A method and system for predicting the nonlinear trend of thermoacoustic oscillations based on flame transfer function

Abstract

This application proposes a method and system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function. The method includes: real-time acquisition of inlet excitation fluctuation signals and heat release rate fluctuation signals of the combustion chamber; identification of the flame transfer function (FTF) based on the inlet excitation fluctuation signals and the heat release rate fluctuation signals; phase correction of the FTF to obtain the corrected real part of the FTF reflecting the internal dynamics of the flame; determination of the linear growth rate of the combustion system; processing of the heat release rate fluctuation signals to extract the steady-state amplitude information of the heat release rate fluctuation signals and determine the nonlinear saturation intensity of the combustion system; and determining whether the current state of the combustion system tends to develop into a stable limiting-loop thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation intensity, and outputting a prediction conclusion. The technical solution proposed in this application can quickly and accurately predict whether the system will develop into destructive thermoacoustic oscillations based solely on online measurable combustion data.

Description

Technical Field

[0001] This application relates to the field of combustion stability monitoring and active control technology, and in particular to a method and system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function. Background Technology

[0002] Thermoacoustic oscillation is a common coupled instability phenomenon in combustion systems, widely found in rocket engines, aero engines, gas turbines, and industrial combustion devices. Its physical essence lies in the phase coupling of combustion heat release fluctuations and sound pressure fluctuations, leading to continuous positive feedback energy accumulation. This eventually forms a large-amplitude, high-intensity limiting loop oscillation, which can severely cause structural fatigue, decreased combustion efficiency, and even equipment damage.

[0003] Currently, the analysis and control of thermoacoustic oscillations mainly fall into two categories: First, frequency domain methods based on linear theory, such as characterizing the linear frequency response of the system using the flame transfer function (FTF) and evaluating linear stability using the Nyquist criterion. However, linear methods cannot predict nonlinear saturation phenomena, i.e., whether the system will be confined to a finite-amplitude limit cycle after instability, nor can they predict the amplitude and formation time of the limit cycle. Second, nonlinear analysis methods based on high-fidelity numerical simulation or detailed chemical reaction mechanisms. While these methods can reveal nonlinear dynamic processes, their computational cost is extremely high, making it difficult to meet the needs of real-time monitoring and rapid response in engineering. In actual engineering, the operating conditions of combustion systems are complex and variable, and their acoustic boundary conditions and fuel characteristics may change in real time. Traditional methods such as "design-stage frequency misalignment" or "offline simulation" are difficult to adapt to the requirements of online control. Existing online monitoring systems are mostly based on linear index threshold alarms, resulting in delayed responses and an inability to distinguish between linear instability and nonlinear limit cycle trends, potentially leading to false alarms or missed alarms. The control process often relies on trial and error based on human experience, which is inefficient and costly. Therefore, there is an urgent need for a solution that can quickly and accurately identify weak nonlinear trends in combustion systems and predict the risk of thermoacoustic oscillations in advance based on online measurable data. Summary of the Invention

[0004] This application provides a method and system for predicting the nonlinear trend of thermoacoustic oscillations based on flame transfer function, which at least solves the technical problem that existing thermoacoustic oscillation analysis schemes cannot quickly predict nonlinear instability trends based on online data, resulting in lag in control.

[0005] The first aspect of this application proposes a method for predicting the nonlinear trend of thermoacoustic oscillations based on a flame transfer function, the method comprising:

[0006] Real-time acquisition of inlet excitation fluctuation signals and heat release rate fluctuation signals of the combustion chamber;

[0007] The flame transfer function (FTF) is obtained by identifying the imported excitation fluctuation signal and the heat release rate fluctuation signal.

[0008] The phase of the FTF is corrected to obtain the real part of the corrected FTF that reflects the internal dynamics of the flame;

[0009] The linear parameters of the combustion system are determined based on the real part of the corrected FTF, and the linear parameters include the linear growth rate.

[0010] The heat release rate fluctuation signal is processed to extract the steady-state amplitude information of the heat release rate fluctuation signal, and the nonlinear parameters of the combustion system are determined based on the steady-state amplitude information. The nonlinear parameters include the nonlinear saturation intensity.

[0011] Based on the linear growth rate and the nonlinear saturation intensity, it is determined whether the current state of the combustion system tends to develop into a stable limiting ring thermoacoustic oscillation, and a prediction conclusion is output.

[0012] Preferably, the step of correcting the phase of the FTF to obtain the corrected real part of the FTF reflecting the internal dynamics of the flame includes:

[0013] The original phase of the FTF is unwrapped to obtain a continuous phase;

[0014] The equivalent time delay is obtained by calculating the derivative of the continuous phase with respect to frequency, and the average value of the equivalent time delay is calculated. The average value of the equivalent time delay is used as the average convection delay time.

[0015] The continuous phase is corrected based on the average convection delay time to obtain the corrected phase;

[0016] Based on the corrected phase and the original amplitude of the FTF, the real part of the corrected FTF is reconstructed.

[0017] Furthermore, determining the linear parameters of the combustion system based on the real part of the corrected FTF includes:

[0018] The frequency corresponding to the maximum value in the real part of the corrected FTF is identified as the dominant mode frequency.

[0019] The linear growth rate is calculated based on the dominant modal frequency, the maximum real part, the preset system coupling strength, and the damping coefficient.

[0020] Furthermore, the processing of the heat release rate fluctuation signal, extracting the steady-state amplitude information of the heat release rate fluctuation signal, and determining the nonlinear parameters of the combustion system based on the steady-state amplitude information includes:

[0021] Perform a Hilbert transform on the heat release rate fluctuation signal to construct an analytical signal of the heat release rate fluctuation signal;

[0022] Calculate the magnitude of the analytical signal and use the magnitude of the analytical signal as the instantaneous envelope of the heat release rate oscillation;

[0023] Extract the expected amplitude value approaching steady state from the instantaneous envelope, and use it as the steady-state limit loop amplitude;

[0024] The nonlinear saturation intensity is calculated based on the linear growth rate and the steady-state limit cycle amplitude.

[0025] Furthermore, the step of determining whether the current state of the combustion system tends to develop into a stable limiting thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation intensity includes:

[0026] If the linear growth rate is greater than zero and the nonlinear saturation intensity is greater than zero, then the combustion system is determined to be prone to forming a stable limiting cycle oscillation.

[0027] If the linear growth rate is greater than zero and the nonlinear saturation intensity is less than or equal to zero, then the combustion system is determined to be trending toward linear instability.

[0028] Furthermore, when the system tends to form a stable limiting cycle oscillation, the method further includes:

[0029] Based on the linear growth rate and the steady-state limit ring amplitude, the amplitude growth process of the combustion system from the current state to the steady-state limit ring is predicted, and the time required to reach a given amplitude threshold is estimated.

[0030] Furthermore, the method also includes:

[0031] When the predicted conclusion is that the combustion system tends to become unstable or tends to form a limiting cycle oscillation, a combustion control command is generated and output.

[0032] The combustion control command is used to adjust the fuel flow rate, air intake, or fuel equivalence ratio.

[0033] A second aspect of this application proposes a system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function, comprising:

[0034] The acquisition module is used to acquire the inlet excitation fluctuation signal and heat release rate fluctuation signal of the combustion chamber in real time;

[0035] The identification module is used to identify the flame transfer function (FTF) based on the inlet excitation fluctuation signal and the heat release rate fluctuation signal.

[0036] A correction module is used to correct the phase of the FTF to obtain the real part of the corrected FTF that reflects the internal dynamics of the flame.

[0037] The first determining module is used to determine the linear parameters of the combustion system based on the real part of the corrected FTF, the linear parameters including the linear growth rate;

[0038] The second determining module is used to process the heat release rate fluctuation signal, extract the steady-state amplitude information of the heat release rate fluctuation signal, and determine the nonlinear parameters of the combustion system based on the steady-state amplitude information. The nonlinear parameters include nonlinear saturation intensity.

[0039] The judgment module is used to determine whether the current state of the combustion system tends to develop into a stable limiting ring thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation intensity, and outputs the prediction conclusion.

[0040] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in the first aspect embodiment.

[0041] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0042] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0043] This application proposes a method and system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function. The method includes: acquiring inlet excitation fluctuation signals and heat release rate fluctuation signals of the combustion chamber in real time; identifying the flame transfer function (FTF) based on the inlet excitation fluctuation signals and the heat release rate fluctuation signals; correcting the phase of the FTF to obtain the corrected real part of the FTF reflecting the internal dynamics of the flame; determining the linear parameters of the combustion system based on the corrected real part of the FTF, the linear parameters including the linear growth rate; processing the heat release rate fluctuation signals to extract the steady-state amplitude information of the heat release rate fluctuation signals, and determining the nonlinear parameters of the combustion system based on the steady-state amplitude information, the nonlinear parameters including the nonlinear saturation intensity; judging whether the current state of the combustion system tends to develop into a stable limiting-loop thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation intensity, and outputting a prediction conclusion. The technical solution proposed in this application, relying solely on online measurable combustion data, can quickly and accurately predict whether the system will develop into destructive thermoacoustic oscillations, and provides early warning and control time windows in advance, thereby achieving active suppression of thermoacoustic instability and avoiding the problems of response lag or reliance on trial and error in traditional methods.

[0044] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0045] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0046] Figure 1 This is a flowchart illustrating a method for predicting the nonlinear trend of thermoacoustic oscillations based on a flame transfer function, according to an embodiment of this application.

[0047] Figure 2 This is a structural diagram of a system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function, according to an embodiment of this application. Detailed Implementation

[0048] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0049] This application proposes a method and system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function. The method includes: real-time acquisition of inlet excitation fluctuation signals and heat release rate fluctuation signals of the combustion chamber; identification of the flame transfer function (FTF) based on the inlet excitation fluctuation signals and the heat release rate fluctuation signals; phase correction of the FTF to obtain the corrected real part of the FTF reflecting the internal dynamics of the flame; determination of linear parameters of the combustion system based on the corrected real part of the FTF, the linear parameters including the linear growth rate; processing of the heat release rate fluctuation signals to extract the steady-state amplitude information of the heat release rate fluctuation signals, and determination of nonlinear parameters of the combustion system based on the steady-state amplitude information, the nonlinear parameters including the nonlinear saturation intensity; judgment of whether the current state of the combustion system tends to develop into a stable limiting-loop thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation intensity, and outputting a prediction conclusion. The technical solution proposed in this application, relying solely on online measurable combustion data, can quickly and accurately predict whether the system will develop into destructive thermoacoustic oscillations, and provides early warning and control time windows, thereby achieving active suppression of thermoacoustic instability and avoiding the problems of response lag or reliance on trial and error in traditional methods.

[0050] The following describes, with reference to the accompanying drawings, a method and system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function, according to embodiments of this application.

[0051] Example 1

[0052] Figure 1 This is a flowchart illustrating a method for predicting the nonlinear trend of thermoacoustic oscillations based on a flame transfer function, according to an embodiment of this application. Figure 1 As shown, the method includes:

[0053] Step 1: Real-time acquisition of inlet excitation fluctuation signal and heat release rate fluctuation signal of combustion chamber;

[0054] Step 2: Identify the flame transfer function (FTF) based on the inlet excitation fluctuation signal and the heat release rate fluctuation signal;

[0055] It should be noted that the physical meaning of the flame transfer function (FTF) is: it represents the system's response to excitation and the properties of the system itself.

[0056] The mathematical method of Flame Transfer Function (FTF) is as follows: in the category of time-invariant linear systems, the system is regarded as a black box. By applying an excitation to one end, the system output is obtained. The result is obtained by system identification (statistical method) of the output. The calculation method is basically some algorithms of cross-correlation divided by autocorrelation. In data-driven systems, the underlying layer is generally least squares.

[0057] Methods for obtaining the flame transfer function (FTF): experimentation and numerical simulation.

[0058] The input in this embodiment is: import incentive. System heat release rate Both methods can be used to derive the flame transfer function (FTF) from the linear time-invariant system, and then use the FTF to derive the four main parameters of the weakly nonlinear system. A phase diagram can then be plotted using these four parameters to determine the occurrence of system events. These four parameters include: linear growth rate... Nonlinear saturation intensity Linear frequency Nonlinear frequency shift coefficient .

[0059] Time-series import pressure fluctuations based on excitation (Or the fluctuation in the import speed u') and the fluctuation in the overall heat release rate inside the flame tube The following data was obtained from the FTF: principal mode frequency Amplitude phase ;

[0060] To ensure phase continuity, phase unrolling is performed. .

[0061] Step 3: Correct the phase of the FTF to obtain the real part of the corrected FTF that reflects the internal dynamics of the flame;

[0062] In this embodiment of the disclosure, step 3 specifically includes:

[0063] The original phase of the FTF is unwrapped to obtain a continuous phase;

[0064] The equivalent time delay is obtained by calculating the derivative of the continuous phase with respect to frequency, and the average value of the equivalent time delay is calculated. The average value of the equivalent time delay is used as the average convection delay time.

[0065] The continuous phase is corrected based on the average convection delay time to obtain the corrected phase;

[0066] Based on the corrected phase and the original amplitude of the FTF, the real part of the corrected FTF is reconstructed.

[0067] It should be noted that the derivative of phase with respect to frequency is calculated. According to the central difference method, ,in These are elements of the angular frequency array;

[0068] Its average value is calculated based on the convection delay. .

[0069] To correct the phase, the convective linear phase shift needs to be removed, leaving only the internal dynamics of the flame to obtain the corrected phase. : .

[0070] Correcting the complex FTF: Then take its real part: .

[0071] Step 4: Determine the linear parameters of the combustion system based on the real part of the corrected FTF, wherein the linear parameters include the linear growth rate;

[0072] In this embodiment of the disclosure, step 4 specifically includes:

[0073] The frequency corresponding to the maximum value in the real part of the corrected FTF is identified as the dominant mode frequency.

[0074] The linear growth rate is calculated based on the dominant modal frequency, the maximum real part, the preset system coupling strength, and the damping coefficient.

[0075] It should be noted that determining the mode that the flame most readily amplifies acoustically, i.e., the peak frequency, is crucial. Actually, the department is .

[0076] linear growth rate ,in The above settings are used. To read the peak value in the previous step, This corresponds to the frequency read in the previous step.

[0077] Step 5: Process the heat release rate fluctuation signal, extract the steady-state amplitude information of the heat release rate fluctuation signal, and determine the nonlinear parameters of the combustion system based on the steady-state amplitude information. The nonlinear parameters include the nonlinear saturation intensity.

[0078] In this embodiment of the disclosure, step 5 specifically includes:

[0079] Perform a Hilbert transform on the heat release rate fluctuation signal to construct an analytic signal of the heat release rate fluctuation signal. ;

[0080] Calculate the magnitude of the analytical signal and use the magnitude of the analytical signal as the instantaneous envelope of the heat release rate oscillation;

[0081] Extract the expected amplitude value approaching steady state from the instantaneous envelope, and use it as the steady-state limit loop amplitude;

[0082] The nonlinear saturation intensity is calculated based on the linear growth rate and the steady-state limit cycle amplitude.

[0083] It should be noted that the construction ,in For the Hilbert transform, its envelope is... The expected value of its steady-state portion is... .

[0084] Based on the above steps, the nonlinear saturation term can be obtained. .

[0085] Meanwhile, in a steady-state swirling flame, a steady-state frequency shift is set. for .

[0086] That is, the above process can yield four important nonlinear parameters.

[0087] Step 6: Based on the linear growth rate and the nonlinear saturation intensity, determine whether the current state of the combustion system tends to develop into a stable limiting ring thermoacoustic oscillation, and output the prediction conclusion.

[0088] In this embodiment of the disclosure, step 6 specifically includes:

[0089] If the linear growth rate is greater than zero and the nonlinear saturation intensity is greater than zero, then the combustion system is determined to be prone to forming a stable limiting cycle oscillation.

[0090] If the linear growth rate is greater than zero and the nonlinear saturation intensity is less than or equal to zero, then the combustion system is determined to be trending toward linear instability.

[0091] Furthermore, when the system tends to form a stable limiting cycle oscillation, the method further includes:

[0092] Based on the linear growth rate and the steady-state limit ring amplitude, the amplitude growth process of the combustion system from the current state to the steady-state limit ring is predicted, and the time required to reach a given amplitude threshold is estimated.

[0093] Furthermore, the method also includes:

[0094] When the predicted conclusion is that the combustion system tends to become unstable or tends to form a limiting cycle oscillation, a combustion control command is generated and output, and a warning light is activated.

[0095] The combustion control command is used to adjust the fuel flow rate, air intake, or fuel equivalence ratio.

[0096] It should be noted that this embodiment achieves Hopf bifurcation identification by tracking the peak value of the real part curve of the FTF; and uses the Hilbert envelope ("physically measured amplitude") to identify Re( The symbols for ) are as follows: If the envelope shows that Ass approaches 0, then there is no saturation and instability; if the envelope shows that Ass is clearly present, then there is a saturation limit cycle. Both of these innovations are based on data-driven calculations, enabling real-time online computation rather than numerical calculations that take several seconds. This allows the system to make judgments and provide suggestions within milliseconds, avoiding the formation of limit cycle oscillations. Hopf bifurcation occurs when system parameters cross a threshold, causing instability from a stable state and spontaneously generating periodic oscillations—that is, the process from a steady state to oscillation.

[0097] At the same time, this method of using graphical tracing instead of traditional limit cycle equations is far more fault-tolerant than numerical methods for finding the roots of equations.

[0098] It features speed, enabling real-time online data acquisition and processing. It detects nonlinearity even when it is weak and predicts the time to reach the limit cycle. The time before reaching the limit cycle is reserved for the online control program of the combustion chamber to fine-tune the equivalence ratio, thereby reducing the risk of thermoacoustic oscillation in the combustion system.

[0099] In summary, the method proposed in this embodiment for predicting the nonlinear trend of thermoacoustic oscillations based on flame transfer function can quickly and accurately predict whether the system will develop into destructive thermoacoustic oscillations based solely on online measurable combustion data, and provide early warning and control time windows in advance, thereby achieving active suppression of thermoacoustic instability and avoiding the problems of delayed response or reliance on trial and error in traditional methods.

[0100] Example 2

[0101] Figure 2 This is a structural diagram of a system for predicting the nonlinear trend of thermoacoustic oscillations based on the flame transfer function, according to an embodiment of this application. Figure 2 As shown, the system includes:

[0102] The acquisition module 100 is used to acquire the inlet excitation fluctuation signal and heat release rate fluctuation signal of the combustion chamber in real time.

[0103] The identification module 200 is used to identify the flame transfer function (FTF) based on the inlet excitation fluctuation signal and the heat release rate fluctuation signal.

[0104] The correction module 300 is used to correct the phase of the FTF to obtain the real part of the corrected FTF that reflects the internal dynamics of the flame.

[0105] The first determining module 400 is used to determine the linear parameters of the combustion system based on the real part of the corrected FTF, the linear parameters including the linear growth rate;

[0106] The second determining module 500 is used to process the heat release rate fluctuation signal, extract the steady-state amplitude information of the heat release rate fluctuation signal, and determine the nonlinear parameters of the combustion system based on the steady-state amplitude information. The nonlinear parameters include nonlinear saturation intensity.

[0107] The judgment module 600 is used to determine whether the current state of the combustion system tends to develop into a stable limiting ring thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation intensity, and outputs a prediction conclusion.

[0108] In this embodiment of the disclosure, the correction module 300 is further configured to:

[0109] The original phase of the FTF is unwrapped to obtain a continuous phase;

[0110] The equivalent time delay is obtained by calculating the derivative of the continuous phase with respect to frequency, and the average value of the equivalent time delay is calculated. The average value of the equivalent time delay is used as the average convection delay time.

[0111] The continuous phase is corrected based on the average convection delay time to obtain the corrected phase;

[0112] Based on the corrected phase and the original amplitude of the FTF, the real part of the corrected FTF is reconstructed.

[0113] In this embodiment of the disclosure, the first determining module 400 is further configured to:

[0114] The frequency corresponding to the maximum value in the real part of the corrected FTF is identified as the dominant mode frequency.

[0115] The linear growth rate is calculated based on the dominant modal frequency, the maximum real part, the preset system coupling strength, and the damping coefficient.

[0116] In this embodiment of the disclosure, the second determining module 500 is further configured to:

[0117] Perform a Hilbert transform on the heat release rate fluctuation signal to construct an analytical signal of the heat release rate fluctuation signal;

[0118] Calculate the magnitude of the analytical signal and use the magnitude of the analytical signal as the instantaneous envelope of the heat release rate oscillation;

[0119] Extract the expected amplitude value approaching steady state from the instantaneous envelope, and use it as the steady-state limit loop amplitude;

[0120] The nonlinear saturation intensity is calculated based on the linear growth rate and the steady-state limit cycle amplitude.

[0121] In this embodiment of the disclosure, the determination module 600 is further configured to:

[0122] If the linear growth rate is greater than zero and the nonlinear saturation intensity is greater than zero, then the combustion system is determined to be prone to forming a stable limiting cycle oscillation.

[0123] If the linear growth rate is greater than zero and the nonlinear saturation intensity is less than or equal to zero, then the combustion system is determined to be trending toward linear instability.

[0124] In this embodiment of the disclosure, the determination module 600 is further configured to:

[0125] When the system tends to form a stable limiting cycle oscillation, the amplitude growth process of the combustion system from the current state to the stable limiting cycle is predicted based on the linear growth rate and the steady-state limiting cycle amplitude, and the time required to reach a given amplitude threshold is estimated.

[0126] In this embodiment of the disclosure, the determination module 600 is further configured to:

[0127] When the predicted conclusion is that the combustion system tends to become unstable or tends to form a limiting cycle oscillation, a combustion control command is generated and output.

[0128] The combustion control command is used to adjust the fuel flow rate, air intake, or fuel equivalence ratio.

[0129] In summary, the system proposed in this embodiment for predicting the nonlinear trend of thermoacoustic oscillations based on flame transfer function can quickly and accurately predict whether the system will develop into destructive thermoacoustic oscillations based solely on online measurable combustion data, and provide early warning and control time windows in advance, thereby achieving active suppression of thermoacoustic instability and avoiding the problems of delayed response or reliance on trial and error in traditional methods.

[0130] Example 3

[0131] To implement the above embodiments, this disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in Embodiment 1.

[0132] Example 4

[0133] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Embodiment 1.

[0134] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0135] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0136] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting nonlinear trends of thermoacoustic oscillations based on flame transfer functions, characterized in that, The method comprises: real-time acquisition of an inlet excitation fluctuation signal and a heat release rate fluctuation signal of a combustion chamber; identification of a flame transfer function (FTF) based on the inlet excitation fluctuation signal and the heat release rate fluctuation signal; correction of a phase of the FTF to obtain a corrected FTF real part reflecting internal dynamics of a flame; determination of linear parameters of a combustion system according to the corrected FTF real part, the linear parameters including a linear growth rate; processing of the heat release rate fluctuation signal to extract steady-state amplitude information of the heat release rate fluctuation signal, and determination of nonlinear parameters of the combustion system according to the steady-state amplitude information, the nonlinear parameters including a nonlinear saturation strength; judgment of whether a current state of the combustion system tends to develop into stable limit cycle thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation strength, and output of a prediction conclusion; wherein the correction of the phase of the FTF to obtain the corrected FTF real part reflecting the internal dynamics of the flame comprises: unwinding processing of an original phase of the FTF to obtain a continuous phase; calculation of a derivative of the continuous phase with respect to frequency to obtain an equivalent time lag, and calculation of an average value of the equivalent time lag as an average convection delay time; correction of the continuous phase based on the average convection delay time to obtain a corrected phase; reconstruction of the corrected FTF real part based on the corrected phase and an original amplitude of the FTF.

2. The method of claim 1, wherein, The determination of the linear parameters of the combustion system according to the corrected FTF real part comprises: identification of a frequency corresponding to a maximum value in the corrected FTF real part as a dominant modal frequency; calculation of the linear growth rate based on the dominant modal frequency, the maximum value of the real part, a preset system coupling strength, and a damping coefficient.

3. The method of claim 2, wherein, The processing of the heat release rate fluctuation signal to extract the steady-state amplitude information of the heat release rate fluctuation signal, and the determination of the nonlinear parameters of the combustion system according to the steady-state amplitude information comprise: Hilbert transformation of the heat release rate fluctuation signal to construct an analytic signal of the heat release rate fluctuation signal; calculation of a modulus of the analytic signal, and taking the modulus of the analytic signal as an instantaneous envelope of the heat release rate oscillation; extraction of an amplitude expectation of the instantaneous envelope when tending to be steady-state as a steady-state limit cycle amplitude; calculation of the nonlinear saturation strength according to the linear growth rate and the steady-state limit cycle amplitude.

4. The method of claim 3, wherein, The judgment of whether the current state of the combustion system tends to develop into the stable limit cycle thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation strength comprises: if the linear growth rate is greater than zero and the nonlinear saturation strength is greater than zero, it is determined that the combustion system tends to form a stable limit cycle oscillation; if the linear growth rate is greater than zero and the nonlinear saturation strength is less than or equal to zero, it is determined that the combustion system tends to be linearly unstable.

5. The method of claim 4, wherein, When the system tends to form the stable limit cycle oscillation, the method further comprises: prediction of an amplitude growth process of the combustion system from the current state to the stable limit cycle based on the linear growth rate and the steady-state limit cycle amplitude, and estimation of a time required to reach a given amplitude threshold.

6. The method of claim 5, wherein, The method further comprises: generating and outputting a combustion regulation instruction when the predicted conclusion is that the combustion system tends to be unstable or tends to form limit cycle oscillation; the combustion regulation instruction is used to adjust fuel flow, air intake or fuel equivalence ratio.

7. A system for predicting nonlinear trends of thermoacoustic oscillations based on flame transfer functions, characterized in that, The system comprises: an acquisition module, configured to acquire an inlet excitation fluctuation signal and a heat release rate fluctuation signal of a combustion chamber in real time; an identification module, configured to identify a flame transfer function (FTF) based on the inlet excitation fluctuation signal and the heat release rate fluctuation signal; a correction module, configured to correct a phase of the FTF to obtain a corrected real part of the FTF reflecting internal dynamics of a flame; a first determination module, configured to determine linear parameters of the combustion system according to the corrected real part of the FTF, the linear parameters comprising a linear growth rate; a second determination module, configured to process the heat release rate fluctuation signal, extract steady-state amplitude information of the heat release rate fluctuation signal, and determine nonlinear parameters of the combustion system according to the steady-state amplitude information, the nonlinear parameters comprising a nonlinear saturation strength; a judgment module, configured to judge whether a current state of the combustion system tends to develop into stable limit cycle thermoacoustic oscillation based on the linear growth rate and the nonlinear saturation strength, and output a predicted conclusion; wherein the correction module is further configured to: perform unwrapping processing on an original phase of the FTF to obtain a continuous phase; calculate a derivative of the continuous phase with respect to frequency to obtain an equivalent time lag, and calculate an average value of the equivalent time lag as an average convection delay time; correct the continuous phase based on the average convection delay time to obtain a corrected phase; reconstruct the corrected real part of the FTF based on the corrected phase and an original amplitude of the FTF.

8. An electronic device, comprising: comprise: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, when the processor executes the program, the method of any one of claims 1-6 is implemented.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-6.

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