Fan blade channel three-dimensional pneumatic optimization system of transonic compressor

Abstract

The invention discloses a three-dimensional pneumatic optimization system for a fan blade channel of a transonic compressor. The system comprises a data acquisition module used for acquiring geometric parameters, fluid characteristic parameters and operation conditions of the fan blade channel; the calculation module is configured to perform simulation analysis on airflow in a fan blade channel based on a computational fluid dynamics method and predict aerodynamic performance indexes under different design parameters; the optimization algorithm module is used for realizing aerodynamic performance maximization or performance index optimization of a fan blade channel according to a preset objective function and constraint conditions; and the result output module is used for outputting the optimized fan blade channel design scheme and the corresponding aerodynamic performance evaluation result to a user. By integrating the advanced CFD technology and the intelligent optimization algorithm, the system can quickly evaluate aerodynamic performance under different design schemes, and the product development cycle is remarkably shortened.

Description

Technical Field

[0001] The present invention belongs to the field of transonic compressor optimization, and more specifically, particularly relates to a three-dimensional aerodynamic optimization system for a fan blade channel of a transonic compressor. Background Art

[0002] In the design of modern aircraft engines and industrial compressors, the performance of the compressor directly affects the efficiency and reliability of the entire system. As a key component, the aerodynamic performance optimization of the fan blade channel of the transonic compressor is particularly important. Traditional design methods rely on experience and trial and error, which is not only time-consuming and labor-intensive, but also difficult to achieve the optimal design effect. With the development of computational fluid dynamics (CFD) technology, aerodynamic optimization methods based on numerical simulation have gradually become mainstream. However, existing CFD optimization systems still face challenges in dealing with complex flow phenomena, capturing shock waves and flow separation; Traditional compressor design mainly relies on engineers’ experience and experimental data. Although this method is effective to a certain extent, it has many shortcomings: Inefficiency: Empirical design and trial-and-error methods require a lot of time and resources for multiple iterations. Each modification requires re-experimentation or numerical simulation verification, resulting in a long design cycle and high costs.

[0003] Lack of accuracy: Traditional methods have difficulty in accurately predicting airflow characteristics due to the lack of accurate mathematical models to describe complex fluid dynamics behaviors, especially under transonic conditions.

[0004] Limited innovation capabilities: Designs that rely on experience are often confined to the framework of known solutions, making it difficult to break through existing technological bottlenecks, thus limiting the possibility of innovation; Based on the above, it is of great significance to develop an efficient and accurate three-dimensional aerodynamic optimization system for the fan blade channel of a transonic compressor. Therefore, we propose a three-dimensional aerodynamic optimization system for the fan blade channel of a transonic compressor. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art, and a three-dimensional aerodynamic optimization system for the fan blade channel of a transonic compressor is proposed. By integrating advanced CFD technology and intelligent optimization algorithms, the system can quickly evaluate the aerodynamic performance under different design schemes and significantly shorten the product development cycle. At the same time, high-precision numerical simulation helps to more accurately predict the performance in actual operation, reducing the demand and cost of physical experiments.

[0006] To achieve the above object, the present invention provides the following technical solutions: A three-dimensional aerodynamic optimization system for a fan blade passage of a transonic compressor, comprising: A data acquisition module is used to obtain geometric parameters, fluid characteristic parameters and operating conditions of the fan blade passage; A calculation module configured to simulate and analyze the airflow in the fan blade channel based on a computational fluid dynamics method and predict aerodynamic performance indicators under different design parameters; An optimization algorithm module, connected to the calculation module, is used to iteratively search for an optimal solution according to a preset objective function and constraint conditions to maximize the aerodynamic performance of the fan blade channel or optimize the performance index; The result output module is used to output the optimized fan blade channel design scheme and the corresponding aerodynamic performance evaluation results to the user.

[0007] Preferably, the calculation module includes: A mesh generation unit, which is used to automatically generate high-quality computational meshes based on the geometry of the fan blade passage; a solver unit configured to perform CFD simulations, including steady-state and unsteady-state flow simulations, and application of turbulence models; The post-processing unit is used to visualize and analyze the CFD simulation results and extract key aerodynamic performance parameters.

[0008] Preferably, the grid generation unit is configured as follows: Input, geometry and boundary conditions of the fan blade passage; Processing, using structured grid generation technology to automatically generate high-quality computational grids; Output, mesh file suitable for CFD simulation; The CFD simulation settings are as follows: Input, initial conditions and boundary conditions configured by the solver; Processing, performing CFD simulations, including steady and unsteady flow simulations; for transonic cases, focusing on shock wave capture and flow separation phenomena; Output, flow field data at the time step or iteration number, including velocity, pressure, temperature, density; Preferably, the solver unit is configured as follows: Input, grid file, fluid characteristic parameters, operating conditions; Processing, configure solver parameters, including selecting appropriate turbulence models, setting boundary conditions, and initializing the flow field; The post-processing unit is specifically configured as follows: Input, flow field data obtained by CFD simulation; Processing, data visualization, and extraction of key aerodynamic performance parameters, including pressure ratio, efficiency, and flow coefficient; Output, aerodynamic performance evaluation results in the form of charts and reports.

[0009] Preferably, the expression of the computational grid is: ; Where u is the kinematic viscosity, is the wall shear stress, and the grid size Δx is determined based on the desired maximum y+ value, where y+ is a dimensionless distance describing the location of the grid points within the boundary layer; The mesh size is determined by the mesh density, which ensures that the aspect ratio, twist and orthogonality of the mesh meet the solver requirements, including in the boundary layer and shock wave regions.

[0010] Preferably, the solver unit is responsible for solving the basic equations of fluid flow, including the continuity equation, momentum equation, energy equation and application of turbulence models; The continuity equation describes the principle of mass conservation, that is, the mass flowing into a control volume per unit time is equal to the rate of increase of the mass in the volume plus the mass flowing out, expressed as: ; Where ρ represents the fluid density, u represents the velocity vector, and abla·(ρu) represents the net mass flow rate of the fluid through the control surface; The momentum equation describes the relationship between the rate of change of momentum of fluid particles and the external force acting on the fluid, and is expressed as: +abla·(ρu×u)=-ablap+abla·τ+f; Where u represents the velocity vector, p represents the pressure, τ is the stress tensor describing the viscous stress, and f is the external force.

[0011] Preferably, the energy equation describes the relationship between the change of internal energy of the fluid and heat transfer and work, and the expression is: +abla·(ρuh)=abla·(kablaT)+ μ; Where e is the internal energy and h is the enthalpy, defined as ; k is the thermal conductivity, μ is the viscous dissipation term, which represents the energy dissipation due to viscosity.

[0012] Preferably, the turbulence model includes two transport equations for turbulent kinetic energy k and turbulent dissipation rate ε, expressed as: ; ; In the formula, G k is the turbulent kinetic energy generation term due to the mean velocity gradient, μ tis the turbulent viscosity, calculated from k and ε; C 1ε , C 2ε , σ k , σ ε is a model constant.

[0013] Preferably, the optimization algorithm module iteratively searches for the optimal solution by presetting the objective function and constraint conditions, with the goal of maximizing the aerodynamic performance of the fan blade channel or optimizing the performance index; The objective function includes key aerodynamic performance parameters such as pressure ratio, efficiency and flow coefficient, and the constraints include geometric parameter restrictions, operating condition restrictions and physical law restrictions.

[0014] Technical effects and advantages of the present invention: The present invention provides a three-dimensional aerodynamic optimization system for a fan blade passage of a transonic compressor. Compared with the conventional technology, the present invention has the following advantages: By integrating advanced CFD technology and intelligent optimization algorithms, the system can quickly evaluate the aerodynamic performance under different design schemes, significantly shortening the product development cycle. At the same time, high-precision numerical simulation helps to more accurately predict the performance in actual operation, reducing the need and cost of physical experiments. With the use of automated grid generation and solver configuration functions, engineers can more flexibly explore new design concepts without having to worry about complex pre-processing. In addition, the application of multi-objective optimization strategies makes it possible to find the best balance between meeting multiple conflicting objectives. By precisely controlling key aerodynamic parameters and taking into account performance under a variety of operating conditions, this optimized system helps improve the reliability and durability of the final product. The system not only solves current challenges, but also provides technical support for more efficient and environmentally friendly power units in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a diagram of the architecture of the three-dimensional aerodynamic optimization system for the fan blade passage of the transonic compressor of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] The present invention provides Figure 1 A three-dimensional aerodynamic optimization system for a fan blade passage of a transonic compressor is shown, which has the effect of improving design efficiency and enhancing innovation; Improve design efficiency Fast evaluation: By integrating CFD technology and intelligent optimization algorithms, the system can quickly evaluate the aerodynamic performance of different design options, significantly shortening the product development cycle.

[0018] Reduced experimental requirements: High-precision numerical simulations help to more accurately predict actual performance, reducing the need and cost of physical experiments.

[0019] Enhanced innovation Explore new design concepts: With automated mesh generation and solver configuration capabilities, engineers have more flexibility to explore new design concepts without having to worry about complex pre-processing.

[0020] Multi-objective optimization: The application of multi-objective optimization strategies makes it possible to find the best balance between satisfying multiple conflicting objectives.

[0021] The three-dimensional aerodynamic optimization system of the fan blade passage based on the above transonic compressor includes: The data acquisition module is used to obtain the geometric parameters, fluid characteristic parameters and operating conditions of the fan blade channel. The data acquisition module is an indispensable part of the three-dimensional aerodynamic optimization system of the fan blade channel of the transonic compressor. It bears the important responsibility of collecting raw data. This process involves the comprehensive acquisition of the geometric parameters, fluid characteristic parameters and operating conditions of the fan blade channel, providing basic support for subsequent calculations and analysis. In terms of geometric parameters, the shape, size and layout of the fan blades need to be accurately measured; these data directly affect the flow characteristics of the airflow, so they must be collected using high-precision instruments and the information obtained must be accurate; laser scanning technology is used to obtain detailed features of the blade surface; and for some internal structures that are difficult to measure directly, CT scanning may be required to achieve non-contact detection; Fluid characteristic parameters mainly include physical properties such as air density and viscosity coefficient, which will change with changes in temperature and pressure factors. In order to obtain more realistic and reliable simulation results, the system should be able to automatically adjust relevant values ​​according to the actual working environment. In addition, the influence of gas composition needs to be considered, because different types of air mixtures may exhibit different behavior patterns. The operating conditions include factors such as rotation speed and air intake angle, which will have a significant impact on the final performance. At different flight altitudes, the external atmospheric conditions will change greatly, which requires our model to be able to flexibly adapt to various situations. At the same time, it is also necessary to consider the wear and aging problems that may occur during long-term operation so as to take preventive measures in advance. It is worth noting that in addition to the above basic contents, the data acquisition module should also have good scalability and compatibility to facilitate the addition of more types of sensors or docking with other external systems in the future; this will not only improve work efficiency, but also further enhance the functionality and flexibility of the entire platform; in short, an efficient and stable data acquisition module is the key to achieving high-quality simulation calculations, and is of great significance to promoting the advancement of aero-engine technology.

[0022] A calculation module configured to simulate and analyze the airflow in the fan blade channel based on a computational fluid dynamics method and predict aerodynamic performance indicators under different design parameters; Furthermore, the calculation module includes: The mesh generation unit is used to automatically generate high-quality computational meshes according to the geometric shape of the fan blade passage; the mesh generation unit settings are as follows: Input, geometry and boundary conditions of the fan blade passage; Processing, using structured grid generation technology to automatically generate high-quality computational grids; Output, mesh file suitable for CFD simulation.

[0023] In addition, the expression for calculating the grid is: ; Where u is the kinematic viscosity, is the wall shear stress, and the grid size Δx is determined based on the desired maximum y+ value, where y+ is a dimensionless distance describing the location of the grid points within the boundary layer; The mesh size is determined by the mesh density and the mesh density, including in the boundary layer and shock wave regions, which ensures that the aspect ratio, twist and orthogonality of the mesh meet the solver requirements.

[0024] Solver unit, configured to perform CFD simulations, including steady-state and unsteady-state flow simulations, and application of turbulence models; CFD simulation settings are as follows: Input, initial conditions and boundary conditions configured by the solver; Processing, performing CFD simulations, including steady and unsteady flow simulations; for transonic cases, focusing on shock wave capture and flow separation phenomena; Output, flow field data at the time step or iteration number, including velocity, pressure, temperature, and density.

[0025] It should be noted that the solver unit settings are specifically as follows: Input, grid file, fluid characteristic parameters, operating conditions; Processing, configure solver parameters, including selecting appropriate turbulence models, setting boundary conditions, and initializing the flow field; The solver unit is responsible for solving the basic equations of fluid flow, including the continuity equation, momentum equation, energy equation, and the application of turbulence models; The continuity equation describes the principle of mass conservation, that is, the mass flowing into a control volume per unit time is equal to the rate of increase of the mass in the volume plus the mass flowing out. The expression is: ; Where ρ represents the fluid density, u represents the velocity vector, and abla·(ρu) represents the net mass flow rate of the fluid through the control surface; The momentum equation describes the relationship between the rate of change of momentum of fluid particles and the external force acting on the fluid. The expression is: +abla·(ρu×u)=-ablap+abla·τ+f; Where u represents the velocity vector, p represents the pressure, τ is the stress tensor, describing the viscous stress, and f is the external force; The energy equation describes the relationship between the change of internal energy of the fluid and heat transfer and work, and the expression is: +abla·(ρuh)=abla·(kablaT)+ μ; Where e is the internal energy and h is the enthalpy, defined as ; k is the thermal conductivity, μ is the viscous dissipation term, which represents the energy dissipation due to viscosity; The turbulence model includes two transport equations for the turbulent kinetic energy k and the turbulent dissipation rate ε, expressed as: ; ; In the formula, G k is the turbulent kinetic energy generation term due to the mean velocity gradient, μ t is the turbulent viscosity, calculated from k and ε; C 1ε , C 2ε , σ k , σ ε is a model constant.

[0026] The post-processing unit is used to visualize and analyze the CFD simulation results and extract key aerodynamic performance parameters. The post-processing unit settings are as follows: Input, flow field data obtained by CFD simulation; Processing, data visualization, and extraction of key aerodynamic performance parameters, including pressure ratio, efficiency, and flow coefficient; Output, aerodynamic performance evaluation results in the form of charts and reports.

[0027] The optimization algorithm module is connected to the calculation module and is used to iteratively search for the optimal solution according to the preset objective function and constraint conditions to maximize the aerodynamic performance of the fan blade channel or optimize the performance index; the optimization algorithm module iteratively searches for the optimal solution according to the preset objective function and constraint conditions, and the goal is to maximize the aerodynamic performance of the fan blade channel or optimize the performance index; The objective function includes key aerodynamic performance parameters such as pressure ratio, efficiency, and flow coefficient, and the constraints include geometric parameter restrictions, operating condition restrictions, and physical law restrictions.

[0028] The result output module is used to output the optimized fan blade channel design and the corresponding aerodynamic performance evaluation results to the user. The result output module is a crucial part of the three-dimensional aerodynamic optimization system of the fan blade channel of the transonic compressor. It is not only responsible for presenting the final design solution obtained through complex calculations and iterative optimization to the user, but also provides a detailed aerodynamic performance evaluation report. This information includes key parameters such as pressure ratio, efficiency, and flow coefficient, helping users to fully understand the performance of the new design under various working conditions.

[0029] In addition, to facilitate understanding and further analysis, the module supports the generation of various forms of output content, such as charts, graphs, and animations, which intuitively display the airflow distribution and shock wave location information. Users can easily identify potential problem areas or improvement points based on these visual materials, so as to make more scientific and reasonable decisions.

[0030] In summary, compared with the traditional technology, the present invention has the following advantages: By integrating advanced CFD technology and intelligent optimization algorithms, the system can quickly evaluate the aerodynamic performance under different design schemes, significantly shortening the product development cycle. At the same time, high-precision numerical simulation helps to more accurately predict the performance in actual operation, reducing the need and cost of physical experiments. With the use of automated grid generation and solver configuration functions, engineers can more flexibly explore new design concepts without having to worry about complex pre-processing. In addition, the application of multi-objective optimization strategies makes it possible to find the best balance between meeting multiple conflicting objectives. By precisely controlling key aerodynamic parameters and taking into account performance under a variety of operating conditions, this optimized system helps improve the reliability and durability of the final product. The system not only solves current challenges, but also provides technical support for more efficient and environmentally friendly power units in the future.

[0031] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A three-dimensional aerodynamic optimization system for fan blade passages of a transonic compressor, characterized in that: include: A data acquisition module is used to obtain geometric parameters, fluid characteristic parameters and operating conditions of the fan blade passage; A calculation module configured to simulate and analyze the airflow in the fan blade channel based on a computational fluid dynamics method and predict aerodynamic performance indicators under different design parameters; An optimization algorithm module, connected to the calculation module, is used to maximize the aerodynamic performance of the fan blade channel or optimize the performance index by iteratively searching for the optimal solution according to the preset objective function and constraint conditions; The result output module is used to output the optimized fan blade channel design scheme and the corresponding aerodynamic performance evaluation results to the user.

2. The three-dimensional aerodynamic optimization system for fan blade passage of a transonic compressor according to claim 1, characterized in that: The calculation module comprises: A mesh generation unit, which is used to automatically generate high-quality computational meshes based on the geometry of the fan blade passage; a solver unit configured to perform CFD simulations, including steady-state and unsteady-state flow simulations, and application of turbulence models; The post-processing unit is used to visualize and analyze the CFD simulation results and extract key aerodynamic performance parameters.

3. The three-dimensional aerodynamic optimization system for fan blade passage of a transonic compressor according to claim 2, characterized in that: The grid generation unit is specifically configured as follows: Input, geometry and boundary conditions of the fan blade passage; Processing, using structured grid generation technology to automatically generate high-quality computational grids; Output, mesh file suitable for CFD simulation; Among them, the CFD simulation settings are as follows: Input, initial conditions and boundary conditions configured by the solver; Processing, performing CFD simulations, including steady and unsteady flow simulations; for transonic cases, focusing on shock wave capture and flow separation phenomena; Output, flow field data at the time step or iteration number, including velocity, pressure, temperature, and density.

4. The three-dimensional aerodynamic optimization system for fan blade passage of a transonic compressor according to claim 3, characterized in that: The solver unit settings are specifically as follows: Input, grid file, fluid characteristic parameters, operating conditions; Processing, configure solver parameters, including selecting appropriate turbulence models, setting boundary conditions, and initializing the flow field; The post-processing unit is specifically configured as follows: Input, flow field data obtained by CFD simulation; Processing, data visualization, and extraction of key aerodynamic performance parameters, including pressure ratio, efficiency, and flow coefficient; Output, aerodynamic performance evaluation results in the form of charts and reports.

5. The three-dimensional aerodynamic optimization system for fan blade passage of a transonic compressor according to claim 4, characterized in that: The expression of the computational grid is: ； Where u is the kinematic viscosity, is the wall shear stress, and the grid size Δx is determined based on the desired maximum y+ value, where y+ is a dimensionless distance describing the location of the grid points within the boundary layer; The mesh size is determined by the mesh density, which ensures that the aspect ratio, twist and orthogonality of the mesh meet the solver requirements, including in the boundary layer and shock wave regions.

6. A three-dimensional aerodynamic optimization system for fan blade passages of a transonic compressor according to claim 4, characterized in that: The solver unit is responsible for solving the basic equations of fluid flow, including the continuity equation, momentum equation, energy equation and application of turbulence models; The continuity equation describes the principle of mass conservation, that is, the mass flowing into a control volume per unit time is equal to the rate of increase of the mass in the volume plus the mass flowing out, expressed as: ； Where ρ represents the fluid density, u represents the velocity vector, and abla·(ρu) represents the net mass flow rate of the fluid through the control surface; The momentum equation describes the relationship between the rate of change of momentum of fluid particles and the external force acting on the fluid, and is expressed as: +abla·(ρu×u)=-ablap+abla·τ+f; Where u represents the velocity vector, p represents the pressure, τ is the stress tensor describing the viscous stress, and f is the external force.

7. A three-dimensional aerodynamic optimization system for fan blade passages of a transonic compressor according to claim 6, characterized in that: The energy equation describes the relationship between the change of internal energy of the fluid and heat transfer and work, and the expression is: +sister(ρuh)=sister(kablaT)+μ; Where e is the internal energy and h is the enthalpy, defined as ; k is the thermal conductivity, μ is the viscous dissipation term, which represents the energy dissipation due to viscosity.

8. A three-dimensional aerodynamic optimization system for fan blade passages of a transonic compressor according to claim 7, characterized in that: The turbulence model includes two transport equations for turbulent kinetic energy k and turbulent dissipation rate ε, expressed as: ； ； In the formula, G k is the turbulent kinetic energy generation term due to the mean velocity gradient, μ t is the turbulent viscosity, calculated from k and ε; C 1ε , C 2ε , σ k , σ ε is a model constant.

9. The three-dimensional aerodynamic optimization system for fan blade passage of a transonic compressor according to claim 1, characterized in that: The optimization algorithm module iteratively searches for the optimal solution by presetting the objective function and constraints, with the goal of maximizing the aerodynamic performance of the fan blade channel or optimizing the performance index; The objective function includes key aerodynamic performance parameters such as pressure ratio, efficiency and flow coefficient, and the constraints include geometric parameter restrictions, operating condition restrictions and physical law restrictions.

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