A method and system for optimizing the fluid topology of an attached jet cavity

Abstract

This invention provides a method and system for fluid topology optimization of an attached jet cavity, relating to the field of aero-engine technology. The method includes: dividing the computational domain using source-term-based numerical simulation, determining constraints based on the basic parameters of the optimization object, establishing a topology optimization model, obtaining the optimal output value of the model by determining whether the model converges, thereby obtaining the final optimized solid material distribution scheme, and geometrically reconstructing the attached jet cavity according to the optimization scheme. By employing a fluid topology optimization method to optimize the structure of the attached jet cavity, a structural optimization with higher design freedom is achieved. Furthermore, the use of source-term-based numerical simulation reduces the difficulty of structural optimization, solving the technical problem of the high difficulty of traditional structural optimization methods in the prior art.

Description

Technical Field

[0001] This invention relates to the field of aero-engine technology, and in particular to a method and system for optimizing the fluid topology of the jet cavity attached to the wall. Background Technology

[0002] In the prior art, in order to reduce the efficiency loss caused by suction surface separation at large turning angles in the high-load design of compressors, Li Yiwen's paper "Coanda jet blade design method and its application in high-load compressors" (University of Chinese Academy of Sciences, [D]. 2018) provides an axial compressor blade that fully utilizes the Coanda effect to control the flow separation of the suction surface of the high-load axial compressor blade. In this method, the jet gas of the Coanda jet blade is provided by an external high-pressure gas source (or bleed gas from the high-pressure stage of the axial compressor), and is finally ejected from the jet slot through the jet cavity inside the blade. Therefore, the jet cavity structure is an important part of the blade design process.

[0003] Currently, structural optimization of the jet cavity has not been carried out, and the internal space of the Coanda jet blades is limited, making traditional cavity structure optimization methods aimed at reducing the total pressure loss within the bleed air chamber quite challenging. In recent years, topology optimization methods have been widely used in the field of structural optimization due to their advantages such as high design freedom, freedom from structural parameterization constraints, and the ability to change the structural topology during optimization. However, existing technologies lack the application of topology optimization methods to the structural optimization of attached-wall jet cavities. Summary of the Invention

[0004] In view of this, the first aspect of the present invention provides a method for optimizing the fluid topology of an attached jet cavity, to solve the technical problem that traditional structural optimization methods in the prior art are difficult to implement. The method includes:

[0005] Based on the spatial region of the wall-attached jet cavity application, a geometric model of the wall-attached jet cavity is constructed, and the geometric model is divided into a computational domain with multiple grid cells.

[0006] Determine the basic parameters of the wall-attached jet cavity, including design variables, optimization objectives, fluid properties, and boundary conditions;

[0007] The constraints are determined based on the basic parameters, an objective function is constructed based on the constraints, and a topology optimization model is established based on the objective function.

[0008] Determine the initial value of the design variable, and based on the initial value, perform a preset number of iterations to update the design variable through the topology optimization model to obtain output information. The output information is the porosity of each grid cell corresponding to the iteration step. Determine whether the iteration convergence condition is met based on the output information. If it is met, obtain the solid material distribution optimization scheme based on the output information.

[0009] The attached jet cavity is geometrically reconstructed according to the solid material distribution optimization scheme, and the flow field analysis is performed on the optimized attached jet cavity structure.

[0010] Furthermore, based on the initial values, the step of iteratively updating the design variables a preset number of times through the topology optimization model to obtain output information includes:

[0011] Based on the initial values ​​of the design variables, the target flow field and the actual flow field are solved in the computational domain to obtain the ideal velocity V of each grid cell. ref and the actual local speed V local ;

[0012] According to the ideal speed V ref and the local actual speed V local The sensitivity of the objective function is obtained by performing sensitivity analysis using the sensitivity analysis method.

[0013] Based on the sensitivity of the objective function, the topology optimization model updates the design variables using a moving asymptote optimization algorithm to obtain the output information.

[0014] Furthermore, the formula for calculating the sensitivity information of the objective function is as follows:

[0015]

[0016] Where Z represents the sensitivity value, V ref V represents the ideal speed. local This represents the actual local speed, and n is the number of iterations.

[0017] Furthermore, among the basic parameters, the design variable is the porosity of each grid cell, and the porosity value is between 0 and 1; the fluid property is ideal air; among the boundary conditions, the inlet boundary adopts the mass inlet condition, the outlet boundary adopts the atmospheric pressure condition, and the other boundaries adopt the solid wall no-slip boundary.

[0018] Furthermore, the optimization objective is the total pressure loss of the jet cavity; the constraint condition is the fluid domain volume ratio of the jet cavity that satisfies the condition of minimizing the total pressure loss.

[0019] Furthermore, the mathematical model expression of the topology optimization model is as follows:

[0020] min.F(r,U,P)

[0021]

[0022] Where F represents the objective function, r, r n U represents porosity, P represents pressure, st (subject to) represents constraints, and μ and μ0 represent the following values: μ0 and μ1. t Let S represent the molecular viscosity and dynamic viscosity, respectively, and ρ represent the fluid density. m,i FV represents the momentum source term (its physical meaning is the resistance value of the grid cell to the flow velocity). n V is the volume of the mesh cell. F Let G represent the defined fluid domain volume fraction, and let x be the constraint function. i x j The coordinate values ​​of the grid cell are given.

[0023] A second aspect of this invention provides a wall-attached jet cavity fluid topology optimization system to address the technical problem of the high difficulty of traditional structural optimization methods in the prior art. The system includes:

[0024] The computational domain partitioning module is used to construct a geometric model of the attached jet cavity based on the spatial region of the application of the attached jet cavity, and to partition the geometric model into a computational domain with multiple grid cells.

[0025] The basic parameter acquisition module is used to determine the basic parameters of the wall-attached jet cavity, including design variables, optimization objectives, fluid properties, and boundary conditions.

[0026] The topology optimization model construction module is used to determine the constraints based on the basic parameters, construct the objective function based on the constraints, and establish the topology optimization model based on the objective function.

[0027] The iterative optimization module is used to determine the initial value of the design variable, and based on the initial value, to perform a preset number of iterative updates on the design variable through the topology optimization model to obtain output information. The output information is the porosity of each grid cell corresponding to the iteration step. The module determines whether the iterative convergence condition is met based on the output information. If it is met, the module obtains the solid material distribution optimization scheme based on the output information.

[0028] The reconstruction analysis module is used to perform geometric reconstruction of the attached-wall jet cavity according to the solid material distribution optimization scheme, and to perform flow field analysis on the optimized attached-wall jet cavity structure.

[0029] A third aspect of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for optimizing the fluid topology of the attached jet cavity.

[0030] A fourth aspect of the present invention also provides a computer-readable storage medium storing a computer program that executes any of the above-described methods for optimizing the fluid topology of an attached jet cavity.

[0031] Compared with existing technologies, the beneficial effects achieved by at least one of the above-mentioned technical solutions adopted in this specification include: providing a fluid topology optimization method for wall-mounted jet cavities, using source-term-based numerical simulation to divide the computational domain, determining constraints based on the basic parameters of the optimization object, establishing a topology optimization model, obtaining the optimal output value of the model by judging whether the model converges, thereby obtaining the final optimized scheme for solid material distribution, and geometrically reconstructing the wall-mounted jet cavity according to the optimization scheme. By using the fluid topology optimization method to optimize the design of the wall-mounted jet cavity structure, compared with traditional design methods, it is not necessary to perform structural parameter optimization and has a higher degree of design freedom; in the optimization process, source-term-based numerical simulation is used, and only one set of computational grids is needed to solve the fluid-solid physics field, avoiding the mesh generation work required for structural changes and reducing the difficulty of structural optimization. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic flowchart of a method for optimizing the fluid topology of an attached jet cavity provided in an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the computational domain, i.e., the mesh generation, of the wall-attached jet cavity provided in an embodiment of the present invention;

[0035] Figure 3 Porosity distribution cloud maps with different optimization steps provided in embodiments of the present invention;

[0036] Figure 4 These are flow field comparison diagrams before and after topology optimization provided in this embodiment of the invention;

[0037] Figure 5 This is a schematic diagram of a wall-attached jet cavity fluid topology optimization system provided in an embodiment of the present invention;

[0038] Figure 6 This is a schematic diagram of a computer device provided in an embodiment of the present invention.

[0039] The figure includes the following reference numerals: 1. Upper end wall; 2. Blade; 3. Jet slit; 500. A fluid topology optimization system for a jet cavity attached to a wall; 510. Computational domain partitioning module; 520. Basic parameter acquisition module; 530. Topology optimization model construction module; 540. Iterative optimization module; 550. Reconstruction analysis module; 601. Memory; 602. Processor. Detailed Implementation

[0040] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0041] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] In this embodiment of the invention, a fluid topology optimization method for the attached jet cavity is provided. By using the fluid topology optimization method to optimize the design of the attached jet cavity structure, compared with the traditional design method, it is not necessary to select structural parameters and has a higher degree of design freedom. In the optimization process, source term-based numerical simulation is used, and the solution of fluid-structure physics field is realized with only one set of computational grid, avoiding the mesh division work required for structural changes and reducing the difficulty of structural optimization.

[0043] like Figure 1 As shown, the method for optimizing the fluid topology of an attached jet cavity provided in this embodiment specifically includes the following steps:

[0044] Step S100: Based on the spatial region of the wall-attached jet cavity application, construct a geometric model of the wall-attached jet cavity, and divide the geometric model into a computational domain with multiple grid cells;

[0045] In practical implementation, a geometric model of the wall-attached jet cavity is established based on the spatial region where it is applied. The geometric model is then divided into N meshes using the mesh generation software ICEM. nodeThe computational domain consists of N grid cells. In specific implementation, the attached jet cavity is embedded in the Coanda jet blade 2. Jet enters the attached jet cavity from the upper endwall 1 and exits at the jet slit 3 at the trailing edge of the blade. A geometric model of the flat jet cavity is established based on the original structure of the attached jet cavity. The geometric model of the attached jet cavity is divided into N grid cells using the mesh generation software ICEM. node The computational domain of each grid cell. The computational domain of the wall-attached jet cavity is as follows: Figure 2 As shown.

[0046] Step S200: Determine the basic parameters of the wall-attached jet cavity, including design variables, optimization objectives, fluid properties, and boundary conditions;

[0047] Step S300: Determine the constraints based on the basic parameters, construct the objective function based on the constraints, and establish a topology optimization model based on the objective function;

[0048] In practice, the basic parameters of the attached jet cavity are determined, and the design variable is the porosity r of each grid cell. n Preferably, among the basic parameters, the porosity r n The value of r is between 0 and 1. n The value of determines the material properties of each grid cell; the fluid property is ideal air; the boundary conditions are selected as mass inlet conditions, atmospheric pressure conditions at the outlet boundary, and solid wall no-slip boundaries for other boundaries; the optimization objective is the total pressure loss of the jet cavity; the constraint condition is the fluid domain volume ratio of the jet cavity that satisfies the condition of minimizing the total pressure loss. In this embodiment, the optimization is set to minimize the total pressure loss, and the optimization constraint is that the fluid domain volume ratio is not less than 0.7.

[0049] Furthermore, the mathematical model expression of the topology optimization model is as follows:

[0050] min.F(r,U,P)

[0051]

[0052] Where F represents the objective function, r, r n U represents porosity, P represents pressure, st (subject to) represents constraints, and μ and μ0 represent the following values: μ0 and μ1. t Let S represent the molecular viscosity and dynamic viscosity, respectively, and ρ represent the fluid density. m,i FV represents the momentum source term (its physical meaning is the resistance value of the grid cell to the flow velocity). n V is the volume of the mesh cell. F Let G represent the defined fluid domain volume fraction, and let x be the constraint function. i xj V represents the coordinate value of the grid cell. In this embodiment, V F The preferred value is 0.7.

[0053] Step S400: Determine the initial value of the design variable. Based on the initial value, perform a preset number of iterations to update the design variable through the topology optimization model to obtain output information. The output information is the porosity of each grid cell corresponding to the iteration step. Determine whether the iteration convergence condition is met based on the output information. If it is met, obtain the solid material distribution optimization scheme based on the output information.

[0054] Furthermore, based on the initial values, the topology optimization model iteratively updates the design variables a preset number of times to obtain output information, including:

[0055] Step S410: Based on the initial values ​​of the design variables, solve for the target flow field and the actual flow field in the computational domain to obtain the ideal velocity V of each grid cell. ref and the actual local speed V local ;

[0056] Step S420: According to the ideal speed V ref and the local actual speed V local The sensitivity of the objective function is obtained by performing sensitivity analysis using the sensitivity analysis method.

[0057] Step S430: Based on the sensitivity of the objective function, the topology optimization model updates the design variables through the moving asymptote optimization algorithm to obtain the output information.

[0058] Furthermore, the formula for calculating the sensitivity of the objective function is as follows:

[0059]

[0060] Where Z represents the sensitivity value, V ref V represents the ideal speed. local This represents the actual local speed, and n is the number of iterations.

[0061] In specific implementation, such as Figure 1 As shown, by selecting appropriate initial values ​​for design variables, a source-term-based numerical simulation method is used to solve for the target flow field and the actual flow field in the optimization computational domain of the attached jet cavity, in order to obtain the reference velocity V of each grid cell. ref and actual speed V local The target flow field is obtained by numerically simulating the computational domain using a high-viscosity fluid, resulting in a flow field without backflow, and the reference velocity V is output. refIn this embodiment, the target flow field preferably uses a fluid dynamic viscosity of 1.0 Pas. Using the same initial values ​​for design variables and boundary conditions as described above, the source term numerical simulation method is used to solve the flow control equations with added source terms to obtain the actual flow field velocity U. To overcome the problem that traditional optimization methods require continuous mesh updates as the target shape changes, this topology optimization method uses a single mesh to discretize the computational domain containing the fluid-structure interaction. Source terms are introduced into the Navier-Stokes equations to simulate the fluid-structure interaction physics. The numerical simulation with added source terms is implemented using the momentum source term model in Ansys CFX (a commercial software), where the source term is the porosity r. n The function is described. Matlab is used to initialize the design cells and output them as a CSV file. In CFX, the CSV file is loaded using the initialization configuration file function to complete the definition of the mesh cell porosity.

[0062] Furthermore, based on the object to be optimized, suitable initial values ​​for design variables are determined. The design variables are iteratively updated a preset number of times using the topology optimization model. The Method of Moving Asymptotes (MMA) algorithm is used to update the design variables and output the new design variables. In each iteration, a sub-optimization problem is established. By solving this sub-optimization problem, the original design variables are updated, and the porosity of each grid cell at that iteration step is output. The iteration convergence is determined by comparing the maximum relative porosity change of each grid cell before and after the update with a preset iteration convergence condition. If the iteration convergence condition is met, the iteration converges, yielding the optimal solution. The topology optimization model outputs the optimal porosity, and the porosity contour plots for each optimization step are shown below. Figure 3 As shown; if the iteration convergence condition is not met, the output mesh cell porosity is fed back to the topology optimization model for re-iteration until the output value of the topology optimization model meets the iteration termination condition.

[0063] Step S500: Based on the solid material distribution optimization scheme, the attached wall jet cavity is geometrically reconstructed to obtain the optimized attached wall jet cavity structure, and the flow field of the optimized attached wall jet cavity structure is analyzed.

[0064] Furthermore, based on the output information of the topology optimization model, the attached jet cavity is geometrically reconstructed to obtain the optimal solid material distribution scheme. The main boundary points of the flow channel profile are extracted, and spline curves are used to fit these boundary points to obtain a smooth flow channel profile. The flow field of the reconstructed jet cavity is solved using the same boundary conditions as above. The final total pressure loss coefficient is reduced from 0.2493 to 0.1851 compared to the original jet cavity, a reduction of 25.66%, and the backflow region in the lower left corner of the jet cavity is effectively suppressed. Figure 4This is a schematic diagram comparing the flow field before and after topology optimization in an embodiment of this application. Figure 4 In the diagram, (a) represents the original jet cavity with ω = 0.2493; and (b) represents the reconstructed jet cavity with ω = 0.1851.

[0065] This embodiment, through the above method, ultimately obtained a wall-mounted jet cavity structure with excellent aerodynamic performance. It overcomes the limitations of traditional optimization methods, offering a high degree of design freedom while enabling the rapid generation of conceptual structural solutions.

[0066] Based on the same inventive concept, this invention also provides a wall-mounted jet cavity fluid topology optimization system, as described in the following embodiments. Since the principle of a wall-mounted jet cavity fluid topology optimization system is similar to that of a wall-mounted jet cavity fluid topology optimization method, the implementation of a wall-mounted jet cavity fluid topology optimization system can refer to the implementation of a wall-mounted jet cavity fluid topology optimization method; repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0067] Figure 5 This is a structural block diagram of a wall-attached jet cavity fluid topology optimization system 500 according to an embodiment of the present invention, as shown below. Figure 5 As shown, the system includes: a computational domain partitioning module 510, used to construct a geometric model of the attached-wall jet cavity based on the spatial region of the application of the attached-wall jet cavity, and to partition the geometric model into a computational domain with multiple grid cells; a basic parameter acquisition module 520, used to determine the basic parameters of the attached-wall jet cavity, the basic parameters including design variables, optimization objectives, fluid properties, and boundary conditions; a topology optimization model construction module 530, used to determine constraints based on the basic parameters, construct an objective function based on the constraints, and establish a topology optimization model based on the objective function; an iterative optimization module 540, used to determine the initial values ​​of the design variables, and based on the initial values, to perform a preset number of iterative updates on the design variables through the topology optimization model to obtain output information, the output information being the porosity of each grid cell corresponding to the iteration step number, and to determine whether the iterative convergence condition is met based on the output information. If it is met, an optimization scheme for solid material distribution is obtained based on the output information; and a reconstruction analysis module 550, used to perform geometric reconstruction of the attached-wall jet cavity based on the optimization scheme for solid material distribution, and to perform flow field analysis on the optimized attached-wall jet cavity structure.

[0068] Furthermore, the iterative optimization module 540 is also used for:

[0069] Based on the initial values ​​of the design variables, the target flow field and the actual flow field are solved in the computational domain to obtain the ideal velocity V of each grid cell. ref and the actual local speed V local ;

[0070] According to the ideal speed V ref and the local actual speed V local The sensitivity of the objective function is obtained by performing sensitivity analysis using the sensitivity analysis method.

[0071] Based on the sensitivity of the objective function, the topology optimization model updates the design variables using a moving asymptote optimization algorithm to obtain the output information.

[0072] Furthermore, the iterative optimization module 540 is also used to calculate the sensitivity information of the objective function as follows:

[0073]

[0074] Where Z represents the sensitivity value, V ref V represents the ideal speed. local This represents the actual local speed, and n is the number of iterations.

[0075] Furthermore, the basic parameter acquisition module 520 is also used for: the design variable being the porosity of each grid cell, the porosity value being between 0 and 1; the fluid property being ideal air; the boundary conditions being selected as mass inlet conditions, atmospheric pressure conditions for the outlet boundary, and solid wall no-slip boundaries for other boundaries; the optimization objective being the total pressure loss of the jet cavity; and the constraint condition being the fluid domain volume ratio of the jet cavity that satisfies the optimization condition of minimizing the total pressure loss.

[0076] Furthermore, the iterative optimization module 540 is also used to: the mathematical model expression of the topology optimization model is as follows:

[0077] min.F(r,U,P)

[0078]

[0079] Where F represents the objective function, r, r n U represents porosity, P represents pressure, st (subject to) represents constraints, and μ and μ0 represent the following values: μ0 and μ1. t Let S represent the molecular viscosity and dynamic viscosity, respectively, and ρ represent the fluid density. m,i FV represents the momentum source term (its physical meaning is the resistance value of the grid cell to the flow velocity). n V is the volume of the mesh cell. FLet G represent the defined fluid domain volume fraction, and let x be the constraint function. i x j The coordinate values ​​of the grid cell are given.

[0080] The present invention achieves the following technical effects through the above embodiments:

[0081] 1. This application optimizes the design of the attached jet cavity structure by constructing a topology optimization model. Compared with the traditional structural design method, it does not require the optimization of structural parameters and has a higher degree of design freedom.

[0082] 2. In the topology optimization process, source term-based numerical simulation is adopted, which can solve the fluid-structure physical field with only one set of computational grid, avoiding the mesh generation work required for structural changes and reducing the optimization difficulty.

[0083] In this embodiment, a computer device is provided, such as... Figure 6 As shown, it includes a memory 601, a processor 602, and a computer program stored in the memory 601 and executable on the processor 602. When the processor 602 executes the computer program, it implements any of the above-described methods for optimizing the fluid topology of the attached jet cavity.

[0084] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0085] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes any of the above-described methods for optimizing the fluid topology of the attached jet cavity.

[0086] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0087] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the fluid topology of an attached jet cavity, characterized in that, include: Based on the spatial region of the wall-attached jet cavity application, a geometric model of the wall-attached jet cavity is constructed, and the geometric model is divided into a computational domain with multiple grid cells. Determine the basic parameters of the wall-attached jet cavity, including design variables, optimization objectives, fluid properties, and boundary conditions; The constraints are determined based on the basic parameters, an objective function is constructed based on the constraints, and a topology optimization model is established based on the objective function. Determine the initial values ​​of the design variables, and based on the initial values, perform a preset number of iterations to update the design variables through the topology optimization model to obtain output information, which is the porosity of each grid cell corresponding to the iteration step. Determine whether the iteration convergence condition is met based on the output information. If it is met, obtain the solid material distribution optimization scheme based on the output information. The mathematical model expression of the topology optimization model is as follows: Where F represents the objective function, r, r n Let U represent porosity, U represent the velocity vector, P represent pressure, st represent the constraint condition, and μ and μ' represent the pressure. t Let S represent the molecular viscosity and dynamic viscosity, respectively, and ρ represent the fluid density. m,i FV represents the momentum source term. n V is the volume of the mesh cell. F Let G represent the defined fluid domain volume fraction, and let x be the constraint function. i x j The coordinate values ​​of the grid cell; The attached jet cavity is geometrically reconstructed according to the solid material distribution optimization scheme to obtain the optimized attached jet cavity structure, and the flow field of the optimized attached jet cavity structure is analyzed.

2. The method for optimizing the fluid topology of an attached jet cavity according to claim 1, characterized in that, The step of iteratively updating the design variables a preset number of times based on the initial values ​​using the topology optimization model to obtain output information includes: Based on the initial values ​​of the design variables, the target flow field and the actual flow field are solved in the computational domain to obtain the ideal velocity V of each of the grid cells. ref and the actual local speed V local ; According to the ideal speed V ref and the local actual speed V local The sensitivity of the objective function is obtained by performing sensitivity analysis using the sensitivity analysis method. The topology optimization model updates the design variables and obtains the output information by using the moving asymptote optimization algorithm and the sensitivity.

3. The method for optimizing the fluid topology of an attached jet cavity according to claim 2, characterized in that, The formula for calculating the sensitivity of the objective function is as follows: Where Z represents the sensitivity value, V ref V represents the ideal speed. local This represents the actual local speed, and n is the number of iterations.

4. The method for optimizing the fluid topology of an attached jet cavity according to claim 1, characterized in that, Among the basic parameters, the design variable is the porosity of each grid cell, and the porosity value is between 0 and 1; the fluid property is ideal air; among the boundary conditions, the inlet boundary adopts the mass inlet condition, the outlet boundary adopts the atmospheric pressure condition, and the other boundaries adopt the solid wall no-slip boundary.

5. The method for optimizing the fluid topology of an attached jet cavity according to claim 4, characterized in that, The optimization objective is the total pressure loss of the jet cavity; the constraint is the fluid domain volume ratio of the jet cavity that satisfies the condition of minimizing the total pressure loss.

6. A fluid topology optimization system for a wall-attached jet cavity, characterized in that, include: The computational domain partitioning module is used to construct a geometric model of the attached jet cavity based on the spatial region of the application of the attached jet cavity, and to partition the geometric model into a computational domain with multiple grid cells. The basic parameter acquisition module is used to determine the basic parameters of the attached jet cavity, including design variables, optimization objectives, fluid properties and boundary conditions. The topology optimization model construction module is used to determine the constraints based on the basic parameters, construct the objective function based on the constraints, and establish the topology optimization model based on the objective function. An iterative optimization module is used to determine the initial values ​​of the design variables, and based on the initial values, to perform a preset number of iterative updates on the design variables through the topology optimization model to obtain output information, wherein the output information is the porosity of each grid cell corresponding to the iteration step; and to determine whether the iterative convergence condition is met based on the output information. If it is met, an optimization scheme for solid material distribution is obtained based on the output information. The iterative optimization module is also used for the mathematical model expression of the topology optimization model, as follows: Where F represents the objective function, r, r n Let U represent porosity, U represent the velocity vector, P represent pressure, st represent the constraint condition, and μ and μ' represent the pressure. t Let S represent the molecular viscosity and dynamic viscosity, respectively, and ρ represent the fluid density. m,i FV represents the momentum source term. n V is the volume of the mesh cell. F Let G represent the defined fluid domain volume fraction, and let x be the constraint function. i x j The coordinate values ​​of the grid cell; The reconstruction analysis module is used to perform geometric reconstruction of the attached-wall jet cavity according to the solid material distribution optimization scheme, and to perform flow field analysis on the optimized attached-wall jet cavity structure.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the fluid topology optimization method for the attached jet cavity as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs a method for optimizing the fluid topology of an attached jet cavity according to any one of claims 1 to 5.

Images