Small bypass ratio turbofan engine performance evaluation method based on model self-adjustment

Abstract

The invention discloses a small bypass ratio turbofan engine performance evaluation method based on model self-adjustment, comprising the following steps: defining a parameter structure for engine performance evaluation, the parameter structure comprising parameters and parameter units selected from engine test flight test data; determining a sampling moment, traversing the test flight test data, and extracting a steady-state section at the sampling moment; updating the sampling time of the test flight stage, repeatedly executing the steady-state segment extraction of the sampling time, and outputting the evaluation coefficient of the actual part characteristic recession amount of the engine; and generating an engine component performance degradation trend according to the sampling time and the engine actual component characteristic degradation amount evaluation coefficient corresponding to the sampling time. According to the technical scheme, the gas path component characteristic characterization parameters of the engine can be corrected by applying the engine self-adjusting model, table conversion processing of real sensor measurement values can be achieved, and then the gas path component performance of the engine can be effectively evaluated under multiple working conditions in the full envelope of the aero-engine.

Description

Technical Field

[0001] The present invention relates to the field of aviation engine health management, and in particular to a performance evaluation method for a low bypass ratio turbofan engine based on model self-adjustment. Background Art

[0002] In the field of civil aviation passenger aircraft, since the operating conditions of large bypass ratio engines are single, the engine performance can be evaluated by monitoring the engine's exhaust temperature margin; for small bypass ratio turbofan engines, their envelope is wide, the operating conditions are changeable, and the state duration and overlap are low. They are more complex than civil aviation turbofan engines and turboshaft and turboprop engines, and it is difficult to find repeated steady-state conditions for performance degradation estimation.

[0003] Therefore, when evaluating the performance of engine components, it is difficult to obtain characteristic data of gas path components under steady-state conditions; if accurate evaluation of engine component performance is required, the characteristic characterization parameters of the engine's gas path components need to be corrected to optimize the evaluation process. Summary of the invention

[0004] To achieve the above object, the present application provides a method for evaluating the performance of a low bypass ratio turbofan engine based on model self-adjustment, comprising the following steps:

[0005] Define the parameter structure of engine performance evaluation, the parameter structure includes parameters and parameter units selected from the engine test flight data; the parameters selected from the engine test flight data include throttle lever angle, speed, section temperature, and section pressure;

[0006] Determine the sampling time, traverse the test flight data, and extract the steady-state segment at the sampling time;

[0007] Update the sampling time of the test flight phase, repeat the steady-state segment extraction at the sampling time, and output the evaluation coefficient of the actual engine component characteristic degradation;

[0008] The engine component performance degradation trend is generated based on the sampling time and the evaluation coefficient of the actual engine component characteristic degradation amount corresponding to the sampling time.

[0009] The steady-state segment extraction at the sampling time includes:

[0010] According to the parameter structure, the steady-state segment is extracted at the sampling time of the test flight data to generate the steady-state segment data; the steady-state segment extraction includes processing the test flight data in the activation steady-state stage, the steady-state stage and the burst end recording stage;

[0011] Loading a self-adjusting model, wherein the self-adjusting model calculates an evaluation coefficient of an actual engine component characteristic decay amount according to steady-state segment data; the evaluation coefficient of an actual engine component characteristic decay amount includes an efficiency characteristic decay coefficient and a flow characteristic decay coefficient;

[0012] Combined with the actual measured parameters of the sensor, the engine total pressure ratio correction coefficient is calculated, and the evaluation coefficient of the actual engine component characteristic degradation amount is corrected to obtain the engine component performance degradation amount based on the engine test flight data at the sampling time.

[0013] Among them, the activation of the stable state stage means that: within a certain sampling time, the fluctuation difference of the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 is less than the preset threshold, and the compressor relative speed is greater than 0.9;

[0014] The steady state stage refers to: within the specified sampling time after entering the activation steady state stage, the throttle lever angle PLA, the engine inlet total pressure tP2, the engine inlet total temperature tT2 and the compressor relative speed are in a stable state;

[0015] The sudden end recording stage refers to: the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 suddenly change.

[0016] Among them, the sensor measured parameters include: throttle lever angle, rotation speed, cross-sectional temperature, and cross-sectional pressure.

[0017] Furthermore, the self-adjusting model calculates the evaluation coefficient of the degradation amount of the actual engine component characteristics according to the steady-state segment data, including the following steps:

[0018] Initialize measurable parameters, which are the influence relationship matrix between the target variable change and the optimization variable; the measurable parameters include: high-pressure rotor speed, low-pressure rotor speed, low-pressure turbine rear total temperature, low-pressure turbine rear total pressure, compressor rear total pressure, compressor inlet total pressure; the target variables include: high-pressure rotor power deviation, low-pressure rotor power deviation, turbine rear total pressure, turbine rear total temperature; the optimization variables include: fuel flow rate, nozzle area or nozzle angle;

[0019] Load the steady-state segment data at the sampling moment;

[0020] According to the target variable of the self-tuning model, the input parameters of the engine are corrected; the corrected input parameters of the engine are constituted as optimization variables;

[0021] The component characteristic characterization parameters are interpolated and supplemented, and the iterative calculation is performed to generate the actual component efficiency characteristic decay coefficient and flow characteristic decay coefficient.

[0022] Furthermore, the horizontal axis of the influence relationship matrix between the target variable change and the optimized variable is the relative rotation speed relative to the total temperature of the engine inlet, and the vertical axis of the influence relationship matrix is ​​the gradient between the efficiency of the engine's gas path rotating components and the total temperature after the engine's low-pressure turbine combustion gas; wherein, the gas path rotating components are the fan, high-pressure compressor, high-pressure turbine, and low-pressure turbine.

[0023] The target variable of the self-adjusting model refers to the deviation between the actual measured parameters of the sensor and the state value output by the self-adjusting model; including: high-pressure rotor power deviation, high-pressure rotor power deviation, turbine after-pressure, turbine after-temperature, high-pressure power difference, low-pressure power difference, and compressor after-total pressure.

[0024] After obtaining the performance degradation of engine components based on engine test and flight data, the evaluation coefficients of the degradation of actual engine component characteristics are normalized.

[0025] Further, the engine component performance degradation trend includes a sampling time and an engine component performance degradation amount corresponding to the sampling time;

[0026] The change of engine component performance degradation amount with sampling time is used to evaluate the performance of the small bypass ratio turbofan engine.

[0027] According to the present invention, the engine self-adjusting model can be used to correct the characteristic characterization parameters of the engine's gas path components, realize the table conversion processing of the real sensor measurement values, and then realize the effective evaluation of the engine's gas path component performance under multiple working conditions within the full envelope of the aircraft engine. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a step diagram of a method for evaluating performance of a low bypass ratio turbofan engine provided in accordance with an embodiment of the present invention;

[0029] Figure 2 is a logic flow chart for extracting steady-state segment of test flight data provided according to an embodiment of the present invention;

[0030] Figure 3 It is a logic flow chart of a method for evaluating the performance of a low bypass ratio turbofan engine provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The low bypass ratio turbofan engine has the characteristics of wide envelope, variable working conditions, low state duration and overlap, so it is difficult to evaluate the performance degradation of the engine by conventional methods such as monitoring the exhaust temperature margin of the engine. According to the characteristics of the self-adjusting model, the present invention is applied to the correction and adjustment of the characteristic characterization parameters of the engine gas path components (fan, compressor, high and low pressure turbine) under multiple working conditions to realize the performance evaluation of the low bypass ratio turbofan engine.

[0032] The specific implementation of the present invention is described in detail below with reference to the accompanying drawings.

[0033] The present invention relates to a method for implementing a performance evaluation method of a low bypass ratio turbofan engine based on model self-adjustment, such as Figure 1 As shown, including:

[0034] Step S100: defining a parameter structure for engine performance evaluation, wherein the parameter structure includes parameters and parameter units selected from engine test flight data;

[0035] Specifically, the parameters selected from the engine test flight data include throttle lever angle, rotation speed, section temperature, and section pressure;

[0036] In addition to selecting parameters for a set of engine test and flight data, a unified unit conversion is also performed to make it meet the input standards of the program.

[0037] Step S110: In this step, the test flight data is traversed to extract the steady-state segment at each sampling time. The specific process is as follows: Figure 2 As shown, the following steps are included:

[0038] Step 1: According to the parameter structure defined in step S100, the steady-state segment at the sampling time is extracted from the test flight data to generate steady-state segment data; wherein the steady-state segment extraction includes processing the test flight data in the following three stages, which are: activating the steady state stage, the steady state stage and the burst end recording stage.

[0039] Specifically, within a certain sampling time, if the fluctuation difference of the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 is less than a preset threshold, and the compressor relative speed is greater than 0.9, it indicates that the engine stable state has been activated and enters the activation stable state stage; wherein the preset threshold is set according to the actual situation, and the fluctuation difference is less than the preset threshold to determine that the fluctuation difference of the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 has no large fluctuation;

[0040] The steady state stage refers to: within the specified sampling time after entering the activation steady state stage, the throttle lever angle PLA, the engine inlet total pressure tP2, the engine inlet total temperature tT2 and the compressor relative speed meet the above conditions and are still in a stable state. At this time, the data length of the steady state time period begins to be recorded, and the start time of the steady state section is recorded;

[0041] The sudden end recording stage refers to: when the subsequent engine operation state does not meet the above conditions, the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 suddenly change; at this time, the data length is stopped and the end time of the steady-state segment is recorded to form the steady-state segment data;

[0042] After completing the above operations at a certain moment, obtain the next moment and continue to extract the steady-state segment until all the test flight data of this experiment are traversed.

[0043] Step 2: Load the self-adjusting model and calculate the evaluation coefficient of the degradation of the actual engine components based on the steady-state data;

[0044] The evaluation coefficients of the characteristic decay of the actual engine components include the efficiency characteristic decay coefficient and the flow characteristic decay coefficient;

[0045] The specific process of calculating the evaluation coefficient of the degradation amount of the actual engine component characteristics based on the steady-state data by the self-adjusting model includes the following steps:

[0046] 1) Obtain and initialize measurable parameters, where the measurable parameters are the influence relationship matrix between the target variable change and the optimization variable;

[0047] Specifically, the measurable parameters include: high-pressure rotor speed, low-pressure rotor speed, total temperature after low-pressure turbine, total pressure after low-pressure turbine, total pressure after compressor, and total pressure at compressor inlet;

[0048] The target variable of the self-adjusting model refers to the deviation between the actual measured parameters of the sensor and the state value output by the self-adjusting model; including: high-pressure rotor power deviation, high-pressure rotor power deviation, turbine after-pressure, turbine after-temperature, high-pressure power difference, low-pressure power difference, total pressure after the compressor; the optimization variables include: fuel flow, nozzle area or nozzle angle.

[0049] Based on the current state characterization parameter being the relative speed of the high-pressure compressor relative to the total temperature at the engine inlet, the horizontal axis of the influence relationship matrix between the target variable change and the optimized variable is the relative speed relative to the total temperature at the engine inlet, and the vertical axis of the influence relationship matrix is ​​the gradient between the efficiency of the engine's gas path rotating components and the total temperature after the engine's low-pressure turbine combustion gas; wherein the gas path rotating components are the fan, high-pressure compressor, high-pressure turbine, and low-pressure turbine.

[0050] On the other hand, the sensor measured parameters include: throttle lever angle, rotation speed, cross-sectional temperature, and cross-sectional pressure.

[0051] 2) Load the steady-state data at the sampling time and input it into the self-adjusting model.

[0052] 3) In engine control, fuel flow, nozzle area, and nozzle angle are open-loop control parameters with inherent deviations. Therefore, the self-adjusting model corrects the input parameters of the engine (fuel flow, nozzle area, or nozzle angle) according to the target variables to ensure that the input parameters of the self-adjusting model are accurate. At this time, the input parameters of the engine are corrected to form optimization variables. Figure 3As shown in , at this time, the fuel self-adjustment and nozzle area or nozzle angle self-adjustment of the self-adjustment model are realized.

[0053] 4) The self-adjusting model also interpolates the component characteristic parameters according to the deviation between the target parameters (such as low-pressure power difference, high-pressure power difference, and total temperature after the turbine) and the actual measured parameters of the sensor, and reversely solves the influence relationship matrix in the measurable parameters to obtain the change in the optimization variable corresponding to the current target variable deviation value, fits the influence value of different target variables on the optimization variable, and subsequently uses the gradient descent and PI control methods to quickly solve the change in the optimization variable, iterates and calculates, generates the actual component efficiency characteristic decay coefficient, and then uses the above method to generate the actual component flow characteristic decay coefficient; Figure 3 As shown in , at this time, the efficiency self-adjustment and flow self-adjustment of the self-adjustment model are realized.

[0054] Step 3: Calculate the engine total pressure ratio correction coefficient based on the actual sensor measured parameters, correct the actual engine component characteristic degradation assessment coefficient, and obtain the engine component performance degradation based on the engine test flight data at the sampling time; Among them, the total pressure ratio is a parameter to measure engine performance, and is a value that can be calculated based on the sensor measurement parameters, specifically the ratio of the total pressure after the low-pressure turbine P6 to the total pressure at the engine inlet P2.

[0055] After obtaining the engine component performance degradation amount based on the engine test flight data, the evaluation coefficient of the actual engine component characteristic degradation amount is normalized.

[0056] Step S120: Update the sampling time of the test flight phase, repeat steps 1 to 3, and output the evaluation coefficient of the actual engine component characteristic degradation until all the steady-state segment data are traversed.

[0057] Step S130: Generate engine component performance degradation trends of engine test flight data.

[0058] The engine component performance degradation trend of the engine test flight data is reflected in the form of a list by the sampling time and the engine component performance degradation amount corresponding to the sampling time; the change of the engine component performance degradation amount with the sampling time is used to evaluate the performance of the small bypass ratio turbofan engine.

[0059] When the present invention implements the performance evaluation of the engine, the sampling data is firstly optimized, the stability reflected in the engine test and flight test data is captured, and the original data that can effectively reflect the performance is extracted; secondly, the component performance evaluation logic is performed through the self-adjusting model. During the self-adjusting process, the relationship characteristics between the target parameters and the actual measured parameters of the sensor are processed to realize multi-angle parameter self-adjustment, thereby obtaining an effective performance evaluation of the actual component.

[0060] The above disclosures are only several specific embodiments of the present invention; however, the present invention is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for evaluating the performance of a low bypass ratio turbofan engine based on model self-adjustment, characterized in that: The following steps are involved: Define a parameter structure for engine performance evaluation, wherein the parameter structure includes parameters and parameter units selected from the engine test flight data; the parameters selected from the engine test flight data include throttle lever angle, rotation speed, cross-sectional temperature, and cross-sectional pressure; Determine the sampling time, traverse the test flight data, and extract the steady-state segment at the sampling time; Updating the sampling time of the test flight phase, repeatedly performing the steady-state segment extraction of the sampling time, and outputting the evaluation coefficient of the characteristic degradation amount of the actual engine component; The engine component performance degradation trend is generated according to the sampling time and the actual engine component characteristic degradation amount evaluation coefficient corresponding to the sampling time.

2. The low bypass ratio turbofan engine performance evaluation method according to claim 1, characterized in that: The steady-state segment extraction at the sampling moment comprises: According to the parameter structure, steady-state segment extraction is performed at the sampling time of the test flight data to generate steady-state segment data; the steady-state segment extraction includes processing the test flight data in the activation steady-state stage, the steady-state stage and the burst end recording stage; Loading a self-adjusting model, wherein the self-adjusting model calculates an evaluation coefficient of an actual engine component characteristic decay amount according to the steady-state segment data; the evaluation coefficient of an actual engine component characteristic decay amount includes an efficiency characteristic decay coefficient and a flow characteristic decay coefficient; Combined with the actual measured parameters of the sensor, the engine total pressure ratio correction coefficient is calculated, the actual engine component characteristic degradation assessment coefficient is corrected, and the engine component performance degradation based on the engine test flight data at the sampling time is obtained.

3. The low bypass ratio turbofan engine performance evaluation method according to claim 2, characterized in that: The activation stable state stage means that: within a certain sampling time, the fluctuation difference of the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 is less than the preset threshold, and the compressor relative speed is greater than 0.9; The steady state stage refers to: within a specified sampling time after entering the activation steady state stage, the throttle lever angle PLA, the engine inlet total pressure tP2, the engine inlet total temperature tT2 and the compressor relative speed are in a stable state; The sudden end recording stage refers to: the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 undergo sudden changes.

4. The low bypass ratio turbofan engine performance evaluation method according to claim 1, characterized in that: The sensor measured parameters include: throttle lever angle, rotation speed, cross-sectional temperature, and cross-sectional pressure.

5. The low bypass ratio turbofan engine performance evaluation method according to claim 1, characterized in that: The self-adjusting model calculates the evaluation coefficient of the actual engine component characteristic degradation amount according to the steady-state segment data, including the following steps: Initialize measurable parameters, which are the influence relationship matrix between the target variable change and the optimization variable; the measurable parameters include: high-pressure rotor speed, low-pressure rotor speed, low-pressure turbine rear total temperature, low-pressure turbine rear total pressure, compressor rear total pressure, compressor inlet total pressure; the target variables include: high-pressure rotor power deviation, low-pressure rotor power deviation, turbine rear total pressure, turbine rear total temperature; the optimization variables include: fuel flow rate, nozzle area or nozzle angle; Load the steady-state segment data at the sampling moment; According to the target variable of the self-adjusting model, the input parameters of the engine are corrected; the input parameters of the engine are corrected to form optimization variables; The component characteristic characterization parameters are interpolated and supplemented, and the iterative calculation is performed to generate the actual component efficiency characteristic decay coefficient and flow characteristic decay coefficient.

6. The low bypass ratio turbofan engine performance evaluation method according to claim 5, characterized in that: The horizontal axis of the influence relationship matrix between the target variable change and the optimization variable is the relative speed relative to the total temperature of the engine inlet, and the vertical axis of the influence relationship matrix is ​​the gradient between the efficiency of the engine's gas path rotating components and the total temperature after the engine's low-pressure turbine combustion gas; the gas path rotating components are the fan, high-pressure compressor, high-pressure turbine, and low-pressure turbine.

7. The low bypass ratio turbofan engine performance evaluation method according to claim 5, characterized in that: The target variable of the self-adjusting model refers to the deviation between the actual measured parameters of the sensor and the state value output by the self-adjusting model; including: high-pressure rotor power deviation, high-pressure rotor power deviation, turbine after-pressure, turbine after-temperature, high-pressure power difference, low-pressure power difference, and compressor after-total pressure.

8. The low bypass ratio turbofan engine performance evaluation method according to claim 1, characterized in that: After the engine component performance degradation amount based on the engine test flight data is obtained, the evaluation coefficient of the actual engine component characteristic degradation amount is normalized.

9. The low bypass ratio turbofan engine performance evaluation method according to claim 1, characterized in that: The engine component performance degradation trend of the engine test flight data includes a sampling time and an engine component performance degradation amount corresponding to the sampling time; The change in the engine component performance degradation amount with the sampling time is used to evaluate the performance of the small bypass ratio turbofan engine.

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