Gas turbine engine performance prediction method, prediction system and readable medium

Abstract

The invention relates to a gas turbine engine performance prediction method, a prediction system and a readable medium. The prediction method comprises the following steps: S1, obtaining a Mach number-total pressure recovery relation or an inlet flow-total pressure recovery relation under the blockage ratio of a target distortion simulation network or simulation board; s2, according to the relation obtained in the S1, flow-pressure ratio characteristics, surge boundary lines and flow-efficiency characteristics of a fan outer culvert and a fan inner culvert / booster stage under the blockage ratio of the target distortion simulation network or simulation board are obtained; s3, obtaining engine performance test data of the simulation net or the simulation board without the target distortion, and obtaining a corrected engine digital model according to the test data; s4, according to the corrected engine digital model, replacing the air inlet characteristics of the digital model; and S5, obtaining the steady-state performance and / or the transition-state performance of the overall performance of the engine under the blockage ratio of the target distortion simulation network or the simulation board in the S1.

Description

Technical Field

[0001] The present invention belongs to the field of engines, and in particular relates to a prediction method, a prediction system and a readable medium for predicting the performance of a gas turbine engine. Background Art

[0002] When a gas turbine engine, such as an aircraft engine, is working within the flight envelope, the pressure at the inlet outlet is uneven due to the influence of the natural wind speed of the outside atmosphere, the angle of attack and the sideslip angle caused by the aircraft's maneuvering flight, or the design of the inlet itself (such as the S-bend inlet), which will cause pressure distortion at the engine inlet. Both theory and practice have shown that pressure distortion at the engine inlet will lead to poor performance such as engine thrust and fuel consumption, and increased exhaust temperature. More importantly, it will lead to a reduction in the surge margin of the engine compressor components. In severe cases, it will also lead to surge / stall of the compressor components, and even engine shutdown. Therefore, during the engine development process, the engine needs to carry out intake distortion tests, that is, add a distortion simulation net or simulation board at the engine inlet. It is particularly important to accurately predict the impact of intake distortion on the engine as a whole before the test to reduce the risk in the test.

[0003] Existing aerodynamic stability research mostly uses the parallel compressor model to predict the impact of intake distortion on engine performance and compressor surge boundary. As a numerical simulation method, the core idea of ​​this method is to divide the compressor under distorted intake into two sub-compressors, one compressor inlet is a non-distorted zone, and the other is a distorted zone. Since the total inlet pressure of the distorted zone is low, it works on the characteristic line close to the surge point, while the non-distorted zone works closer to the blockage point. After finding the working points of the distortion zone and the non-distorted zone, the total compressor parameters under distorted intake are averaged in some way. The disadvantage of this method is that the calculation accuracy is poor and radial distortion cannot be considered. The intake distortion of modern large bypass ratio civil turbofan engines includes both circumferential distortion and radial distortion, so this method is difficult to meet the prediction accuracy requirements of the experiment.

[0004] Therefore, there is a need in the art for a prediction method that can accurately predict the impact of intake distortion on engine performance and compressor surge boundary. Summary of the invention

[0005] An object of the present invention is to provide a method for predicting the performance of a gas turbine engine.

[0006] An object of the present invention is to provide a gas turbine engine performance prediction system.

[0007] An object of the present invention is to provide a computer readable medium.

[0008] According to one aspect of the present invention, a method for predicting the performance of a gas turbine engine comprises: S1. obtaining a Mach number-total pressure recovery relationship or an inlet flow-total pressure recovery relationship under a blockage ratio of a target distortion simulation network or simulation board; S2. obtaining, based on the relationship obtained in S1, the flow-pressure ratio characteristics and surge boundary line and flow-efficiency characteristics of the fan duct and fan duct / boost stage under a blockage ratio of a target distortion simulation network or simulation board; S3. obtaining engine performance test data without a target distortion simulation network or simulation board, and obtaining a modified engine digital model based on the test data; S4. obtaining, based on the modified engine digital model, a modified engine digital model; S5. obtaining a modified engine digital model based on the modified engine digital model; S6. obtaining a modified engine digital model based on the modified engine digital model; S7. obtaining a modified engine digital model based on the modified engine digital model; S8. obtaining a modified engine digital model based on the modified engine digital model; S9. obtaining a modified engine digital model based on the modified engine digital model; S10. obtaining a modified engine digital model based on the modified engine digital model; S11. obtaining a modified engine digital model based on the modified engine digital model; S12. obtaining a modified engine digital model based on the modified engine digital model; S13. obtaining a modified engine digital model Model, replace the inlet duct characteristics of the digital model with the relationship between Mach number and total pressure recovery or the relationship between inlet flow and total pressure recovery under the blockage ratio of the target distortion simulation network or simulation board obtained in S1; replace the characteristics of the fan boost stage of the digital model with the flow-pressure ratio characteristics and surge boundary line of the fan duct and fan duct / boost stage, as well as the flow-efficiency characteristics obtained in S2, to obtain the final corrected engine digital model; S5. According to the final corrected engine digital model, obtain the steady-state performance and / or transitional performance of the overall engine performance under the blockage ratio of the target distortion simulation network or simulation board of S1.

[0009] A gas turbine engine performance prediction system according to another aspect of the present invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above prediction method when executing the computer program.

[0010] According to another aspect of the present invention, a computer readable medium has a computer program thereon, and the program is executed by a processor to implement the steps in the prediction method described above that can be implemented by the computer program.

[0011] In summary, the beneficial effects of the prediction method, prediction system and computer-readable medium introduced in the above embodiments include but are not limited to that, during the detailed design and development stage of the engine, after the resistance characteristic blowing test of the distortion simulation net or simulation board, the intake distortion test of the fan boost stage components and the whole machine performance admission test have been carried out, through simple data analysis and processing, the overall performance of the large bypass ratio turbofan engine under intake distortion can be predicted more accurately and reasonably, including the engine characteristic section temperature, pressure, flow parameters, and engine thrust, fuel consumption and other performance parameters, so as to carry out the whole machine distortion test of the engine, which is particularly suitable for conducting pre-test performance evaluation, formulating test plans and risk estimation for newly developed engine models or engines with no development experience. Provides a feasible and reliable method. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, properties and advantages of the present invention will become more apparent through the following description in conjunction with the accompanying drawings and embodiments. It should be noted that the drawings are only examples and are not drawn to scale, and should not be used as a limitation on the actual scope of protection required by the present invention, wherein:

[0013] Figure 1 It is a flowchart of an embodiment of the prediction method of the present invention.

[0014] Figure 2 It is a flowchart of another embodiment of the prediction method of the present invention.

[0015] Figure 3 It is the inlet flow-total pressure recovery coefficient curve of the distortion network.

[0016] Figure 4 It is a flow-pressure ratio characteristic diagram.

[0017] Figure 5 It is a flow-efficiency characteristic diagram. DETAILED DESCRIPTION

[0018] The following discloses various different implementation methods or embodiments of the subject technical solution. To simplify the disclosure, specific examples of various elements and arrangements are described below. Of course, these are only examples and are not intended to limit the scope of protection of the present invention.

[0019] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0020] It is understood that flow charts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that, according to the actual situation, the previous or following operations are not necessarily performed precisely in order. Other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0021] The following prediction of gas turbine engines is carried out by taking a turbofan engine as an example. A turbofan engine (turbofan engine) generally refers to a turbojet engine in which the fan (or low-pressure compressor) outlet airflow enters two ducts, the inner duct and the outer duct respectively.

[0022] refer to Figure 1 As shown, in some embodiments, a method for predicting gas turbine engine performance may include the following steps:

[0023] S1. Obtain the relationship between Mach number and total pressure recovery or the relationship between inlet flow and total pressure recovery under the blockage ratio of the target distortion simulation network or simulation plate.

[0024] Specifically, in some embodiments, Figure 2 As shown, a wind blowing test of the resistance characteristics of the distortion simulation net or simulation plate can be carried out to obtain the following blockage ratios of several sets of distortion simulation nets or simulation plates: Figure 3 The Mach number-total pressure recovery coefficient curve or the inlet flow-total pressure recovery coefficient curve shown. The meaning of the distortion simulation net and simulation plate here is similar to the general meaning in this field, that is, a net-like or plate-like structure simulating intake distortion for the engine that needs to carry out intake distortion test.

[0025] If there is a wind blowing test database of the resistance characteristics of the target distortion simulation network or simulation plate, the Mach number-total pressure recovery coefficient curve or the inlet flow-total pressure recovery coefficient curve under the blockage ratio of the target distortion simulation network or simulation plate can be obtained by linear interpolation.

[0026] S2. Based on the relationship obtained in S1, obtain the flow-pressure ratio characteristics and flow-efficiency characteristics, flow-efficiency characteristics and surge boundary line of the fan outer duct and fan inner duct / boost stage under the blockage ratio of the target distortion simulation network or simulation board.

[0027] Specifically, in some embodiments, Figure 2 As shown, the fan / boost stage component intake distortion test can be performed, and the fan culvert and fan culvert / boost stage flow-pressure ratio characteristics and surge boundary lines (such as Figure 4 shown), flow-efficiency characteristics (such as Figure 5 shown).

[0028] Alternatively, if relevant data are available, then the flow-pressure ratio characteristics, surge boundary line, and flow-efficiency characteristics of the fan duct and fan content / boost stage of the existing database of flow-pressure ratio characteristics, surge boundary line, and flow-efficiency characteristics of the fan duct and fan content / boost stage of the simulation network or simulation board corresponding to different blockage ratios can be directly obtained through linear interpolation to obtain the flow-pressure ratio characteristics, surge boundary line, and flow-efficiency characteristics of the fan duct and fan content / boost stage under the blockage ratio of the target distortion simulation network or simulation board.

[0029] S3. Obtain an engine performance test without a target distortion simulation network or simulation board, and obtain a corrected engine digital model based on the test data.

[0030] refer to Figure 2 In some embodiments, the specific steps may be to carry out an engine performance test (without distortion network), and obtain engine characteristic section temperature, pressure, flow parameters, and engine thrust, fuel consumption and other performance parameters in 3 to 5 engine stable states. In order to make the engine fully stable, the engine should stay in each state for a long enough time, and the recommended stay time is not less than 15 minutes. The engine measurement section should be as many as possible. The recommended measurement sections and parameters for a large bypass ratio turbofan engine include: engine inlet section flow, temperature, pressure, high pressure compressor inlet temperature, pressure, fan outlet temperature, pressure, high pressure turbine outlet temperature, pressure, low pressure turbine outlet temperature, pressure, etc. And according to the data of the performance test, the engine benchmark overall performance model (which can also be called an engine digital model or mathematical model) is corrected. The correction process mainly corrects the engine characteristic section temperature, pressure, flow parameters, and engine thrust, fuel consumption and other performance parameters of the tested engine, so that the deviation between the calculation result of the digital model and the test result meets the accuracy requirement and is less than the error threshold. Preferably, the deviation can be no more than 2%, so that a more accurate digital model can be obtained.

[0031] S4. According to the modified digital engine model, the intake duct characteristics of the digital model are replaced with the Mach number-total pressure recovery relationship or the inlet flow-total pressure recovery relationship under the blockage ratio of the target distortion simulation network or simulation board obtained in S1; the fan boost stage characteristics of the digital model are replaced with the flow-pressure ratio characteristics and surge boundary line of the fan duct and fan duct / boost stage, as well as the flow-efficiency characteristics obtained in S2, to obtain the final modified digital engine model.

[0032] S5. Based on the finally corrected digital engine model, obtain the steady-state performance and / or transitional performance of the overall engine performance under the blockage ratio of the target distortion simulation network or simulation board of S1.

[0033] Specifically, refer to Figure 2 For steady-state performance, the steady-state performance of the engine under the blockage ratio corresponding to the multiple sets of distortion simulation nets or simulation boards in S1 can be calculated, including the remaining surge margin of each compression component, and the performance parameters of the steady-state dwell step of the engine tested on the day can be estimated in combination with the engine's resonance band, maximum exhaust temperature or speed. The calculation should include all simulation nets or simulation boards to be tested, and the evaluation state should be from engine slow to maximum speed state. Similarly, for transient performance, the acceleration and deceleration performance of the engine under the blockage ratio corresponding to the several sets of distortion simulation nets or simulation boards in S1, the performance of the starting, acceleration and deceleration processes (speed, flow, temperature, pressure, etc.), the starting, acceleration and deceleration time, and the remaining surge margin of each compression component can be calculated. The calculation should include all simulation nets or simulation boards to be tested, and the evaluation state should be from engine slow to maximum speed state.

[0034] The prediction method introduced in the above embodiment can quickly and accurately predict the influence of intake distortion on engine performance. The principle is that the inventor found that it is different from the parallel compressor model to predict the influence of intake distortion on engine performance. It is based on the unique law of intake distortion of a large bypass ratio turbofan engine. The pressure distortion is mainly concentrated in the outer duct, and the influence on the inner duct is relatively small. Therefore, when considering the overall intake pressure distortion of the engine: the distortion is completely mixed at the engine outlet, and the distortion will cause obvious changes in the fan outer duct characteristics, but the influence on the total pressure loss from the OGV (fan outlet guide vane) outlet to the engine outlet is negligible; and the distortion will cause certain changes in the characteristics of the fan inner duct / boost stage, and the influence on the high-pressure compressor characteristics is negligible, and will not cause performance changes of other components. Therefore, the scheme of the above embodiment can quickly predict the influence of intake distortion on engine performance by replacing only the characteristics of the intake duct and the fan boost stage under the intake turning condition in the modified engine overall performance calculation model. At the same time, simple processing of the test data (or database data) can obtain the characteristics of the intake duct and the fan boost stage under the target blockage ratio as the input of the performance model, which greatly improves the accuracy compared with the traditional calculation method.

[0035] It can be understood that the above embodiments can also be implemented in a system, that is, the present application also provides a prediction system for gas turbine engine performance, including a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the prediction method introduced in the above embodiments.

[0036] It should be noted that the above-mentioned memory, processor, and database are not limited to a specific memory, processor, or database. For example, in some cases, both the memory and the processor can have a distributed structure. For example, they can include a memory and a processor located at the test equipment end and the backend cloud, respectively, and the test equipment end and the backend cloud jointly implement the above-mentioned life prediction method. Furthermore, in an embodiment adopting a distributed structure, each step can adjust the specific execution terminal according to actual conditions, and the specific scheme of each step implemented in a specific terminal should not limit the scope of protection of the present invention.

[0037] Another aspect of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps of the prediction method described in the above embodiment are implemented. Please refer to the above description for details, which will not be repeated here.

[0038] In addition, it can be understood that the above-mentioned computer-readable storage medium can also be in a system form, that is, it includes multiple computer-readable storage sub-media, so as to jointly implement the steps of the prediction method described above through multiple computer-readable storage media.

[0039] In summary, the beneficial effects of the prediction method, prediction system and computer-readable medium introduced in the above embodiments include but are not limited to that, during the detailed design and development stage of the engine, after the resistance characteristic blowing test of the distortion simulation net or simulation board, the intake distortion test of the fan boost stage components and the whole machine performance admission test have been carried out, through simple data analysis and processing, the overall performance of the large bypass ratio turbofan engine under intake distortion can be predicted more accurately and reasonably, including the engine characteristic section temperature, pressure, flow parameters, and engine thrust, fuel consumption and other performance parameters, so as to carry out the whole machine distortion test of the engine, which is particularly suitable for conducting pre-test performance evaluation, formulating test plans and risk estimation for newly developed engine models or engines with no development experience. Provides a feasible and reliable method.

[0040] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0041] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0042] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any media that facilitates the transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0043] Although the present invention is disclosed as above in terms of preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for predicting gas turbine engine performance, characterized in that: include: S1. Obtain the relationship between Mach number and total pressure recovery or the relationship between inlet flow and total pressure recovery under the blockage ratio of the target distortion simulation network or simulation plate; S2. According to the relationship obtained in S1, the flow-pressure ratio characteristics, surge boundary line, and flow-efficiency characteristics of the fan duct and fan duct / boost stage under the blockage ratio of the target distortion simulation network or simulation board are obtained; S3. Obtain engine performance test data without target distortion simulation network or simulation board, and obtain a corrected engine digital model based on the test data; S4. According to the revised digital engine model, the inlet duct characteristics of the digital model are replaced with the relationship between Mach number and total pressure recovery or the relationship between inlet flow and total pressure recovery under the blockage ratio of the target distortion simulation network or simulation board obtained in S1; the characteristics of the fan boost stage of the digital model are replaced with the flow-pressure ratio characteristics and surge boundary line of the fan duct and fan duct / boost stage obtained in S2, as well as the flow-efficiency characteristics, to obtain the final revised digital engine model; S5. Based on the finally corrected digital engine model, obtain the steady-state performance and / or transitional performance of the overall engine performance under the blockage ratio of the target distortion simulation network or simulation board of S1.

2. The method for predicting engine performance according to claim 1, characterized in that: In said S1, the step of obtaining the relationship between Mach number and total pressure recovery or the relationship between inlet flow and total pressure recovery under the blockage ratio of the target distortion simulation net or simulation plate comprises: Conduct a wind blowing test on the resistance characteristics of the target distortion simulation net or simulation plate to obtain multiple sets of Mach number-total pressure recovery coefficient curves or inlet flow-total pressure recovery coefficient curves under the blockage ratio of the target distortion simulation net or simulation plate; or According to the existing wind blowing test database of the resistance characteristics of the distortion simulation network or simulation plate, the Mach number-total pressure recovery coefficient curve or the inlet flow-total pressure recovery coefficient curve under the blockage ratio of the target distortion simulation network or simulation plate is directly obtained or obtained through linear interpolation.

3. The method for predicting engine performance according to claim 1, characterized in that: In said S2, the step of obtaining the flow-pressure ratio characteristics, surge boundary line, and flow-efficiency characteristics of the fan duct and fan duct / boost stage under the blockage ratio of the target distortion simulation network or simulation board includes: Conduct an intake distortion test of a fan / boost stage component, and obtain the flow-pressure ratio characteristics, surge boundary line, and flow-efficiency characteristics of the fan culvert and fan culvert / boost stage under the corresponding blockage ratio according to the multiple sets of target distortion simulation networks or simulation boards corresponding to step S1; or According to the existing database of flow-pressure ratio characteristics, surge boundary line and flow-efficiency characteristics of the fan duct and fan content / boost stage of the simulation network or simulation board corresponding to different blockage ratios, the flow-pressure ratio characteristics, surge boundary line and flow-efficiency characteristics of the fan duct and fan content / boost stage under the blockage ratio of the target distortion simulation network or simulation board are obtained by directly obtaining through linear interpolation.

4. The method for predicting engine performance according to claim 1, characterized in that: In said S3, the step of acquiring engine performance test data without the target distortion simulation network or simulation board and obtaining a corrected engine digital model according to the test data comprises: The measurement parameters of multiple measurement sections of the engine are obtained by measuring in multiple engine steady states, wherein the multiple measurement sections and parameters include: flow rate, temperature, and pressure of the engine inlet section, temperature and pressure of the high-pressure compressor inlet section, temperature and pressure of the fan outlet section, temperature and pressure of the high-pressure turbine outlet section, and temperature and pressure of the low-pressure turbine outlet section; The baseline digital engine model is corrected based on the test data until the deviation between the corrected model and the test data is within a threshold.

5. The method for predicting engine performance according to claim 4, characterized in that: The baseline digital engine model is corrected based on the test data until the corrected model is within 2% of the test data.

6. The method for predicting engine performance according to claim 4, characterized in that: In the process of obtaining the measurement parameters of multiple measurement sections of the engine by measuring multiple engine stable states, the residence time of each state is more than 15 minutes.

7. The method for predicting engine performance according to claim 1, characterized in that: In said S5, the step of obtaining the steady-state performance of the overall engine performance under the blockage ratio of the target distortion simulation network or simulation board of said S1 comprises: Calculate the engine steady-state performance under the blockage ratio of the target distortion simulation network or simulation board corresponding to the S1, including the remaining surge margin of the compressor, and calculate the performance parameters of the steady-state dwell step of the engine test according to the resonance band, maximum exhaust temperature or speed of the engine.

8. The method for predicting engine performance according to claim 1, characterized in that: In said S5, the step of obtaining the transition state performance of the overall engine performance under the blockage ratio of the target distortion simulation network or simulation board of said S1 comprises: Calculate the engine acceleration and deceleration performance under the blockage ratio of the target distortion simulation network or simulation board corresponding to the S1, the starting, acceleration and deceleration process performance, including speed, flow, temperature, pressure, etc., the starting, acceleration and deceleration time, and the remaining surge margin of the compressor.

9. A gas turbine engine performance prediction system, characterized in that: include: a memory for storing instructions executable by a processor; A processor, configured to execute the instructions to implement the prediction method according to any one of claims 1 to 8.

10. A computer readable medium having a computer program thereon, characterized in that The program is executed by a processor to implement the steps in the prediction method according to any one of claims 1 to 8 that can be implemented by a computer program.

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