Compressor sand ingestion wear robustness optimization design method and system, equipment and medium

Abstract

This invention discloses a compressor sand ingestion wear robustness optimization design method, system, equipment, and medium. First, preliminary aerodynamic optimization is performed to ensure that the compressor's factory performance does not degrade compared to traditional designs. Then, a high-precision real-time simulation method for compressor blade wear is constructed. By updating the coupling relationship between blade geometry and wear rate in real time, it accurately simulates blade geometric wear and aerodynamic performance degradation caused by sand ingestion during compressor operation in an outdoor environment. Next, based on this real-time simulation method, sand ingestion uncertainty analysis is performed on the preliminary design scheme, and wear-sensitive design parameters are optimized to improve the compressor's sand ingestion wear robustness, resulting in an optimized design scheme. Finally, the aerodynamic performance of the optimized design scheme is verified. This method can improve the wear resistance of compressors without reducing their aerodynamic performance as in traditional designs. The entire optimization process requires no manual design experience and helps alleviate the performance degradation of turboshaft engines.

Description

Technical Field

[0001] This invention relates to the field of compressor aerodynamics and thermodynamics, and in particular to a compressor sand ingestion wear robustness optimization design method and system, electronic equipment, and computer-readable storage medium. Background Technology

[0002] When gas turbine engines, especially turboshaft engines, operate in harsh field environments, the intake of sand, dust, and other particles and impurities causes changes in the blade profile and flow channel due to prolonged and continuous scouring and wear. This leads to the compressor's aerodynamic performance deviating from its design state, reducing compressor efficiency and surge margin, decreasing bleed air engine performance, and even causing surge, seriously endangering field use or flight safety.

[0003] Existing compressor design methods, when considering the impact of external foreign objects or sand and dust wear, take two approaches. On the one hand, they try to mitigate the impact of sand and dust wear by adding external equipment such as particle separators or by adding compressor blade coatings, but this is not conducive to engine performance, reliability, and cost control. On the other hand, they rely on engineering experience to resist wear by increasing geometric parameters such as compressor blade thickness and chord length, or by improving design specifications to increase margins. However, this approach is subjective and limited, as different designers may have different understandings and judgments, leading to unstable design results and making it difficult to guarantee that the impact of sand and dust wear on compressor performance can be effectively reduced under various complex operating conditions.

[0004] Furthermore, compressor blade wear research is primarily based on experimental and simulation methods. Experimental methods, due to their high cost and long development cycle, have limitations in engineering applications. Simulation methods, building upon existing wear models, offer advantages such as low cost and short development cycle. Currently, compressor blade wear simulation methods fail to consider the real-time changes in wear rate during the wear process, lack understanding of the wear process, and have insufficient accuracy in simulating wear volume. Summary of the Invention

[0005] This invention provides a robust optimization design method and system for compressor sand ingestion wear, electronic equipment, and computer-readable storage medium. It can improve wear resistance without affecting the aerodynamic performance of the compressor. The entire optimization process does not require manual design experience and can effectively reduce the impact of sand and dust wear on compressor performance under the complex operating conditions of turboshaft engines.

[0006] According to one aspect of the present invention, a method for optimizing the robustness of compressor sand ingestion wear is provided, comprising the following:

[0007] The aerodynamic performance of the compressor at the time of manufacture was optimized to obtain a preliminary design scheme.

[0008] A high-precision real-time simulation method for blade wear is constructed to update the coupling relationship between blade geometry and wear rate in real time, thereby accurately calculating the amount of blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in an external environment.

[0009] Uncertainty analysis was conducted on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor. Based on the high-precision real-time simulation method of blade wear, the design parameters of wear-sensitive blades in the preliminary design scheme were further optimized to obtain the compressor wear-resistant optimized design scheme.

[0010] Verify the aerodynamic performance of the compressor wear-resistant optimization design scheme. If its aerodynamic performance meets the design specifications, the optimized design scheme will be adopted as the final design scheme; otherwise, iterative optimization will continue.

[0011] Furthermore, the process of constructing the high-precision real-time simulation method for blade wear includes the following:

[0012] Generate compressor blade mesh, set the blade surface solid wall boundary as UDF moving boundary, set the corresponding contact mesh as deformable mesh, and give the compressor sand ingestion wear simulation conditions;

[0013] The compressor flow field was calculated to obtain the blade wear rate, and the total sand ingestion wear time was divided into multiple wear periods.

[0014] At the end of the current wear period, the blade surface wear amount in three dimensions is calculated based on the blade surface wear rate and the duration of the wear period.

[0015] Based on the amount of blade wear in three dimensions during the current wear period, determine the coordinates of the solid wall boundary after wear. Update the wall grid point coordinates through UDF and update the computational domain grid before performing sand ingestion wear simulation for the next wear period.

[0016] The simulation was iterated until all wear periods were completed. The total wear amount was obtained by summing the blade surface wear amount in all wear periods. The aerodynamic performance degradation of the compressor during the first overhaul period was determined based on the blade surface wear amount in different wear periods.

[0017] Furthermore, the blade surface wear over a certain period of time is calculated based on the following formula:

[0018] ;

[0019] Where, ΔH i E represents the blade surface wear during the i-th wear period. i ρ represents the wall wear rate during the i-th wear period, ρ represents the blade material density, and Δt represents the wear rate during the i-th wear period. i This represents the duration of the i-th wear period.

[0020] Furthermore, the total sand ingestion wear time is divided into multiple wear periods of equal duration; or, the total sand ingestion wear time is divided into segmented time distribution curves with approximately constant wear rates, and the duration of each wear period is obtained by corresponding to the curves.

[0021] Furthermore, the process of performing uncertainty analysis on the changes in blade aerodynamic performance caused by sand ingestion during the compressor's first overhaul period, and further optimizing the wear-sensitive blade design parameters in the preliminary design scheme based on a high-precision real-time simulation method for blade wear, to obtain the compressor wear-resistant optimized design scheme includes the following:

[0022] Uncertainty parameters for compressor blade wear are set, and sensitivity analysis is performed on the uncertainty parameters to determine the key wear design parameters that are sensitive to blade wear.

[0023] Based on key wear design parameters, a high-precision real-time simulation method for blade wear is used to simulate real-time blade wear, so as to construct sample data on the impact of key wear design parameters on compressor aerodynamic performance.

[0024] A data-driven model of the impact of key wear design parameters on aerodynamic performance was trained based on a sample dataset. The expected value and variance of the compressor's aerodynamic performance during the first overhaul period were used as the objective function to find the optimal wear-resistant design scheme for the compressor.

[0025] Furthermore, the process of optimizing the aerodynamic performance of the compressor at the time of manufacture includes the following:

[0026] Sensitivity analysis was conducted based on the compressor's design parameters to determine the key design parameters that are sensitive to aerodynamic performance.

[0027] An aerodynamic performance model was constructed to simulate the impact of key styling design parameters on aerodynamic performance. After training the aerodynamic performance model, a multi-objective genetic algorithm was used to optimize the key styling design parameters to obtain the preliminary design scheme of the compressor at the time of delivery.

[0028] The aerodynamic performance of the preliminary design scheme is verified. If the aerodynamic performance does not meet the design requirements, the sample size of the aerodynamic performance model is increased and the iteration is repeated until the aerodynamic performance of the preliminary design scheme meets the design requirements.

[0029] Furthermore, the process of verifying the aerodynamic performance of the optimized design scheme includes the following:

[0030] Statistical analysis is performed on the compressor design point efficiency and surge margin under the condition of first overhaul period to end of service life. If the statistical index results meet the design index requirements, the optimized design scheme is adopted as the final design scheme; otherwise, it returns to the factory optimization design stage for iteration.

[0031] In addition, the present invention also provides a compressor sand ingestion wear robustness optimization design system, comprising:

[0032] The factory performance optimization module is used to optimize the aerodynamic performance of the compressor at the time of delivery and obtain a preliminary design scheme.

[0033] The blade wear real-time simulation module is used to build a high-precision real-time simulation method for blade wear, so as to update the coupling relationship between blade geometry and wear rate in real time, thereby accurately calculating the amount of blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in the field environment.

[0034] The wear resistance optimization module is used to perform uncertainty analysis on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor. Based on the high-precision real-time simulation method of blade wear, the design parameters of wear-sensitive blades in the preliminary design scheme are further optimized to obtain the wear resistance optimization design scheme of the compressor.

[0035] The aerodynamic performance verification module is used to verify the aerodynamic performance of the compressor wear-resistant optimization design scheme. If its aerodynamic performance meets the design specifications, the optimized design scheme will be adopted as the final design scheme; otherwise, iterative optimization will continue.

[0036] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0037] In addition, the present invention provides a computer-readable storage medium for storing a computer program for optimizing the robustness of compressor sand ingestion wear, wherein the computer program executes the steps of the method described above when run on a computer.

[0038] The present invention has the following beneficial effects:

[0039] This invention discloses a compressor sand ingestion wear robustness optimization design method. The method first performs a preliminary aerodynamic design based on traditional compressor factory aerodynamic performance optimization design methods to ensure that the compressor's factory performance does not degrade compared to the traditional design. Then, a high-precision real-time simulation method for compressor blade wear is proposed. By updating the coupling relationship between blade geometry and wear rate in real time, it accurately simulates blade geometric wear and aerodynamic performance degradation caused by sand ingestion during compressor operation in an outdoor environment. Next, based on this real-time simulation method, the sand ingestion uncertainty analysis of the preliminary compressor design is further performed, optimizing the compressor wear-sensitive design parameters and improving the compressor's sand ingestion wear robustness, resulting in a wear-resistant optimized design scheme for the compressor. Finally, the aerodynamic performance of the optimized design scheme is verified. This method can improve the wear resistance of compressors without reducing the aerodynamic performance of traditional designs. The entire optimization process requires no manual design experience and can effectively improve the expected value and variance of compressor efficiency, margin, and other aerodynamic performance during the initial overhaul period, helping to alleviate the performance degradation of turboshaft engines.

[0040] In addition, the compressor sand ingestion wear robustness optimization design system of the present invention also has the above-mentioned advantages.

[0041] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0042] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0043] Figure 1 This is a flowchart illustrating the compressor sand ingestion wear robustness optimization design method according to a preferred embodiment of this application;

[0044] Figure 2 yes Figure 1 A schematic diagram of the sub-process of step S10;

[0045] Figure 3 yes Figure 1 A schematic diagram of the sub-process of step S20;

[0046] Figure 4 This is a schematic diagram of dividing the total wear time into multiple wear periods in a preferred embodiment of this application;

[0047] Figure 5 yes Figure 1 A schematic diagram of the sub-process of step S30;

[0048] Figure 6This is a schematic diagram of the combined compressor selected for example verification in the preferred embodiment of this application;

[0049] Figure 7 This is a schematic diagram of the wear rate distribution of the combined compressor obtained by simulation analysis during the example verification in the preferred embodiment of this application;

[0050] Figure 8 This is a schematic diagram illustrating the sensitivity of the influence of the blade geometry parameters on the average wear amount obtained during instance verification in the preferred embodiment of this application;

[0051] Figure 9 This is a schematic diagram illustrating the sensitivity of the centrifugal impeller blade profile and flow channel geometry parameters to the average wear amount obtained during example verification in the preferred embodiment of this application.

[0052] Figure 10 This is a schematic diagram of the probability density distribution of sand intake per unit time during instance verification in a preferred embodiment of this application;

[0053] Figure 11 This is a schematic diagram illustrating the uncertainty range of the inlet blade angle of the axial rotor blade tip profile obtained during instance verification in a preferred embodiment of this application.

[0054] Figure 12 This is a schematic diagram illustrating the uncertainty range of the leading edge radius of the axial rotor blade tip profile obtained during instance verification in a preferred embodiment of this application;

[0055] Figure 13 This is a schematic diagram illustrating the uncertainty range of the chord length of the axial rotor blade tip profile obtained during instance verification in a preferred embodiment of this application;

[0056] Figure 14 This is a schematic diagram showing the uncertainty range of the outlet blade angle of the centrifugal impeller root section obtained during example verification in a preferred embodiment of this application;

[0057] Figure 15 This is a schematic diagram showing the uncertainty range of the second flow channel control point at the root section of the centrifugal impeller obtained during instance verification in a preferred embodiment of this application;

[0058] Figure 16 This is a schematic diagram showing the uncertainty range of the third flow channel control point of the centrifugal impeller root section obtained during instance verification in a preferred embodiment of this application.

[0059] Figure 17 This is a schematic diagram illustrating the prediction accuracy of the aerodynamic performance model for compressor efficiency constructed during instance verification in the preferred embodiment of this application.

[0060] Figure 18This is a schematic diagram illustrating the prediction accuracy of the aerodynamic performance model constructed during instance verification in the preferred embodiment of this application for compressor surge margin.

[0061] Figure 19 This is a schematic diagram of the solution set used in the preferred embodiment of this application for multi-objective optimization using a multi-objective genetic algorithm during instance verification;

[0062] Figure 20 This is a schematic diagram showing the mean distribution of compressor efficiency under the influence of blade wear in the prototype scheme of the combined compressor obtained during the instance verification in the preferred embodiment of this application.

[0063] Figure 21 This is a schematic diagram of the variance distribution of compressor efficiency under the influence of blade wear, obtained during the instance verification of the preferred embodiment of this application.

[0064] Figure 22 This is a schematic diagram showing the mean distribution of compressor surge margin under the influence of blade wear in the prototype scheme of the combined compressor obtained during the instance verification in the preferred embodiment of this application.

[0065] Figure 23 This is a schematic diagram of the variance distribution of the compressor surge margin under the influence of blade wear, obtained during the instance verification of the preferred embodiment of this application.

[0066] Figure 24 This is a schematic diagram showing the mean distribution of compressor efficiency under the influence of blade wear, obtained during the instance verification of the preferred embodiment of this application;

[0067] Figure 25 This is a schematic diagram of the variance distribution of compressor efficiency under the influence of blade wear, obtained during the instance verification of the preferred embodiment of this application;

[0068] Figure 26 This is a schematic diagram showing the mean distribution of compressor surge margin under the influence of blade wear in the optimized design scheme of the combined compressor obtained during the instance verification in the preferred embodiment of this application.

[0069] Figure 27 This is a schematic diagram of the variance distribution of the compressor surge margin under the influence of blade wear, obtained from the example verification of the preferred embodiment of this application;

[0070] Figure 28 This is a schematic diagram of the module structure of a compressor sand ingestion wear robustness optimization design system according to another embodiment of this application. Detailed Implementation

[0071] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0072] Reference Figure 1 A preferred embodiment of this application provides a compressor sand ingestion wear robustness optimization design method, including the following:

[0073] Step S10: Optimize the aerodynamic performance of the compressor at the time of manufacture to obtain a preliminary design scheme;

[0074] Step S20: Construct a high-precision real-time simulation method for blade wear to update the coupling relationship between blade geometry and wear rate in real time, thereby accurately calculating the amount of blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in the field environment.

[0075] Step S30: Conduct uncertainty analysis on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor, and further optimize the design parameters of wear-sensitive blades in the preliminary design scheme based on the high-precision real-time simulation method of blade wear to obtain the compressor wear-resistant optimized design scheme;

[0076] Step S40: Verify the aerodynamic performance of the compressor wear-resistant optimization design scheme. If its aerodynamic performance meets the design specifications, the optimized design scheme shall be adopted as the final design scheme; otherwise, iterative optimization shall continue.

[0077] It is understood that the compressor sand ingestion wear robustness optimization design method in this embodiment first performs a preliminary aerodynamic design based on the traditional compressor factory aerodynamic performance optimization design method to ensure that the compressor factory performance does not decrease compared to the traditional design. Then, a high-precision real-time simulation method for compressor blade wear is proposed. By updating the coupling relationship between blade geometry and wear rate in real time, it accurately simulates the blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in an outdoor environment. Next, based on this real-time simulation method, the sand ingestion uncertainty analysis of the preliminary compressor design scheme is further performed to optimize the compressor wear-sensitive design parameters, improve the compressor sand ingestion wear robustness, and obtain the compressor wear-resistant optimized design scheme. Finally, the aerodynamic performance of the optimized design scheme is verified. This method can improve the wear resistance of the compressor without reducing the aerodynamic performance of the traditional design compressor. The entire optimization process does not require manual design experience and can effectively improve the expected value, variance, and other statistical performance of the compressor's efficiency, margin, and other aerodynamic performance during the first overhaul period, which helps to alleviate the performance degradation of turboshaft engines.

[0078] Among them, such as Figure 2 As shown, in step S10, the process of optimizing the aerodynamic performance of the compressor at the time of manufacture includes the following:

[0079] Step S11: Based on the compressor's design parameters, conduct a sensitivity analysis to determine the key design parameters that are sensitive to aerodynamic performance;

[0080] Step S12: Construct an aerodynamic performance model to simulate the influence of key styling design parameters on aerodynamic performance. After training the aerodynamic performance model, use a multi-objective genetic algorithm to optimize the key styling design parameters and obtain the preliminary design scheme of the compressor at the time of delivery.

[0081] Step S13: Verify the aerodynamic performance of the preliminary design scheme. If the aerodynamic performance does not meet the design requirements, increase the sample size of the aerodynamic performance model and iterate again until the aerodynamic performance of the preliminary design scheme meets the design requirements.

[0082] Specifically, firstly, the design parameters affecting the compressor's aerodynamic performance are defined. Then, the active subspace method is used to perform sensitivity analysis on these parameters, thereby identifying the key design parameters sensitive to aerodynamic performance, which are then used as the key parameters for aerodynamic performance optimization. The active subspace method is existing technology, and its specific principles will not be elaborated here. Next, a neural network is used to construct an aerodynamic performance model to simulate the impact of the key design parameters on aerodynamic performance. After creating a training dataset, the aerodynamic performance model is trained. The process of creating the training dataset is existing technology and will not be elaborated here. Then, a multi-objective genetic algorithm is used to perform multi-objective optimization of the compressor's efficiency and surge margin under pressure ratio constraints. The optimal solution in the Pareto front is selected as the preliminary design scheme for the compressor's factory optimization. Then, a high-precision CFD simulation method is used to verify the aerodynamic performance of the preliminary design scheme. If the compressor efficiency and surge margin of the preliminary design scheme meet the design requirements, the preliminary design scheme is deemed to meet the requirements. If the compressor efficiency and surge margin of the preliminary design scheme do not meet the design requirements, the sample data of the key styling design parameters are increased, and the aerodynamic performance model is retrained and iterated until the compressor efficiency and surge margin of the preliminary design scheme meet the design requirements.

[0083] It is understood that this invention can accurately determine the key styling design parameters that are sensitive to aerodynamic performance by performing sensitivity analysis on the compressor styling design parameters, providing an optimization direction for subsequent factory-delivered aerodynamic performance optimization. Then, by constructing a proxy model of the impact of the key styling design parameters on aerodynamic performance, optimization is carried out to achieve the optimized design of the key styling design parameters and obtain a preliminary design scheme. Finally, the aerodynamic performance of the preliminary design scheme is verified to ensure that the aerodynamic performance of the compressor at the time of delivery meets the design specifications.

[0084] In addition, such as Figure 3As shown, in step S20, the process of constructing a high-precision real-time simulation method for blade wear includes the following:

[0085] Step S21: Generate compressor blade mesh, set the blade surface solid wall boundary as UDF moving boundary, set the corresponding contact mesh as deformable mesh, and give the compressor sand ingestion wear simulation conditions;

[0086] Step S22: Calculate the compressor flow field to obtain the blade wear rate, and divide the total sand ingestion wear time into multiple wear periods;

[0087] Step S23: At the end of the current wear period, calculate the blade surface wear amount in three dimensions based on the blade surface wear rate and the duration of the wear period;

[0088] Step S24: Determine the coordinates of the solid wall boundary position after wear based on the blade surface wear amount in three dimensions during the current wear period, update the wall mesh point coordinates through UDF, update the computational domain mesh, and then perform sand ingestion wear simulation for the next wear period.

[0089] Step S25: Iterate continuously until the sand ingestion wear simulation of all wear periods is completed. The total wear amount is obtained by summing the blade surface wear amount of all wear periods, and the aerodynamic performance degradation of the compressor during the first overturning period is determined based on the blade surface wear amount of different wear periods.

[0090] Specifically, the compressor blade mesh is first generated using conventional methods. Then, the solid wall boundary of the blade surface is set as the UDF moving boundary, and the corresponding contact mesh is set as a deformable mesh. Conditions for the compressor sand ingestion wear simulation are given, such as flow field boundary conditions, turbulence model, sand inlet velocity, particle size distribution, mass flow rate, and wear model. Next, the compressor flow field is calculated to obtain the wall wear rate (i.e., blade surface wear rate), and the total sand ingestion wear time is divided into multiple wear periods, such as... Figure 4 As shown, the total wear time can be divided into multiple wear periods of equal duration, i.e. Figure 4 The straight line segment in the curve, or the distribution curve that divides the total wear time into multiple time periods with approximately constant wear rates, i.e. Figure 4 The curve segments in the curve, and the magnitude of each time period in the curve, need to be obtained through experiments or simulation results under high-density equal time periods. The duration of each wear period can be obtained by corresponding to the curve. In addition, the specific calculation process of steady flow field and unsteady flow field is existing technology, and the specific principles will not be elaborated here.

[0091] Next, at the end of the current wear period, the blade surface wear in the x, y, and z three-dimensional directions is calculated based on the wall wear rate and the duration of the wear period. Specifically, the blade surface wear in each dimension during a certain period is calculated based on the following formula:

[0092] ;

[0093] Where, ΔH i E represents the blade surface wear during the i-th wear period. i ρ represents the wall wear rate during the i-th wear period, ρ represents the blade material density, and Δt represents the wear rate during the i-th wear period. i This represents the duration of the i-th wear period.

[0094] Then, based on the amount of blade surface wear in three dimensions during the current wear period, the coordinates of the blade solid wall boundary after wear damage can be determined. The coordinates of the wall grid points are updated by UDF, and the computational domain grid is updated by the elastic optical kinetic mesh algorithm. Then, the sand ingestion wear simulation is performed for the next wear period.

[0095] By continuously iterating through sand ingestion wear simulations until simulations for all wear stages are completed, the total wear amount (i.e., the geometric wear of the blades due to sand ingestion) can be obtained by summing the blade surface wear amounts for all wear stages. The aerodynamic performance degradation of the compressor during the initial overhaul period can then be determined based on the blade surface wear amounts for different wear stages. The principle behind determining the aerodynamic performance degradation of the compressor during the initial overhaul period based on blade surface wear amounts for different wear stages is existing technology and will not be elaborated upon here. The total wear amount is calculated using the following formula:

[0096] ;

[0097] Where N represents the total wear time and M represents the number of wear periods.

[0098] It is understood that this invention sets the blade surface solid wall boundary as the UDF moving boundary, the corresponding contact mesh as the deformable mesh, and divides the total wear time into multiple wear periods. After each wear period, the blade surface wear amount in each wear period can be calculated, and the computational domain mesh is updated according to the blade surface wear amount in the current wear period before the sand ingestion wear simulation of the next wear period is performed. This achieves real-time blade wear simulation. Compared with conventional flow field simulation, which sets the blade surface solid wall boundary as a fixed boundary and the blade surface wear rate as a fixed value, the real-time blade wear simulation of this invention considers the damage and deformation of the blade geometry under the action of sand ingestion wear, which in turn affects the flow field and causes the blade surface wear rate distribution to change. This realizes fluid-structure interaction analysis under wear action, thereby achieving high-precision simulation of blade sand ingestion wear and greatly improving the accuracy and precision of blade wear simulation analysis.

[0099] In addition, such as Figure 5 As shown, step S30 specifically includes the following:

[0100] Step S31: Set the uncertainty parameters for compressor blade wear, and perform sensitivity analysis on the uncertainty parameters to determine the key wear design parameters that are sensitive to blade wear;

[0101] Step S32: Based on the key wear design parameters, a high-precision real-time simulation method for blade wear is used to perform real-time blade wear simulation in order to construct sample data on the impact of key wear design parameters on the compressor aerodynamic performance;

[0102] Step S33: Train a data-driven model of the impact of key wear design parameters on aerodynamic performance based on the sample dataset. Use the expected value and variance of the compressor's aerodynamic performance during the first overhaul period as the objective function to find the optimal wear-resistant design scheme for the compressor.

[0103] Specifically, to optimize the compressor's wear robustness, uncertainty parameters for compressor blade wear are first defined. Then, the active subspace method is used for sensitivity analysis to determine the geometric parameters sensitive to blade wear (i.e., key wear design parameters). Next, based on these key wear design parameters, a high-precision real-time simulation method for blade wear is employed to simulate real-time blade wear. This simulation can accurately simulate the aerodynamic performance degradation during sand ingestion wear, assessing the impact of key design parameters on compressor aerodynamic performance loss during sand ingestion wear. Sample data on the relationship between key wear design parameters and compressor aerodynamic performance loss is constructed, creating a sample point dataset. Then, a data-driven model of the impact of blade wear-sensitive design parameters on aerodynamic performance is built based on a neural network. The model is trained using the previously constructed sample point dataset until it meets the accuracy requirements. The trained data-driven model accurately reflects the mapping relationship between key wear design parameters and aerodynamic performance loss.

[0104] Then, taking the expectation and variance of aerodynamic performance (efficiency and surge margin, etc.) under the influence of sand ingestion uncertainty during the first overhaul period of the compressor as the objective function, a multi-objective genetic algorithm is used to optimize the design parameters. The optimization scheme in the Pareto front is selected as the design result of sand ingestion wear robustness, thus obtaining the compressor wear resistance optimization design scheme.

[0105] In addition, in step S40, the process of verifying the aerodynamic performance of the optimized design scheme includes the following:

[0106] Statistical analysis is performed on the compressor design point efficiency and surge margin under the condition of first overhaul period to end of service life. If the statistical index results meet the design index requirements, the optimized design scheme is adopted as the final design scheme; otherwise, it returns to the factory optimization design stage for iteration.

[0107] Specifically, the Monte Carlo method is used to analyze the expected value and variance of the compressor's design point efficiency and surge margin after the compressor reaches its first overhaul period. If both the expected value and variance of efficiency and surge margin meet the design requirements, the optimized design scheme is adopted as the final design scheme. If it does not meet the design requirements, the process returns to step S10, where the suboptimal scheme in the Pareto front is used as the preliminary design scheme for iterative optimization until the design requirements are met. Optionally, step S40 can also use a high-precision CFD simulation method to verify the aerodynamic performance of the optimized design scheme. If the compressor efficiency and surge margin of the optimized design scheme meet the design requirements, it can also be adopted as the final design scheme.

[0108] In addition, to demonstrate the effectiveness of this optimization design method, this application also includes a case study. Specifically, a sand-swallowing simulation was conducted using a combined three-stage axial-flow compressor and a single-stage centrifugal compressor as the model. The structure of this combined compressor is as follows: Figure 6 As shown, the first seven rows of blades form the axial flow stage, consisting of the 0th stage guide vane (S0), the first-stage axial flow rotor and stator (R1, S1), the second-stage rotor and stator (R2, S2), and the third-stage rotor and stator (R3, S3). The last three rows of blades form the centrifugal stage, consisting of the centrifugal impeller (IM), the radial diffuser (RD), and the axial diffuser (AD). When sand particles enter the compressor, the solid particles collide with the compressor's metal walls, leading to wear and tear on the blades and flow channels. Figure 7 Based on the wear distribution cloud map of the combined compressor obtained from simulation analysis, it can be found that the axial stage rotor blade tip section airfoil, centrifugal impeller airfoil, and centrifugal hub are the areas with more severe wear. Therefore, this application conducts a sensitivity analysis of the styling design parameters on geometric wear for these two typical areas to clarify the key styling design parameters that are sensitive to wear.

[0109] In this study, the Design of the First-Stage Axial-Flow Rotor Blade Tip Section Design Parameters was discretized using the DOE method to obtain 50 different blade tip geometries. Sand ingestion simulations were then conducted on single rotors with these 50 different blade tip airfoil schemes to obtain the average wear rate of the airfoil. Furthermore, the sensitivity analysis of the combined compressor airfoil design parameters to the average wear rate of sand ingestion was conducted using the active subspace method, revealing the sensitivity of the airfoil geometry parameters to the average wear rate. Figure 8 As shown, Figure 8 The vertical axis represents various geometric parameters, and the horizontal axis represents the average wear. This shows the inlet blade angle, leading edge radius, and chord length. Figure 8 The first, third, and seventh items (from bottom to top) are the three parameters that have the most significant impact on the average wear rate. Similarly, the sensitivity of centrifugal impeller blade profile and flow channel parameters to the average wear rate of sand ingestion is analyzed. Based on the active subspace method, the sensitivity of the influence of centrifugal impeller blade profile and flow channel geometry parameters on the average wear rate is obtained as follows: Figure 9As shown, Figure 9 The vertical axis represents various geometric parameters, and the horizontal axis represents the average wear rate. For a centrifugal impeller, this includes the outlet blade angle, and the coordinates of the second and third flow channel control points. Figure 9 The 2nd, 23rd, and 24th items from bottom to top are the three parameters that have the most significant impact on average wear.

[0110] Assuming the amount of sand ingested per unit time by a turboshaft engine within its flight envelope follows a normal distribution, such as Figure 10 As shown, the expected sand throughput per unit time is 1.62 g / h, with a variance of 0.3. This is determined through discretization. Figure 10 The 16 different sand intake scenarios shown (i.e. Figure 10 The hollow circle on the horizontal axis is used to calculate the values ​​of the inlet blade angle, leading edge radius, and chord length in the key wear design parameters of the axial rotor blade tip profile based on the first overturning period of 1000 hours. The uncertainty range of the three parameters is as follows: Figures 11 to 13 As shown, the horizontal axis represents the change in parameters relative to before wear, and the vertical axis represents the probability density corresponding to that amount of wear. From Figures 11 to 13 As can be seen, the statistical laws governing the wear variations of the inlet blade angle, leading edge radius, and chord length are quite complex and do not follow a common probability distribution; they can be approximated as a non-central normal distribution. Similar to the axial flow stage rotor, the uncertainty range of the outlet blade angle and the coordinates of the second and third flow channel control points at the centrifugal impeller root section is obtained as follows: Figures 14 to 16 As shown.

[0111] This application established a database of 300 sample points on the impact of key 3D modeling parameters of a combined compressor on its aerodynamic performance based on design experience. A surrogate model of the impact of these key 3D modeling parameters on aerodynamic performance was trained using an artificial neural network method. This model's prediction accuracy for the combined compressor's efficiency and surge margin is as follows: Figure 17 and Figure 18 As shown. Furthermore, this invention employs a multi-objective genetic algorithm to optimize the factory performance of the combined compressor, and the sample point solution set distribution is as follows. Figure 19 As shown, a typical scheme on the Pareto front was selected to carry out robust optimization design of aerodynamic performance. After verification by three-dimensional numerical simulation, the aerodynamic performance of the factory-optimized preliminary design scheme was found to be 0.48% higher and 1.46% higher than that of the prototype scheme, without reducing the flow rate and pressure ratio.

[0112] Through wear-resistance robustness optimization of the initial design at the factory, sensitivity analysis revealed that the key wear design parameters are the inlet blade angle, leading edge radius, and chord length of the axial stage rotor blade tip section, and the outlet blade angle, coordinates of the second and third flow channel control points of the centrifugal impeller root section. Considering that the combined compressor includes three axial stages and one centrifugal stage, there are 14 corresponding key wear design parameters. An artificial neural network was used to establish a surrogate model between the key wear design parameters and the aerodynamic performance loss of the combined compressor, and based on... Figures 11 to 16 The probability density function within the given range of key wear design parameters is used to analyze the expected value (mean) and variance of the combined compressor's aerodynamic performance during the first overhaul period using the Monte Carlo method. Multiple samples are randomly selected from the surrogate model dataset of key wear design parameters and combined compressor aerodynamic performance losses. The expected value and variance are calculated for each sample. The average of the sample statistics is used to estimate the overall expected value and variance. Finally, the distribution of the sample statistics is presented graphically. The histograms showing the distribution of the mean and variance of the compressor efficiency under the influence of blade wear in the prototype combined compressor scheme are shown below. Figure 20 and Figure 21 As shown, the overall expected value of the compressor efficiency is 0.7638, and the overall variance is 0.0005. The distribution histograms of the mean and variance of the compressor surge margin of the prototype combined compressor under the influence of blade wear are shown below. Figure 22 and Figure 23 As shown, the overall expected value of the surge margin is 0.1148, and the overall variance is 0.0004. The histograms showing the distribution of the mean and variance of the compressor efficiency under the influence of blade wear in the optimized design scheme, analyzed using the Monte Carlo method, are shown below. Figure 24 and Figure 25 As shown, the overall expected value of the compressor efficiency is 0.7745, and the overall variance is 0.0003. The distribution histograms of the mean and variance of the compressor surge margin under the influence of blade wear in the optimized design scheme are shown below. Figure 26 and Figure 27 As shown, the overall expected value of the surge margin is 0.1285, and the overall variance is 0.0001. Therefore, it is evident that the optimized design scheme of this invention significantly improves upon the prototype variance in terms of both compressor efficiency and the expected and variance values ​​of the surge margin.

[0113] In summary, the optimized design method of this invention improves compressor efficiency by 0.48% and surge margin by 1.46% under the condition that the compressor's design flow rate and pressure ratio at the factory performance point do not decrease. Under the design flow rate and pressure ratio, and considering the sand intake during the first overturning period of 1000 hours, the optimized design scheme improves the expected efficiency by 1.07 percentage points and reduces the variance by 40% compared to the prototype scheme. It also improves the expected surge margin by 1.37 percentage points and reduces the variance by 75%, demonstrating significant performance optimization effects.

[0114] In addition, such as Figure 28 As shown, another embodiment of the present invention also provides a compressor sand ingestion wear robustness optimization design system, preferably employing the compressor sand ingestion wear robustness optimization design method as described above. The system includes:

[0115] The factory performance optimization module is used to optimize the aerodynamic performance of the compressor at the time of delivery and obtain a preliminary design scheme.

[0116] The blade wear real-time simulation module is used to build a high-precision real-time simulation method for blade wear, so as to update the coupling relationship between blade geometry and wear rate in real time, thereby accurately calculating the amount of blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in the field environment.

[0117] The wear resistance optimization module is used to perform uncertainty analysis on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor. Based on the high-precision real-time simulation method of blade wear, the design parameters of wear-sensitive blades in the preliminary design scheme are further optimized to obtain the wear resistance optimization design scheme of the compressor.

[0118] The aerodynamic performance verification module is used to verify the aerodynamic performance of the compressor wear-resistant optimization design scheme. If its aerodynamic performance meets the design specifications, the optimized design scheme will be adopted as the final design scheme; otherwise, iterative optimization will continue.

[0119] It is understood that the compressor sand ingestion wear robustness optimization design system of this embodiment first performs a preliminary aerodynamic design based on the traditional compressor factory aerodynamic performance optimization design method to ensure that the compressor factory performance does not decrease compared with the traditional design. Then, a high-precision real-time simulation method for compressor blade wear is proposed. By updating the coupling relationship between blade geometry and wear rate in real time, it accurately simulates the blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in the field environment. Next, based on this real-time simulation method, the sand ingestion uncertainty analysis of the preliminary compressor design scheme is further performed to optimize the compressor wear-sensitive design parameters, improve the compressor sand ingestion wear robustness, and obtain the compressor wear-resistant optimized design scheme. Finally, the aerodynamic performance of the optimized design scheme is verified. This system can improve the wear resistance of the compressor without reducing the aerodynamic performance of the traditional design compressor. The entire optimization process does not require manual design experience and can effectively improve the expected value, variance, and other statistical performance of the compressor's efficiency, margin, and other aerodynamic performance during the first overhaul period, which helps to alleviate the performance degradation of turboshaft engines.

[0120] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0121] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for optimizing the robustness of compressor sand ingestion wear, the computer program executing the steps of the method described above when run on a computer.

[0122] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0123] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0124] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0128] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A robust optimization design method for sand ingestion wear in an air compressor, characterized in that, Includes the following: The aerodynamic performance of the compressor at the time of manufacture was optimized to obtain a preliminary design scheme. A high-precision real-time simulation method for blade wear is constructed to update the coupling relationship between blade geometry and wear rate in real time, thereby accurately calculating the amount of blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in an external environment. Uncertainty analysis was conducted on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor. Based on the high-precision real-time simulation method of blade wear, the design parameters of wear-sensitive blades in the preliminary design scheme were further optimized to obtain the wear-resistant optimized design scheme of the compressor. Verify the aerodynamic performance of the compressor wear-resistant optimization design scheme. If its aerodynamic performance meets the design specifications, the optimized design scheme shall be adopted as the final design scheme; otherwise, iterative optimization shall continue. The process of constructing a high-precision real-time simulation method for blade wear includes the following: Generate compressor blade mesh, set the blade surface solid wall boundary as UDF moving boundary, set the corresponding contact mesh as deformable mesh, and give the compressor sand ingestion wear simulation conditions; The compressor flow field was calculated to obtain the blade wear rate, and the total sand ingestion wear time was divided into multiple wear periods. At the end of the current wear period, the blade surface wear amount in three dimensions is calculated based on the blade surface wear rate and the duration of the wear period. Based on the amount of blade wear in three dimensions during the current wear period, determine the coordinates of the solid wall boundary after wear. Update the wall grid point coordinates through UDF and update the computational domain grid before performing sand ingestion wear simulation for the next wear period. The simulation of sand ingestion wear was continuously iterated until all wear periods were completed. The total wear amount was obtained by summing the blade surface wear amount of all wear periods, and the aerodynamic performance degradation of the compressor during the first overhaul period was determined based on the blade surface wear amount of different wear periods.

2. The compressor sand ingestion wear robustness optimization design method as described in claim 1, characterized in that, The blade surface wear over a certain period of time is calculated based on the following formula: Where, ΔH i E represents the blade surface wear during the i-th wear period. i Let ρ represent the wall wear rate during the i-th wear period, ρ represent the blade material density, and Δt represent the wear rate during the i-th wear period. i This represents the duration of the i-th wear period.

3. The compressor sand ingestion wear robustness optimization design method as described in claim 1, characterized in that, The total sand ingestion wear time is divided into multiple wear periods of equal duration; or, the total sand ingestion wear time is divided into a segmented time distribution curve with an approximately constant wear rate, and the duration of each wear period is obtained by corresponding to the curve.

4. The compressor sand ingestion wear robustness optimization design method as described in claim 1, characterized in that, The process of performing uncertainty analysis on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor, and further optimizing the design parameters of wear-sensitive blades in the preliminary design scheme based on a high-precision real-time simulation method for blade wear, to obtain the compressor wear-resistant optimized design scheme includes the following: Uncertainty parameters for compressor blade wear are set, and sensitivity analysis is performed on the uncertainty parameters to determine the key wear design parameters that are sensitive to blade wear. Based on key wear design parameters, a high-precision real-time simulation method for blade wear is used to simulate real-time blade wear, so as to construct sample data on the impact of key wear design parameters on compressor aerodynamic performance. A data-driven model of the impact of key wear design parameters on aerodynamic performance was trained based on a sample dataset. The expected value and variance of the compressor's aerodynamic performance during the first overhaul period were used as the objective function to find the optimal wear-resistant design scheme for the compressor.

5. The compressor sand ingestion wear robustness optimization design method as described in claim 1, characterized in that, The process of optimizing the aerodynamic performance of the compressor at the time of manufacture includes the following: Sensitivity analysis was conducted based on the compressor's design parameters to determine the key design parameters that are sensitive to aerodynamic performance. An aerodynamic performance model was constructed to simulate the impact of key styling design parameters on aerodynamic performance. After training the aerodynamic performance model, a multi-objective genetic algorithm was used to optimize the key styling design parameters to obtain the preliminary design scheme of the compressor at the time of delivery. The aerodynamic performance of the preliminary design scheme is verified. If the aerodynamic performance does not meet the design requirements, the sample size of the aerodynamic performance model is increased and the iteration is repeated until the aerodynamic performance of the preliminary design scheme meets the design requirements.

6. The compressor sand ingestion wear robustness optimization design method as described in claim 1, characterized in that, The process of verifying the aerodynamic performance of the optimized design scheme includes the following: Statistical analysis is performed on the compressor design point efficiency and surge margin under the condition of first overhaul period. If the statistical index results meet the design index requirements, the optimized design scheme is adopted as the final design scheme; otherwise, it returns to the factory optimization design stage for iteration.

7. A compressor sand ingestion wear robustness optimization design system, employing the compressor sand ingestion wear robustness optimization design method as described in any one of claims 1 to 6, characterized in that, include: The factory performance optimization module is used to optimize the aerodynamic performance of the compressor at the time of delivery and obtain a preliminary design scheme. The blade wear real-time simulation module is used to build a high-precision real-time simulation method for blade wear, so as to update the coupling relationship between blade geometry and wear rate in real time, thereby accurately calculating the amount of blade geometric wear and aerodynamic performance degradation caused by sand ingestion when the compressor is working in the field environment. The wear resistance optimization module is used to perform uncertainty analysis on the changes in blade aerodynamic performance caused by sand ingestion during the first overhaul period of the compressor. Based on the high-precision real-time simulation method of blade wear, the design parameters of wear-sensitive blades in the preliminary design scheme are further optimized to obtain the wear resistance optimization design scheme of the compressor. The aerodynamic performance verification module is used to verify the aerodynamic performance of the compressor wear-resistant optimization design scheme. If its aerodynamic performance meets the design specifications, the optimized design scheme will be adopted as the final design scheme; otherwise, iterative optimization will continue.

8. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium for storing a computer program for optimizing the robustness of compressor sand ingestion wear, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 6.

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