Method, system and readable medium for predicting performance of a gas turbine engine

Abstract

The present application relates to a method for predicting the performance of a gas turbine engine, a system for predicting the performance of a gas turbine engine and a readable medium. The method comprises: S1. obtaining the relationship between the Mach number and total pressure recovery or the relationship between the inlet flow and total pressure recovery at the blockage ratio of the target distortion simulation net or simulation board; S2. obtaining the flow-pressure ratio characteristics and surge boundary line, flow-efficiency characteristics of the fan outer and inner and booster stage at the blockage ratio of the target distortion simulation net or simulation board according to the relationship obtained in S1; S3. obtaining the engine performance test data without the target distortion simulation net or simulation board, and obtaining a corrected engine digital model according to the test data; S4. replacing the inlet duct characteristics of the digital model according to the corrected engine digital model; and S5. obtaining the steady-state performance and / or transient-state performance of the overall performance of the engine at the blockage ratio of the target distortion simulation net or simulation board in S1.

Description

Technical Field

[0001] This invention belongs to the field of engines, and particularly relates to a method, system, and readable medium for predicting the performance of a gas turbine engine. Background Technology

[0002] Gas turbine engines, such as aircraft engines, experience uneven pressure distribution at the engine inlet when operating within their flight envelope. This is due to factors such as natural atmospheric wind speeds, angles of attack and sideslip angles caused by aircraft maneuvers, and the design of the inlet itself (e.g., S-curve inlets). Both theory and practice show that this pressure distortion leads to decreased engine thrust and fuel consumption, increased exhaust temperature, and, more importantly, reduced surge margin in the compressor components. In severe cases, it can cause compressor surge / stall, or even engine shutdown. Therefore, during engine development, inlet distortion testing is necessary, which involves adding a distortion simulation net or plate to the engine inlet. Accurately predicting the impact of inlet distortion on the engine before testing is crucial to minimizing risks during the experiment.

[0003] Existing aerodynamic stability studies often employ parallel compressor models to predict the impact of inlet distortion on engine performance and compressor surge boundaries. This method, as a numerical simulation technique, is based on the core idea of ​​dividing the compressor under distorted inlet conditions into two sub-compressors: one with an inlet in the undistorted region and the other with a distorted region. Due to the lower total inlet pressure, the distorted region operates near the surge point on its characteristic curve, while the undistorted region operates closer to the surge point. After determining the operating points of the distorted and undistorted regions, the overall compressor parameters under distorted inlet conditions are obtained by averaging them using some method. The drawbacks of this method are its poor computational accuracy and inability to account for radial distortion. Modern high-bypass turbofan engines suffer from both circumferential and radial distortion in their inlet distortion, making it difficult for this method to meet the experimental requirements for prediction accuracy.

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

[0006] One object of the present invention is to provide a predictive system for the performance of a gas turbine engine.

[0007] One 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 includes: S1. Obtaining the Mach number-total pressure recovery relationship or the inlet flow-total pressure recovery relationship under a blockage ratio of a target distortion simulation network or simulation plate; S2. Based on the relationship obtained in S1, obtaining the flow-pressure characteristics and surge boundary line and flow-efficiency characteristics of the fan bypass and fan inner / booster stages under a blockage ratio of the target distortion simulation network or simulation plate; S3. Obtaining engine performance test data without a target distortion simulation network or simulation plate, and obtaining a corrected engine digital model based on the test data; S4. Based on the corrected engine digital model... The model is modified by replacing the intake characteristics of the digital model with the Mach number-total pressure recovery relationship or inlet flow-total pressure recovery relationship under the blockage ratio of the target distortion simulation network or simulation plate obtained in S1; replacing the characteristics of the fan booster stage of the digital model with the flow-pressure characteristics and surge boundary line of the fan bypass and fan inward / booster stage obtained in S2, as well as the flow-efficiency characteristics, to obtain the final modified engine digital model; S5. Based on the final modified engine digital model, the steady-state performance and / or transient performance of the overall engine performance under the blockage ratio of the target distortion simulation network or simulation plate obtained in S1 are obtained.

[0009] According to another aspect of the present invention, a gas turbine engine performance prediction system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above prediction method.

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

[0011] In summary, the beneficial effects of the prediction methods, prediction systems, and computer-readable media described in the above embodiments include, but are not limited to, the ability to accurately and reasonably predict the overall performance of a high-bypass turbofan engine under intake distortion, including engine characteristic section temperature, pressure, and flow parameters, as well as engine thrust and fuel consumption, through simple data analysis and processing, after the engine has undergone drag characteristic blowing tests on distortion simulation nets or simulation boards, fan turbocharger component intake distortion tests, and overall engine performance recording tests during the detailed design and development stage of the engine. This provides a feasible and reliable method for conducting overall engine distortion tests, especially suitable for newly developed engine models or engines without development experience, for conducting pre-test performance evaluation, developing test plans, and risk assessment. Attached Figure Description

[0012] The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments. It should be noted that the drawings are merely illustrative and are not drawn to scale, and should not be construed as limiting the scope of protection actually claimed by the present invention, wherein:

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

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

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

[0016] Figure 4 This is a schematic diagram of the flow-pressure ratio characteristic.

[0017] Figure 5 This is a schematic diagram of flow-efficiency characteristics. Detailed Implementation

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

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

[0020] It is understood that flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that, depending on the actual situation, the preceding or following operations may not necessarily be performed precisely in sequence. Other operations may be added to these processes, or one or more steps may be removed from them.

[0021] The following predictions for gas turbine engines will be made using turbofan engines as an example. Turbofan engines generally refer to turbojet engines in which the exhaust airflow from the fan (or low-pressure compressor) enters two separate bypass ducts: the inner duct and the outer duct.

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

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

[0024] Specifically, in some embodiments, such as Figure 2 As shown, this could involve conducting a wind test on the resistance characteristics of a distorted simulation mesh or plate to obtain several sets of blockage ratios for the distorted simulation mesh or plate. Figure 3 The Mach number-total pressure recovery coefficient curve or inlet flow-total pressure recovery coefficient curve shown are examples. The meanings of "distortion simulation mesh" and "simulation plate" here are similar to their usual meanings in this field, referring to mesh or plate-like structures used to simulate intake distortion in engines requiring intake distortion testing.

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

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

[0027] Specifically, in some embodiments, such as Figure 2 As shown, this can be a fan / booster stage component intake distortion test, using multiple sets of target distortion simulation networks or simulation boards corresponding to step S1 to obtain the flow-pressure characteristics and surge boundary lines of the fan bypass and fan inner / booster stage at the corresponding blockage ratio (e.g., ...). Figure 4 As shown), flow-efficiency characteristics (such as) Figure 5 shown).

[0028] Alternatively, if relevant data already exists, the flow-pressure characteristics and surge boundary lines and flow-efficiency characteristics of the fan bypass and fan induct / booster stages under the target distortion simulation network or simulation board at different congestion ratios can be obtained directly by linear interpolation from the existing database of fan bypass and fan induct / booster stage flow-pressure characteristics and surge boundary lines and flow-efficiency characteristics for the fan bypass and fan induct / booster stages under different congestion ratios.

[0029] S3. Obtain engine performance tests without target distortion simulation networks or simulation boards, and obtain corrected engine digital models based on the test data.

[0030] refer to Figure 2 In some embodiments, the specific steps may include conducting engine performance admission tests (without distortion nets), measuring engine characteristic section temperature, pressure, and flow parameters, as well as engine thrust, fuel consumption, and other performance parameters at 3-5 engine stable states. To ensure sufficient engine stability, the dwell time in each state should be sufficiently long, with a recommended dwell time of no less than 15 minutes. The engine measurement sections should be as numerous as possible. Recommended measurement sections and parameters for high-bypass turbofan engines include: engine inlet flow rate, temperature, and pressure; high-pressure compressor inlet temperature and pressure; fan outlet temperature and pressure; high-pressure turbine outlet temperature and pressure; and low-pressure turbine outlet temperature and pressure. Furthermore, based on the performance admission test data, the engine's baseline overall performance model (which can also be called the engine digital model or mathematical model) is corrected. This correction process mainly involves adjusting the engine characteristic section temperature, pressure, and flow parameters, as well as the engine thrust, fuel consumption, and other performance parameters, to ensure that the deviation between the digital model's calculation results and the test results meets accuracy requirements and is less than the error threshold. Preferably, the deviation can be no greater than 2%, thus obtaining a more accurate digital model.

[0031] S4. Based on the modified engine digital model, replace the intake characteristics of the digital model with the Mach number-total pressure recovery relationship or inlet flow-total pressure recovery relationship under the blockage ratio of the target distortion simulation network or simulation plate obtained in S1; replace the characteristics of the fan booster stage of the digital model with the flow-pressure characteristics and surge boundary line of the fan bypass and fan inward / booster stage obtained in S2, as well as the flow-efficiency characteristics, to obtain the final modified engine digital model.

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

[0033] Specifically, refer to Figure 2 For steady-state performance, the calculation can be performed on the engine's steady-state performance under the blockage ratios of multiple sets of distorted simulation networks or simulation plates corresponding to S1, including the remaining surge margin of each compression component. Combined with the engine's resonance band, maximum exhaust temperature, or engine speed, the performance parameters of the engine's steady-state dwell stage for the day's test can be estimated. The calculation should include all simulation networks or simulation plates to be tested, and the evaluation state should range from engine idle to maximum speed. Similarly, for transient performance, the calculation can be performed on the engine's acceleration and deceleration performance under the blockage ratios of several sets of distorted simulation networks or simulation plates corresponding to S1, including starting and acceleration / deceleration process performance (speed, flow rate, temperature, pressure, etc.), starting and acceleration / deceleration times, and the remaining surge margin of each compression component. The calculation should include all simulation networks or simulation plates to be tested, and the evaluation state should range from engine idle to maximum speed.

[0034] The principle behind the prediction method described in the above embodiments, which enables rapid and accurate prediction of the impact of intake distortion on engine performance, lies in the inventors' discovery that, unlike the parallel compressor model's prediction of the impact of intake distortion on engine performance, this method is based on the unique characteristics of intake distortion in high-bypass turbofan engines. The distortion is primarily concentrated in the outer bypass, with a relatively small impact on the inner bypass. Therefore, when considering the overall intake pressure distortion of the engine: the distortion is completely mixed at the engine outlet, leading to significant changes in the characteristics of the fan outer bypass, but the impact on the total pressure loss from the OGV (outlet guide vane) outlet to the engine outlet is negligible; furthermore, the distortion causes some changes in the characteristics of the fan inner bypass / boost stage, but the impact on the high-pressure compressor characteristics is negligible, and it does not lead to changes in the performance of other components. Therefore, the above embodiment can quickly predict the impact of intake distortion on engine performance by simply replacing the characteristics of the intake duct and fan turbo stage under intake turning conditions in the modified overall engine performance calculation model. At the same time, the characteristics of the intake duct and fan turbo stage under the target blockage ratio can be obtained by simple processing of the test data (or database data) and used as input for the performance model, which greatly improves the accuracy compared with traditional calculation methods.

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

[0036] It should be noted that the aforementioned memory, processor, and database are not limited to a specific memory, processor, or database. For example, in some cases, the memory and processor can have a distributed structure. For instance, it can include memory and processor located at the test equipment end and the backend cloud end, respectively, with the test equipment end and the backend cloud end jointly implementing the aforementioned lifetime prediction method. Furthermore, in embodiments employing a distributed structure, the specific execution terminal for each step can be adjusted according to actual conditions, and the specific implementation scheme of each step on a particular terminal should not limit the scope of protection of this invention.

[0037] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the prediction method as described in the above embodiments. For details, please refer to the description above, which will not be repeated here.

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

[0039] In summary, the beneficial effects of the prediction methods, prediction systems, and computer-readable media described in the above embodiments include, but are not limited to, the ability to accurately and reasonably predict the overall performance of a high-bypass turbofan engine under intake distortion, including engine characteristic section temperature, pressure, and flow parameters, as well as engine thrust and fuel consumption, through simple data analysis and processing, after the engine has undergone drag characteristic blowing tests on distortion simulation nets or simulation boards, fan turbocharger component intake distortion tests, and overall engine performance recording tests during the detailed design and development stage of the engine. This provides a feasible and reliable method for conducting whole-engine distortion tests, especially suitable for newly developed engine models or engines without development experience, for conducting pre-test performance evaluation, developing test plans, and risk assessment.

[0040] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using 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. The general-purpose processor may be a microprocessor, but in alternatives, it 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, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0041] The steps of the methods or algorithms 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 both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0042] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or via a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0043] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A method of predicting performance of a gas turbine engine, characterized by, Comprise: S1. Obtain the relationship of Mach number-total pressure recovery or the relationship of inlet flow rate-total pressure recovery of the target distortion simulation net or simulation plate at the blockage ratio; S2. According to the relationship obtained in S1, obtain the flow rate-pressure ratio characteristics and surge boundary line, flow rate-efficiency characteristics of the fan outer and fan inner / boost stage of the target distortion simulation net or simulation plate at the blockage ratio; S3. Obtain the engine performance test data without the target distortion simulation net or simulation plate, and obtain the corrected engine digital model according to the test data; S4. According to the corrected engine digital model, replace the inlet duct characteristics of the digital model with the relationship of Mach number-total pressure recovery or the relationship of inlet flow rate-total pressure recovery of the target distortion simulation net or simulation plate at the blockage ratio obtained in S1; replace the characteristics of the fan boost stage of the digital model with the flow rate-pressure ratio characteristics and surge boundary line, and flow rate-efficiency characteristics of the fan outer and fan inner / boost stage 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 transient-state performance of the overall performance of the engine of the target distortion simulation net or simulation plate at the blockage ratio in S1.

2. The method of predicting engine performance according to claim 1, wherein In S1, the step of obtaining the relationship of Mach number-total pressure recovery or the relationship of inlet flow rate-total pressure recovery of the target distortion simulation net or simulation plate at the blockage ratio comprises: Perform a resistance characteristic blow test of the target distortion simulation net or simulation plate, and obtain a plurality of sets of Mach number-total pressure recovery coefficient curves or inlet flow rate-total pressure recovery coefficient curves of the target distortion simulation net or simulation plate at the blockage ratio; or Directly obtain or obtain by linear interpolation the Mach number-total pressure recovery coefficient curves or inlet flow rate-total pressure recovery coefficient curves of the target distortion simulation net or simulation plate at the blockage ratio according to an existing resistance characteristic blow test database of the distortion simulation net or simulation plate.

3. The method of predicting engine performance according to claim 1, wherein In S2, the step of obtaining the flow rate-pressure ratio characteristics and surge boundary line, flow rate-efficiency characteristics of the fan outer and fan inner / boost stage of the target distortion simulation net or simulation plate at the blockage ratio comprises: Perform an inlet distortion test of the fan / boost stage components, and obtain the flow rate-pressure ratio characteristics and surge boundary line, flow rate-efficiency characteristics of the fan outer and fan inner / boost stage at the corresponding blockage ratio of the plurality of sets of target distortion simulation nets or simulation plates corresponding to step S1; or Directly obtain or obtain by linear interpolation the flow rate-pressure ratio characteristics and surge boundary line, flow rate-efficiency characteristics of the fan outer and fan inner / boost stage of the target distortion simulation net or simulation plate at the blockage ratio according to an existing database of the flow rate-pressure ratio characteristics and surge boundary line, flow rate-efficiency characteristics of the fan outer and fan inner / boost stage of the simulation net or simulation plate corresponding to different blockage ratios.

4. The method of predicting engine performance according to claim 1, wherein In S3, the step of obtaining the engine performance test data without the target distortion simulation net or simulation plate, and obtaining the corrected engine digital model according to the test data comprises: In the multiple engine steady state measurements, the measured parameters of the multiple measurement sections of the engine are obtained, including the flow rate, temperature and pressure of the engine inlet section, the temperature and pressure of the high pressure compressor inlet section, the temperature and pressure of the fan outlet section, the temperature and pressure of the high pressure turbine outlet section, and the temperature and pressure of the low pressure turbine outlet section. The reference engine digital model is corrected according to the test data until the deviation of the corrected model from the test data is within a threshold value.

5. The method of predicting engine performance according to claim 4, wherein, The reference engine digital model is corrected according to the test data until the deviation of the corrected model from the test data is within 2%.

6. The method of predicting engine performance according to claim 4, wherein In the measurement process of obtaining the measured parameters of the multiple measurement sections of the engine in the multiple engine steady state measurements, the residence time of each state is more than 15 minutes.

7. The method of predicting engine performance according to claim 1, wherein In the S5, the step of obtaining the steady state performance of the overall performance of the engine at the blockage ratio of the target distortion simulation network or simulation board of the S1 includes: The engine steady state performance corresponding to the blockage ratio of the target distortion simulation network or simulation board of the S1 is calculated, including the remaining surge margin of the compressor, and the performance parameters of the steady state residence step of the engine test are calculated according to the resonance band, maximum exhaust temperature or rotating speed of the engine.

8. The method of predicting engine performance according to claim 1, wherein, In the S5, the step of obtaining the transient state performance of the overall performance of the engine at the blockage ratio of the target distortion simulation network or simulation board of the S1 includes: The engine acceleration and deceleration performance, starting, acceleration and deceleration process performance, starting, acceleration and deceleration time, and the remaining surge margin of the compressor corresponding to the blockage ratio of the target distortion simulation network or simulation board of the S1 are calculated.

9. A system for predicting performance of a gas turbine engine, characterized by, It includes: A memory for storing instructions executable by a processor; A processor for executing the instructions to implement the prediction method of any one of claims 1-8.

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

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