Axial flow compressor blade anti-erosion optimization method facing sand and dust environment

Abstract

The invention discloses an axial flow compressor blade anti-erosion optimization method facing a sand and dust environment, and belongs to the technical field of compressor blade anti-erosion design and optimization. According to the method, on the basis of a gas-solid two-phase flow erosion mechanism in a sand and dust environment, links such as working condition analysis, parameterization platform building, constrained multi-objective optimization method design, model building and optimization and index verification are integrated, and an integrated anti-erosion optimization process is formed. CFturbo, AutoGrid, CFX and Matlab are called in a unified mode through a Python script, automatic coupling of geometric modeling, grid division, gas-solid coupling simulation and an intelligent optimization algorithm of the blade is achieved, and collaborative optimization of aerodynamic performance and erosion resistance can be achieved under the constraint of erosion wear. Compared with a traditional method which depends on experience modification or single simulation, the method has the advantages that the optimization efficiency is remarkably improved while the simulation precision is guaranteed, the personal error risk is reduced, and the method can flexibly adapt to different compressor models and diversified operation conditions.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optimization design and analysis of axial flow compressor blade erosion wear, and particularly relates to an anti-erosion optimization method for axial flow compressor blades in a sand and dust environment. BACKGROUND

[0002] The axial flow compressor is a key component of an aero-engine, and its main function is to compress the incoming air in stages to provide a stable high-pressure air source for the combustion chamber. In actual application, when the engine is in a complex environment such as desert, plateau or arid area, a large number of sand and dust particles will enter the compressor along with the airflow and continuously impact the blade surface. This continuous particle erosion not only causes damage to the blade material, but also can cause the shape of the blade to change, thereby reducing the aerodynamic efficiency and reducing the surge margin, ultimately affecting the service life and reliability of the engine. Therefore, sand and dust erosion has become an important problem restricting the safe service and long-term operation of the axial flow compressor in harsh environments.

[0003] At present, the research paths for the sand and dust erosion problem mainly include experimental methods and traditional numerical simulation. Experiments can directly reflect the performance degradation characteristics of the blade under the action of sand and dust, but they have long test periods, high costs, and are difficult to fully cover different particle size distributions and operating conditions; numerical simulation can reveal the gas-solid two-phase flow and erosion mechanism to some extent, but it generally relies on simplified assumptions and is difficult to accurately reproduce complex unsteady flow fields and nonlinear wear laws. At the same time, the existing optimization process often needs manual repeated modification of parameters and boundary conditions, which is not only inefficient, but also prone to error accumulation, resulting in insufficient reliability of the prediction results.

[0004] With the continuous progress of simulation technology and intelligent optimization algorithms, the overall optimization method based on parameterized modeling and automatic coupling has gradually attracted attention. By integrating geometric modeling, mesh generation, gas-solid coupling calculation and multi-objective optimization in a unified framework, it is expected to achieve rapid evaluation and balance of the aerodynamic performance and anti-erosion ability of the blade in the design stage. However, there is currently a lack of a method that can systematically integrate the above aspects and take into account the engineering adaptability and scalability, making it difficult to meet the actual needs of anti-erosion optimization of compressor blades in a sand and dust environment.

[0005] Therefore, how to construct an overall solution method covering operating condition analysis, modeling simulation, wear prediction and multi-objective optimization to realize systematic evaluation and efficient optimization of the anti-erosion performance of axial flow compressor blades in a sand and dust environment has become a core problem that needs to be solved in this field. SUMMARY

[0006] In view of the above technical problems, the present application provides a sand-dust environment-oriented axial flow compressor blade anti-erosion optimization method. The method integrates working condition analysis, parameterized platform construction, constrained multi-objective optimization method design, model construction, and optimization and index verification to construct an integrated design and analysis process. With the help of the method, the erosion and wear law and aerodynamic performance change of the axial flow compressor blade can be efficiently predicted and balanced optimized, thereby providing strong support for the structural design, performance improvement, reliability evaluation and whole life management of the compressor, and having significant engineering application value and popularization prospect.

[0007] The present application solves the above problems by the following technical means: A sand-dust environment-oriented axial flow compressor blade anti-erosion optimization method, characterized by comprising the following construction steps: Step 1) Working condition analysis: The axial flow compressor is designed in CFturbo, and the sand-dust working condition is analyzed, the sand-dust concentration is set to 53mg / m 3 , and the sand-dust particle size range is set to 0~80 microns; Step 2) Parameterized platform construction and constrained multi-objective optimization method design: The constrained multi-objective optimization method is designed, applied to the erosion and wear optimization design of the axial flow compressor, specifically including designing the Kriging-based constrained multi-objective optimization method and building the parameterized platform. At the same time, the parameterized simulation platform is established based on Python, Cfturbo, AutoGrid, CFX and Matlab software are called, parameterized modeling, parameterized grid division, parameterized simulation and parameterized calculation are completed, and the optimization design of the axial flow compressor is realized; Step 3) Experimental design: The candidate variables include the rotor blade inlet installation angle , the rotor blade outlet installation angle , the rotor blade leading edge angle , the rotor blade trailing edge angle , the rotor meridian flow passage profile parameter, and the rotor angle-preserving mapping distribution. The initial sample points are extracted using Latin hypercube, and the weight analysis is performed using Pearson correlation analysis technology to determine the optimization variables and value range; Step 4) Model construction: The model building includes an optimization target model and an erosion wear constraint model. The optimization target model is composed of a flow model and an efficiency model, and is used to represent the aerodynamic performance of the compressor under different optimization variables. The erosion wear constraint model is composed of a wear rate concentration model, and is used to reflect the erosion wear of the blade surface in the sand and dust environment. The above two types of models form a mutual coupling relationship of target optimization and constraint conditions, and through collaborative modeling and iterative updating, the comprehensive balance of aerodynamic performance and erosion resistance is realized, thereby providing a basis for subsequent optimization solution. Step 5) optimization and index verification: The designed constraint multi-objective optimization method is used to complete the optimization design of the axial flow compressor under the erosion wear constraint, to generate a Pareto solution set, to update the model in Cfturbo, to implement grid division in AutoGrid, to complete simulation in CFX, to verify whether the optimization index meets the requirements, and to analyze the erosion morphology difference of the blade at 10%, 50% and 90% of the blade height before and after optimization.

[0008] Preferably, the engineering analysis step is a basic link of the blade erosion resistance optimization design, and the specific steps include: Step 1) design modeling and baseline performance establishment: The axial flow compressor is taken as the research object, design modeling is completed in CFturbo, structured grid division is performed through AutoGrid, and the aerodynamic characteristic calculation is performed on the model by using the CFX solver to verify the design point efficiency and pressure ratio, thereby establishing an original performance database to provide a baseline for the subsequent performance degradation quantification after the blade erosion wear; Step 2) sand and dust environment parameter calibration: According to the fine sand concentration (53mg / m 3 ) and particle distribution requirements provided by the first party, boundary conditions are provided for the gas-solid two-phase coupling simulation; Step 3) erosion wear baseline establishment: The gas-solid two-phase flow simulation is carried out in the sand and dust environment, the high wear area of the blade is identified, the performance degradation is quantified, and the design point index is extracted as a comparison baseline for subsequent optimization.

[0009] Preferably, in the parameterization platform building and constraint multi-objective optimization method design step, the specific steps of the Kriging-based constraint multi-objective optimization method design include: Step 1) generate initial sample points in the feasible space through DOE, and calculate the target function value and constraint violation degree; Step 2) establish independent Kriging models for the target function and the constraint condition, wherein the target model outputs the predicted value and the uncertainty, and the constraint model is used to predict the feasibility probability; Step 3) Design a multi-objective acquisition function with constraints to maintain a balance between exploration and development while considering the reliability of constraint satisfaction; Step 4) Use a global optimization algorithm to select new candidate points, calculate the true objective and constraint values, and update the sample library and Kriging model. Repeat until the preset condition is met, which is the convergence criterion. Select the Pareto optimal solution set that meets the constraint condition from the historical samples to provide a solution space for subsequent optimization.

[0010] Preferably, in the parameterization platform building and constraint-based multi-objective optimization method design step, the specific steps of the parameterization platform building include: Step 1) Export the design parameters in CFturbo, generate.cft and.cft-batch files, and output the.geomTurbo file required by AutoGrid; Step 2) Import the.geomTurbo file in AutoGrid, complete mesh generation and generate the.mesh file; Step 3) Record the simulation process in CFX, set boundary conditions, calculation formulas, and output formats, generate.pre and.cse files, and record the results; Step 4) Write an optimization algorithm based on Kriging in Matlab, read the simulation result data, update the surrogate model and collect new sample points, and output the Pareto solution set; Step 5) Batch process the above steps through Python scripts to realize automatic execution of the entire parameterization process.

[0011] Preferably, the blade erosion resistance optimization process further includes an experimental design step, which includes the following specific steps: Step 1) Select blade geometric parameters that affect the optimization objective based on considering the wear rate concentration constraint; Step 2) Set the upper and lower bounds of the candidate variables and use Latin hypercube sampling to generate initial sample points; Step 3) Perform gas-solid coupling simulation on the initial sample points under sand and dust conditions to obtain efficiency, flow rate, and wear rate concentration, and establish a multidisciplinary database; Step 4) Use Pearson correlation analysis method to sort the candidate variables and determine the key optimization variables and their ranges to provide data support for subsequent optimization.

[0012] Preferably, the specific steps of the model building include: Step 1) Based on the optimization variables selected in the experimental design stage, determine the range and use Latin hypercube sampling to generate initial sample points; obtain the corresponding objective and constraint response values through parameterized simulation; Step 2) Establish the Kriging model for the aerodynamic performance objective function and the Kriging model for the erosion constraint function, respectively; Step 3) Generate new sampling points using a constrained multi-objective optimization algorithm and update the Kriging model to improve prediction accuracy; Step 4) When the model accuracy reaches the convergence condition, stop the iteration and output the Pareto solution set to provide a solution space for subsequent optimization.

[0013] Preferably, the specific steps for optimization and indicator verification include: Step 1) Based on the established Kriging model, solve the Pareto solution set using a constrained multi-objective optimization algorithm; Step 2) Construct the following calculation formula to select the final optimization result from the Pareto front:

[0014] In the formula, and This indicates the efficiency and flow rate of the prototype compressor. and To find the efficiency and flow of the midpoint of the Pareto front, calculate the midpoint of the Pareto front. The point with the largest value is the final optimization result; Step 3) Compare the erosion wear distribution of the blades before and after optimization, analyze the erosion pattern with CFD flow field data, and evaluate the wear improvement effect of key parts.

[0015] Preferably, in the model building step, when constructing the Kriging model, the initial value of the parameter θ of the correlation function is set to 10, with a lower limit of 1 and an upper limit of 10. 6 .

[0016] The erosion resistance optimization method for axial compressor blades in sandy and dusty environments proposed in this invention has the following beneficial effects: 1) By adopting parametric modeling and automated integration process, this invention overcomes the shortcomings of traditional methods where modeling, mesh generation and optimization are independent and inefficient, and achieves a high degree of integration and automation of the design process, which significantly improves the overall design efficiency and reduces errors caused by human operation.

[0017] 2) By introducing a multi-objective optimization method with erosion damage constraints during the optimization process, this invention breaks through the limitation of existing designs that overemphasize aerodynamic performance while neglecting blade wear, and achieves a synergistic improvement in aerodynamic performance and erosion resistance, thus better meeting the engineering application needs in complex environments.

[0018] 3) Due to the introduction of the data-driven correlation analysis and variable screening technology, the application effectively solves the problems of large calculation amount and low optimization efficiency in the high-dimensional design space, can significantly reduce the calculation cost under the premise of ensuring the calculation accuracy, and improve the convergence speed and efficiency of optimization.

[0019] 4) By realizing the integrated integration of simulation and optimization process, the application overcomes the shortcomings of disconnection between simulation and optimization and lack of closed-loop verification in the prior art, makes the optimization result more accurate and engineering applicability, and ensures that the design method can be effectively verified and applied under actual working conditions.

[0020] 5) The implementation of the method can not only realize the collaborative optimization of the aerodynamic performance and erosion resistance of the axial flow compressor blade in the sand dust environment, but also provide systematic theoretical basis and technical support for blade structure design, operation and maintenance and life improvement, thereby improving the adaptability, safety and reliability of the aero-engine in complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is the anti-erosion optimization method of the axial flow compressor blade in the application; Figure 2 is the basic flowchart of working condition analysis in the application; Figure 3 is the design flowchart of the multi-objective optimization method with constraints in the application; Figure 4 is the basic flowchart of parameterization platform construction in the application; Figure 5 is the basic flowchart of experimental design in the application; Figure 6 is the basic flowchart of building an agent model in the application; Figure 7 is the specific flowchart of optimization and index verification in the application Figure 8 is the three-dimensional model of the axial flow compressor in the application; Figure 9 is the Pareto front of the optimization solution of the axial flow compressor in the application; Figure 10 is the three-dimensional waterfall diagram of the wear rate concentration at different blade heights of the pressure surface of the moving blade before and after optimization in the application; Figure 11This refers to the average volume fraction of particles in the flow field region at different blade heights before and after optimization in this invention. Detailed Implementation

[0023] In the description of this invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0024] The present invention will now be described in detail with reference to the accompanying drawings.

[0025] Example 1 like Figure 1 As shown in the figure, this embodiment describes the overall process of an optimization method for erosion resistance of axial compressor blades in sandy and dusty environments. This method is based on the gas-solid two-phase flow erosion mechanism in sandy and dusty environments, integrating operational condition analysis, parameterized platform construction and constrained multi-objective optimization method design, model building, optimization, and index verification, forming an integrated erosion resistance optimization process.

[0026] Specifically, the construction steps include the following: Step 1) Operating Condition Analysis: An axial compressor was designed in CFturbo, and sand and dust conditions were analyzed, with the sand and dust concentration set at 53 mg / m³. 3 The specified particle size range for sand and dust is 0~80 micrometers; Step 2) Parameterization platform construction and design of constrained multi-objective optimization method: A constrained multi-objective optimization method was designed and applied to the erosion and wear optimization design of an axial compressor. Simultaneously, a parametric simulation platform was built based on Python, utilizing Cfturbo, AutoGrid, CFX, and Matlab software to complete parametric modeling, parametric mesh generation, parametric simulation, and parametric calculation, thereby achieving the optimized design of the axial compressor. Step 3) Experimental Design: Candidate variables were identified, including the rotor blade inlet installation angle. Rotor blade outlet installation angle , rotor blade leading edge angle , rotor blade trailing edge angle , rotor meridional passage profile parameters, rotor conformal mapping distribution, using Latin hypercube to extract initial sample points, using Pearson correlation analysis technique to analyze weights, determining optimization variables and value range; Step 4) model building: The model building includes an optimization target model and an erosion wear constraint model. The optimization target model is composed of a flow model and an efficiency model, which is used to represent the aerodynamic performance of the compressor under different optimization variables. The erosion wear constraint model is composed of a wear rate concentration model, which is used to reflect the erosion wear of the blade surface in the sand and dust environment. The above two types of models form a mutual coupling relationship of target optimization and constraint conditions. Through collaborative modeling and iterative updating, the comprehensive balance of aerodynamic performance and erosion resistance is realized, providing a basis for subsequent optimization solution; Step 5) optimization and index verification: The designed multi-objective optimization method with constraints is used to complete the optimization design of the axial compressor under the constraint of erosion wear, and a Pareto solution set is generated. The model is updated in Cfturbo, the grid is divided in AutoGrid, and the simulation is completed in CFX. Whether the optimization index meets the requirements is verified, and the erosion morphology difference of the blade at 10%, 50%, and 90% of the blade height before and after optimization is analyzed.

[0027] Example two The basic link of the axial compressor blade erosion resistance optimization method is working condition analysis. This embodiment describes the working condition analysis process. The basic process of the working condition analysis is as shown in Figure 2 , and the specific steps are as follows: Step 1) design modeling and baseline performance establishment: Taking the axial compressor as the research object, the design modeling is completed in CFturbo, the structured grid is divided through AutoGrid, and the aerodynamic characteristic calculation is performed on the model by using the CFX solver to verify the design point efficiency and pressure ratio, thereby establishing the original performance database to provide a baseline for the subsequent performance degradation quantification after blade erosion wear; Step 2) sand and dust environment parameter calibration: According to the fine sand concentration (53mg / m 3 ) and particle distribution requirements provided by the first party, the boundary conditions are provided for the gas-solid two-phase coupling simulation; Step 3) erosion wear baseline establishment: In the sand and dust environment, the gas-solid two-phase flow simulation is carried out, the high wear area of the blade is identified, the performance degradation is quantified, and the design point index is extracted as a comparison baseline for subsequent optimization.

[0028] Example three The method for optimizing the erosion resistance of the axial flow compressor blade is a Kriging-based multi-objective optimization method with constraints. This embodiment describes the design of the Kriging-based multi-objective optimization method with constraints. The specific process of the Kriging-based multi-objective optimization method with constraints is as shown in Figure 3 The specific steps are as follows: Step 1: Generate initial sample points in the feasible space through DOE, calculate the target function value and the constraint violation degree; Step 2: Establish independent Kriging models for the target function and the constraint condition respectively, wherein the target model outputs the predicted value and the uncertainty, and the constraint model is used to predict the feasibility probability; Step 3: Design a multi-objective acquisition function with constraints to maintain a balance between exploration and development while considering the reliability of constraint satisfaction; Step 4: Select a new candidate point by using a global optimization algorithm, update the sample library and the Kriging model after calculating the true target and constraint value, and execute in a loop until a preset condition is reached, wherein the preset condition is a convergence criterion, and the Pareto optimal solution set that satisfies the constraint condition is selected from the historical samples to provide a solution space for subsequent optimization.

[0029] Embodiment Four In order to improve the efficiency and reliability of the optimization design of the axial flow compressor blade in a sand and dust environment, this embodiment introduces a parameterization platform. The platform uses Python scripts to uniformly schedule CFturbo, AutoGrid, CFX and Matlab, realizes the automatic execution of geometric modeling, mesh division, boundary condition setting, simulation calculation and post-processing, and avoids the defects of low efficiency and easy error caused by manual step-by-step operation. The process of the parameterization platform is as shown in Figure 4 The specific steps are as follows: Step 1: Export the design parameters in CFturbo, generate.cft and.cft-batch files, and output.geomTurbo files required by AutoGrid; Step 2: Import the.geomTurbo file in AutoGrid, complete mesh division and generate the.mesh file; Step 3: Record the simulation process in CFX, set the boundary conditions, calculation formula and output format, generate.pre and.cse files, and record the results; Step 4: Write an optimization algorithm based on Kriging in Matlab, read the simulation result data, update the surrogate model and collect new sample points, and output the Pareto solution set; Step 5: Batch process the above steps through Python scripts to realize the automatic execution of the whole process of parameterization.

[0030] Embodiment five The key link of the method for optimizing the erosion resistance of an axial flow compressor blade is experimental design. This embodiment describes the experimental design. The specific process of the experimental design is as shown in Figure 5 The specific steps are as follows: Step 1) On the basis of considering the concentration constraint of the wear rate, the blade geometric parameters affecting the optimization target are screened; Step 2) The upper and lower bounds of the candidate variables are set, and the Latin hypercube sampling method is used to generate initial sample points; Step 3) The initial sample points are simulated under the sand dust working condition to obtain the efficiency, flow rate and wear rate concentration, and a multidisciplinary database is established; Step 4) The Pearson correlation analysis method is used to sort the candidate variables to determine the key optimization variables and their ranges, thereby providing data support for subsequent optimization.

[0031] Embodiment six In order to effectively depict the aerodynamic performance and erosion wear response relationship of the axial flow compressor blade in the sand dust environment, this embodiment introduces a data-driven proxy model method to replace the traditional explicit physical modeling. This method realizes efficient characterization of the complex fluid-solid coupling process by constructing an approximate mapping relationship between the input variables and the output target. Among various proxy models, the Kriging method is selected as the main tool for building the compressor multi-objective performance model because it has nonlinear fitting capability, uncertainty quantification characteristics and active learning potential. For the sand dust erosion constraint problem, this embodiment simultaneously establishes a Kriging constraint model of erosion damage, and gradually improves the prediction accuracy through an iterative updating strategy to form an adaptive framework of collaborative updating of the target model and the constraint model, thereby providing reliable support for subsequent multi-objective optimization. The model building process is as shown in Figure 6 The specific steps are as follows: Step 1) Based on the optimization variables screened in the experimental design stage, the ranges are determined and the initial sample points are generated by using the Latin hypercube sampling; the corresponding target and constraint response values are obtained through parameterized simulation; Step 2) The Kriging model of the aerodynamic performance target function and the Kriging model of the erosion constraint function are respectively established; Step 3) A multi-objective optimization algorithm with constraints is used to generate new sample points, and the Kriging model is updated to improve the prediction accuracy; Step 4) When the model accuracy reaches the convergence condition, the iteration is stopped and the Pareto solution set is output, thereby providing a solution space for subsequent optimization.

[0032] Embodiment seven To achieve synergistic optimization of aerodynamic performance and erosion resistance of axial compressor blades in a dusty environment, this embodiment embeds the optimization target model and erosion damage constraint model into a constrained multi-objective optimization algorithm, defining efficiency and flow rate as optimization objectives and erosion damage as constraints. Using this optimization method, the Pareto front solution set of the blade design is obtained, and the results are compared and verified with aerodynamic performance simulations. Furthermore, an optimized blade geometry model is generated using a parametric platform, and CFD simulations are used to analyze the erosion differences of key parts such as the blade leading edge and tip before and after optimization, extracting wear data in the blade height direction. Combined with flow field distribution characteristics, the improvement effect on dust accumulation areas is quantitatively analyzed. The overall process is as follows: Figure 7 As shown, the specific steps are as follows: Step 1) Based on the established Kriging model, solve the Pareto solution set using a constrained multi-objective optimization algorithm; Step 2) Construct the following calculation formula to select the final optimization result from the Pareto front:

[0033] In the formula, and This indicates the efficiency and flow rate of the prototype compressor. and To find the efficiency and flow of the midpoint of the Pareto front, calculate the midpoint of the Pareto front. The point with the largest value is the final optimization result; Step 3) Compare the erosion wear distribution of the blades before and after optimization, analyze the erosion pattern with CFD flow field data, and evaluate the wear improvement effect of key parts.

[0034] Example 8 Taking a certain type of axial compressor as an example, this paper further illustrates the application of the erosion resistance optimization method for axial compressor blades in sandy and dusty environments. Figure 8 As shown, the three-dimensional model of this axial compressor consists of inlet guide vanes, first-stage stationary vanes, first-stage moving vanes, second-stage stationary vanes, outlet guide vanes, hub, casing, airflow inlet, airflow outlet, and two rotor-stationary interfaces. The design speed of this axial compressor is... n The design flow rate is 32927 r / min. Q The design pressure is 5.38 kg / s, the design pressure ratio is 1.582, and the design efficiency is... η It is 86%.

[0035] Table 1 shows the fine sand concentration of 53 mg / m³. 3 Particle size distribution.

[0036] Table 1. Fine sand concentration is 53 mg / m³ 3 Particle size distribution

[0037] Table 2 shows the baseline values of the design parameters considered in this example and their variation ranges.

[0038] Table 2 Baseline values and ranges of axial compressor design parameters

[0039] The erosion-resistant optimization design problem of the axial compressor blade can be described by the following formula,

[0040] In the formula, η , Q are the design point efficiency and outlet flow, respectively; E is the allowable average wear rate concentration.

[0041] According to the method, 40 initial sample points are first collected, and are updated for 273 iterations until the model converges. The converged surrogate model is used as the fitness function, and the MOPSO algorithm is called to search the Pareto front in the optimization space. The algorithm parameters are: initial population size 200, Pareto library capacity 100, and iteration number 500. Figure 9 The obtained Pareto front is shown, and the optimal solution is selected therefrom.

[0042] Table 3 shows the key design parameters before and after optimization.

[0043] Table 3 Comparison of parameters of the axial compressor before and after optimization

[0044] Table 4 gives the comparison results of performance parameters before and after optimization. As can be seen from the table, the compressor flow rate is increased from 5.343 kg / s to 5.743 kg / s after optimization, with an increase of 7.49%; the efficiency is increased from 75.23% to 77.06%, with an efficiency increase of 1.83%.

[0045] Table 4 Comparison of performance parameters of the axial compressor before and after optimization

[0046] In order to further analyze the wear characteristics of the axial compressor in the sand dust environment before and after optimization, the wear distribution of the moving blade under the design point working condition is compared. Figure 10The three-dimensional waterfall diagrams of the wear rate concentration of the pressure surface of the moving blade at different blade heights before and after optimization are shown, wherein the horizontal coordinate is the Z-direction (axial direction) coordinate of the blade node, and the smaller the value, the closer to the leading edge of the blade. The results show that the overall erosion rate density of the pressure surface of the moving blade is low except for the local wear of the tip and the trailing edge near the casing, and therefore it is recommended to take measures such as coating to meet the anti-wear design requirements.

[0047] In order to further reveal the formation mechanism of wear near the tip region, the particle average volume fraction of the flow field region at different blade heights is extracted and analyzed, as shown in Figure 11 The results show that the particle average volume fraction gradually increases with the increase of the blade height, indicating that the particles are obviously gathered to the tip of the blade under the action of centrifugal force. At 90% of the blade height, the trajectory of the particles when they first enter the flow field is basically consistent with the inclination angle of the leading edge of the moving blade, resulting in that most of the particles pass above the leading edge, so that the local wear distribution of the leading edge is not significant. The main wear in this region is caused by the direct impact of particles on the pressure surface of the trailing edge. Since this position is in the outlet flow field of the moving blade, a large number of particles do not collide with the suction surface of the adjacent moving blade after being reflected, but directly enter the flow field of the next static blade, so that the trailing edge becomes the concentrated area of the tip wear. This phenomenon fully illustrates that the centrifugal gathering effect and the local flow field structure of the trailing edge jointly cause the erosion wear of the trailing edge region near the tip to be more significant.

[0048] In practical application, this method can organically link the working condition analysis, geometric modeling, gas-solid coupling simulation, data processing and multi-objective optimization in the sand and dust environment, realize the full-process automatic execution from the input condition to the optimization result. Designers only need to set the corresponding parameters to quickly obtain the comparison results of the blade aerodynamic performance and anti-erosion performance under different sand and dust working conditions, so as to conveniently carry out the design and optimization under multiple working conditions. At the same time, this method has intelligent data processing capability and efficient proxy modeling mechanism, which can significantly reduce the calculation cost on the premise of ensuring the prediction accuracy. In addition, by introducing advanced gas-solid two-phase flow numerical method and improved erosion damage prediction model, and combining with the multi-objective optimization strategy with constraints, the aerodynamic efficiency and anti-erosion ability of the blade are improved, and the reliability and engineering applicability of the optimization result are ensured.

[0049] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the erosion resistance of axial compressor blades in sandy and dusty environments, characterized in that, The following are the construction steps: Step 1) Operating Condition Analysis: An axial compressor was designed in CFturbo, and sand and dust conditions were analyzed, with the sand and dust concentration set at 53 mg / m³. 3 The specified particle size range for sand and dust is 0~80 micrometers; Step 2) Parameterization platform construction and design of constrained multi-objective optimization method: A constrained multi-objective optimization method based on Kriging was designed, and a parameterized platform was built. A parameterized simulation platform was established based on Python, and Cfturbo, AutoGrid, CFX and Matlab software were called to complete parameterized modeling, parameterized mesh generation, parameterized simulation and parameterized calculation, so as to realize the optimized design of axial flow compressor. Step 3) Experimental Design: Candidate variables were identified, including the rotor blade inlet installation angle. Rotor blade outlet installation angle rotor blade leading edge angle rotor blade trailing edge angle Rotor meridional flow channel profile parameters, rotor conformal mapping distribution, initial sample points were extracted using Latin hypercube, and Pearson correlation analysis was used for weight analysis to determine the optimization variables and their value ranges. Step 4) Model building: The model construction includes an optimization target model and an erosion wear constraint model. The optimization target model consists of a flow rate model and an efficiency model, which are used to characterize the aerodynamic performance of the compressor under different optimization variables. The erosion wear constraint model consists of a wear rate concentration model, which is used to reflect the erosion wear of the blade surface in a sand and dust environment. The optimization target model and the erosion wear constraint model form a relationship of mutual coupling between target optimization and constraint conditions. Through collaborative modeling and iterative updates, a comprehensive balance between aerodynamic performance and erosion resistance is achieved. Step 5) Optimization and Metric Validation: The constrained multi-objective optimization method was used to complete the optimization design of the axial compressor under erosion and wear constraints. Pareto solution set was generated, the model was updated in Cfturbo, the mesh was generated by AutoGrid, and the simulation was completed by CFX. The optimization index was verified to meet the requirements, and the differences in erosion morphology at 10%, 50%, and 90% blade height before and after optimization were analyzed.

2. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, The operating condition analysis steps include the following specific steps: Step 1) Design Modeling and Baseline Performance Establishment: Taking the axial compressor as the research object, the design model was completed in CFturbo, the structured mesh was generated by AutoGrid, and the aerodynamic characteristics of the model were calculated by CFX solver to verify the design point efficiency and pressure ratio. Then, the original performance database was established to provide a benchmark for the subsequent quantification of performance degradation after blade erosion and wear. Step 2) Calibrating sand and dust environmental parameters: Based on the fine sand concentration (53 mg / m³) 3 The requirements for particle distribution provide boundary conditions for gas-solid two-phase coupled simulation; Step 3) Establishing the erosion wear benchmark: Gas-solid two-phase flow simulation was conducted in a sandy environment to identify high wear areas on the blades, quantify performance degradation, and extract design point indicators as a benchmark for subsequent optimization.

3. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, The steps involved in setting up a parameterized platform and designing a constrained multi-objective optimization method include the following: Step 1) Generate initial sample points in the feasible space using DOE, and calculate the objective function value and the degree of constraint violation; Step 2) Establish independent Kriging models for the objective function and constraints respectively, where the objective model outputs the predicted value and uncertainty, and the constraint model is used to predict the feasibility probability; Step 3) Design a constrained multi-objective acquisition function to maintain a balance between exploration and development, while considering the reliability of constraint satisfaction; Step 4) Use the global optimization algorithm to select new candidate points, calculate the true target and constraint values, update the sample library and Kriging model, and repeat the process until the preset conditions are met. The preset conditions are the convergence criteria. Select the Pareto optimal solution set that meets the constraint conditions from the historical samples to provide a solution space for subsequent optimization.

4. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, The steps involved in setting up a parameterized platform and designing a constrained multi-objective optimization method include the following: Step 1) Export the design parameters in CFturbo, generate .cft and .cft-batch files, and output the .geomTurbo file required by AutoGrid; Step 2) Import the .geomTurbo file into AutoGrid to complete the grid generation and generate the .mesh file; Step 3) Record the simulation process in CFX, set boundary conditions, calculation formulas and output format, generate .pre and .cse files, and record the results; Step 4) Write an optimization algorithm based on Kriging in Matlab, read the simulation result data, update the surrogate model and collect new sample points, and output the Pareto solution set; Step 5) Use a Python script to batch process the above steps to automate the entire parameterized process.

5. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, The blade erosion resistance optimization process also includes an experimental design step, the specific steps of which include: Step 1) Based on the wear rate concentration constraint, screen the blade geometric parameters that affect the optimization objective; Step 2) Set the upper and lower bounds of the candidate variables and use the Latin hypercube sampling method to generate initial sample points; Step 3) Perform gas-solid coupling simulation on the initial sample points under sand and dust conditions to obtain efficiency, flow rate and wear rate concentration, and establish a multidisciplinary database; Step 4) Use Pearson correlation analysis to rank the candidate variables, determine the key optimization variables and their range, and provide data support for subsequent optimization.

6. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, The specific steps for building the model include: Step 1) Based on the optimization variables selected in the experimental design phase, determine the range and generate initial sample points using Latin hypercube sampling; obtain the corresponding target and constraint response values ​​through parametric simulation; Step 2) Establish the Kriging model for the aerodynamic performance objective function and the Kriging model for the erosion constraint function, respectively; Step 3) Generate new sampling points using a constrained multi-objective optimization algorithm and update the Kriging model to improve prediction accuracy; Step 4) When the model accuracy reaches the convergence condition, stop the iteration and output the Pareto solution set to provide a solution space for subsequent optimization.

7. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, The specific steps for optimization and indicator verification include: Step 1) Based on the established Kriging model, solve the Pareto solution set using a constrained multi-objective optimization algorithm; Step 2) Construct the following calculation formula to select the final optimization result from the Pareto front: In the formula, and This indicates the efficiency and flow rate of the prototype compressor. and To find the efficiency and flow of the midpoint of the Pareto front, calculate the midpoint of the Pareto front. The point with the largest value is the final optimization result; Step 3) Compare the erosion wear distribution of the blades before and after optimization, analyze the erosion pattern with CFD flow field data, and evaluate the wear improvement effect of key parts.

8. The erosion resistance optimization method for axial compressor blades in sandy and dusty environments according to claim 1, characterized in that, In the model building steps, when constructing the Kriging model, the initial value of the parameter θ of the relevant function is set to 10, with a lower limit of 1 and an upper limit of 10. 6 .

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