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 aero-engine combustion chamber

Abstract

The present application relates to a kind of statistical inference method of aero-engine combustion chamber net damping coefficient, statistical inference system, statistical inference equipment and computer readable storage medium.The statistical inference method includes S1, obtains the dynamic pressure sampling data of combustion chamber;S2, calculates pressure autocorrelation function value;S3, based on pressure autocorrelation function value, deduce the analytic function relationship satisfied between pressure autocorrelation function value, eigenfrequency and damping coefficient;S4, according to the principle of Bayesian estimation, based on autocorrelation function value, infers the joint probability density function of eigenfrequency and damping coefficient;S5, sample value is obtained from joint probability density function, and eigenfrequency and damping coefficient are estimated based on sample value.The present application proposes a kind of statistical inference method of aero-engine combustion chamber net damping coefficient, statistical inference system, statistical inference equipment and computer readable storage medium, and the damping coefficient of aero-engine combustion chamber can be effectively estimated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aero-engine simulation professional design, and particularly relates to a statistical inference method, a statistical inference system, a statistical inference device and a computer readable storage medium for net damping coefficient of aero-engine combustion chamber. BACKGROUND

[0002] Aero-engine development is a complex engineering with high technology, long development cycle and great difficulty, which is cross-disciplinary, cross-professional and multi-agent highly collaborative. An aero-engine is mainly composed of a compressor, a combustion chamber and a turbine, and the combustion chamber is the core component that provides power. In the 2020 Annual Guide to Aero-Engines and Gas Turbines published by the National Academy of Sciences, it is indicated that, in order to achieve the low emission target, the future aero-engine combustion technology will continue to develop in the direction of lean premixing. Although the application of lean premixing combustion technology can greatly reduce nitrogen oxide emissions, it is easy to cause instability in the combustion chamber, and in severe cases, it can cause extreme phenomena such as self-ignition, backfire and flameout, which threatens the safe operation of the aero-engine. Accurate understanding of the atomized combustion process in the engine combustion chamber and organization optimization play a crucial role in the development of a new generation of engines with low emissions and high safety.

[0003] Lean premixing combustion technology is highly sensitive to thermal-acoustic instability: when the combustion chamber is operated under adverse conditions, the positive coupling between acoustic pressure and unstable heat release will lead to self-excited oscillation. This can cause flame ejection, increased pollutant formation, and in the worst case, serious damage to the hardware. In order to avoid unacceptably high pulsation levels, it is necessary to ensure that the dissipation of acoustic energy always exceeds the acoustic driving of the flame. For this purpose, the sign (positive or negative) of the damping coefficient of the combustion chamber indicates whether the amplitude in the combustion chamber decays or grows exponentially over time. A reliable method for determining the damping rate of the combustion chamber is crucial for the technical application of real-time monitoring of stability levels and verification of stability analysis results.

[0004] The most natural way to determine the damping rate of the combustion chamber is to introduce an external force 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 applying a least squares fit to the filtered decay pressure signal envelope. Both methods place high demands on signal quality and the intrinsic frequency must be known in advance. In this regard, progress has been made in decay rate measurements in premixing combustion chambers by applying statistical methods. However, when moving from laboratory-scale facilities to full-engine conditions, it is often not feasible to introduce an external force into a stably operating combustion chamber, making it difficult to accurately infer the net damping coefficient of a certain mode. SUMMARY

[0005] Aiming at the above problems of the prior art, the present application 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 aero-engine combustion chamber, which can effectively estimate the damping coefficient of the aero-engine combustion chamber.

[0006] Specifically, the present application provides a statistical inference method for the net damping coefficient of an aero-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 between the pressure autocorrelation function value, an eigenfrequency and a damping coefficient based on the pressure autocorrelation function value;

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

[0011] S5, sampling the eigenfrequency and the damping coefficient 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 an embodiment of the present application, in step S2, a sampling time interval is calculated according to a sampling frequency of the dynamic pressure sampling data, an autocorrelation sequence of the sampled dynamic pressure value is calculated based on the definition of the autocorrelation function, and the equal-difference sequence of the time interval is taken as the independent variable of the autocorrelation function, and the autocorrelation sequence is taken as the dependent variable of the autocorrelation function.

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

[0014] According to an embodiment of the present application, the analytical function relationship is:

[0015]

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

[0017] According to an embodiment of the present application, in step S5, Gibbs sampling technique is employed to sample sample values of the natural frequency and the damping coefficient from the joint probability density function.

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

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

[0020] An acquisition unit is configured to obtain dynamic pressure sampling data of the combustion chamber.

[0021] A calculation unit is configured to calculate a pressure autocorrelation function value based on the dynamic pressure sampling data.

[0022] A function relationship establishment unit is configured to derive an analytical function relationship satisfied among the pressure autocorrelation function value, the natural frequency and the damping coefficient based on the pressure autocorrelation function value.

[0023] An inference unit is configured to model and regress unknown parameters in the analytical function according to the Bayesian estimation principle, and to infer a joint probability density function of the natural frequency and the damping coefficient based on the autocorrelation function value.

[0024] A sampling unit is configured to sample sample values of the natural frequency and the damping coefficient from the joint probability density function.

[0025] An estimation unit is configured to estimate the natural frequency and the damping coefficient based on the sample values.

[0026] According to an embodiment of the present application, the sampling unit employs Gibbs sampling technique to sample sample values of the natural frequency and the damping coefficient from the joint probability density function.

[0027] The present application also provides a statistical inference device of the net damping coefficient of a combustion chamber of an aero-engine, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the statistical inference method of the net damping coefficient of the combustion chamber of the aero-engine according to any one of the preceding embodiments when executing the computer program.

[0028] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executable on a processor to implement the steps of the statistical inference method of the net damping coefficient of the combustion chamber of the aero-engine according to any one of the preceding embodiments.

[0029] The application provides 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 aero-engine combustion chamber.

[0030] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are intended to provide further explanation of the application, as claimed. BRIEF DESCRIPTION OF DRAWINGS

[0031] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute apart of this application, illustrate embodiments of the application, and together with the description serve to explain the principles of the application.

[0032] In the drawings:

[0033] Figure 1 A flow chart of a statistical inference method for a net damping coefficient of an aero-engine combustion chamber is shown.

[0034] Figure 2 A structural schematic diagram of a statistical inference system for a net damping coefficient of an aero-engine combustion chamber is shown. DETAILED DESCRIPTION

[0035] It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict.

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0037] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0038] The relative arrangement of parts and steps, numerical expressions, and numerical values set forth in the examples are not intended to limit the scope of the present application unless otherwise specifically stated. It is to be understood that the drawings are not necessarily to scale of the various parts shown in the drawings. Techniques, methods, and apparatus known to those of ordinary skill can not be discussed in detail because such techniques, methods, and apparatus are considered to be part of the state of the art. In all examples shown and discussed herein, any specific value is to be interpreted as illustrative only and not as a limitation. Thus, other examples of example embodiments can have different values. It is noted that like numbers and letters on the figures identify like parts throughout the several views, and therefore, further discussion of such parts is not necessary unless otherwise noted.

[0039] In the description of the present application, it is to be understood that the orientation or positional relationships indicated by terms such as "front", "back", "up", "down", "left", "right", "lateral", "vertical", "horizontal", and "top", "bottom" are generally based on the orientation or positional relationships shown in the drawings, and are merely intended to facilitate the description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the scope of protection of the present application; the orientation terms "inner", "outer" refer to the inner and outer of the contour of the parts themselves.

[0040] In addition, it should be noted that the use of the terms "first", "second", and the like does not imply any particular meaning, unless otherwise stated, and therefore cannot be understood as limiting the scope of protection of the present application. In addition, although the terms used in the present application are selected from well-known and commonly used terms, some of the terms mentioned in the specification of the present application can be selected by the applicant according to his or her judgment, and the detailed meanings thereof are explained in the relevant part of the description. In addition, the present application is to be understood not only by the actual terms used, but also by the meaning implied by each term.

[0041] Figure 1A flow chart of a statistical inference method of a net damping coefficient of an aero-engine combustion chamber according to an embodiment of the present application is shown. As shown in the figure, the present application provides a statistical inference method of a net damping coefficient of an aero-engine combustion chamber, comprising the steps of:

[0042] S1, obtaining a plurality of modal mixed combustion chamber dynamic pressure sampling data;

[0043] S2, calculating a 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, modeling and regressing unknown parameters in the analytical function according to the Bayesian estimation principle, and inferring a joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function value. The Bayesian estimation principle starts from the prior knowledge of the parameters and the sample, unlike the classical statistical estimation, which no longer regards the parameters as an unknown deterministic variable, but as an unknown random variable, and through observation of the sample, the prior probability density of the parameters is transformed into the posterior probability density.

[0046] S5, sampling the eigenfrequency and the damping coefficient 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.

[0047] It should be noted that in order to extract the net damping coefficient of a certain mode from the plurality of modal mixed combustion chamber pressure sampling data, the traditional method needs to be filtered first, and signals of different frequencies need to be separated, but the selection of the filter and the filtering range is very difficult. The traditional method puts forward very high requirements for the quality of the measuring pressure signal device, 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 application is to infer the net damping coefficient of the aero-engine combustion chamber according to the dynamic pressure data, to calculate the pressure autocorrelation function value, and then to infer the joint probability density function of the eigenfrequency and the net damping coefficient, and to sample the eigenfrequency and the damping coefficient from the joint probability density function to obtain the estimated value. The obtained net damping coefficient can be used for real-time health diagnosis, fault prediction of aero-engine operation, and 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, the autocorrelation sequence of the sampling dynamic pressure value is calculated based on the definition of the autocorrelation function, and the equal difference series (starting from 0) of the time interval is taken as the independent variable of the autocorrelation function, and the autocorrelation sequence is taken 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 according to the basic principles of thermodynamics 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] wherein 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) introduces a Markov process into the Monte Carlo simulation, constructs a Markov chain, and makes the stationary distribution of the chain the posterior distribution of the to-be-estimated parameters, and generates samples of the posterior distribution through the chain. The Gibbs sampling technique is a high-dimensional sample sampling algorithm for MCMC, which can approximately sample a sample sequence of a certain dimension variable from a multi-dimensional variable joint probability distribution when direct sampling is difficult.

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

[0055] Figure 2 The structural schematic diagram of the statistical inference system of the net damping coefficient of the combustion chamber of an aero-engine is shown. As shown in the figure, the present application also provides a statistical inference system 200 of the net damping coefficient of the combustion chamber of an aero-engine, which is suitable for the statistical inference method of the net damping coefficient of the combustion chamber of an aero-engine described above. The statistical inference system 200 comprises:

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

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

[0058] A function relationship establishment unit 203 is configured 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] The inference unit 204 is configured 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] The sampling unit 205 is configured to sample the joint probability density function to obtain sample values of the eigenfrequency and the damping coefficient;

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

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

[0063] The present application also provides an aero-engine combustion chamber net damping coefficient statistical inference device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the aero-engine combustion chamber net damping coefficient statistical inference methods.

[0064] The present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executable on the processor to implement the steps of any one of the aero-engine combustion chamber net damping coefficient statistical inference methods.

[0065] The specific implementation and technical effects of the statistical inference system, the statistical inference device and the computer readable storage medium can be referred to the above-mentioned embodiments of the aero-engine combustion chamber net damping coefficient statistical inference method provided by the present application, and will not be repeated here.

[0066] The present application provides an aero-engine combustion chamber net damping coefficient statistical inference method, a statistical inference system, a statistical inference device and a computer readable storage medium, and the main feature is that the Bayesian estimation principle is adopted to establish an inference model, and a joint probability density function to which the eigenfrequency and the net damping coefficient are subjected is inferred, then the Gibbs sampling technique is adopted 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 sample values. The net damping coefficient of the aero-engine combustion chamber obtained by the present application can be used for real-time health diagnosis, fault prediction of engine operation, and the damping coefficient obtained from the test data is also helpful for the forward simulation design in the design of the combustion chamber components.

[0067] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0068] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein can be implemented or performed 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 can be a microprocessor, but in the alternative, the processor can be any conventional processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0069] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, 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 the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0070] In one or more exemplary embodiments, the functions described can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0071] Various modifications and variations can be made to the above exemplary implementations without departing from the spirit and scope of the application. Therefore, it is intended that the application cover modifications and variations of the application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A statistical inference method of net damping coefficient of a combustion chamber of an aero-engine, comprising the steps of: S1, obtaining dynamic pressure sampling data of the combustion chamber; S2, calculating pressure autocorrelation function values based on the dynamic pressure sampling data; S3, deriving an analytical function relationship satisfied between the pressure autocorrelation function values, an eigenfrequency and a damping coefficient based on the pressure autocorrelation function values; S4, modeling and regressing unknown parameters in the analytical function according to a Bayesian estimation principle, inferring a joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function values; S5, sampling sample values of the eigenfrequency and the damping coefficient from the joint probability density function by using a Gibbs sampling technique, and estimating the eigenfrequency and the damping coefficient based on the sample values; wherein the analytical function relationship is: where k pp represents an autocorrelation function value, τ represents a sampling time interval, i represents a mode, ω i represents an eigen angular frequency, v i represents a net damping coefficient.

2. The statistical inference method of claim 1, wherein, In step S2, a sampling time interval is calculated according to a sampling frequency of the dynamic pressure sampling data, an autocorrelation sequence of the sampling dynamic pressure values is calculated based on a definition of the autocorrelation function, and an arithmetic sequence of the time interval is taken as an independent variable of the autocorrelation function and the autocorrelation sequence is taken as a dependent variable of the autocorrelation function.

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

4. The statistical inference method of claim 1, wherein, In step S5, a mean value or a median value of the sample values is calculated as the eigenfrequency and the damping coefficient.

5. A system for statistical inference of net damping coefficient of an aeroengine combustor, adapted to the method for statistical inference of net damping coefficient of an aeroengine combustor according to claim 1, characterized by, The statistical inference system comprises: an acquisition unit configured to obtain dynamic pressure sampling data of the combustion chamber; a calculation unit configured to calculate pressure autocorrelation function values based on the dynamic pressure sampling data; a function relationship establishment unit configured to derive an analytical function relationship satisfied between the pressure autocorrelation function values, an eigenfrequency and a damping coefficient based on the pressure autocorrelation function values; the analytical function relationship is: where k pp represents an autocorrelation function value, τ represents a sampling time interval, i represents a mode, ω i represents an eigen angular frequency, v i represents a net damping coefficient; an inference unit configured to model and regress unknown parameters in the analytical function according to a Bayesian estimation principle, and to infer a joint probability density function of the eigenfrequency and the damping coefficient based on the autocorrelation function values; a sampling unit configured to sample sample values of the eigenfrequency and the damping coefficient from the joint probability density function by using a Gibbs sampling technique; an estimation unit configured to estimate the eigenfrequency and the damping coefficient based on the sample values.

6. An apparatus for statistical inference of a net damping coefficient of a combustion chamber of an aeroengine, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the statistical inference method of the net damping coefficient of the combustion chamber of the aero-engine according to any one of claims 1-4 when executing the computer program.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the steps of the statistical inference method of the net damping coefficient of the combustion chamber of the aero-engine according to any one of claims 1-4 when executed by the processor.

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