CN110657939A - Flutter critical prediction method and device - Google Patents

Flutter critical prediction method and device Download PDF

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Publication number
CN110657939A
CN110657939A CN201910812897.0A CN201910812897A CN110657939A CN 110657939 A CN110657939 A CN 110657939A CN 201910812897 A CN201910812897 A CN 201910812897A CN 110657939 A CN110657939 A CN 110657939A
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response signal
output response
target characteristic
acquiring
flutter
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Inventor
郭洪涛
路波
吴军强
余立
吕彬彬
寇西平
闫昱
曾开春
杨兴华
张昌荣
查俊
雷鹏轩
郭鹏
刘大伟
马晓永
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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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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
    • G01M9/02—Wind tunnels
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M9/00—Aerodynamic testing; Arrangements in or on wind tunnels
    • G01M9/08—Aerodynamic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/13—Differential equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • 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

Abstract

The embodiment of the application provides a flutter critical prediction method and a device, wherein the flutter critical prediction method comprises the following steps: acquiring an output response signal, and performing ensemble average calculation on the output response signal to obtain a random attenuation mark of the output corresponding signal; according to the random attenuation mark, obtaining a target characteristic equation coefficient of an output response signal through fitting calculation; and calculating and acquiring a flutter critical point of the output response signal according to the target characteristic equation coefficient. By adopting the flutter critical prediction method and device provided by the embodiment of the application, the flutter boundary in the wind tunnel test can be accurately predicted.

Description

Flutter critical prediction method and device
Technical Field
The application relates to the technical field of wind tunnel tests, in particular to a flutter critical prediction method and device.
Background
At present, in the process of wind tunnel test, the most common criterion for judging the flutter boundary is realized by monitoring the damping ratio. However, since the damping is more dispersed than the recognition, the flutter boundary cannot be accurately acquired.
In view of the above, how to provide a method for accurately acquiring a flutter boundary is a problem to be solved at present.
Disclosure of Invention
The application provides a flutter critical prediction method, a flutter critical prediction device, computer equipment and a readable storage medium.
In a first aspect, the present application provides a method for predicting chatter criticality, applied to a computer device, the method including:
acquiring an output response signal, and performing ensemble average calculation on the output response signal to obtain a random attenuation mark of the output corresponding signal;
obtaining a target characteristic equation coefficient of the output response signal through fitting calculation according to the random attenuation mark;
and calculating and obtaining the flutter critical point of the output response signal according to the target characteristic equation coefficient.
Optionally, the obtaining an output response signal and performing ensemble averaging calculation on the output response signal to obtain a random attenuation label of the output corresponding signal includes:
acquiring an output response signal, and filtering the output response signal to acquire a model response subsample;
and performing ensemble average calculation on the model response subsample to obtain a random attenuation mark of the output corresponding signal.
Optionally, the obtaining, according to the random attenuation marker, a target characteristic equation coefficient of the output response signal through fitting calculation includes:
acquiring a target matrix in the random number attenuation mark;
calculating and acquiring a target characteristic value according to the target matrix;
and calculating to obtain a target characteristic equation coefficient according to the target characteristic value.
Optionally, the calculating a flutter critical point of the obtained output response signal according to the target characteristic equation coefficient includes:
calculating to obtain modal variables through a modal coupling mechanism according to the target characteristic equation coefficient;
and acquiring a flutter critical point of the output response signal according to the modal variable.
In a second aspect, the present application provides a flutter criticality prediction apparatus for use in a computer device, the apparatus comprising:
the acquisition module is used for acquiring an output response signal and performing ensemble average calculation on the output response signal to obtain a random attenuation mark of the output corresponding signal;
the fitting module is used for obtaining a target characteristic equation coefficient of the output response signal through fitting calculation according to the random attenuation mark;
and the calculation module is used for calculating and acquiring the flutter critical point of the output response signal according to the target characteristic equation coefficient.
Optionally, the obtaining module is specifically configured to:
acquiring an output response signal, and filtering the output response signal to acquire a model response subsample;
and performing ensemble average calculation on the model response subsample to obtain a random attenuation mark of the output corresponding signal.
Optionally, the fitting module is specifically configured to:
acquiring a target matrix in the random number attenuation mark;
calculating and acquiring a target characteristic value according to the target matrix;
and calculating to obtain a target characteristic equation coefficient according to the target characteristic value.
Optionally, the calculation module is specifically configured to:
calculating to obtain modal variables through a modal coupling mechanism according to the target characteristic equation coefficient;
and acquiring a flutter critical point of the output response signal according to the modal variable.
In a third aspect, the present application provides a computer device, the computer device being communicatively connected to a terminal device, the computer device comprising a processor and a non-volatile memory storing computer instructions, wherein when the computer instructions are executed by the processor, the computer device executes the flutter critical prediction method according to the first aspect.
In a fourth aspect, the present application provides a readable storage medium, which includes a computer program, where the computer program controls a computer device where the readable storage medium is located to execute the method for predicting chatter criticality according to the first aspect.
Compared with the prior art, the beneficial effects provided by the application comprise: by adopting the flutter critical prediction method and device provided by the embodiment of the application, the problem of inaccurate prediction caused by other interference factors on flutter boundary prediction in a wind tunnel test can be solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below. It is appreciated that the following drawings depict only certain embodiments of the application and are therefore not to be considered limiting of its scope. For a person skilled in the art, it is possible to derive other relevant figures from these figures without inventive effort.
FIG. 1 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present disclosure;
FIG. 2 is a schematic block diagram illustrating a flow of steps of a flutter threshold prediction method according to an embodiment of the present disclosure;
FIG. 3 is a schematic block diagram illustrating a flow of sub-steps of step S201 in FIG. 2;
FIG. 4 is a block diagram illustrating a flow of substeps of step S202 of FIG. 2;
FIG. 5 is a schematic block diagram illustrating a flow of sub-steps of step S203 in FIG. 2;
fig. 6 is a block diagram schematically illustrating a structure of a chattering threshold prediction apparatus according to an embodiment of the present application.
Icon: 100-a computer device; 110-means for prediction of chatter criticality; 1101-an acquisition module; 1102-a fitting module; 1103-a calculation module; 111-a memory; 112-a processor; 113-communication unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic block diagram of a computer device 100 according to an embodiment of the present disclosure. The computer apparatus 100 comprises a flutter criticality predicting device 110, a memory 111, a processor 112 and a communication unit 113.
The elements of the memory 111, the processor 112 and the communication unit 113 are directly or indirectly electrically connected to each other to realize 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 Memory 111 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 Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is used for storing a program, and the processor 112 executes the program after receiving the execution instruction. The communication unit 113 is used for establishing a communication connection between the computer device 100 and another device (such as a user terminal) via a network, and for receiving and transmitting data via the network. For example, in the present embodiment, the computer apparatus 100 performs data communication with an external apparatus through the communication unit 113.
Referring to fig. 2, fig. 2 is a schematic block diagram illustrating a flow of steps of a flutter threshold prediction method according to an embodiment of the present application. The method comprises steps S201 to S203.
Step S201, obtaining an output response signal, and performing ensemble averaging calculation on the output response signal to obtain a random attenuation mark of the output corresponding signal.
And step S202, obtaining a target characteristic equation coefficient of the output response signal through fitting calculation according to the random attenuation mark.
And step S203, calculating and acquiring a flutter critical point of the output response signal according to the target characteristic equation coefficient.
Referring to fig. 3, fig. 3 is a schematic block diagram illustrating a flow of sub-steps of step S201 in fig. 2. In the present embodiment, step S201 may include sub-step S2011 and sub-step S2012.
And a substep S2011 of obtaining an output response signal, and filtering the output response signal to obtain a model response subsample.
And a substep S2012, which is to perform ensemble average calculation on the model response subsample to obtain a random attenuation mark of the output corresponding signal.
In this embodiment, a plurality of filter models are set to eliminate random disturbance responses caused by wind tunnel airflow noise and electromagnetic interference through ensemble averaging calculation, so as to obtain effective physical vibration responses (i.e. effective test data excluding disturbance information) caused by test initial conditions. It should be understood that, when only the initial displacement is defined as the initial condition of the sub-sample truncation, after the ensemble averaging calculation, the impulse responses caused by the initial velocities cancel each other out due to the equal probability of the positive and negative initial velocities, so that the effective physical vibration response may be the aerodynamic excitation response caused by the initial displacement, and as the filtering models are more, the number of filtering times increases, and the random attenuation mark of the corresponding signal can be obtained.
Referring to fig. 4, fig. 4 is a schematic block diagram illustrating a flow of sub-steps of step S202 in fig. 2. In this embodiment, step S202 may include sub-steps S2021-S2023.
Substep S2021, obtain the target matrix in the random number attenuation label.
And a substep S2022 of calculating and obtaining a target characteristic value according to the target matrix.
And a substep S2023 of calculating a target characteristic equation coefficient according to the target characteristic value.
In this embodiment, the test model in the wind tunnel test process can be regarded as an elastic system with degrees of freedom, and the differential equation of vibration motion can be expressed by the following equation:
Figure RE-GDA0002287874820000061
wherein [ M ] is mass, [ C ] is damping, [ K ] is stiffness matrix. Thus, the free response expression at any point on the test model may be:
Figure RE-GDA0002287874820000071
wherein s isiIs a plurality of numbers. Based on this, a complex exponential parametric model can be used to represent the noise pollution signal with a sampling length N:
Figure RE-GDA0002287874820000072
wherein n iskTo observe noise, | bi | is amplitude,
Figure RE-GDA00022878748200000713
is a phase, αiAs damping coefficient, ωiThe circle frequency, Δ is the sampling interval, M is the number of sinusoids, k is 0,1 … n-1.
It is possible to order:
zi＝exp(αi+jωi)
at the same time, can define:
Figure RE-GDA0002287874820000073
Figure RE-GDA0002287874820000074
Figure RE-GDA0002287874820000075
wherein L is a beam parameter, and when M is not less than L not more than N-M, the singular eigenvalue z can be considerediSatisfies the following conditions:
(X1-ziX0)qi＝0
pi(X1-ziX0)＝0
based on this, the noise can be contaminated with the signal ykAre defined according to the same form asY0And Y1Using truncated pseudo-inverse of rank P in combinationAnd
Figure RE-GDA0002287874820000078
approximate replacement pseudo-inverse
Figure RE-GDA0002287874820000079
And
Figure RE-GDA00022878748200000710
the definition may be:
Figure RE-GDA00022878748200000711
wherein, { sigma0i, i ═ 1, …, P } is Y0P characteristic values of v0iAnd u0iFor corresponding feature vectors, V0＝{v01,…,v0M}，U0＝{u01,…,u0M}，A＝ diag{σ01,…,σ0MAnd are of
Figure RE-GDA00022878748200000712
Similar definitions are possible. Then, the singular eigenvalues ziThe estimate of (i.e., the target eigenvalue) may be obtained by computing the eigenvalues of the P × P asymmetric matrix (i.e., the target matrix) as follows:
ZEis exactly the P characteristic values
Figure RE-GDA0002287874820000082
P non-zero eigenvalues. Finding singular eigenvalues ziThen, the corresponding characteristic equation coefficient a can be obtainedi(i.e., target feature equation coefficients).
Referring to fig. 5, fig. 5 is a schematic block diagram illustrating a flow of sub-steps of step S203 in fig. 2. In the present embodiment, step S203 may include sub-step S2031 and sub-step S2032.
And a substep S2031 of calculating a modal variable through a modal coupling mechanism according to the target characteristic equation coefficient.
And a substep S2032 of obtaining a flutter critical point of the output response signal according to the modal variable.
In this embodiment, the experimental model can be regarded as an elastic system with a second-order degree of freedom, and therefore, the characteristic equation can be expressed as:
G(z)＝z4+a1z3+a2z2+a3z+a4
while the modal coupling mechanism variable F can be definedzComprises the following steps:
Figure RE-GDA0002287874820000083
wherein the content of the first and second substances,
Figure RE-GDA0002287874820000084
characteristic equation coefficient a obtained through the previous stepsiObtaining a1、a2、a3And a4Can obtain the values of X and Y, and can calculate the modal coupling mechanism variable F according to X and YzThe value is obtained.
In this embodiment, when Fz>At 0, the elastic system is stable. When F is presentzAt 0, the elastic system reaches the flutter threshold. With increasing wind speed, FzMay be linearly decreasing. Thus, by plotting FzThe curve that varies with wind speed can be fitted to extrapolate the flutter threshold point (i.e. the flutter threshold point at which the response signal is output).
Referring to fig. 6, fig. 6 is a block diagram schematically illustrating a structure of a flutter threshold prediction apparatus 110 according to an embodiment of the present disclosure. The chatter-critical prediction apparatus 110 includes:
the obtaining module 1101 is configured to obtain an output response signal, and perform ensemble averaging on the output response signal to obtain a random attenuation flag of the output corresponding signal.
And a fitting module 1102, configured to obtain a target characteristic equation coefficient of the output response signal through fitting calculation according to the random attenuation marker.
And a calculating module 1103, configured to calculate a chattering critical point of the obtained output response signal according to the target characteristic equation coefficient.
Further, the obtaining module 1101 is specifically configured to:
and acquiring an output response signal, and filtering the output response signal to acquire a model response subsample.
And performing ensemble average calculation on the model response subsample to obtain a random attenuation mark of the output corresponding signal.
Further, the fitting module 1102 is specifically configured to:
acquiring a target matrix in the random number attenuation mark;
calculating and acquiring a target characteristic value according to the target matrix;
and calculating to obtain a target characteristic equation coefficient according to the target characteristic value.
Further, the calculating module 1103 is specifically configured to:
calculating to obtain modal variables through a modal coupling mechanism according to the target characteristic equation coefficient;
and acquiring a flutter critical point of the output response signal according to the modal variable.
The embodiment of the application provides computer equipment, the computer equipment is in communication connection with terminal equipment, the computer equipment comprises a processor and a nonvolatile memory, and computer instructions are stored in the nonvolatile memory, and when the computer instructions are executed by the processor, the computer equipment executes the flutter critical prediction method.
The embodiment of the application provides a readable storage medium, which includes a computer program, and the computer program controls a computer device on which the readable storage medium is located to execute the aforementioned flutter critical prediction method when running.
In summary, by using the flutter critical prediction method and device provided by the embodiment of the application, the problem of inaccurate flutter boundary of the wind tunnel test caused by other interference information can be avoided.
The above description is only a preferred embodiment of the present application and is not intended to limit 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 critical prediction method is applied to computer equipment, and comprises the following steps:
acquiring an output response signal, and performing ensemble average calculation on the output response signal to obtain a random attenuation mark of the output corresponding signal;
obtaining a target characteristic equation coefficient of the output response signal through fitting calculation according to the random attenuation mark;
and calculating and obtaining the flutter critical point of the output response signal according to the target characteristic equation coefficient.
2. The method of claim 1, wherein obtaining the output response signal and performing a ensemble averaging of the output response signal to obtain the signature of random attenuation of the output response signal comprises:
acquiring an output response signal, and filtering the output response signal to acquire a model response subsample;
and performing ensemble average calculation on the model response subsample to obtain a random attenuation mark of the output corresponding signal.
3. The method of claim 1, wherein obtaining target characteristic equation coefficients for the output response signal from the stochastic decay marks by fitting calculations comprises:
acquiring a target matrix in the random number attenuation mark;
calculating and acquiring a target characteristic value according to the target matrix;
and calculating to obtain a target characteristic equation coefficient according to the target characteristic value.
4. The method of claim 1, wherein said calculating a critical point for flutter for said output response signal based on said target characteristic equation coefficients comprises:
calculating to obtain modal variables through a modal coupling mechanism according to the target characteristic equation coefficient;
and acquiring a flutter critical point of the output response signal according to the modal variable.
5. A flutter criticality prediction apparatus for use in a computer device, the apparatus comprising:
the acquisition module is used for acquiring an output response signal and performing ensemble average calculation on the output response signal to obtain a random attenuation mark of the output corresponding signal;
the fitting module is used for obtaining a target characteristic equation coefficient of the output response signal through fitting calculation according to the random attenuation mark;
and the calculation module is used for calculating and acquiring the flutter critical point of the output response signal according to the target characteristic equation coefficient.
6. The apparatus of claim 5, wherein the obtaining module is specifically configured to:
acquiring an output response signal, and filtering the output response signal to acquire a model response subsample;
and performing ensemble average calculation on the model response subsample to obtain a random attenuation mark of the output corresponding signal.
7. The apparatus of claim 5, wherein the fitting module is specifically configured to:
acquiring a target matrix in the random number attenuation mark;
calculating and acquiring a target characteristic value according to the target matrix;
and calculating to obtain a target characteristic equation coefficient according to the target characteristic value.
8. The apparatus of claim 5, wherein the computing module is specifically configured to:
calculating to obtain modal variables through a modal coupling mechanism according to the target characteristic equation coefficient;
and acquiring a flutter critical point of the output response signal according to the modal variable.
9. A computer device communicatively coupled to a terminal device, the computer device comprising a processor and a non-volatile memory storing computer instructions that, when executed by the processor, cause the computer device to perform the flutter criticality prediction method of any one of claims 1-4.
10. A readable storage medium comprising a computer program which, when executed, controls a computer device on which the readable storage medium resides to perform the flutter criticality prediction method according to any one of claims 1-4.
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Application publication date: 20200107