A model-based self-adjusting performance evaluation method for small-bypass-ratio turbofan engines

Abstract

The application discloses a kind of based on model self-adjusting small bypass ratio turbofan engine performance evaluation method, comprising the following steps: defining the parameter structure of engine performance evaluation, parameter structure includes the parameter selected from engine test flight data, parameter unit;Determine sampling time, traverse test flight data, carry out steady-state section extraction of sampling time;Update the sampling time of test flight stage, repeat the steady-state section extraction of sampling time, output engine actual component characteristic degradation amount evaluation coefficient;According to sampling time and the engine actual component characteristic degradation amount evaluation coefficient corresponding to sampling time, generate engine component performance degradation trend.According to the above technical solution, the engine self-adjusting model can be used to correct the engine air path component characteristic representation parameter, realize the table conversion processing of real sensor measurement value, and then realize the effective evaluation of engine air path component performance under multiple working conditions in the full envelope of aeroengine.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aero-engine health management, in particular to a small-bypass-ratio turbofan engine performance evaluation method based on model self-adjustment. BACKGROUND

[0002] In the field of civil aviation passenger aircraft, since the working condition of large-bypass-ratio engines is single, the performance of the engine can be evaluated by monitoring the exhaust temperature margin of the engine; for small-bypass-ratio turbofan engines, the envelope domain is wide, the working condition is variable, the state duration and overlap degree are low, and it is 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 the characteristic data of the gas path components under steady-state conditions; if accurate evaluation of the performance of engine components is required, the characteristic parameters of the gas path components of the engine need to be corrected, and the evaluation process needs to be optimized. SUMMARY

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

[0005] Defining the parameter structure of engine performance evaluation, the parameter structure including parameters selected from engine test flight data, parameter units; the parameters selected from the engine test flight data include throttle lever angle, speed, cross-section temperature, cross-section pressure;

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

[0007] Update the sampling time of the test flight stage, repeatedly execute the steady-state section extraction of the sampling time, and output the engine actual component characteristic degradation amount evaluation coefficient;

[0008] According to the sampling time and the engine actual component characteristic degradation amount evaluation coefficient corresponding to the sampling time, the engine component performance degradation trend is generated.

[0009] The steady-state section extraction of the sampling time comprises:

[0010] According to the parameter structure, the steady-state section extraction of the sampling time is performed on the test flight data to generate steady-state section data; the steady-state section extraction includes processing of the test flight data in the active steady state stage, the steady state stage and the sudden end record stage;

[0011] Load a self-adjusting model, which calculates an engine actual component characteristic degradation amount evaluation coefficient according to steady state section data; the engine actual component characteristic degradation amount evaluation coefficient includes an efficiency characteristic degradation coefficient and a flow characteristic degradation coefficient;

[0012] In combination with a sensor measured parameter, an engine total pressure ratio correction coefficient is calculated, the engine actual component characteristic degradation amount evaluation coefficient is corrected, and an engine component performance degradation amount based on engine test flight data at a sampling time is obtained.

[0013] The activated steady state stage refers to 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 a preset threshold, and the compressor relative speed is greater than 0.9;

[0014] The steady state stage refers to that, within a specified sampling time after entering the activated 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 steady state;

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

[0016] The sensor measured parameters include the throttle lever angle, the speed, the cross-section temperature, and the cross-section pressure.

[0017] Further, the self-adjusting model calculates the engine actual component characteristic degradation amount evaluation coefficient according to the steady state section data, including the following steps:

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

[0019] Load the steady state section data at the sampling time;

[0020] Correct the input parameters of the engine according to the target variables of the self-adjusting model; the input parameters of the engine after correction constitute the optimization variables;

[0021] Interpolate and supplement the component characteristic representation parameters, iteratively calculate, and generate the actual component efficiency characteristic degradation coefficient and the flow characteristic degradation coefficient.

[0022] Further, the horizontal axis of the influence relationship matrix between the target variable change and the optimization variable is the relative speed relative to the engine inlet total temperature, and the vertical axis of the influence relationship matrix is the gradient between the efficiency of the engine air path rotating component and the engine low-pressure turbine gas after total temperature; wherein the air path rotating component is a fan, a high-pressure compressor, a high-pressure turbine, and a low-pressure turbine.

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

[0024] After obtaining the engine component performance degradation amount based on the engine test flight data, the engine actual component characteristic degradation amount evaluation coefficient is 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 engine component performance degradation amount changes with the sampling time, and is used to evaluate the performance of the small-bypass-ratio turbofan engine.

[0027] According to the present application, by using the engine self-adjusting model, the air path component characteristic representation parameters of the engine can be corrected, the sensor measurement value conversion processing can be realized, and the effective evaluation of the air path component performance of the engine under multiple working conditions within the full envelope of the aero-engine can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a small-bypass-ratio turbofan engine performance evaluation method step diagram provided according to an embodiment of the present application;

[0029] Figure 2 is a test flight data steady-state section extraction logic flowchart diagram provided according to an embodiment of the present application;

[0030] Figure 3 is a small-bypass-ratio turbofan engine performance evaluation method logic flowchart diagram provided according to an embodiment of the present application. DETAILED DESCRIPTION

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

[0032] The specific implementation manners of the present application are described in detail below with reference to the accompanying drawings of the specification.

[0033] The present application relates to a kind of based on model self-adjusting low-bypass-ratio turbofan engine performance evaluation method implementation steps, as shown in Figure 1 Including:

[0034] Step S100: define the parameter structure of engine performance evaluation, parameter structure includes the parameter selected from engine test flight data, parameter unit;

[0035] Specifically, the parameter selected from engine test flight data includes throttle lever angle, speed, section temperature, section pressure;

[0036] In addition to the parameter selection of a certain group of engine test flight data obtained, it is also uniformly unit conversion, so that it meets the input standard of procedure.

[0037] Step S110: in this link, test flight data is traversed, and the steady-state section extraction of each sampling time is carried out, and the specific process is as shown in Figure 2 Including the following steps:

[0038] Step one: according to the parameter structure defined in step S100, the steady-state section extraction of sampling time is carried out from test flight data, to generate steady-state section data;Wherein, steady-state section extraction includes the processing of test flight data in the following three stages, and the three stages are: active stable state stage, steady-state stage and sudden end record stage.

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

[0040] The steady-state stage refers to: within the specified sampling time after entering the active stable state stage, throttle lever angle PLA, engine inlet total pressure tP2, engine inlet total temperature tT2 and compressor relative speed meet the above conditions, and still in stable state, at this time, start recording the length of steady-state time period data, and record the starting time of steady-state section;

[0041] The said sudden end record stage refers to: when the subsequent engine operating state does not meet the above conditions, throttle lever angle PLA, engine inlet total pressure tP2, engine inlet total temperature tT2 occur mutation;At this time, stop recording data length and record the end time of steady-state section, to form steady-state section 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 test flight data for this experiment has been traversed.

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

[0044] The evaluation coefficients for the actual degradation of engine component characteristics include the efficiency characteristic degradation coefficient and the flow characteristic degradation coefficient.

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

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

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

[0048] The target variable of the self-adjusting model refers to the deviation between the 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; the optimization variables include: fuel flow rate, nozzle area or nozzle angle.

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

[0050] On the other hand, the sensor's measured parameters include: throttle lever angle, rotational 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) Because fuel flow rate, nozzle area, and nozzle angle are open-loop control parameters in engine control and have inherent biases, the self-tuning model corrects the engine's input parameters (fuel flow rate, nozzle area, or nozzle angle) based on the target variable to ensure the accuracy of the self-tuning model's input parameters. At this point, the corrected engine input parameters constitute the optimization variables; for example... Figure 3At this time, the fuel self-adjustment and the nozzle area or nozzle angle self-adjustment of the self-adjustment model are realized.

[0053] 4) The self-adjustment model also interpolates the component characteristic parameter according to the deviation of the target parameter (such as: low-pressure power difference, high-pressure power difference, total temperature after the turbine) and the sensor measured parameter, inversely solves the influence relationship matrix in the measurable parameter to obtain the change amount of 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 method to quickly solve the change amount of the optimization variable, iteratively calculates, generates the actual component efficiency characteristic degradation coefficient, and then generates the actual component flow characteristic degradation coefficient by the above method; for example Figure 3 At this time, the efficiency self-adjustment and the flow self-adjustment of the self-adjustment model are realized.

[0054] Step three: combined with the sensor measured parameter, the engine total pressure ratio correction coefficient is calculated, the actual engine component characteristic degradation amount evaluation coefficient is corrected, and the engine component performance degradation amount based on the engine test flight data at the sampling time is obtained; wherein the total pressure ratio is a parameter for measuring the engine performance, and is a value that can be calculated according to the sensor measured parameter, specifically the ratio of the low-pressure turbine total pressure P6 to the engine inlet total pressure P2.

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

[0056] Step S120: updating the sampling time in the test flight stage, repeatedly executing steps one to three, outputting the actual engine component characteristic degradation amount evaluation coefficient, until the steady-state segment data is completely traversed.

[0057] Step S130: generating the engine component performance degradation trend of the engine test flight data.

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

[0059] When the engine performance evaluation is realized, the sampling data is first optimized, the stability embodied in the engine test flight data is captured, and the original data that can effectively reflect the performance is extracted; secondly, through the self-adjustment model, the component performance evaluation logic is performed, in the self-adjustment process, the relationship characteristics between the target parameter and the sensor measured parameter are processed, the multi-angle parameter self-adjustment is realized, and then the effective performance evaluation of the actual component can be obtained.

[0060] The above disclosure is merely a few specific embodiments of the present application, but the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the scope of the present application.

Claims

1. A method for performance evaluation of a model-based self-tuning low-bypass-ratio turbofan engine, characterized in that, The method comprises the following steps: defining a parameter structure of engine performance evaluation, the parameter structure comprising parameters selected from engine test flight data, and units of the parameters; the parameters selected from the engine test flight data comprising throttle lever angle, rotating speed, cross-section temperature, and cross-section pressure; determining a sampling time, and performing steady-state section extraction at the sampling time on the test flight data; updating the sampling time of the test flight stage, repeatedly performing the steady-state section extraction at the sampling time, and outputting an engine actual component characteristic degradation amount evaluation coefficient; 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; wherein the steady-state section extraction at the sampling time comprises: according to the parameter structure, generating steady-state section data by performing steady-state section extraction on the test flight data at the sampling time; the steady-state section extraction comprises processing of the test flight data in an activated steady state stage, a steady state stage, and a sudden end recording stage; the activated steady state stage refers to a stage in which, 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 a preset threshold, and the compressor relative rotating speed is greater than 0.9; the steady state stage refers to a stage in which, within a specified sampling time after the activated steady state stage, the throttle lever angle PLA, the engine inlet total pressure tP2, the engine inlet total temperature tT2, and the compressor relative rotating speed are in a steady state; the sudden end recording stage refers to a stage in which the throttle lever angle PLA, the engine inlet total pressure tP2, and the engine inlet total temperature tT2 suddenly change; loading a self-adjusting model, the self-adjusting model calculating an engine actual component characteristic degradation amount evaluation coefficient according to the steady-state section data; the engine actual component characteristic degradation amount evaluation coefficient comprising an efficiency characteristic degradation coefficient and a flow characteristic degradation coefficient; combining the measured parameters of the sensor, calculating an engine total pressure ratio correction coefficient, correcting the engine actual component characteristic degradation amount evaluation coefficient, and obtaining an engine component performance degradation amount based on the engine test flight data at the sampling time; wherein the self-adjusting model calculates the engine actual component characteristic degradation amount evaluation coefficient according to the steady-state section data, comprising the following steps: initializing measurable parameters, the measurable parameters being an influence relationship matrix between target variable changes and optimization variables; the measurable parameters comprising high-pressure rotor rotating speed, low-pressure rotor rotating speed, low-pressure turbine rear total temperature, low-pressure turbine rear total pressure, compressor rear total pressure, and compressor inlet total pressure; the target variables comprising high-pressure rotor power deviation, low-pressure rotor power deviation, turbine rear total pressure, and turbine rear total temperature; the optimization variables comprising fuel flow, nozzle area, or nozzle angle; loading the steady-state section data at the sampling time; correcting the input parameters of the engine according to the target variables of the self-adjusting model; the input parameters of the engine after correction constituting the optimization variables; performing interpolation and supplement on component characteristic representation parameters, iteratively calculating, and generating actual component efficiency characteristic degradation coefficients and flow characteristic degradation coefficients.

2. The low-bypass-ratio turbofan engine performance evaluation method according to Claim 1, wherein The sensor measured parameters include: throttle lever angle, rotation speed, cross section temperature, and cross section pressure.

3. The low-bypass-ratio turbofan engine performance evaluation method of claim 1, wherein, The horizontal axis of the influence relationship matrix between the target variable change and the optimization variable is relative rotation speed relative to engine inlet total temperature, and the vertical axis of the influence relationship matrix is the gradient between the efficiency of the gas path rotating component of the engine and the engine low pressure turbine gas after total temperature; the gas path rotating component is a fan, a high pressure compressor, a high pressure turbine, and a low pressure turbine.

4. The low-bypass-ratio turbofan engine performance evaluation method of claim 1, wherein, The target variable of the self-adjusting model refers to the deviation between the sensor measured parameters and the state value output by the self-adjusting model; and includes: 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.

5. The low-bypass-ratio turbofan engine performance evaluation method of claim 1, wherein, After obtaining the engine component performance degradation amount based on the engine test flight data, the engine actual component characteristic degradation amount evaluation coefficient is normalized.

6. The low-bypass-ratio turbofan engine performance evaluation method of claim 1, wherein 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 engine component performance degradation amount changes with the sampling time, and is used for evaluating the small-bypass-ratio turbofan engine performance.

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