Figure DEST_PATH_IMAGE023
Resulting in a broken vibration. Namely:
Figure DEST_PATH_IMAGE025
（2）
responding to the intercepted vibration signal
Figure DEST_PATH_IMAGE026
From the time of starting
Figure 208425DEST_PATH_IMAGE020
Moving to the left origin to obtain a series of time-shifted random process functions
Figure DEST_PATH_IMAGE027
Wherein
Figure DEST_PATH_IMAGE028
Namely:
Figure DEST_PATH_IMAGE030
（3）
the statistics of equation (3) are averaged to yield:
Figure DEST_PATH_IMAGE032
（4）
due to excitation
Figure DEST_PATH_IMAGE033
Is a smooth random excitation with an average value of 0, and the vibration response of the system
Figure 927989DEST_PATH_IMAGE002
、
Figure 330151DEST_PATH_IMAGE016
Also a smooth random response vibration with a mean value of 0, i.e. having
Figure DEST_PATH_IMAGE034
=0、
Figure DEST_PATH_IMAGE035
And = 0. Thus:
Figure DEST_PATH_IMAGE036
（5）
obtained thus far
Figure DEST_PATH_IMAGE037
The initial displacement is A, and the initial velocity is 0.
Optionally, in this embodiment of the present application, the expression of the excitation response signal is:
Figure DEST_PATH_IMAGE038
(6)
in the formula (6),
Figure 202292DEST_PATH_IMAGE002
representing the stimulus response signal;Mrepresenting the modal order of the system;
Figure DEST_PATH_IMAGE039
represents a white noise excitation;
Figure DEST_PATH_IMAGE040
to measure noise;
Figure 117027DEST_PATH_IMAGE003
is shown as
Figure 136936DEST_PATH_IMAGE004
Natural frequencies of order modes;
Figure 77210DEST_PATH_IMAGE005
is shown as
Figure 600595DEST_PATH_IMAGE004
Damping ratio of order mode.
In step S102, identifying modal parameters in the free vibration response based on a MatrixPencil algorithm, specifically including: expressing the free vibration response in a discrete time form to obtain a discrete time response signal; constructing a Hankel matrix based on the discrete time response signal to obtain a first matrix; singular value decomposition is carried out on the first matrix, and optimization processing is carried out to obtain a second matrix and a third matrix; constructing a matrix bundle based on the second matrix and the third matrix; and calculating the characteristic value of the matrix bundle to obtain the modal parameter.
The above steps are described below with reference to specific equations.
That is, obtained in step S101
Figure 296019DEST_PATH_IMAGE037
And identifying the modal parameters by adopting a matrix beam method.
Firstly, the free vibration response is expressed in a discrete time form, and the expression is as follows:
Figure DEST_PATH_IMAGE041
(7)
in the formula (7), the first and second groups,k=1,2,…,N，Ncounting the number of sampling points;
Figure DEST_PATH_IMAGE042
to measure noise;
Figure DEST_PATH_IMAGE043
is the pole of the system;
Figure DEST_PATH_IMAGE044
is a sampling period;
Figure 924928DEST_PATH_IMAGE003
is shown as
Figure 465631DEST_PATH_IMAGE004
Natural frequencies of order modes;
Figure 312364DEST_PATH_IMAGE005
denotes the first
Figure 913109DEST_PATH_IMAGE004
Damping ratio of order mode;Mrepresenting the modal order of the system.
Constructing a Hankel matrix Y based on the discrete time response signal to obtain a first matrix, wherein the expression of the first matrix is as follows:
Figure DEST_PATH_IMAGE045
(8)
in the formula (8), the first and second groups of the chemical reaction are shown in the specification,Lfor matrix beam parameters, takeN/3~N/2。
Singular value decomposition is carried out on the first matrix to obtain:
Y=UXV T (9)
subjecting the obtained product toVFront of the matrixMA dominant right singular neighbor formation (L+1)×MMatrix of dimensions
Figure DEST_PATH_IMAGE046
Deletion of
Figure 110873DEST_PATH_IMAGE046
Get one from the last row ofL×MMatrix of dimensionsV a (ii) a Simultaneous deletion
Figure 642217DEST_PATH_IMAGE046
Get one from the first row element removalL×MMatrix of dimensionsV b . Wherein the content of the first and second substances,V a andV b namely the second matrix and the third matrix. Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE047
；
Figure DEST_PATH_IMAGE048
；
Figure DEST_PATH_IMAGE050
indicating transposition.
Based on the second matrixV a And a third matrixV b Constructing a matrix bundle yields:
Figure 343457DEST_PATH_IMAGE051
(10)
and calculating characteristic values of the matrix bundle to obtain modal parameters. That is, equation (10) is converted to
Figure DEST_PATH_IMAGE052
；
Figure DEST_PATH_IMAGE053
Is thatV a- The pseudo-inverse matrix of (2). Solve outGAfter the characteristic value is obtained, the modal parameter (including the modal parameter) can be obtained through mathematical operation
Figure 583945DEST_PATH_IMAGE003
And
Figure 987114DEST_PATH_IMAGE005
）；
Figure DEST_PATH_IMAGE055
representing a preset constant.
Optionally, in step S103, the preset model is a convolutional neural network model. Correspondingly, the determining the flutter boundary prediction value based on the modal parameter and a preset model specifically includes:
and inputting the modal parameters into the convolutional neural network model to obtain flutter boundary predicted values corresponding to the modal parameters.
Referring to fig. 3, the convolutional neural network model is obtained by the following steps: step S201-step S204.
Step S201: and acquiring sample data, wherein the sample data comprises sample modal parameters and flutter boundary numerical values corresponding to the sample modal parameters.
Step S202: the sample data is divided into a training set and a test set.
Step S203: and inputting the training set into the constructed initial model for training until the model converges.
Step S204: and checking the initial model through the test set, and when the verification accuracy exceeds a preset threshold value, taking the model as the convolutional neural network model.
Correspondingly, the sample modal parameters are obtained through the following steps: obtaining free vibration response of a sample; extracting a sample free attenuation response of the system from the excitation response by a random decrement method; and identifying modal parameters in the sample free attenuation response based on a Matrix Xencil algorithm.
In the embodiment of the present application, the convolutional neural network model includes four convolutional layers, two pooling layers, and three full-connected layers.
In the embodiment of the present application, the preset model is a convolutional neural network model. That is, the embodiment of the application is combined with a trained neural network model to realize the prediction of the flutter boundary, so that the optimal solution can be quickly found out, and the calculation efficiency is improved. And the neural network model has strong parallelism and applicability, and can continuously perform iterative learning and continuously optimize a prediction result.
Optionally, the preset model is a flutter margin model.
Wherein the flutter margin model has an expression as follows:
Figure DEST_PATH_IMAGE056
（11）
in the formula (11), the reaction mixture,FMthe flutter margin is represented by the amount of flutter,
Figure 604040DEST_PATH_IMAGE007
and
Figure 159786DEST_PATH_IMAGE008
natural frequencies representing different order modes;
Figure 367913DEST_PATH_IMAGE009
and
Figure 9110DEST_PATH_IMAGE010
representing the damping ratio of different order modes.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present invention further provides a flutter boundary prediction apparatus 200, including:
an obtaining module 201, configured to obtain a free vibration response; wherein the free decay response of the system is extracted from the excitation response by a random decrement method.
And the identification module 202 is configured to identify a modal parameter in the free vibration response based on a MatrixPencil algorithm.
A determining module 203, configured to determine the flutter boundary prediction value based on the modal parameter and a preset model.
Optionally, the preset model is a convolutional neural network model, and the determining module 203 is specifically configured to input the modal parameter into the convolutional neural network model to obtain a flutter boundary prediction value corresponding to the modal parameter.
Correspondingly, the device further comprises a training module.
The training module is specifically used for acquiring sample data, wherein the sample data comprises sample modal parameters and flutter boundary numerical values corresponding to the sample modal parameters; dividing the sample data into a training set and a test set; inputting the training set into a constructed initial model for training until the model converges; and checking the initial model through the test set, and when the verification accuracy exceeds a preset threshold value, taking the model as the convolutional neural network model.
Optionally, the training module is further specifically configured to obtain a free vibration response of the sample; extracting a sample free attenuation response of the system from the excitation response by a random decrement method; and identifying modal parameters in the sample free attenuation response based on a Matrix Xencil algorithm.
Optionally, the obtaining module 201 is specifically configured to obtain a vibration response signal of a test point under white noise excitation; intercepting the vibration response signal based on a set interception amplitude; moving the time starting point of the intercepted vibration response signal to obtain a time-shifted random process function; determining the free-vibration response based on the stochastic process function.
Wherein the stimulus response signal is expressed by:
Figure DEST_PATH_IMAGE057
wherein, the first and the second end of the pipe are connected with each other,
Figure 147836DEST_PATH_IMAGE002
representing the stimulus response signal;Mrepresenting the modal order of the system;f(t) Represents a white noise excitation;n(t) To measure noise;
Figure 885985DEST_PATH_IMAGE003
is shown as
Figure 202697DEST_PATH_IMAGE004
Natural frequencies of order modes;
Figure 393507DEST_PATH_IMAGE005
is shown as
Figure 821077DEST_PATH_IMAGE004
Damping ratio of order mode.
Optionally, the identification module 202 is specifically configured to express the free vibration response in a discrete time form to obtain a discrete time response signal; constructing a Hankel matrix based on the discrete time response signal to obtain a first matrix; singular value decomposition is carried out on the first matrix, and optimization processing is carried out to obtain a second matrix and a third matrix; constructing a matrix bundle based on the second matrix and the third matrix; and calculating the characteristic value of the matrix bundle to obtain the modal parameter.
Optionally, the preset model is a flutter margin model; wherein the flutter margin model has an expression as follows:
Figure 882574DEST_PATH_IMAGE056
wherein the content of the first and second substances,FMthe flutter margin is represented by the amount of flutter,
Figure 635767DEST_PATH_IMAGE007
and
Figure 313873DEST_PATH_IMAGE008
natural frequencies representing different orders of modes;
Figure 341872DEST_PATH_IMAGE009
and
Figure 978914DEST_PATH_IMAGE010
representing the damping ratio of different order modes. It should be noted that, as those skilled in the art can clearly understand, for convenience and simplicity of description, for a specific working process of the above-described system, apparatus and unit, reference may be made to a corresponding process in the foregoing method embodiment, and details are not repeated herein.
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 apparatus embodiments are merely illustrative, and for example, the division of the units into only one type of logical function may be implemented in other ways, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. 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, second, third, 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 (10)

1. A flutter boundary prediction method applied to an electronic device, the method comprising:
obtaining a free vibration response; extracting free attenuation response of the system from the excitation response by a random decrement method;
identifying modal parameters in the free vibration response based on a matrix xPincil algorithm;
and determining a flutter boundary predicted value based on the modal parameters and a preset model.
2. The method according to claim 1, wherein the preset model is a convolutional neural network model, and the determining a flutter boundary prediction value based on the modal parameters and the preset model comprises:
inputting the modal parameters into the convolutional neural network model to obtain flutter boundary predicted values corresponding to the modal parameters;
wherein the convolutional neural network model is obtained by the following steps:
obtaining sample data, wherein the sample data comprises sample modal parameters and flutter boundary numerical values corresponding to the sample modal parameters;
dividing the sample data into a training set and a test set;
inputting the training set into a constructed initial model for training until the model converges;
and checking the initial model through the test set, and when the verification accuracy exceeds a preset threshold value, taking the model as the convolutional neural network model.
3. The method of flutter boundary prediction according to claim 2, wherein the sample modal parameters are obtained by the steps comprising:
obtaining free vibration response of a sample; extracting a sample free attenuation response of the system from the excitation response by a random decrement method;
and identifying modal parameters in the sample free attenuation response based on a Matrix Xencil algorithm.
4. The method of flutter boundary prediction according to claim 1, wherein the obtaining a free-vibration response comprises:
obtaining a vibration response signal of a test point under the excitation of white noise;
intercepting the vibration response signal based on a set interception amplitude;
moving the time starting point of the intercepted vibration response signal to obtain a time-shifted random process function;
determining the free-vibration response based on the stochastic process function.
5. The flutter boundary prediction method according to claim 4, wherein an excitation response signal is expressed by:
Figure DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE002
representing the stimulus response signal;Mrepresenting the modal order of the system;f(t) Represents a white noise excitation;n(t) To measure noise;
Figure DEST_PATH_IMAGE003
is shown as
Figure DEST_PATH_IMAGE004
Natural frequencies of order modes;
Figure DEST_PATH_IMAGE005
is shown as
Figure 404751DEST_PATH_IMAGE004
Damping ratio of order mode.
6. The method according to claim 1, wherein the identifying modal parameters in the free vibration response based on a MatrixPencil algorithm comprises:
expressing the free vibration response in a discrete time form to obtain a discrete time response signal;
constructing a Hankel matrix based on the discrete time response signal to obtain a first matrix;
singular value decomposition is carried out on the first matrix, and optimization processing is carried out to obtain a second matrix and a third matrix;
constructing a matrix bundle based on the second matrix and the third matrix;
and calculating the characteristic value of the matrix bundle to obtain the modal parameter.
7. The flutter boundary prediction method according to claim 1, wherein the preset model is a flutter margin model;
wherein the flutter margin model has an expression as follows:
Figure DEST_PATH_IMAGE006
；
wherein the content of the first and second substances,FMthe flutter margin is represented by the amount of flutter,
Figure DEST_PATH_IMAGE007
and
Figure DEST_PATH_IMAGE008
natural frequencies representing different order modes;
Figure DEST_PATH_IMAGE009
and
Figure DEST_PATH_IMAGE010
representing the damping ratio of different order modes.
8. A flutter boundary predicting device applied to an electronic device, the device comprising:
the acquisition module is used for acquiring free vibration response; extracting free attenuation response of the system from the excitation response by a random decrement method;
the identification module is used for identifying modal parameters in the free vibration response based on a matrix xPincil algorithm;
and the determining module is used for determining a flutter boundary predicted value based on the modal parameters and a preset model.
9. An electronic device, comprising: a processor and a memory, the processor and the memory connected;
the memory is used for storing programs;
the processor is configured to execute a program stored in the memory to perform the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when executed by a computer, performs the method of any one of claims 1-7.
CN202210943989.4A 2022-08-08 2022-08-08 Flutter boundary prediction method and device, electronic equipment and storage medium Pending CN115017963A (en)

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