Self-adaptive aero-engine large-range component performance correction method and device

Abstract

The invention provides an aero-engine wide-range component performance self-adaptive correction method and device, and the method comprises the steps: building an aero-engine component-level nonlinear model according to the aero-thermal characteristics of components of an aero-engine; a component characteristic self-adaptive correction factor is introduced to adjust the model performance, matching of a model simulation result and a target measurement parameter is taken as an optimization target, and component performance matching is converted into a self-adaptive optimization problem; establishing a curved surface fitting database, and constructing an adaptive correction factor curved surface fitting model through a moving least square method; the characteristics of the model component are adjusted through the self-adaptive correction factor curved surface, so that the output result of the aero-engine component-level simulation model is kept consistent with the measurement parameters under the large-range working condition, and the large-range correction of the component characteristic diagram is realized. According to the method, the simulation output of the corrected model can be kept consistent with the target engine within a large working condition range, and the deviation degree of the actual characteristics can be accurately represented through the self-adaptive correction result.

Description

Technical Field

[0001] The present invention relates to the technical field of aero-engines, and in particular to a method and a device for adaptively correcting the performance of a wide range of aero-engine components. Background Art

[0002] High-fidelity component-level models are essential for modeling, monitoring, and diagnosis of aero-engine performance. The accuracy of simulation models depends largely on the accuracy of component drawings. Component characteristics are usually drawn by engine manufacturers through a large number of test runs, which is costly and generally not provided to the outside world. Due to differences in test environments, test methods, and inlet and outlet flow fields, there are differences between component characteristics obtained based on component tests and engine installation characteristics. As the engine is used for a longer time, the characteristics of each component degrade, resulting in differences between component characteristics and test characteristics. Therefore, it is relatively difficult for engine testers and users to obtain accurate component characteristics. In order to improve the accuracy of engine simulation prediction, it is necessary to continuously adjust the general component parameters according to the actual measurement parameters to reverse and update the engine component characteristics so that the model simulation results match the test measurement results.

[0003] The traditional adaptive correction method for aircraft engine component performance has the following disadvantages:

[0004] 1. The traditional adaptive correction factor model construction method only fits a single working point of the equal speed line, ignoring the full use of the characteristic diagram information contained in other working points, resulting in a small correction range of the component characteristic diagram, deviation in the calculation of the adaptive correction factor, and inaccurate model correction;

[0005] 2. The currently disclosed performance adaptation methods are mainly based on engine steady-state measurement parameters. However, in the full life cycle operation conditions of commercial high bypass ratio turbofan engines, the steady-state operating point mainly appears in the cruise state, and the range of variation on the component characteristic diagram is small. Only using steady-state data is not enough to carry out large-scale performance adaptive correction;

[0006] 3. The various multi-operating point correction technologies currently proposed lack comprehensive consideration of the correlation between the actual physical information of the engine and the characteristics of the components, resulting in low utilization of multi-operating point measurement parameter information, a narrow scope of application of the correction algorithm, and low correction accuracy. Summary of the invention

[0007] In view of the defects in the prior art, the purpose of the present invention is to provide a method and device for adaptively correcting the performance of a wide range of aircraft engine components, which can not only make the corrected model simulation output consistent with the target engine within a larger range of operating conditions, but also accurately characterize the degree of deviation of actual characteristics through the adaptive correction results.

[0008] To solve the above problems, the technical solution of the present invention is:

[0009] A method for adaptively correcting performance of a wide range of aircraft engine components, comprising the following steps:

[0010] According to the aerodynamic and thermal characteristics of aircraft engine components, a nonlinear model of aircraft engine components is established;

[0011] The adaptive correction factor of component characteristics is introduced to adjust the model performance, and the matching of model simulation results with target measurement parameters is taken as the optimization goal, which transforms the component performance matching into an adaptive optimization problem.

[0012] Establish a surface fitting database and construct an adaptive correction factor surface fitting model through the moving least squares method;

[0013] By adjusting the model component characteristics through adaptive correction factor surfaces, the output results of the aircraft engine component-level simulation model can be consistent with the measured parameters under a wide range of working conditions, thus achieving a wide range of corrections to the component characteristic diagram.

[0014] Preferably, the step of establishing a nonlinear model at the aircraft engine component level according to the aerodynamic and thermodynamic characteristics of the aircraft engine components specifically includes: according to the calculation method of the variable specific heat of the working fluid and the balance of the joint working of the components, an object-oriented method is used to abstract the working fluid thermophysical property model and different module classes from the engine, and according to the aerodynamic and thermodynamic characteristics of the aircraft engine components, an engine component-level nonlinear model is established, and the Newton-Raphson method and the improved Euler method are used to solve it in the steady state and transition state respectively.

[0015] Preferably, the step of introducing an adaptive correction factor of component characteristics to adjust model performance, taking the matching of model simulation results with target measurement parameters as the optimization goal, and converting component performance matching into an adaptive optimization problem specifically includes: in order to solve the problem that the established gas path performance model does not match the actual engine performance, introducing an adaptive correction factor of component characteristics to adjust model performance, taking the matching of model simulation results with measurement parameters as the goal, integrating adaptive calculation with model simulation, and using the extended Newton-Raphson method to iteratively solve.

[0016] Preferably, the component characteristic adaptive correction factor is adjusted to gradually reduce the deviation between the simulation model output and the actual engine measurement value. The measurement deviation is defined as: m,i =(Y model,i -Y meas,i ) / Y meas,i , where Ymodel represents the model prediction, Ymeas represents the sensor measurement value, and for n sensor measurement parameters, the combined measurement parameter deviation vector em is: e m =[e m,1 ,e m,2 ,em,3 ,…,e m,n ] T , define the adaptive correction factor AF of the fan, boost stage, high pressure compressor, and turbine gas path components, and combine the adaptive correction factor iteration vector:

[0017] AF i =[AFW FAN ,AFE FAN ,AFW Booster ,…,AFE LPT ].

[0018] Preferably, since the changes of the adaptive correction factor iteration vector AF and the common working equation iteration vector v will affect the engine performance simulation results at the same time, the engine common working balance equation and the measurement parameter deviation iteration equation are combined to construct a new iteration vector and target residual vector, e = [ee m ]; v' = [v AF].

[0019] Preferably, the step of establishing a surface fitting database and constructing an adaptive correction factor surface fitting model by moving least squares method specifically includes: in view of the problem of matching multiple working points between the model and the actual engine within the flight envelope, a characteristic diagram adjustment method based on an adaptive correction factor surface is proposed, adaptive calculation is performed through a unified process based on steady-state and transition state measurement data to obtain characteristic line parameters and adaptive correction factors of component working points under different working conditions, and the relationship between the characteristic line parameters of the working point and the adaptive correction factor is established by using moving least squares method fitting, thereby realizing calibration of non-design point characteristic diagrams over a wider range.

[0020] Preferably, the fitting function in the moving least squares method is a set of coefficient vector functions a(x) and basis functions p(x), where x is a spatial coordinate, and the moving least squares approximation near a certain node represents f(x):

[0021]

[0022] Preferably, the steps of establishing an adaptive correction factor fitting surface by moving least squares method are as follows:

[0023] Meshing the fitting area;

[0024] Loop through each grid point x;

[0025] Determine the size of the support domain for grid point x;

[0026] Determine the sampling points contained in the influence area of ​​x;

[0027] Calculate shape functions;

[0028] Calculate the function value at the grid point x;

[0029] End the grid point loop;

[0030] Connect the mesh points to form a fitted surface.

[0031] Furthermore, the present invention also provides an apparatus for adaptively correcting the performance of a wide range of aircraft engine components, comprising a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the method for adaptively correcting the performance of a wide range of aircraft engine components as described above by executing the executable instructions.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention introduces transient variable working condition measurement parameters, converts transient self-adaptation into a steady-state solution process, and realizes large-scale correction of component characteristic diagrams.

[0034] 2. By introducing adaptive correction factors to adjust the characteristic diagram of model components, and taking the matching of model simulation results with measured data as the optimization goal, the component performance matching is transformed into an adaptive optimization problem, which can effectively improve the accuracy and reliability of the model.

[0035] 3. The present invention constructs an adaptive correction factor surface fitting model through the moving least squares method, which can not only make the simulation output of the corrected model consistent with the target engine within a larger operating range, but also accurately characterize the degree of deviation of the actual characteristics through the adaptive correction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0037] Figure 1 This is a flowchart of a method for adaptively correcting performance of a wide range of aircraft engine components according to the present invention;

[0038] Figure 2 A detailed flow chart of the method for adaptively correcting the performance of a wide range of aircraft engine components of the present invention;

[0039] Figure 3 To fit the correction factor surface diagram for fan flow;

[0040] Figure 4 To fit the correction factor surface diagram for fan efficiency;

[0041] Figure 5 This is a comparison diagram of the boost stage flow-pressure ratio characteristics before and after adaptive correction;

[0042] Figure 6This is a comparison diagram of the boost stage flow-efficiency characteristics before and after adaptive correction;

[0043] Figure 7 This is a comparison chart of the error of the measurable sensor parameter T3 of the engine before and after correction;

[0044] Figure 8 This is a comparison chart of the error of the measurable sensor parameter P45 of the engine before and after correction;

[0045] Fig. 9 This is the error comparison diagram of the unmeasurable parameter thrust FN before and after correction;

[0046] Fig.10 This is a comparison chart of the errors of the unmeasurable parameter combustion chamber outlet total temperature T4 before and after correction. DETAILED DESCRIPTION

[0047] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0048] Specifically, the present invention provides a method for adaptively correcting the performance of a wide range of aircraft engine components, such as Figure 1 and Figure 2 As shown, the method comprises the following steps:

[0049] S1: According to the aerodynamic and thermal characteristics of aircraft engine components, a nonlinear model of aircraft engine components is established;

[0050] Specifically, according to the calculation method of variable specific heat of working fluid and the balance of joint working of components, an object-oriented method is adopted to abstract the working fluid thermophysical property model and different module classes from the engine. According to the aerodynamic and thermal characteristics of aircraft engine components, an engine component-level nonlinear model is established, and the Newton-Raphson method and improved Euler method are used to solve it in the steady state and transition state respectively.

[0051] Aircraft engines are highly complex nonlinear systems. Their performance is affected by the joint operation and control laws of various components, and there are mutually restrictive relationships: (1) flow continuity; (2) pressure balance; (3) power balance between the compressor and turbine on the same rotor; (4) the physical speed of the compression component and the turbine on the same rotor is equal. For the large bypass ratio turbofan engine studied in this invention, the joint operation of the engine components must satisfy the following balance equations:

[0052]

[0053] In the case of non-design points, the engine performance parameters under equilibrium conditions cannot be directly obtained. For the simulation of commercial high bypass ratio turbofan engines, the unknown quantities are the oil-gas ratio f, the bypass ratio BPR, the low-pressure rotor speed NL, and the fan characteristic line auxiliary interpolation β 1 , boost stage characteristic line auxiliary interpolation β 2 , High pressure compressor characteristic line auxiliary interpolation β 3 , High-pressure turbine characteristic line auxiliary interpolation β 4 , low pressure turbine characteristic line auxiliary interpolation β 5 The common working equations are solved by the Newton-Raphson (NR) iteration method, and the solution process is the model matching process. The corresponding residual vector e = [e 1 ,e 2 ,e 3 ,e 4 ,e 5 ,e 6 ,e 7 ,e 8 ], iterative vector v = [NL, NH, BPR, β 1 ,β 2 ,β 3 ,β 4 ,β 5 ], the update formula for the k+1th iteration v is:

[0054] v k+1 =v k -J -1 e k

[0055] In the formula, v k+1 and e k+1 are the guessed value parameter vector and the residual vector of the joint working equation for the kth iteration respectively, and J is the Jacobian matrix of the modified guessed value.

[0056] In the dynamic phase, the high and low pressure rotor speeds are updated by the rotor dynamics formula:

[0057] PW LPT η Lspool -PW LPC -PW FAN =dNLt×NL×Θ Lspool ×(π / 30) 2

[0058] PW HPT η Hspool -PW HPC =dNHt×NH×Θ Hspool ×(π / 30) 2

[0059] Where: Θ Lspoolis the moment of inertia of the low-pressure shaft, dNLt (dNL / dt) is the rate of change of the low-pressure shaft speed, Θ Hspool is the moment of inertia of the high-pressure shaft, and dNHt (dNH / dt) is the rate of change of the high-pressure shaft speed.

[0060] S2: Introduce component characteristic adaptive correction factors to adjust model performance, take the matching of model simulation results and measurement parameters as the optimization goal, and transform component performance matching into an adaptive optimization problem;

[0061] Specifically, in order to address the problem that the established gas path performance model does not match the actual engine performance, an adaptive correction factor of component characteristics is introduced to adjust the model performance. With the goal of matching the model simulation results with the measured parameters, the adaptive calculation is integrated with the model simulation, and the extended NR method is used for the solution.

[0062] According to the characteristics of commercial high bypass ratio turbofan engine components, the core rotating component flow and efficiency adaptive correction factors are defined as follows:

[0063]

[0064] The calculation of performance adaptation is an inverse mathematical process. The vector of the corresponding adaptive correction factor AF is found according to the deviation between the model and the actual engine measurement parameters. The iterative idea is to gradually reduce the deviation between the simulation model output and the actual engine measurement value by adjusting the component characteristic adaptive correction factor. The measurement deviation is defined as follows:

[0065] e m,i =(Y model,i -Y meas,i ) / Y meas,i

[0066] Where Ymodel represents the model prediction and Ymeas represents the sensor measurement value. For n sensor measurement parameters, the combined measurement parameter deviation vector e m :

[0067] e m =[e m,1 ,e m,2 ,e m,3 ,…,e m,n ] T

[0068] With reference to the sensor settings of a typical commercial high bypass ratio turbofan engine, in this embodiment, the low-pressure rotor speed NL, the high-pressure rotor speed NH, the fuel flow rate Wf, the outer duct inlet total pressure P13, the high-pressure compressor inlet total pressure P25, the high-pressure compressor inlet total temperature T25, the high-pressure compressor outlet total pressure P3, the high-pressure compressor outlet total temperature T3, the low-pressure turbine inlet total pressure P45, the low-pressure turbine outlet total pressure P5, and the low-pressure turbine outlet total temperature T5 are selected as the target engine measurement parameters.

[0069] Define the adaptive correction factor AF for the fan, boost stage, high pressure compressor, and turbine gas path components, and combine the adaptive correction factor iteration vector: AF i =[AFW FAN ,AFE FAN ,AFW Booster ,…,AFE LPT ].

[0070] Since the changes of the adaptive correction factor iteration vector AF and the co-working equation iteration vector v will affect the engine performance simulation results at the same time, the present invention combines the engine co-working balance equation with the measurement parameter deviation iteration equation to construct a new iteration vector and target residual vector, e = [ee m ]; v' = [v AF].

[0071] The performance adaptation problem at this time is equivalent to an expansion based on the performance simulation calculation, from which the extended Newton-Raphson method iteration formula can be obtained:

[0072] v′ k+1 =v′ k -J -1 e′ k

[0073] In the extended Newton-Raphson method, the component performance adaptive parameter n to be solved and the number of target measurement parameters m are usually not equal, and a solution in the sense of least squares can be obtained.

[0074] S3: Establish a surface fitting database and construct an adaptive correction factor surface fitting model by moving least squares method;

[0075] Specifically, a characteristic diagram adjustment method based on an adaptive correction factor surface is proposed to match the model with the actual engine at multiple operating points within the flight envelope. Adaptive calculations are performed through a unified process based on steady-state and transition state measurement data to obtain the characteristic line parameters and adaptive correction factors of component operating points under different operating conditions. The moving least squares method is used to fit the relationship between the characteristic line parameters of the operating point and the adaptive correction factor, thereby achieving characteristic diagram calibration of a wider range of non-design points.

[0076] Step S2 can calculate the adaptive correction factor AF of the engine component performance at each working point on the steady-state and transient working lines and the characteristic line parameters of the component working point.

[0077] Mobile least squares (MLS) constructs an adaptive correction factor surface. MLS is a method widely used in computer graphics and auxiliary geometric design, which can generate smooth surfaces from discrete data points. The basic idea of ​​MLS is: for a given target point, its attributes are expressed as the weighted average of the surrounding data points using the least squares method. Compared with the traditional least squares method, the fitting function in MLS is not a polynomial, but a set of coefficient vector functions a(x) and basis functions p(x), where x is the spatial coordinate, and the moving least squares approximation f(x) near a node is:

[0078]

[0079] The steps to establish the adaptive correction factor surface through MLS are as follows: (1) grid the fitting area; (2) loop through each grid point x; (3) determine the size of the support domain of the grid point x; (4) determine the sampling points contained in the influence area of ​​x; (5) calculate the shape function; (6) calculate the function value at the grid point x; (7) end the grid point loop; (8) connect the grid points to form a fitting surface.

[0080] Common basis functions include linear basis functions, quadratic basis functions, etc. For the adaptive correction factor surface fitting problem of the present invention, the basis function is selected as p(x)=[1,x,y,x 2 ,xy,y 2 ]. The correction factor surface for fan flow and efficiency is fitted as follows Figure 3 and Figure 4 shown.

[0081] S4: By adjusting the model component characteristics through adaptive correction factor surfaces, the output results of the aircraft engine component-level simulation model are kept consistent with the measured parameters under a wide range of operating conditions, thus achieving a wide range of corrections to the component characteristic diagram.

[0082] Figure 5 This is a comparison chart of the booster stage flow-pressure ratio characteristics before and after correction. Figure 6This is a comparison diagram of the boost stage flow-efficiency characteristic before and after correction, where the corrected characteristic diagram includes single-point correction, steady-state WPAM (Wide-range Performance Adaptation Method) correction and transient WPAM correction. The single-point correction scales the speed line as a whole without changing the shape of the characteristic diagram, resulting in a significant difference between the calibrated speed line and the reference speed line. The WPAM method proposed in the present invention can not only scale the speed line, but also rotate it to better align with the reference speed line. The two overlap in certain areas, indicating that the matching degree between the mathematical model and the real engine has been improved, and also indicating that the current corrected component characteristic diagram is in a relatively correct position.

[0083] In addition, the steady-state calibration of WPAM using only steady-state measurements is not as effective as the transient calibration of WPAM, because the transient operating conditions can provide more data information for the fitted surface, making the transient measurements contribute to better WPAM performance. It should be noted that the velocity lines after calibration with WPAM do not completely match the reference velocity lines, because the working points used do not cover all the working areas of the component. Outside the map area covered by the measured working points, the reliability of the fitted adaptive correction factor surface will inevitably decrease. In order to assign adaptation factors to working points far away from the fitting area, the method of taking boundary values ​​is used to avoid surface deformation and ensure that the corrected mapping conforms to the mechanism.

[0084] Figure 7 , Figure 8 Table 1 is a comparison chart of the errors of the engine sensor parameters T3 and P45 before and after correction. Table 1 is a comparison chart of the errors of all sensors after correction by different adaptive methods. The transient WPAM-Transient calibration model has the smallest prediction error, while the steady-state WPAM-Steady calibration model has a larger error than the WPAM-Transient calibration model at certain time points. The accuracy of the PAM calibration model is poor, and the prediction error of some parameters even exceeds that of the original model.

[0085]

[0086] Table 1

[0087] Fig. 9 and Fig.10 The following is a comparison of the error of the unmeasurable parameters thrust FN and combustion chamber outlet total temperature T4 before and after correction. Although the direct optimization target of performance adaptation is the measured parameters of the engine, it is worth noting that the adjustment of the characteristic map will affect the calculation of all engine performance parameters. Therefore, performance adaptation can also reduce the prediction error of the unmeasurable parameters of the engine, among which the WPAM-Transient accurate model shows the lowest error.

[0088] Furthermore, the present invention also provides an apparatus for adaptively correcting the performance of a wide range of aircraft engine components, comprising a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the method for adaptively correcting the performance of a wide range of aircraft engine components as described above by executing the executable instructions.

[0089] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for adaptively correcting the performance of a wide range of aircraft engine components, characterized in that: The method comprises the following steps: According to the aerodynamic and thermal characteristics of aircraft engine components, a nonlinear model of aircraft engine components is established; The adaptive correction factor of component characteristics is introduced to adjust the model performance, and the matching of model simulation results with target measurement parameters is taken as the optimization goal, which transforms the component performance matching into an adaptive optimization problem. Establish a surface fitting database and construct an adaptive correction factor surface fitting model through the moving least squares method; By adjusting the model component characteristics through adaptive correction factor surfaces, the output results of the aircraft engine component-level simulation model can be consistent with the measured parameters under a wide range of working conditions, thus achieving a wide range of corrections to the component characteristic diagram.

2. The method for adaptively correcting the performance of a wide range of aircraft engine components according to claim 1, characterized in that: The steps of establishing a nonlinear model of aircraft engine component level according to the aerodynamic and thermodynamic characteristics of aircraft engine components specifically include: according to the calculation method of variable specific heat of working fluid and the balance of joint working of components, using object-oriented method to abstract working fluid thermophysical property model and different module classes from the engine, establishing a nonlinear model of engine component level according to the aerodynamic and thermodynamic characteristics of aircraft engine components, and solving it by Newton-Raphson method and improved Euler method in steady state and transition state respectively.

3. The method for adaptively correcting performance of a wide range of aircraft engine components according to claim 1, characterized in that: The method introduces a component characteristic adaptive correction factor to adjust the model performance, takes the matching of the model simulation results with the target measurement parameters as the optimization goal, and converts the component performance matching into an adaptive optimization problem. Specifically, the method includes: in order to solve the problem that the established gas path performance model does not match the actual engine performance, introduces a component characteristic adaptive correction factor to adjust the model performance, takes the matching of the model simulation results with the measurement parameters as the goal, integrates the adaptive calculation with the model simulation, and uses the extended Newton-Raphson method to iteratively solve.

4. The method for adaptively correcting performance of a wide range of aircraft engine components according to claim 3, characterized in that: By adjusting the adaptive correction factor of component characteristics, the deviation between the simulation model output and the actual engine measurement value is gradually reduced. The measurement deviation is defined as: e m,i =(Y model,i -Y meas,i ) / Y meas,i , where Ymodel represents the model prediction, Ymeas represents the sensor measurement value, and for n sensor measurement parameters, the combined measurement parameter deviation vector em is: e m =[e m,1 ,e m,2 ,e m,3 ,…,e m,n ] T , define the adaptive correction factor AF of the fan, boost stage, high pressure compressor, and turbine gas path components, and combine the adaptive correction factor iteration vector: <h2 style=";text-align:left;direction:ltr">AF<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =[AFW<h2 style=";text-align:left;direction:ltr"> FAN <h2 style=";text-align:left;direction:ltr"> AFE<h2 style=";text-align:left;direction:ltr"> FAN <h2 style=";text-align:left;direction:ltr"> AFW<h2 style=";text-align:left;direction:ltr"> Booster <h2 style=";text-align:left;direction:ltr"> ,…,AFE<h2 style=";text-align:left;direction:ltr"> LPT <h2 style=";text-align:left;direction:ltr"> ]。 5. The method for adaptively correcting performance of a wide range of aircraft engine components according to claim 4, characterized in that: Since the changes of adaptive correction factor iteration vector AF and common working equation iteration vector v will affect the engine performance simulation results at the same time, the engine common working balance equation and the measurement parameter deviation iteration equation are combined to construct a new iteration vector and target residual vector, e = [ee m ];v ’ =[v AF].

6. The method for adaptively correcting performance of a wide range of aircraft engine components according to claim 1, characterized in that: The steps of establishing a surface fitting database and constructing an adaptive correction factor surface fitting model by using a moving least squares method specifically include: in view of the problem of matching multiple working points between the model and the actual engine within the flight envelope, a characteristic diagram adjustment method based on an adaptive correction factor surface is proposed, adaptive calculation is performed through a unified process based on steady-state and transition state measurement data to obtain characteristic line parameters and adaptive correction factors of component working points under different working conditions, and the relationship between the characteristic line parameters of the working point and the adaptive correction factor is established by using the moving least squares method to fit, thereby realizing calibration of non-design point characteristic diagrams over a wider range.

7. The method for adaptively correcting performance of a wide range of aircraft engine components according to claim 6, characterized in that: The fitting function in the moving least squares method is a set of coefficient vector functions a(x) and basis functions p(x), where x is a spatial coordinate, and the moving least squares approximation near a node represents f(x):

8. The method for adaptively correcting performance of a wide range of aircraft engine components according to claim 7, characterized in that: The steps to establish an adaptive correction factor fitting surface using the moving least squares method are as follows: Meshing the fitting area; Loop through each grid point x; Determine the size of the support domain for grid point x; Determine the sampling points contained in the influence area of ​​x; Calculate shape functions; Calculate the function value at the grid point x; End the grid point loop; Connect the mesh points to form a fitted surface.

9. An adaptive correction device for the performance of a wide range of aircraft engine components, characterized in that: The apparatus comprises a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the method for adaptively correcting performance of a wide range of aircraft engine components as claimed in any one of claims 1 to 8 by executing the executable instructions.

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