A flow simulation parallel partition computing method, system, device and medium

Abstract

The present application relates to the technical field of computational fluid dynamics, and discloses a flow simulation parallel partition calculation method, system, equipment and medium, wherein the method comprises: obtaining a geometric model and initial parameters of a flow field to be calculated, and dividing the flow field to be calculated into an outer flow region and an inner flow region according to flow characteristics; setting a connecting surface of the outer flow region and the inner flow region as a self-defined boundary, and generating a data exchange file with the self-defined boundary identifier as a name; assigning two groups of independent processes to perform parallel flow control equation solving on the outer flow region and the inner flow region respectively; in the solving process, updating physical quantities of a self-defined boundary virtual grid through the data exchange file to realize flow information exchange between the outer flow region and the inner flow region, and then realizing boundary condition updating, and finally outputting a flow field simulation result. The present application decouples high-speed outer flow simulation and low-speed inner flow simulation, and both provide boundary conditions for each other in the process of simultaneous calculation, which can effectively improve the simulation calculation convergence speed.

Description

Technical Field

[0001] This invention relates to the field of computational fluid dynamics, and in particular to a method, system, device, and medium for parallel partitioned computation of flow simulation. Background Technology

[0002] After the boundary layer of a high-speed aircraft transitions from laminar to turbulent flow, the friction coefficient and heat flux of the surface increase by approximately 3-5 times. This poses a significant challenge to the thermal protection system and aerodynamic design of the aircraft surface. Microporous surfaces are a potentially effective technology for delaying the transition of the high-speed boundary layer and achieving heat reduction and drag reduction; numerical simulation is an essential means of studying this technology. High-speed boundary layer flow on microporous surfaces is characterized by the coexistence of high-speed external flow and extremely low-speed internal flow, which presents a significant challenge for numerical simulation.

[0003] There are currently two numerical simulation methods:

[0004] One approach is direct solution, treating the interface between the microporous region and the external flow field as an internal surface and solving the governing equations simultaneously across the entire field without manual intervention. However, this method suffers from slow convergence and low computational efficiency. This is because hypersonic external flow is compressible, requiring a compressible solver for simulation. In contrast, the flow inside the micropores is low-speed incompressible. Using a compressible solver for incompressible flow encounters stiffness issues, leading to extremely slow convergence or even non-convergence. Furthermore, the slow convergence of low-speed microporous internal flow further restricts the convergence speed of high-speed external flow.

[0005] Another approach is a partitioned calculation method, which divides the flow into an external flow region and a micropore internal flow region. Then, a step-by-step partitioned solution is implemented, using interface conditions to couple the iterative solutions of the two regions. While solving the external flow region, the micropore internal flow region solution is paused. Once the external flow region solution converges, the external flow calculation is paused again. Based on the calculation results, the distribution of physical quantities such as heat flux and pressure at the interface between the external and internal flows is updated. Then, the solution for the micropore internal flow region begins. Once the micropore internal flow region solution converges, the continuity of physical quantities at the interface is checked for accuracy. If the accuracy check is met, the calculation ends; otherwise, the iterative solution continues. This method iterates between the two regions, resulting in low solution efficiency and low accuracy, and is also affected by the boundary conditions at the interface.

[0006] Therefore, it is necessary to develop new numerical simulation methods to improve the computational convergence speed and efficiency of this type of flow simulation, which simultaneously exhibits characteristics of high-speed outflow and low-speed inflow, while ensuring accuracy. Summary of the Invention

[0007] To address the problems of slow convergence speed and low computational efficiency in existing technologies, this invention proposes a parallel partitioned computation method, system, device, and medium for flow simulation. This method decouples high-speed external flow simulation from low-speed internal flow simulation, allowing both to provide boundary conditions for each other during simultaneous computation, thereby effectively improving the convergence speed of the simulation.

[0008] The technical solution adopted in this invention is as follows:

[0009] A method for parallel partitioned computation in flow simulation includes:

[0010] Obtain the geometric model and initial parameters of the flow field to be calculated, and divide the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics;

[0011] Set the connection surface between the outflow area and the inflow area as a custom boundary, generate a data exchange file named with the custom boundary identifier, and clarify the connection relationship between the custom boundaries of the two areas;

[0012] Two independent processes are allocated to solve the flow control equations in parallel for the outflow region and the inflow region, respectively. During the solution process, the physical quantities of the custom boundary virtual mesh are updated through the data exchange file to realize the exchange of flow information between the outflow region and the inflow region.

[0013] Based on the physical quantities of the virtual mesh cells of the exchanged custom boundary and the physical quantities of the cells inside the custom boundary, the physical quantities on the custom boundary are interpolated to realize the boundary condition update of the custom boundary of the external and internal flow regions; after the calculation of the external and internal flow regions meets the convergence condition, the flow field simulation results are output.

[0014] Furthermore, dividing the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics includes: dividing the external flow region and the internal flow region according to the flow characteristics of the computational domain, wherein the internal flow region is the region inside the micropores, and the external flow region is the flow field region outside the micropores.

[0015] Furthermore, the step of setting the connection surface between the outflow area and the inflow area as a custom boundary, generating a data exchange file named after the custom boundary identifier, and clarifying the connection relationship of the custom boundaries of the two areas includes: implementing the custom boundary identifier through grid block index and connection surface index, generating a data exchange file named after the custom boundary identifier, and the data exchange file containing the connection surface association relationship.

[0016] Furthermore, the computational resource allocation ratio of the two independent processes is determined based on the number of grids and computational complexity in the outflow region and the inflow region, and the flow control equations include inviscid flow equations and viscous flow equations.

[0017] Furthermore, in the solution process, different Courant numbers are used in different partitions to control computational stability and efficiency. The physical quantities of the custom boundary virtual mesh are updated by custom boundary identifiers to realize the exchange of flow information between the external and internal flow regions.

[0018] Furthermore, the physical quantities of the custom boundary virtual mesh include density, velocity components, pressure, and energy, which are obtained through numerical interpolation of three adjacent mesh layers on the connecting surface.

[0019] Furthermore, after the calculations of the external and internal flow regions meet the convergence conditions, the flow field simulation results are output, including: outputting the calculation results of the external and internal flow regions according to a preset step ratio, and finally integrating them into the overall flow field simulation results; the preset step ratio matches the solution accuracy requirements of the two regions.

[0020] A streaming simulation parallel partitioned computing system includes:

[0021] The flow field partitioning module is configured to acquire the geometric model and initial parameters of the flow field to be calculated, and to divide the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics.

[0022] The custom boundary identifier module is configured to set the connection surface between the external and internal drainage areas as a custom boundary, generate a data exchange file named with the custom boundary identifier, and clarify the connection relationship between the custom boundaries of the two areas;

[0023] The parallel computing and data exchange module is configured to allocate two independent processes to solve the flow control equations in parallel in the outer and inner flow regions, respectively. During the solution process, the physical quantities of the custom boundary virtual mesh are updated through the data exchange file to realize the exchange of flow information between the outer and inner flow regions.

[0024] The coupled calculation and result output module is configured to interpolate the physical quantities on the custom boundary based on the physical quantities of the exchanged custom boundary virtual mesh unit and the physical quantities of the custom boundary internal unit, thereby updating the boundary conditions of the custom boundary in the external and internal flow regions; after the calculation meets the convergence condition, the flow field simulation results are output.

[0025] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the flow simulation parallel partitioning computation method.

[0026] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the flow simulation parallel partitioning computation method.

[0027] The beneficial effects of this invention are as follows:

[0028] 1. This invention decouples the computation of the high-speed outflow region and the micropore inflow region. Each region employs a solution technique suitable for its own flow characteristics and accelerated convergence methods, without mutually restricting each other, thus improving the overall convergence speed of the flow field. For example, a compressible solver can be used in the high-speed outflow region, while an incompressible solver or a compressible solver with preprocessing capabilities can be used in the micropore inflow region. Furthermore, the modeled seepage equations can be solved in the micropore region. By improving the convergence speed of the micropore inflow, the overall convergence speed of the flow field is improved.

[0029] 2. Unlike existing partitioned computation-interface coupling methods, this invention improves computational efficiency while maintaining accuracy. Firstly, existing partitioned computation-interface coupling methods cannot calculate two regions simultaneously; the calculation of the outer flow region must be paused when the inner flow region is being calculated, and vice versa. The partitioned computation method proposed in this invention allows simultaneous calculations, thus improving efficiency. Secondly, existing partitioned computation-interface coupling methods require additional interface boundary conditions at the interface. Different interface boundary conditions significantly affect the calculation results and cannot guarantee accuracy. The partitioned computation method proposed in this invention does not require additional interface boundary conditions. It only needs to output the physical quantities of the three adjacent internal mesh units at the custom boundary of one region. After the other region reads in, these values ​​are used as the values ​​of the virtual mesh units at the custom boundary of that region. By interpolating these values ​​with the physical quantities of the adjacent internal mesh units at the custom boundary of that region, the custom boundary conditions are obtained, ensuring the accuracy of the interface and the overall computation. Attached Figure Description

[0030] Figure 1 This is a flowchart of a parallel partitioning calculation method for flow simulation according to Embodiment 1 of the present invention.

[0031] Figure 2 This is a schematic diagram of the computational region division in Embodiment 2 of the present invention.

[0032] Figure 3 This is a schematic diagram of the custom boundary conditions in Embodiment 2 of the present invention.

[0033] Figure 4 This is a schematic diagram of the custom boundary marker in Embodiment 2 of the present invention.

[0034] Figure 5 This is a schematic diagram of the partitioned computing parallel strategy of Embodiment 2 of the present invention.

[0035] Figure 6 This is a flowchart of the interface data exchange strategy in Embodiment 2 of the present invention.

[0036] Figure 7 This is a flowchart of the partition calculation process in Embodiment 2 of the present invention.

[0037] Figure 8 This is a comparison diagram of the velocity profiles calculated by partitioning and by non-partitioning in Embodiment 2 of the present invention, wherein sub-figure (a) is the flow velocity profile and sub-figure (b) is the normal velocity profile.

[0038] Figure 9 This is a comparison chart of the convergence curves of partitioned calculation and non-partitioned calculation in Embodiment 2 of the present invention. Detailed Implementation

[0039] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] like Figure 1 As shown, this embodiment provides a method for parallel partitioned computation in flow simulation, including:

[0042] Obtain the geometric model and initial parameters of the flow field to be calculated, and divide the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics;

[0043] Set the connection surface between the outflow area and the inflow area as a custom boundary, generate a data exchange file named with the custom boundary identifier, and clarify the connection relationship between the custom boundaries of the two areas;

[0044] Two independent processes are allocated to solve the flow control equations in parallel for the outflow region and the inflow region, respectively. During the solution process, the physical quantities of the custom boundary virtual mesh are updated through the data exchange file to realize the exchange of flow information between the outflow region and the inflow region.

[0045] Based on the physical quantities of the virtual mesh cells of the exchanged custom boundary and the physical quantities of the cells inside the custom boundary, the physical quantities on the custom boundary are interpolated to realize the boundary condition update of the custom boundary of the external and internal flow regions; after the calculation of the external and internal flow regions meets the convergence condition, the flow field simulation results are output.

[0046] It should be noted that this method, through partitioned parallel computing, can effectively reduce the computational load of a single computing region and improve the computational efficiency of the overall flow field simulation. The custom boundary setting and data exchange mechanism ensure the accuracy of boundary information transmission during partitioned computing, making the flow field simulation results more consistent with the actual flow conditions and improving the simulation accuracy.

[0047] Preferably, in this embodiment, the flow field is divided into an outer flow region and an inner flow region according to the flow characteristics of the computational domain. The inner flow region is the area within the micropores, and the outer flow region is the flow field area outside the micropores. Specifically, when partitioning the flow field to be computed, the regions in the flow field containing micropore structures are first identified. The flow field space inside the micropores is defined as the inner flow region, and the flow field space outside the micropores is defined as the outer flow region. During the partitioning process, the boundary between the two regions is clearly defined based on the geometric boundaries of the micropores and the flow characteristics of the surrounding flow field, ensuring that the boundaries of the inner and outer flow regions are consistent with the boundaries of the actual flow region.

[0048] It should be noted that dividing the flow field into zones based on its characteristics allows for more accurate simulation of the flow state in different regions. The flow characteristics within the micropores are complex; dividing it into a separate inner flow region allows for the use of more suitable solution strategies, improving the computational accuracy of this region while avoiding interference from the calculation of the outer flow region on the simulation of the flow field within the micropores.

[0049] Preferably, a custom boundary identifier is implemented using grid block indexes and connection surface indexes, generating a data exchange file named after the custom boundary identifier. This data exchange file contains the connection surface relationships. Specifically, after completing the flow field partitioning, the grid blocks of each computational region are numbered to form a grid block index, and the connection surfaces between two regions are numbered to form a connection surface index. The grid block index and the connection surface index are combined to serve as the identifier for the custom boundary. Then, a data exchange file is created with this identifier as the filename. The file contains detailed records of the correspondence between the connection surfaces of the external and internal flow regions, clarifying the association information between different connection surfaces and providing a basis for subsequent data exchange.

[0050] It should be noted that using grid block indexes and connect surface indexes to construct custom boundary identifiers enables precise location and differentiation of custom boundaries, avoiding confusion between different boundaries. The explicit connect surface relationships in the data exchange files ensure the accuracy and efficiency of data exchange between the two regions, guaranteeing the correct transmission of boundary information.

[0051] Preferably, the allocation ratio of computational resources between the two independent processes is determined based on the number of grid cells and computational complexity in the external and internal flow regions. The flow control equations include inviscid flow equations and viscous flow equations. Specifically, the number of grid cells in the external and internal flow regions is first counted, and the difficulty of solving the flow control equations in both regions is analyzed to assess the computational complexity. Combining the assessment results of the number of grid cells and computational complexity, the allocation ratio of computational resources to the two regions is determined, with more computational resources allocated to regions with a larger number of grid cells and higher computational complexity. When solving the flow control equations, based on the flow characteristics of the flow field, either inviscid flow equations or viscous flow equations are selected to describe and solve the flow state in the corresponding region.

[0052] It should be noted that allocating computational resources based on the number of grid cells and computational complexity enables the rational use of computational resources, avoids waste, and further improves the efficiency of parallel computing. The flexible selection of inviscid and viscous flow equations can adapt to the flow characteristics of different flow field regions, broadening the applicability of this parallel partitioned computation method for flow simulation.

[0053] Preferably, different Courant numbers are used in different partitions during the solution process to control computational stability and efficiency. Physical quantities of the custom boundary virtual mesh are updated through custom boundary markers, enabling flow information exchange between the outflow and inflow regions. Specifically, when solving in parallel for the outflow and inflow regions, different Courant numbers are set according to the mesh scale, flow velocity, and other characteristics of the two regions. While ensuring computational stability, the Courant number is adjusted according to the computational needs of different partitions to balance computational efficiency and accuracy. During data exchange, based on the custom boundary markers, the virtual mesh cells whose physical quantities need updating are accurately located, and the computational data for the corresponding region is transferred to the virtual mesh, completing the update of the virtual mesh physical quantities and thus realizing the exchange of flow information between the two regions.

[0054] It should be noted that different Courant numbers are used for different partitions, which can optimize the calculation process for each region and improve computational efficiency while ensuring computational stability. By updating the virtual mesh physical quantities through custom boundary markers, accurate transmission of boundary information can be achieved, ensuring the coupling effect of partitioned calculations and improving the accuracy of the overall flow field simulation.

[0055] Preferably, the physical quantities of the custom boundary virtual mesh include density, velocity components, pressure, and energy. These physical quantities are obtained through numerical interpolation of three adjacent layers of meshes at the connecting surface. Specifically, when updating the physical quantities of the custom boundary virtual mesh, the required physical quantities are clearly defined as density, velocity components, pressure, and energy. In each calculation step, the physical quantity data of the three adjacent layers of meshes at the connecting surface are extracted, a suitable numerical interpolation algorithm is selected, and the corresponding physical quantity values ​​of the virtual mesh are calculated based on the physical quantity values ​​of the adjacent meshes, thereby completing the update of the virtual mesh physical quantities.

[0056] It should be noted that selecting key physical quantities such as density, velocity components, pressure, and energy for transmission can comprehensively reflect the flow state of the flow field and ensure the integrity of boundary information during the partitioned calculation process. Using three layers of adjacent grids for numerical interpolation to obtain physical quantities can improve the calculation accuracy of physical quantities in the virtual grid, reduce interpolation errors, and further enhance the accuracy of the flow field simulation results.

[0057] Preferably, the calculation results of the outer flow region and the inner flow region are output according to a preset step ratio, and finally integrated into a full-field flow field simulation result; the preset step ratio matches the solution accuracy requirements of the two regions. Specifically, when setting the calculation output rules, the output step ratio of the calculation results of the two regions is determined according to the solution accuracy requirements of the outer flow region and the inner flow region. During the calculation process, the calculation data of the outer flow region and the inner flow region are output separately according to this step ratio. After the calculations of both regions meet the convergence conditions, the output calculation results of each region are integrated and stitched together to form a complete full-field flow field simulation result.

[0058] It should be noted that setting the step ratio for outputting calculation results based on the required solution accuracy can reduce unnecessary data output and lower the pressure on data storage and processing while ensuring simulation accuracy. The integration of the full-field flow simulation results achieves effective fusion of the partitioned calculation results, providing complete data support for subsequent flow field analysis.

[0059] Accordingly, this embodiment also provides a parallel partitioned computing system for mobile simulation, including:

[0060] The flow field partitioning module is configured to acquire the geometric model and initial parameters of the flow field to be calculated, and to divide the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics.

[0061] The custom boundary identifier module is configured to set the connection surface between the external and internal drainage areas as a custom boundary, generate a data exchange file named with the custom boundary identifier, and clarify the connection relationship between the custom boundaries of the two areas;

[0062] The parallel computing and data exchange module is configured to allocate two independent processes to solve the flow control equations in parallel in the outer and inner flow regions, respectively. During the solution process, the physical quantities of the custom boundary virtual mesh are updated through the data exchange file to realize the exchange of flow information between the outer and inner flow regions.

[0063] The coupled calculation and result output module is configured to interpolate the physical quantities on the custom boundary based on the physical quantities of the exchanged custom boundary virtual mesh unit and the physical quantities of the custom boundary internal unit, thereby updating the boundary conditions of the custom boundary in the external and internal flow regions; after the calculation meets the convergence condition, the flow field simulation results are output.

[0064] It should be noted that this parallel partitioned computation system for flow simulation employs a modular system architecture design, breaking down each stage of the parallel partitioned computation for flow simulation into separate modules. Each module focuses on performing a specific function, enhancing the system's flexibility and maintainability. The modules work collaboratively, automating the flow field partitioning, parallel computation, data exchange, and result output processes, effectively improving the efficiency and accuracy of flow field simulation.

[0065] Example 2

[0066] This embodiment provides a parallel partitioned calculation method for flow simulation, including: decoupling the calculation of a high-speed outflow region and a low-speed inflow region; using different CFL numbers (convergence condition judgment numbers) and accelerated convergence techniques to perform independent loop calculations in the two regions respectively; after calculating a certain number of steps, exchanging information at the custom boundary of the two regions to provide new boundary conditions to each other until the calculation converges.

[0067] It should be noted that this method, while ensuring the calculation accuracy of both high-speed outflow and low-speed inflow, fully leverages the advantages of low-speed inflow, such as small mesh size, fast calculation speed, and the ability to be accelerated by preprocessing, thus significantly improving the convergence speed of low-speed inflow calculations. This method is suitable for efficient and high-precision numerical simulation of flows exhibiting both high-speed outflow and low-speed inflow characteristics.

[0068] Specifically, the parallel partitioning calculation method for flow simulation in this embodiment can be implemented using the following steps:

[0069] 1. Mesh generation and boundary settings: After the mesh is generated, the interface between the high-speed outflow region and the low-speed inflow region of the computational mesh is set as a custom boundary condition.

[0070] 2. Mesh reading and setting custom boundary identifiers: The grid block index and interface index of the custom boundary in step 1 are used to set the identifier for the custom boundary of each process. The identifier of the custom boundary of the high-speed outflow region is composed of the grid block index and interface index of the corresponding low-speed inflow region. The identifier of the custom boundary of the low-speed inflow region is composed of the grid block index and interface index of the corresponding high-speed outflow region.

[0071] 3. Parallel computation: All processes are divided into two groups. The first group simulates high-speed outflow, outputting the physical quantities of the three-layer mesh inside the custom boundary to a file named with an identifier every n1 steps, and simultaneously generating an identifier file to indicate that the physical quantities have been updated. The second group calculates low-speed inflow, outputting the physical quantities of the three-layer mesh inside the custom boundary to a file named with an identifier every n2 steps, and simultaneously generating a corresponding identifier file to indicate that the physical quantities have been updated. Both groups solve different or the same governing equations and use different CFL numbers for computation. The output step numbers n1 and n2 satisfy the following relationship: the ratio of n1 to n2 is roughly equivalent to the ratio of the mesh size in the process with the largest mesh size in the first group to the mesh size in the process with the largest mesh size in the second group.

[0072] 4. Interface data coupling and boundary updates. Each step of the process containing the custom boundary monitors whether the corresponding identifier file has been generated. If the identifier file has been generated, the updated physical quantities are read as the physical quantities of the virtual unit at the custom boundary, and coupled with the physical quantities at the custom boundary in this process for calculation. The boundary conditions at the custom boundary are obtained and updated, and the identifier file is deleted. This iterative process continues until the calculation converges.

[0073] Example 3

[0074] This embodiment is based on embodiment 2:

[0075] This embodiment provides a parallel partitioned computation method for flow simulation, taking a flat plate with microchannels as an example for detailed explanation. The specific steps are as follows.

[0076] 1. Mesh generation and boundary settings.

[0077] The entire computational grid is divided into a high-speed external flow region and a micropore internal flow region, as referenced. Figure 2 Set the interface between the high-speed outflow region and the microporous inflow region as a custom boundary, refer to... Figure 3 .

[0078] 2. Mesh import and setting custom boundary markers.

[0079] refer to Figure 4 Based on the case where different blocks (blk-2-1-9, blk-1) have the same face name (dom-17) under the custom boundary conditions, a custom boundary identifier is set. Figure 4 In the calculation, block number 1 of blk-1 and boundary number 03 of dom-17 are used, so their boundary identifier is 01-03. Similarly, block number 10 of blk-2-1-9 and boundary number 05 of dom-17 are used, so their boundary identifier is 10-05. In subsequent calculations, files 01-03.dat and 10-05.dat are generated to store physical quantities, as well as files 01-03.done and 10-05.done to indicate whether the physical quantities have been updated.

[0080] 3. Parallel computing.

[0081] The parallel process is set up by dividing all processes into two groups. The first group is used to simulate high-speed outflow, outputting the physical quantities at the custom boundary of the high-speed outflow region every n1 steps, and generating an identifier file to indicate that the physical quantities have been updated. The second group is used to calculate low-speed inflow, outputting the physical quantities at the custom boundary of the micropore inflow region every n2 steps, and generating the corresponding identifier file to indicate that the physical quantities have been updated. The specific process is as follows.

[0082] refer to Figure 5The relationship between parallel computing processes and mesh blocks is configured. The mesh for the high-speed outflow region is set in the first process group, and the mesh for the microporous inflow region is set in the second process group. The two processes solve the compressible Navier-Stokes equations independently. During the calculation, independent CFL numbers are set for the two regions. To ensure computational stability and accelerate convergence, the CFL value is smaller in the high-speed outflow region and larger in the low-speed inflow region. In the high-speed outflow region, the physical quantities at the three inner layers of the custom boundary are output every n1 steps in the file 01-03.dat, and an identification file 01-03.done is generated simultaneously. In the microporous inflow region, the physical quantities at the three inner layers of the custom boundary are output every n2 steps in the file 09-05.dat, and an identification file 09-05.done is generated simultaneously. The ratio of n1 to n2 is equivalent to the ratio of the mesh size in the process with the largest mesh size in the first process group to the mesh size in the process with the largest mesh size in the second process group.

[0083] 4. Interface coupling, update boundaries.

[0084] The interface data is coupled, and the process containing the custom boundary monitors at each step whether the corresponding identifier file has been generated. If the identifier file has been generated, the updated physical quantity is read as the physical quantity of the virtual unit at the custom boundary, and coupled with the physical quantity at the custom boundary of this process for calculation to obtain and update the boundary conditions at the custom boundary, while the identifier file is deleted; for details, refer to [link to relevant documentation]. Figure 6 .

[0085] In this embodiment, the entire parallel partitioning computation process for flow simulation can be referred to Figure 7 .

[0086] As a comparison and verification, numerical simulations of a plate with microgrooves were performed using both the method of solving the Navier-Stokes equations simultaneously across the entire field (without partitioning) and the partitioned calculation method of this embodiment.

[0087] like Figure 8 This section presents a comparison of velocity profiles calculated using partitioned and non-partitioned methods, showing the flow velocity profile and normal velocity profile at the center of the first microchannel. Sub-figure (a) shows the flow velocity profile, with the horizontal axis u representing the dimensionless flow velocity and the vertical axis y representing the dimensionless normal height; sub-figure (b) shows the normal velocity profile, with the horizontal axis v representing the dimensionless normal velocity and the vertical axis y representing the dimensionless normal height. Figure 8 It can be seen that partitioned calculation and non-partitioned calculation have the same calculation results.

[0088] Figure 9 A comparison of convergence curves for partitioned and non-partitioned computation is presented, where the horizontal axis n represents the number of computation steps, and the vertical axis L2 represents the L2 error of the density (dimensionless). From Figure 9It can be seen that the convergence speed is about doubled after using partitioned calculation.

[0089] Example 4

[0090] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the parallel partitioned computing method for flow simulation according to any one of Embodiments 1 to 3. The computer program can be in the form of source code, object code, executable file, or some intermediate form.

[0091] Specifically, the computer device's memory pre-stores a completed computer program, which contains the code logic to implement each step of the parallel partitioned calculation for flow simulation. When flow field simulation calculation is required, the processor calls the computer program stored in the memory and executes operations such as region division, boundary marking, parallel calculation, data exchange, coupled calculation, and result output according to the instruction sequence set by the program, thus completing the entire flow field simulation process.

[0092] It should be noted that this computer device, through the coordinated operation of hardware and software, provides a reliable operating platform for the implementation of the parallel partitioning calculation method for flow simulation, giving full play to the computing power of the processor and the storage function of the memory, and ensuring the smooth execution of the calculation process and the stable output of results.

[0093] Example 5

[0094] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the parallel partitioning computation method for flow simulation according to any one of Embodiments 1 to 3. The computer program can be in the form of source code, object code, executable file, or some intermediate form. The storage medium includes any entity or device capable of carrying computer program code, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0095] Specifically, a computer-readable storage medium stores a computer program containing parallel partitioned computational logic for flow simulation in a suitable storage format. When the storage medium is connected to a device with a processor, the processor can read and load the computer program in the storage medium and execute the corresponding computational steps step by step according to the instructions in the program, thereby realizing the complete process from obtaining flow field parameters to outputting simulation results.

[0096] It should be noted that the existence of computer-readable storage media enables the program of the flow simulation parallel partitioning computation method to be conveniently stored, transmitted and used, improving the versatility and portability of the computation method and facilitating its deployment and application on different computing devices.

[0097] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

[0098] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A method for parallel partitioned computation in flow simulation, characterized in that, include: Obtain the geometric model and initial parameters of the flow field to be calculated, and divide the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics; Set the connection surface between the outflow area and the inflow area as a custom boundary, generate a data exchange file named with the custom boundary identifier, and clarify the connection relationship between the custom boundaries of the two areas; Two independent processes are allocated to solve the flow control equations in parallel for the outflow and inflow regions, respectively. During the solution process, the physical quantities of the custom boundary virtual mesh are updated through the data exchange file to realize the exchange of flow information between the external and internal flow regions; Based on the physical quantities of the virtual mesh cells of the exchanged custom boundary and the physical quantities of the cells inside the custom boundary, the physical quantities on the custom boundary are interpolated to realize the boundary condition update of the custom boundary of the outer and inner flow regions; after the calculation of the outer and inner flow regions meets the convergence condition, the flow field simulation results are output. The step of dividing the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics includes: dividing the external flow region and the internal flow region according to the flow characteristics of the computational domain, wherein the internal flow region is the region inside the micropores and the external flow region is the flow field region outside the micropores; The step of setting the connection surface between the outflow area and the inflow area as a custom boundary, generating a data exchange file named after the custom boundary identifier, and clarifying the connection relationship of the custom boundaries of the two areas includes: implementing the custom boundary identifier through grid block index and connection surface index, generating a data exchange file named after the custom boundary identifier, and the data exchange file containing the connection surface association relationship.

2. The method for parallel partitioned computation of flow simulation according to claim 1, characterized in that, The computational resource allocation ratio of the two independent processes is determined based on the number of grids and computational complexity in the outflow and inflow regions. The flow control equations include inviscid flow equations and viscous flow equations.

3. The method for parallel partitioned computation of flow simulation according to claim 1, characterized in that, In the solution process, different Courant numbers are used in different partitions to control computational stability and efficiency. The physical quantities of the custom boundary virtual mesh are updated by custom boundary identifiers to realize the exchange of flow information between the external and internal flow regions.

4. The method for parallel partitioned computation of flow simulation according to claim 3, characterized in that, The physical quantities of the custom boundary virtual mesh include density, velocity components, pressure, and energy. These physical quantities are obtained through numerical interpolation of three adjacent mesh layers on the connecting surface.

5. The method for parallel partitioned computation of flow simulation according to claim 1, characterized in that, After the calculations of the external and internal flow regions meet the convergence conditions, the flow field simulation results are output, including: outputting the calculation results of the external and internal flow regions according to a preset step ratio, and finally integrating them into the overall flow field simulation results; the preset step ratio matches the solution accuracy requirements of the two regions.

6. A parallel partitioned computing system for fluid simulation, characterized in that, include: The flow field partitioning module is configured to acquire the geometric model and initial parameters of the flow field to be calculated, and to divide the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics. The custom boundary identifier module is configured to set the connection surface between the external and internal drainage areas as a custom boundary, generate a data exchange file named with the custom boundary identifier, and clarify the connection relationship between the custom boundaries of the two areas; The parallel computing and data exchange module is configured to allocate two independent processes to solve the flow control equations in parallel in the outer and inner flow regions, respectively. During the solution process, the physical quantities of the custom boundary virtual mesh are updated through the data exchange file to realize the exchange of flow information between the external and internal flow regions; The coupled calculation and result output module is configured to interpolate the physical quantities on the custom boundary based on the physical quantities of the exchanged custom boundary virtual mesh cells and the physical quantities of the custom boundary internal cells, thereby realizing the boundary condition update of the custom boundary in the external and internal flow regions. Once the convergence condition is met, the flow field simulation results will be output. The step of dividing the flow field to be calculated into an external flow region and an internal flow region according to the flow characteristics includes: dividing the external flow region and the internal flow region according to the flow characteristics of the computational domain, wherein the internal flow region is the region inside the micropores and the external flow region is the flow field region outside the micropores; The step of setting the connection surface between the outflow area and the inflow area as a custom boundary, generating a data exchange file named after the custom boundary identifier, and clarifying the connection relationship of the custom boundaries of the two areas includes: implementing the custom boundary identifier through grid block index and connection surface index, generating a data exchange file named after the custom boundary identifier, and the data exchange file containing the connection surface association relationship.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the flow simulation parallel partitioning calculation method according to any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the flow simulation parallel partitioning calculation method according to any one of claims 1-5.

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