Statistical inference method, statistical inference system and statistical inference equipment for net damping coefficient of aero-engine combustion chamber and computer readable storage medium

Abstract

The invention relates to a statistical inference method, a statistical inference system and statistical inference equipment for a net damping coefficient of an aero-engine combustion chamber and a computer readable storage medium. The statistical inference method comprises the following steps: S1, obtaining dynamic pressure sampling data of a combustion chamber; s2, calculating a pressure autocorrelation function value; s3, based on the pressure autocorrelation function value, deriving an analytic function relationship satisfied among the pressure autocorrelation function value, the eigenfrequency and the damping coefficient; s4, according to the Bayesian estimation principle, inferring a joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value; and S5, sampling from the joint probability density function to obtain a sample value, and estimating the eigenfrequency and the damping coefficient based on the sample value. The invention provides a statistical inference method, a statistical inference system and statistical inference equipment for the net damping coefficient of an aero-engine combustion chamber and a computer readable storage medium. The damping coefficient of the aero-engine combustion chamber can be effectively estimated.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft engine simulation professional design, and in particular to a statistical inference method, a statistical inference system, a statistical inference device and a computer-readable storage medium for a net damping coefficient of an aircraft engine combustion chamber. Background Art

[0002] The development of aircraft engines is a complex project that is interdisciplinary, cross-professional, and highly coordinated among multiple subjects. It has the characteristics of high technical content, long development cycle, and high development difficulty. Aircraft engines are mainly composed of three major components: compressor, combustion chamber, and turbine, among which the combustion chamber is the core component that provides power. The National Academy of Sciences of the United States stated in its 2020 Annual Guide for Aircraft Engines and Gas Turbines that in order to achieve low emission goals, future aircraft engine combustion technology will continue to develop in the direction of lean premixing. Although the application of lean premixed combustion technology can greatly reduce nitrogen oxide emissions, it is very easy to cause instability in the combustion chamber. In severe cases, extreme phenomena such as spontaneous combustion, backfire, and flameout may occur, threatening the safe operation of aircraft engines. Accurately understanding the atomization combustion process and organizational optimization in the engine combustion chamber plays a vital role in the development of a new generation of low-emission and high-safety engines.

[0003] Lean premixed combustion technology is highly sensitive to thermoacoustic instabilities: when the combustor operates under unfavorable conditions, the positive coupling between the acoustic pressure and the unstable heat release leads to self-excited oscillations. This can cause flame ejection, increased pollutant formation and, in the worst case, severe damage to the hardware. In order to avoid unacceptably high pulsation levels, it must be ensured that the dissipation of the acoustic energy always exceeds the acoustic drive of the flame. To this end, the sign (positive or negative) of the damping coefficient of the combustor indicates whether the amplitude of the oscillations in the combustor decays over time or grows exponentially. A reliable method for determining the damping rate of the combustor is essential for real-time monitoring of the stability level and for the technical application of the verification of the stability analysis results.

[0004] The most natural way to determine the damping rate of a combustion chamber is to introduce external forces at the resonant frequency and measure the decay rate after stopping the excitation. Assuming that the signal-to-noise ratio is low enough, the damping rate can be calculated by a least squares fit applied to the envelope of the filtered decaying pressure signal. Both methods place high demands on the signal quality and the eigenfrequencies must be known in advance. In this regard, progress has been made by applying statistical methods to decay rate measurements in premixed combustion chambers. However, when transferring from laboratory-scale facilities to full-engine conditions, it is usually not feasible to introduce external forces into a steadily operating combustion chamber, making it difficult to accurately infer the net damping coefficient of a certain mode. Summary of the invention

[0005] In view of the above problems in the prior art, the present invention proposes a statistical inference method, a statistical inference system, a statistical inference device and a computer-readable storage medium for the net damping coefficient of an aircraft engine combustion chamber, which can effectively estimate the damping coefficient of the aircraft engine combustion chamber.

[0006] Specifically, the present invention proposes a statistical inference method for the net damping coefficient of an aircraft engine combustion chamber, comprising the steps of:

[0007] S1, obtaining dynamic pressure sampling data of the combustion chamber;

[0008] S2, calculating a pressure autocorrelation function value based on the dynamic pressure sampling data;

[0009] S3, deriving an analytical function relationship satisfied by the pressure autocorrelation function value, the eigenfrequency and the damping coefficient based on the pressure autocorrelation function value;

[0010] S4, modeling and regressing the unknown parameters in the analytical function according to the Bayesian estimation principle, and inferring the joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value;

[0011] S5, sampling from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient, and estimating the eigenfrequency and the damping coefficient based on the sample values.

[0012] According to one embodiment of the present invention, in step S2, the sampling time interval is calculated according to the sampling frequency of the dynamic pressure sampling data, the autocorrelation sequence of the sampled dynamic pressure values ​​is calculated based on the definition of the autocorrelation function, the arithmetic progression of the time interval is used as the independent variable of the autocorrelation function, and the autocorrelation sequence is used as the dependent variable of the autocorrelation function.

[0013] According to one embodiment of the present invention, in step S3, an analytical function relationship satisfied between the pressure autocorrelation function value, the eigenfrequency and the damping coefficient is derived based on basic thermodynamic principles and mathematical tools, wherein the mathematical tools at least include the residue theorem and the Wiener-Khinchin theorem.

[0014] According to one embodiment of the present invention, the analytical function relationship is:

[0015]

[0016] where k pp represents the autocorrelation function value, τ represents the sampling time interval, i represents the mode, ω i represents the eigenangular frequency, v i represents the net damping coefficient.

[0017] According to an embodiment of the present invention, in step S5, Gibbs sampling technology is used to sample the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient.

[0018] According to an embodiment of the present invention, in step S5, the mean value or median value of the sample values ​​is calculated as the eigenfrequency and the damping coefficient.

[0019] The present invention also provides a statistical inference system for the net damping coefficient of an aircraft engine combustion chamber, which is applicable to the above-mentioned statistical inference method, and the statistical inference system comprises:

[0020] An acquisition unit, used to obtain dynamic pressure sampling data of the combustion chamber;

[0021] A calculation unit, used for calculating a pressure autocorrelation function value based on the dynamic pressure sampling data;

[0022] A functional relationship establishing unit, used for deriving an analytical functional relationship satisfied between a pressure autocorrelation function value, an eigenfrequency and a damping coefficient based on the pressure autocorrelation function value;

[0023] An inference unit, used for modeling and regressing unknown parameters in the analytical function according to the Bayesian estimation principle, and inferring the joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value;

[0024] A sampling unit, used for sampling from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient;

[0025] An estimating unit is used to estimate the eigenfrequency and the damping coefficient based on the sample value.

[0026] According to an embodiment of the present invention, the sampling unit adopts Gibbs sampling technology to sample from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient.

[0027] The present invention also provides a statistical inference device for the net damping coefficient of an aircraft engine combustion chamber, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the statistical inference method for the net damping coefficient of the aircraft engine combustion chamber described in any one of the above items are implemented.

[0028] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the statistical inference method of the net damping coefficient of the combustion chamber of an aircraft engine as described in any of the above items are implemented.

[0029] The present invention provides a statistical inference method, a statistical inference system, a statistical inference device and a computer-readable storage medium for the net damping coefficient of an aircraft engine combustion chamber. The method adopts the Bayesian estimation principle and starts from the prior knowledge and samples of the parameters. The parameters are no longer regarded as an unknown definite variable, but as an unknown random variable. By observing the samples, the prior probability density of the parameters is converted into a posterior probability density, and then the parameters are estimated, thereby effectively estimating the damping coefficient of the aircraft engine combustion chamber.

[0030] It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are included to provide further explanation of the present invention, and are incorporated into and constitute a part of this application. The accompanying drawings illustrate embodiments of the present invention and together with the description serve to explain the principle of the present invention.

[0032] In the attached figure:

[0033] Figure 1 A flowchart of a statistical inference method for the net damping coefficient of an aircraft engine combustion chamber according to an embodiment of the present invention is shown.

[0034] Figure 2 A schematic structural diagram of a statistical inference system for the net damping coefficient of an aircraft engine combustion chamber according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0036] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0038] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the application. Meanwhile, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to actual proportional relationships. The technology, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be considered as a part of the specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0039] In the description of the present application, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "lateral, vertical, perpendicular, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction, and therefore cannot be understood as limiting the scope of protection of the present application; the directional words "inside and outside" refer to the inside and outside relative to the contours of each component itself.

[0040] In addition, it should be noted that the use of words such as "first" and "second" to define components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above words have no special meaning and cannot be understood as limiting the scope of protection of this application. In addition, although the terms used in this application are selected from well-known and commonly used terms, some terms mentioned in the specification of this application may be selected by the applicant at his or her discretion, and their detailed meanings are explained in the relevant parts of the description of this article. In addition, it is required to understand this application not only by the actual terms used, but also by the meaning implied by each term.

[0041] Figure 1A flowchart of a statistical inference method for a net damping coefficient of an aircraft engine combustion chamber according to an embodiment of the present invention is shown. As shown in the figure, the present invention provides a statistical inference method for a net damping coefficient of an aircraft engine combustion chamber, comprising the steps of:

[0042] S1, obtaining the dynamic pressure sampling data of the combustion chamber with multiple modes mixed;

[0043] S2, calculating the pressure autocorrelation function value based on the dynamic pressure sampling data;

[0044] S3, deriving an analytical function relationship satisfied between the pressure autocorrelation function value, the eigenfrequency and the damping coefficient based on the pressure autocorrelation function value;

[0045] S4, based on the Bayesian estimation principle, the unknown parameters in the analytical function are modeled and regressed, and the joint probability density function of the eigenfrequency and the damping coefficient is inferred based on the autocorrelation function value. The Bayesian estimation principle starts from the prior knowledge and samples of the parameters. Different from the classical statistical estimation, the parameters are no longer regarded as an unknown deterministic variable, but as an unknown random variable. By observing the samples, the prior probability density of the parameters is converted into the posterior probability density.

[0046] S5, sampling the sample values ​​of the eigenfrequency and the damping coefficient from the joint probability density function, and estimating the eigenfrequency and the damping coefficient based on the sample values.

[0047] It should be noted that to extract the net damping coefficient of a certain mode from the combustion chamber pressure sampling data of multiple mixed modes, the traditional method needs to filter first to separate the signals of different frequencies, but the selection of filters and filtering ranges is very difficult. The traditional method places high demands on the quality of the equipment for measuring the pressure signal, and the eigenfrequency must be known in advance, so it is difficult to accurately infer the net damping coefficient of a certain mode. The statistical inference method provided by the present invention is to infer the net damping coefficient of the combustion chamber of an aircraft engine based on dynamic pressure data, and to infer the joint probability density function of the eigenfrequency and the net damping coefficient by calculating the value of the pressure autocorrelation function, and to obtain the estimated values ​​of the eigenfrequency and the damping coefficient by sampling from the joint probability density function. The obtained net damping coefficient can be used for real-time health diagnosis and fault prediction of aircraft engine operation, and in the design of combustion chamber components, obtaining the damping coefficient from the test data is also helpful for forward simulation design.

[0048] Preferably, in step S2, the sampling time interval is calculated according to the sampling frequency of the dynamic pressure sampling data, and the autocorrelation sequence of the sampled dynamic pressure values ​​is calculated based on the definition of the autocorrelation function, with the arithmetic progression of the time interval (starting from 0) as the independent variable of the autocorrelation function and the autocorrelation sequence as the dependent variable of the autocorrelation function.

[0049] Preferably, in step S3, an analytical function relationship satisfied between the pressure autocorrelation function value, the eigenfrequency and the damping coefficient is derived based on basic thermodynamic principles and mathematical tools, wherein the mathematical tools at least include the residue theorem and the Wiener-Khinchin theorem.

[0050] Preferably, the analytical function relationship is:

[0051]

[0052] where k pp represents the autocorrelation function value, τ represents the sampling time interval, i represents the mode, ω i represents the eigenangular frequency, v i represents the net damping coefficient.

[0053] Preferably, in step S5, the Gibbs sampling technique is used to sample the sample values ​​of the eigenfrequency and the damping coefficient from the joint probability density function. It should be noted that the Markov chain Monte Carlo method (MCMC for short) introduces the Markov process into the Monte Carlo simulation, constructs a Markov chain, and makes its stable distribution the posterior distribution of the parameter to be estimated, and generates samples of the posterior distribution through this chain. The Gibbs sampling technique is a high-dimensional sample sampling algorithm used in MCMC, which can approximately sample a sample sequence of a certain dimensional variable from the joint probability distribution of multi-dimensional variables when it is difficult to sample directly.

[0054] Preferably, in step S5, the mean or median of the sample values ​​is calculated as the estimated values ​​of the eigenfrequency and the damping coefficient.

[0055] Figure 2 The structure diagram of the statistical inference system of the net damping coefficient of the combustion chamber of an aircraft engine according to one embodiment of the present invention is shown. As shown in the figure, the present invention also provides a statistical inference system 200 of the net damping coefficient of the combustion chamber of an aircraft engine, which is applicable to the statistical inference method of the net damping coefficient of the combustion chamber of an aircraft engine mentioned above. The statistical inference system 200 includes:

[0056] An acquisition unit 201 is used to obtain dynamic pressure sampling data of the combustion chamber;

[0057] A calculation unit 202, used to calculate a pressure autocorrelation function value based on dynamic pressure sampling data;

[0058] A function relationship establishing unit 203, used to derive an analytical function relationship satisfied between the pressure autocorrelation function value, the eigenfrequency and the damping coefficient based on the pressure autocorrelation function value;

[0059] An inference unit 204 is used to model and regress unknown parameters in the analytical function according to the Bayesian estimation principle, and infer a joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value;

[0060] A sampling unit 205, used for sampling from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient;

[0061] The estimation unit 206 is used to estimate the eigenfrequency and the damping coefficient based on the sample values.

[0062] Preferably, the sampling unit 205 adopts Gibbs sampling technology to sample the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient.

[0063] The present invention also provides a statistical inference device for the net damping coefficient of an aircraft engine combustion chamber, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any of the aforementioned statistical inference methods for the net damping coefficient of an aircraft engine combustion chamber are implemented.

[0064] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the aforementioned statistical inference methods for the net damping coefficient of an aircraft engine combustion chamber are implemented.

[0065] Among them, the specific implementation methods and technical effects of the statistical inference system, statistical inference device, and computer-readable storage medium can all be referred to the embodiment of the statistical inference method of the net damping coefficient of the aircraft engine combustion chamber provided by the present invention above, and will not be repeated here.

[0066] The present invention provides a statistical inference method, a statistical inference system, a statistical inference device and a computer-readable storage medium for the net damping coefficient of an aircraft engine combustion chamber. The main features are that an inference model is established using the Bayesian estimation principle to infer the joint probability density function obeyed by the eigenfrequency and the net damping coefficient, and then the Gibbs sampling technique is used to sample the eigenfrequency and the net damping coefficient from the joint probability density function, and then the eigenfrequency and the net damping coefficient are estimated based on the sampled values. The net damping coefficient of the aircraft engine combustion chamber obtained by the present invention can be used for real-time health diagnosis and fault prediction of engine operation. In the design of combustion chamber components, obtaining the damping coefficient from test data is also helpful for forward simulation design.

[0067] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0068] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0069] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0070] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0071] It will be apparent to those skilled in the art that various modifications and variations may be made to the above exemplary embodiments of the present invention without departing from the spirit and scope of the present invention. Therefore, it is intended that the present invention covers modifications and variations of the present invention that fall within the scope of the appended claims and their equivalent technical solutions.

Claims

1. A statistical inference method for the net damping coefficient of an aircraft engine combustion chamber, comprising the steps of: S1, obtaining dynamic pressure sampling data of the combustion chamber; S2, calculating a pressure autocorrelation function value based on the dynamic pressure sampling data; S3, deriving an analytical function relationship satisfied by the pressure autocorrelation function value, the eigenfrequency and the damping coefficient based on the pressure autocorrelation function value; S4, modeling and regressing the unknown parameters in the analytical function according to the Bayesian estimation principle, and inferring the joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value; S5, sampling from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient, and estimating the eigenfrequency and the damping coefficient based on the sample values.

2. The statistical inference method according to claim 1, characterized in that: In step S2, the sampling time interval is calculated according to the sampling frequency of the dynamic pressure sampling data, and the autocorrelation sequence of the sampled dynamic pressure values ​​is calculated based on the definition of the autocorrelation function, with the arithmetic progression of the time interval as the independent variable of the autocorrelation function and the autocorrelation sequence as the dependent variable of the autocorrelation function.

3. The statistical inference method according to claim 1, characterized in that: In step S3, an analytical function relationship satisfied between the pressure autocorrelation function value, the eigenfrequency and the damping coefficient is derived according to basic thermodynamic principles and mathematical tools, wherein the mathematical tools at least include the residue theorem and the Wiener-Khinchin theorem.

4. The statistical inference method according to claim 3, characterized in that: The analytical function relationship is: where k pp represents the autocorrelation function value, τ represents the sampling time interval, i represents the mode, ω i represents the eigenangular frequency, v i represents the net damping coefficient.

5. The statistical inference method according to claim 1, characterized in that: In step S5, Gibbs sampling technique is used to sample the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient.

6. The statistical inference method according to claim 5, characterized in that: In step S5, the mean value or median value of the sample values ​​is calculated as the eigenfrequency and damping coefficient.

7. A statistical inference system for the net damping coefficient of an aircraft engine combustion chamber, applicable to the statistical inference method for the net damping coefficient of an aircraft engine combustion chamber as claimed in claim 1, characterized in that: The statistical inference system comprises: An acquisition unit, used to obtain dynamic pressure sampling data of the combustion chamber; A calculation unit, used for calculating a pressure autocorrelation function value based on the dynamic pressure sampling data; A functional relationship establishing unit, used for deriving an analytical functional relationship satisfied between a pressure autocorrelation function value, an eigenfrequency and a damping coefficient based on the pressure autocorrelation function value; An inference unit, used for modeling and regressing unknown parameters in the analytical function according to the Bayesian estimation principle, and inferring the joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value; A sampling unit, used for sampling from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient; An estimating unit is used to estimate the eigenfrequency and the damping coefficient based on the sample value.

8. The statistical inference system for net damping coefficient of an aircraft engine combustion chamber according to claim 7, characterized in that: The sampling unit adopts Gibbs sampling technology to sample from the joint probability density function to obtain sample values ​​of the eigenfrequency and the damping coefficient.

9. A statistical inference device for the net damping coefficient of an aircraft engine combustion chamber, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the processor implements the steps of the statistical inference method of the net damping coefficient of the aircraft engine combustion chamber as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the statistical inference method of the net damping coefficient of the combustion chamber of an aircraft engine as claimed in any one of claims 1 to 6 are implemented.

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