CN112883483B - Method, equipment and memory for checking and verifying aeroengine model - Google Patents
Method, equipment and memory for checking and verifying aeroengine model Download PDFInfo
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Abstract
The invention provides an aeroengine model checking and verifying method, which comprises the following steps: inputting an algorithm model; solving the error of the algorithm model to obtain an error uncertainty; comparing the performance measurement data of the whole machine with the output data of the algorithm model to obtain accuracy and actual deviation; and calculating uncertainty of the model error according to the confidence range, the actual deviation and the error uncertainty. The invention also provides equipment and a memory comprising the method. The method is suitable for checking and verifying the overall performance model of the aeroengine, and performing code checking, calculation checking and model verification on the simulation model in the built computer. The error uncertainty, the precision, the actual deviation, the uncertainty and the like of the model are calculated, so that the error and the uncertainty of the model are qualitatively and quantitatively described, and a theoretical basis is provided for further model precision improvement.
Description
Technical Field
The invention relates to the field of aeroengines, in particular to a method, equipment and a memory for checking and verifying an aeroengine model.
Background
The development of the aeroengine is a complex system engineering, and relates to a plurality of subjects such as air power, combustion, heat transfer, control, structure, strength and the like, the development period is long, the cost is high, the risk is high, the time and the cost can be greatly saved by modeling and simulation in a computer, meanwhile, parameters required by a test can be acquired to expose the design problem, the development efficiency and quality are improved, and therefore, the high-precision simulation model has great significance on the research and development of the aeroengine. The overall performance model of the whole engine can be used for various stages of scheme design, detailed design, overall test (performance monitoring and fault diagnosis), use and operation maintenance, the overall performance model of the engine is used for knowing the characteristics of the engine, obtaining all section parameters and performance parameters of the engine, and the result of each design stage of the engine is evaluated and verified to be matched with the actual situation, so that the accuracy and the confidence of the model are obtained. However, an effective model accuracy and confidence evaluation system is lacking at present, manual evaluation is required for a specific model, and flexibility is not provided.
Disclosure of Invention
In order to solve at least one of the technical problems, the invention provides an aeroengine model checking and verifying method and equipment memory, which are used for qualitatively and quantitatively evaluating the influence of factors on the accuracy and the confidence of overall performance modeling and simulation, evaluating the coincidence degree of simulation data and test data, and combing uncertain factors influencing the model accuracy so as to achieve the technical purposes of guiding model correction and accuracy improvement. The invention adopts the following technical scheme:
an aircraft engine model checking and verifying method comprises the following steps:
inputting an algorithm model;
solving the error of the algorithm model to obtain an error uncertainty;
comparing the performance measurement data of the whole machine with the output data of the algorithm model to obtain accuracy and actual deviation;
and calculating uncertainty of the model error according to the confidence range, the actual deviation and the error uncertainty.
Further, the error uncertainty is calculated according to the following equation:
where f is each equilibrium equation in the nonlinear system of overall performance equations, N L Wherein the rotation speed of the low-pressure shaft is N H Is the rotation speed of a high-pressure shaft pi cl Is the fan pressure ratio, pi ch Is the pressure ratio of the air compressor, pi th Is the pressure drop ratio of the high-pressure turbine and pi tl Is the low-pressure turbine drop pressure ratio, epsilon is equation control error, deltae is engine key section calculation error, deltae 1 Is the uncertainty of the error epsilon ∞ Is the final iteration error.
Further, the algorithm model calculation comprises iteration step length and initial deviation, and the algorithm model iteration error, calling times, running time and convergence are calculated according to the iteration step length and the initial deviation.
Further, the accuracy is obtained by the following expression:
wherein y is Precision of Is the accuracy of the algorithm model; y is Simulation of The algorithm model calculates the obtained value; y is Measurement of Is a performance parameter and/or an engine key cross-section parameter.
Further, the uncertainty is calculated by the following equation:
e＝Δe 1 +max(Δe 2 +Δe error )
where e is uncertainty, Δe 1 Is the uncertainty of solving the error, deltae 2 Is the confidence range of the measured parameter, Δe error Is the actual deviation of the model calculation from the test output.
Further, the measurement data is steady-state data after the throttle lever of the engine reaches a specified state.
Further, processing the measured data, processing dead pixels and abnormal values, carrying out processing on the measured parameters of the key sections of each engine, and obtaining the average value of the key sections of the engine.
Further, the initial deviation is less than 9%.
According to yet another aspect of the present invention, there is provided a readable storage medium having executable instructions thereon that, when executed, cause a computer to perform the method of aircraft engine model verification and validation.
According to yet another aspect of the present invention, there is provided a computing device comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to perform the one aeroengine model checking and verification method by the one or more processors.
Compared with the prior art, the invention has the advantages that: the invention provides an aeroengine model checking and verifying method which is suitable for checking and verifying an aeroengine overall performance model and is used for performing code checking, calculation checking and model verification on a simulation model in a built computer. The error uncertainty, the precision, the actual deviation, the uncertainty and the like of the model are calculated, so that the error and the uncertainty of the model are qualitatively and quantitatively described, and a theoretical basis is provided for further model precision improvement.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention.
FIG. 1 is a block diagram of an exemplary computing device;
FIG. 2 is a flow chart of an embodiment of the present invention;
FIG. 3 is a schematic diagram showing the implementation steps of S1 in FIG. 2;
FIG. 4 is a schematic diagram showing the implementation steps of S2 in FIG. 2;
FIG. 5 is a schematic diagram showing the implementation step S3 in FIG. 2;
FIG. 6 is a schematic diagram showing the implementation step of S4 in FIG. 2;
FIG. 7 is a schematic diagram of the overall performance modeling and simulation and verification steps;
FIG. 8 is the final iteration error for different algorithms at different initial deviations;
FIG. 9 is an iterative error comparison of three algorithms;
FIG. 10 is the effect of different iteration steps on convergence speed;
FIG. 11 is a comparison of measured data with simulation results.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 is a block diagram of an example computing device 100 arranged to implement an aircraft engine model verification and validation method in accordance with the present invention. In a basic configuration 102, computing device 100 typically includes a system memory 106 and one or more processors 104. The memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processing including, but not limited to: a microprocessor (μp), a microcontroller (μc), a digital information processor (DSP), or any combination thereof. The processor 104 may include one or more levels of caches, such as a first level cache 110 and a second level cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations, the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory including, but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. The system memory 106 may include an operating system 120, one or more programs 122, and program data 128. In some implementations, the program 122 may be configured to execute instructions on an operating system by the one or more processors 104 using the program data 128.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to basic configuration 102 via bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display terminal or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication via one or more I/O ports 158 and external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.). An example communication device 146 may include a network controller 160, which may be arranged to facilitate communication with one or more other computing devices 162 via one or more communication ports 164 over a network communication link.
The network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media in a modulated data signal, such as a carrier wave or other transport mechanism. A "modulated data signal" may be a signal that has one or more of its data set or changed in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or special purpose network, and wireless media such as acoustic, radio Frequency (RF), microwave, infrared (IR) or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 100 may be implemented as part of a small-sized portable (or mobile) electronic device such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that may include any of the above functions. Computing device 100 may also be implemented as a personal computer including desktop and notebook computer configurations.
Wherein the one or more programs 122 of the computing device 100 include instructions for performing an aeroengine model checking and verification method according to the present invention.
FIG. 2 illustrates a flow chart of an aircraft engine model verification and validation method according to one embodiment of the invention. The aircraft engine model checking and verification method starts at step S1, although steps S3-S5 may be provided in a device and a memory containing the aircraft engine model checking and verification method. A complete aircraft engine model checking and verification method will be set forth below.
S1) making a total flow of checking and verifying the overall performance model. Analyzing the overall performance model modeling and simulation process, clearly dividing a conceptual model, a mathematical model and a calculation model, establishing a model checking and verification process according to the three stages, identifying factors affecting the model precision and uncertainty of each link, determining work done by each link and a theoretical method adopted by each link, and forming an overall framework for overall performance model checking and verification. The specific implementation method is shown in fig. 3, and comprises the following steps:
s11) define the specific process of overall performance model modeling and simulation. From the perspective of modeling and simulation, the method comprises three parts of a conceptual model, a mathematical model and a calculation model.
The conceptual model is a mechanism model, and the mechanism analysis adopts a component method for modeling, namely, according to pressure balance, power balance and flow balance, the mechanism analysis complies with the aerodynamic thermodynamic theorem in the working process. The aeroengine mathematical model is a mathematical description of a conceptual model, and the aeroengine component method modeling is a nonlinear equation set established according to flow continuity, pressure balance and power balance, and expresses the internal rule of a real physical system in a mathematical expression form. The calculation model is a mathematical model expressed by a computer language and is solved on a computer, and the calculation model comprises a numerical solution algorithm and a normalized code expression.
S12) establishing a step of checking and simulating an overall performance model to form an overall guide.
As shown in fig. 7, the method comprises three parts of code checking, calculation checking and model verification, wherein the code checking is to check the code conversion process from a data model to a calculation model, so as to ensure the conversion from a logic operation and a mathematical formula to a computer code to be correct; the calculation and check are carried out on the error and uncertainty evaluation of the solving process of the nonlinear equation system of the overall performance; and the model verification is to compare the coincidence degree of test data and model output data under the same test condition after code checking and calculation checking, and give the final error and uncertainty of the simulation model.
S2) checking codes, and checking the correctness of the code conversion process from the mathematical model to the simulation model, wherein the code conversion process comprises static test and dynamic test. The specific implementation process is as shown in fig. 4:
s21) code static test. The overall performance model developed by the invention is programmed by adopting the python language, and the static test does not need to run the overall code. The static state includes the following parts: (1) Checking whether the block diagram and the data flow diagram of the simulation program are correct or not by combining codes; (2) Checking whether the programming process of the core algorithm code is correct or not by combining algorithm description (pseudo code); (3) The method and the system are used for checking whether codes are correct or not in the aspects of parameter naming, annotation writing, input and output, judging conditions, loop boundaries, exception handling and the like, and checking whether key subroutines (sub-modules) are correct or not.
S22) code dynamic test. The dynamic test needs to give program input, check the output of the program or the condition that the program cannot be executed, locate the program error, judge that the program output accords with the actual rule, search the logic error of the code, and the like. The dynamic test of the invention can be divided into a steady state test, a transition state test, a parameter sensitivity calculation analysis test, interface interaction, a curve display test and the like according to program functions.
S3) calculating and checking, and estimating model errors and uncertainty caused by a numerical method for solving the nonlinear equation set. The factors affecting the model error by the numerical method mainly include: different numerical algorithms, stability of the algorithm, convergence speed of the algorithm.
∑|f(x i )| 2 ＜ε，x i E X, formula (1)
X＝[N L ,N H ,π cl ,π ch ,π ch ,π tl ]
Where f is each equilibrium equation in the nonlinear system of overall performance equations, N L Wherein the rotation speed of the low-pressure shaft is N H Is the rotation speed of a high-pressure shaft pi cl Is the fan pressure ratio, pi ch Is the pressure ratio of the air compressor, pi th Is the pressure drop ratio of the high-pressure turbine and pi tl Is the low pressure turbine drop ratio and ε is the control error of the equation.
The specific implementation method is shown in fig. 5, and comprises the following steps:
s31) solving the error contrast of different numerical algorithms of the nonlinear equation set.
Uncertainty of definition error (2)
Wherein the method comprises the steps ofIs the calculation error of the key section of the engine.
Wherein Δe is the calculation error of the key section of the engine, Δe 1 Is the uncertainty of the error epsilon ∞ Is the final iteration error.
S32) stability assessment of the algorithm using the initial bias.
S33) evaluating the convergence speed of the algorithm using the control error and the iteration step.
S4) model verification, namely comparing the deviation of test and model output data under the same input condition, and evaluating the accuracy of the model representing the real engine under the specific condition. The specific implementation process is shown in fig. 7.
S41) evaluating the accuracy and confidence of the test data. And when the model is verified, the test run measurement data of the whole machine performance is compared with the simulation model output, and the accuracy degree and the confidence interval of the test data are required to be determined. The comparison of test data mainly adopts steady state data, and the test data extracts steady state data after an engine throttle lever reaches a certain state according to the characteristics of a steady state model, and mainly comprises the following steps: (1) Steady state data extraction, making a unified steady state data extraction template; (2) Preliminary processing of test data, namely when the number of measurement flow parameter points is large, processing dead pixels and abnormal values, and carrying out processing of measurement parameters of each section to obtain a section average value; (3) And estimating a confidence interval of the measured parameter according to the measurement precision of the sensor and the test data processing process.
S42) establishing a comparison method of the test data and the simulation output data.
The whole machine performance test is often carried out under different atmospheric conditions, and the ground bench test is carried out under the same rotating speed in the test process, but because of the different surrounding atmospheric conditions, the thrust, the fuel consumption rate, the section parameters and the like of the engine are greatly different, and meanwhile, the characteristic curves of all the parts are related to the converted rotating speed, so that the data can be compared, and the test data is converted into the standard atmospheric conditions by utilizing a similar theory. And (3) verifying a steady-state model, and comparing the deviation of a model result and an actual system measurement result to represent the accuracy of the model. The deviation of the model verification is given by formula (3).
Wherein y is Precision of Is the accuracy of the algorithm model; y is Simulation of The algorithm model calculates the obtained value; y is Measurement of Is a performance parameter and/or an engine key cross-section parameter.
S43) combining the checking and verifying processes to construct an uncertainty formula for evaluating the model error, wherein the uncertainty formula for evaluating the model error is formula (4):
e＝Δe 1 +max(Δe 2 +Δe error ) Formula (4)
Where e is uncertainty, Δe 1 Is the uncertainty of solving the error, deltae 2 Is the confidence range of the measured parameter, Δe error Is the actual deviation of the model calculation from the test output.
The invention adopts three methods of Newton-Lapherson algorithm (N-R algorithm), N+1 point residue method and Broyden rank 1 method to verify the steps of S3-S5 and further explain the checking and verifying method of the aeroengine model, and the process is as follows.
S3) calculating and checking. Calculating the precision and error range of the three methods; the error of the initial value deviating from the true value is used as a given input, so that the stability of the algorithm is evaluated, and the maximum range of the initial value deviation is given; and the convergence rate of the algorithm is estimated and analyzed by utilizing the control error and the iteration step length of the numerical algorithm, and the suggested iteration step length and the control error are given.
S31) solving the error contrast of different numerical algorithms of the nonlinear equation set. The invention compares numerical solution errors of three methods of Newton-Laportson method (N-R), broyden rank 1 method and N+1 point residue method, and the numerical solution formulas of the three algorithms are as follows:
the iterative formula of the N-R method is shown as formula (5):
the iterative formula of the Broyden rank 1 method is shown as formula (6):
the iterative formula of the n+1 point residue method is shown as formula (7):
in the formulas (5) (6) (7), x= [ N L ,N H ,π cl ,π ch ,π th ,π tl ]Is an iteration vector of the nonlinear equation set of the overall performance, n is the number of iteration variables n= 6,k is the number of iterations, X k Is the iteration vector of the kth iteration number; f is continuous, pressure balanced and effectiveA nonlinear equation set consisting of rate balances; a, a k Is the coefficient assigned to each iteration vector; a is that k Is the kth iteration matrix.
The iteration error of different numerical solution algorithms is compared and set, different iteration initial value vector deviations are respectively 2%, 3%, 5% and 9% for three algorithms at a design point, and the control error is set to epsilon=0.002. The advantages and disadvantages of the three algorithms compared in terms of algorithm convergence, final iteration error, key section error, etc. are shown in table 1.
Table 1 comparison of three numerical algorithms
Conclusion of comparison of different algorithms, by analyzing and comparing iteration errors, calling times, running time, convergence and the like of the three algorithms under different initial value deviations (each algorithm is given with an iteration step length of 0.01), the initial deviation value of a set solution is 2%, 3%, 5% and 9%, the three algorithms can converge within the range of 5% of the initial deviation of the solution, the minimum iteration error of N-R can be controlled within the range of 0.002, the function calling times are minimum, the running time is minimum, and the performance is optimal.
S32) stability assessment of the algorithm using the initial bias. The stability of the algorithm is evaluated under four initial values of 2%, 3%, 5% and 9% in combination with the table 1, so that a conclusion is drawn that the iterative initial value deviation at the design point is controlled within 5%, the numerical solution algorithm can stably converge, and the equation is significant. Referring to fig. 8, it can be seen that the N-R method obtains a better iteration effect at the same initial offset value. And the convergence rate is higher and the convergence final value error is small, which is an ideal method, see the figure 9,N-R method.
S33) evaluating the convergence speed of the algorithm using the control error and the iteration step. The iteration step should be 0.001 to 0.3 in consideration of convergence speed and accuracy. In the comparative experiment, the iteration step length of the solution is set as 0.001,0.005,0.01,0.015 and 0.02 around the design point, and the influence of the iteration step length on iteration errors, the calling times of error equations and the running time of the algorithm is evaluated.
TABLE 2 influence of different iteration steps on the convergence of a numerical solving algorithm
| Iteration step | Iterative error | Number of calls | Run time | Whether or not to converge |
| 0.001 | 0.00011 | 32 | 0.461s | Convergence of |
| 0.005 | 0.00021 | 40 | 0.470s | Convergence of |
| 0.01 | 0.00154 | 40 | 0.463s | Convergence of |
| 0.015 | 0.00265 | 40 | 0.483s | Convergence of |
| 0.02 | 0.030618 | 408 | 1.265s | Convergence of |
From the analysis of table 2 and fig. 10, it can be seen that the smaller the iteration step length is, the smaller the final iteration error is, and the higher the solution accuracy is, and the iteration error with the iteration step length of 0.01 and below can meet the solution accuracy requirement by comprehensively considering the iteration speed, convergence and final error. Therefore, when solving by using the N-R method, the iteration step length is 0.01 or less, and the solution meeting the precision error can be obtained.
Summarizing the three steps S31), S32), S33) in the calculation check, the numerical solution algorithm is sensitive to the initial value, and when the initial value deviation is 9% and above in the vicinity of the design point, the algorithm does not converge, so the initial deviation should be less than 9%. The method comprises the steps of carrying out a first treatment on the surface of the The convergence of N-R in the three algorithms is best, and near the design point, when the initial value given error is within 2%, the equation control deviation epsilon=0.001, the uncertainty of the critical section parameter error is within 1%, and when the initial value deviation is 5%, the uncertainty of the section parameter error is within 1.8%.
S41) evaluating the accuracy and confidence of the test data, and setting the measurement parameter confidence interval as shown in table 3.
TABLE 3 measurement parameters and confidence intervals
S42) establishing a comparison method of the test data and the simulation output data.
The data types of the comparison are simple, and the values calculated by the performance parameters and the section parameters and the model can be directly compared with those shown in fig. 11.
S43) combines the checking and verification process.
And (3) checking a conclusion of solving an algorithm model, wherein an N-R method is adopted, equation control deviation epsilon=0.001 is set, iteration step length is 0.01, when the initial value given deviation is within a range of 2% in design point calculation, the uncertainty of the error of thrust F is about 2%, the uncertainty of NH error is about 2.4%, and the uncertainty of the exhaust temperature error of the low-pressure turbine is about 2.1%.
The invention is characterized in that an engineering practical checking and verifying method for the overall performance model of the aeroengine is constructed, and the overall performance model is qualitatively and quantitatively evaluated. In each stage of scheme design, detailed design, complete machine test (performance monitoring, fault diagnosis), use, operation and maintenance, all section parameters and performance parameters of the reliable engine are given, and then the result of the design stage is evaluated and verified. The invention provides a checking and verifying process based on a modeling and simulating process, establishes detailed steps of code checking, calculation checking and model verification, defines precision factors influencing the simulation model in each link, can guide effective improvement and improvement of model precision, and has stronger engineering application value and significance.
The method is suitable for checking and verifying the overall performance model of the aeroengine, performs code checking, calculation checking and model verification on the simulation model in the built computer, qualitatively and quantitatively describes the error and uncertainty of the model, and provides a theoretical basis for further model precision improvement.
Meanwhile, the engineering personnel can store the executable instructions prepared by the method in the readable storage medium, and when the executable instructions are executed, the computer can execute the operations included in the aeroengine model checking and verifying method. Wherein the one or more programs are stored in the memory and configured to perform operations comprised by the above-described aeroengine model checking and verification method by the one or more processors. The memory and the processor are included in the computing device.
It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the various methods of the present invention in accordance with instructions in the program code stored in the memory.
By way of example, and not limitation, computer readable media comprise computer storage media and communication media. Computer-readable media include computer storage media and communication media. Computer storage media stores information such as computer readable instructions, data structures, program modules, or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.
It should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various disclosed aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is for performing functions performed by elements for purposes of this disclosure.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (8)
1. The checking and verifying method for the aeroengine model is characterized by comprising the following steps of:
inputting an algorithm model;
solving the error of the algorithm model to obtain an error uncertainty;
comparing the performance measurement data of the whole machine with the output data of the algorithm model to obtain accuracy and actual deviation;
calculating uncertainty of the model error according to the confidence range, the actual deviation and the error uncertainty;
the error uncertainty is calculated according to the following formula:
where f is each equilibrium equation in the nonlinear system of overall performance equations, N L Wherein the rotation speed of the low-pressure shaft is N H Is the rotation speed of a high-pressure shaft pi cl Is the fan pressure ratio, pi ch Is the pressure ratio of the air compressor, pi th Is the pressure drop ratio of the high-pressure turbine and pi tl Is the low-pressure turbine drop pressure ratio, epsilon is equation control error, deltae is engine key section calculation error, deltae 1 Is the uncertainty of the error epsilon ∞ Is the final iteration error;
the uncertainty is calculated by the following equation:
e＝Δe 1 +max(Δe 2 +Δe error )
where e is uncertainty, Δe 1 Is the uncertainty of solving the error, deltae 2 Is the confidence range of the measured parameter, Δe error Is the actual deviation of the model calculation from the test output.
2. The aircraft engine model checking and verification method as claimed in claim 1, wherein: the algorithm model calculation comprises iteration step length and initial deviation, and the iteration error, the calling times, the running time and the convergence of the algorithm model are calculated according to the iteration step length and the initial deviation.
3. The aircraft engine model checking and verification method as claimed in claim 1, wherein:
the accuracy is obtained by the following equation:
wherein, the y precision is the precision of the algorithm model; y simulation is the calculation of an algorithm model to obtain a value; the y-measurement is a performance parameter and/or an engine key cross-section parameter.
4. The aircraft engine model checking and verification method as claimed in claim 1, wherein: the measurement data are steady-state data after the throttle lever of the engine reaches a specified state.
5. The aircraft engine model checking and verifying method as defined in claim 4, wherein: and processing the measurement data, processing dead pixels and abnormal values, carrying out processing on the measurement parameters of the key sections of each engine, and obtaining the average value of the key sections of the engine.
6. An aircraft engine model checking and verification method as claimed in claim 2, wherein: the initial deviation is less than 9%.
7. A readable storage medium having executable instructions thereon that, when executed, cause a computer to perform an aeroengine model checking and verification method as included in any of claims 1-6.
8. A computing device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to perform an aeroengine model checking and verification method as included in any of claims 1-6 by the one or more processors.
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