Abnormal flameout prediction method for scramjet engine

Abstract

The invention belongs to the technical field of scramjet engine control, and particularly relates to a scramjet engine abnormal flameout prediction method which is mainly composed of combustion chamber real-time pressure principal component analysis, pressure oscillation maximum principal component extraction, standard deviation calculation, flameout risk degree calculation and flameout risk feedback links. Real-time analysis is conducted according to the actually measured combustion chamber pressure curve, the flameout risk degree value of the scramjet engine is obtained and fed back to the flight control system for disposal, flameout risk prediction of the scramjet engine is achieved, and it is guaranteed that the scramjet engine works outside the flameout boundary and operates stably and reliably.

Description

Technical Field

[0001] The present invention belongs to the technical field of scramjet engine control, and more specifically, relates to a scramjet engine abnormal flameout prediction method with principal component analysis and flameout risk degree assessment function as the core. Background Art

[0002] As a power device for hypersonic vehicles, scramjet engines have the characteristics of high specific impulse under flight conditions above Mach 6, which has obvious advantages over other power cycle modes. Therefore, scramjet engines have become one of the core technologies of hypersonic vehicles, and the research on various mechanisms and application technologies of scramjet engines has become a hot topic in recent years.

[0003] Affected by the air intake environment, flight conditions and equivalence ratio, there is a risk of scramjet engines flameout during cruising. If the engine flames out during cruising, the aircraft will directly lose power, causing unpredictable consequences.

[0004] After decades of theoretical research at home and abroad, the principle of scramjet engines has been basically understood. In recent years, scramjet engines are in the verification flight stage, and their flameout limit protection system is not perfect. There is no public literature available for querying the abnormal flameout prediction criteria and early warning system of scramjet. Abnormal flameout prediction of scramjet engines refers to the evaluation of the flameout risk level when the engine is working at the flameout boundary and timely feedback to the flight control system to prevent the engine from entering the abnormal flameout state. Scramjet engines are different from traditional aircraft engines. Due to their special working environment and state, there are great difficulties in monitoring and predicting the abnormal flameout state of their engines. The main manifestations are: scramjet engines work in supersonic combustion states, the engine flameout process has nonlinear, non-steady-state, and mutation characteristics, and the abnormal flameout of the engine occurs in milliseconds. It is not easy to observe obvious features of scramjet engines before abnormal flameout. Therefore, it is necessary to use data mining methods to extract the features before flameout, and it is also necessary to design a special evaluation function to quantitatively evaluate the flameout risk level, and provide abnormal flameout prediction and feedback. Summary of the invention

[0005] In view of the defects of the prior art, the present invention provides a prediction method suitable for abnormal flameout of a scramjet and meets the special use requirements of a scramjet engine.

[0006] The specific technical solutions of the present invention are as follows: A method for predicting abnormal flameout of a scramjet engine comprises the following steps: (1) Preprocessing of measured curve of engine combustion chamber pressure: (1.1) Downsample the raw data of the engine combustion chamber and retain 50 sampling points per second; (1.2) Eliminate the trend item of the processed combustion chamber pressure data; (2) Principal component analysis of combustion chamber pressure: (2.1) Decomposing the combustion chamber pressure data of the scramjet engine after being processed in step (1) using the principal component analysis method; (2.2) Calculate the variance contribution rate of the main components after decomposition and extract the component with the largest pressure pulsation amplitude; (3) Engine risk assessment: (3.1) Calculate the average pressure of the combustion chamber for a fixed time length; (3.2) Calculate the standard deviation of the pressure in the combustion chamber for a fixed time length; (3.3) Calculate the engine stall risk assessment function; (4) Feedback link of risk level value: (4.1) Feedback the flameout risk level of the scramjet engine to the flight control system; (4.2) The flight control system will adjust the aircraft attitude and refuel the engine according to the degree of flameout risk, so that the engine can get out of the flameout boundary as soon as possible.

[0007] As a preferred method, in the principal component analysis step (2.1) of the combustion chamber pressure, the principal component analysis method in unsupervised learning is used to extract the principal components of the measured combustion chamber pressure of the scramjet engine. In step (2.2), select the principal component with the largest combustion chamber pressure pulsation amplitude y max This component reflects the combustion oscillation characteristics of the combustion chamber. Large combustion pressure pulsations are more likely to cause the risk of flameout.

[0008] As a preferred method, in step (3.3), the engine flameout risk level assessment is: For the largest principal component y max Fixed step size N The standard deviation calculation formula is ; In the formula, p i The largest principal component y max The data values ​​in μ For fixed step length N The average value of the combustion chamber pressure,

[0009] The absolute value of the difference between the combustion chamber pressure before and during combustion is used as the calculation basis for the degree of engine flameout, and a dimensionless calculation is performed. The mapping relationship that characterizes the degree of engine flameout risk is defined as ; In the formula, is the average pressure in the combustion chamber before engine combustion; It is the average pressure of the combustion chamber when the engine is working normally. It is the engine stall risk assessment function.

[0010] As a preferred method, the scramjet engine works in a supersonic combustion state, and the flameout process is completed in milliseconds. The characteristics of the scramjet engine before abnormal flameout are not easy to be directly observed. In order to predict the abnormal flameout of the scramjet engine, it is necessary to mine the statistical laws of the data from the engine's combustion chamber pressure data. The present invention uses the principal component analysis method in unsupervised learning to extract the principal components of the measured combustion chamber pressure of the scramjet engine. In step (2.2), it is assumed that the measured combustion chamber pressure x yes m dimensional random variable, Σ is the combustion chamber pressure x The covariance matrix of Σ has eigenvalues ​​of The eigenvalues ​​and corresponding eigenvectors are , then the combustion chamber pressure x No. k The main components of combustion are:

[0011] β mk is the mth eigenvector of the kth combustion principal component, x m yes x The mth column vector of ;

[0012] x No. k The variance of the combustion principal component is: ;

[0013] definition x No. k Combustion main component y k The variance contribution rate is y k The ratio of the variance of to the sum of all variances is calculated as follows: .

[0014] The beneficial effects of the present invention compared with the prior art are: The present invention uses principal component analysis to extract the main components of the engine combustion chamber pressure, among which the components with larger pressure pulsation represent the combustion oscillation characteristics of the engine. This principal component analysis method screens out data that have little impact on abnormal engine flameout, further improving the prediction accuracy of engine flameout. The flameout risk function is used to quantify the engine flameout risk degree, and specific quantitative indicators are given. Feeding back the predicted engine flameout risk degree value to the flight control system can effectively avoid abnormal flameout during the operation of the scramjet engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the pressure change curve of the combustion chamber after pretreatment.

[0016] Figure 2 is the third-order principal component obtained through principal component analysis.

[0017] Figure 3 This is the engine misfire degree assessment curve. DETAILED DESCRIPTION

[0018] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0019] The embodiment provides a method for predicting abnormal flameout of a scramjet engine, comprising the following steps: (1) Preprocessing of measured curve of engine combustion chamber pressure: (1.1) Downsample the raw data of the engine combustion chamber and retain 50 sampling points per second; (1.2) Eliminate trend items from the processed combustion chamber pressure data; (2) Principal component analysis of combustion chamber pressure: (2.1) Decomposing the combustion chamber pressure data of the scramjet engine after being processed in step (1) using the principal component analysis method; (2.2) Calculate the variance contribution rate of the main components after decomposition and extract the component with the largest pressure pulsation amplitude; (3) Engine risk assessment: (3.1) Calculate the average pressure of the combustion chamber for a fixed time length ; (3.2) Calculate the standard deviation of the pressure in the combustion chamber for a fixed time length ; In the formula, p i The largest principal component y max The data values ​​in μ For fixed step length N The average value of the combustion chamber pressure, (3.3) Calculate the engine stall risk assessment function; (4) Feedback link of risk level value: (4.1) Feedback the flameout risk level of the scramjet engine to the flight control system; (4.2) The flight control system will adjust the aircraft attitude and refuel the engine according to the degree of flameout risk, so that the engine can get out of the flameout boundary as soon as possible.

[0020] As a preferred method, in the principal component analysis step (2.1) of the combustion chamber pressure, the principal component analysis method in unsupervised learning is used to extract the principal components of the measured combustion chamber pressure of the scramjet engine. In step (2.2), select the principal component with the largest combustion chamber pressure pulsation amplitude y max This component reflects the combustion oscillation characteristics of the combustion chamber. Large combustion pressure pulsations are more likely to cause the risk of flameout.

[0021] As a preferred method, in step (3.3), the engine flameout risk level assessment is: For the largest principal component y max Fixed step size N The standard deviation calculation formula is ; In the formula, p i The largest principal component y max The data values ​​in μ For fixed step length N The average value of the combustion chamber pressure,

[0022] The absolute value of the difference between the combustion chamber pressure before and during combustion is used as the calculation basis for the degree of engine flameout, and a dimensionless calculation is performed. The mapping relationship that characterizes the degree of engine flameout risk is defined as ; In the formula, is the average pressure in the combustion chamber before engine combustion; It is the average pressure of the combustion chamber when the engine is working normally. It is the engine stall risk assessment function.

[0023] As a preferred method, the scramjet engine works in a supersonic combustion state, and the flameout process is completed in milliseconds. The characteristics of the scramjet engine before abnormal flameout are not easy to be directly observed. In order to predict the abnormal flameout of the scramjet engine, it is necessary to mine the statistical laws of the data from the engine's combustion chamber pressure data. The present invention uses the principal component analysis method in unsupervised learning to extract the principal components of the measured combustion chamber pressure of the scramjet engine. In step (2.2), it is assumed that the measured combustion chamber pressure x yes m dimensional random variable, Σ is the combustion chamber pressure x The covariance matrix of Σ has eigenvalues ​​of The eigenvalues ​​and corresponding eigenvectors are , then the combustion chamber pressure x No. k The main components of combustion are: ; β mk is the mth eigenvector of the kth combustion principal component, x m yes x The mth column vector of ;

[0024] x No. k The variance of the combustion principal component is:

[0025] definition x No. k Combustion main component y k The variance contribution rate is y k The ratio of the variance of to the sum of all variances is calculated as follows:

[0026] like Figure 1 As shown. First, it is necessary to collect the pressure curve of the scramjet engine combustion chamber. After downsampling and eliminating trend items, the engine combustion chamber pressure curve is Figure 1 The curve shown is the combustion chamber pressure curve under normal engine operation. After 333 seconds, the engine abnormally stalled.

[0027] Then, the principal component analysis method is used to decompose the scramjet engine combustion chamber pressure data to obtain the main components of the scramjet engine combustion chamber pressure. Figure 2The three main components of the combustion chamber pressure are shown in Figure 2. y 1 , y 2 and y 3 The variance contribution rates of the three components are 71.23%, 19.89% and 8.87% respectively. y 3 It has the largest pressure pulsation amplitude and can better reflect the combustion oscillation characteristics of the scramjet engine. y 3 Defined as y max When the scramjet combustion chamber pressure pulsates greatly, its combustion stability is poor and there is a greater risk of flameout.

[0028] For the principal component y 3 Standard deviation for a fixed time step S Calculate the standard deviation S Substitute into the engine flameout risk assessment function f d The abnormal flameout degree assessment curve of the scramjet engine is obtained, such as Figure 3 shown. Figure 3 It can be seen that after 80s, the amplitude of the flameout risk assessment function increases significantly, and two peaks appear, which indicates that the engine has a greater flameout risk.

[0029] The flameout risk assessment value of the scramjet engine is fed back to the flight control system, which then performs actions such as aircraft attitude adjustment and engine refueling according to the flameout risk level, so as to get the engine out of the flameout boundary as soon as possible.

[0030] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for predicting abnormal flameout of a scramjet engine, characterized in that The steps include: (1) Preprocessing of measured curve of engine combustion chamber pressure: (1.1) Downsample the raw data of the engine combustion chamber and retain 50 sampling points per second; (1.2) Eliminate the trend item of the processed combustion chamber pressure data; (2) Principal component analysis of combustion chamber pressure: (2.1) Decomposing the combustion chamber pressure data of the scramjet engine after being processed in step (1) using the principal component analysis method; (2.2) Calculate the variance contribution rate of the main components after decomposition and extract the component with the largest pressure pulsation amplitude; (3) Engine risk assessment: (3.1) Calculate the average pressure of the combustion chamber for a fixed time length; (3.2) Calculate the standard deviation of the pressure in the combustion chamber for a fixed time length; (3.3) Calculate the engine stall risk assessment function; (4) Feedback link of risk level value: (4.1) Feedback the flameout risk level of the scramjet engine to the flight control system; (4.2) The flight control system will adjust the aircraft attitude and refuel the engine according to the degree of flameout risk, so that the engine can get out of the flameout boundary as soon as possible.

2. The method for predicting abnormal flameout of a scramjet engine according to claim 1 is characterized in that: In the principal component analysis step (2.1) of the combustion chamber pressure, the principal component analysis method in unsupervised learning is used to extract the principal components of the measured combustion chamber pressure of the scramjet engine. In step (2.2), select the principal component with the largest combustion chamber pressure pulsation amplitude y max .

3. The method for predicting abnormal flameout of a scramjet engine according to claim 2 is characterized in that: In step (3.3), the engine flameout risk level is assessed. For the largest principal component y max Fixed step size N The standard deviation calculation formula is ； In the formula, p i The largest principal component y max The data values ​​in μ For fixed step length N The average value of the combustion chamber pressure, ； The absolute value of the difference between the combustion chamber pressure before and during combustion is used as the calculation basis for the degree of engine flameout, and a dimensionless calculation is performed. The mapping relationship that characterizes the degree of engine flameout risk is defined as ; In the formula, is the average pressure in the combustion chamber before engine combustion; It is the average pressure of the combustion chamber when the engine is working normally. It is the engine stall risk assessment function.

4. The method for predicting abnormal flameout of a scramjet engine according to claim 1, characterized in that: In step (2.2), it is assumed that the measured combustion chamber pressure x yes m dimensional random variable, Σ is the combustion chamber pressure x The covariance matrix of Σ has eigenvalues ​​of The eigenvalues ​​and corresponding eigenvectors are , then the combustion chamber pressure x No. k The main components of combustion are: ； β mk is the mth eigenvector of the kth combustion principal component, x m yes x The mth column vector of ; x No. k The variance of the combustion principal component is: ； definition x No. k Combustion main component y k The variance contribution rate is y k The ratio of the variance of to the sum of all variances is calculated as follows: 。

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