Simulation method and device for high-speed coupling flow of incoming flow and porous medium

Abstract

This invention discloses a simulation method and apparatus for coupled flow of high-speed incoming flow and porous media. The method involves acquiring the solids content and characteristic scale of the porous media to be simulated and constructing a cylindrical array model. Based on this model, a computational domain is determined. The cylindrical array model is then divided into partitions to obtain computational grid data, balancing near-wall analytical accuracy with overall computational efficiency. The cylindrical wall boundary is then determined within the computational grid data, and high-speed compressible incoming flow conditions and slip boundary conditions are set to generate target grid data. This allows the simulation to accurately reflect wall slip and temperature jump effects in the high-speed slip flow region. Finally, based on fluid dynamics control algorithms and turbulence models, the target grid data is numerically iteratively solved to obtain flow field distribution data. This achieves refined simulation of the pore scale of coupled flow of high-speed incoming flow and porous media, while ensuring a balance between computational accuracy and efficiency.

Description

Technical Field

[0001] This invention relates to the field of porous media fluid mechanics, and in particular to a simulation method and apparatus for high-speed incoming flow coupled with porous media flow. Background Technology

[0002] Currently, porous media have broad application prospects in the aerospace field, such as for aircraft sweat cooling, thermal protection systems, and flow control structures. For simulating the coupled flow between porous media and high-speed incoming flow, commonly used methods mainly include two categories: empirical mathematical modeling and numerical simulation. Empirical mathematical modeling methods are typically based on the equivalent physical properties of the porous media and idealized flow assumptions to establish corresponding mathematical models to describe the seepage behavior or overall drag characteristics within the porous media. Among these, Darcy's law is the most fundamental theoretical model, which describes the flow in porous media under low Reynolds number conditions by establishing a linear relationship between flow velocity and pressure drop. Numerical simulation methods, on the other hand, directly simulate the coupled flow process within the porous media or between it and external incoming flow using computational fluid dynamics techniques. This typically requires meshing the flow region and solving the governing equations using numerical methods such as the finite volume method, finite difference method, or finite element method to obtain the flow field distribution and overall aerodynamic characteristics.

[0003] Existing methods for simulating coupled flow between porous media and high-speed incoming flows rely on empirical mathematical models, which are ill-suited for high-speed compressible flows and cannot resolve flow details at the pore scale. While numerical simulation methods can directly solve the Navier-Stokes equations, they require explicit modeling of the complex three-dimensional pore geometry of real porous media. This leads to extremely difficult mesh generation and massive computational scale, making it difficult to balance accuracy with computational efficiency, thus limiting their application in engineering parameter research and rapid design. Summary of the Invention

[0004] This invention provides a simulation method and apparatus for high-speed incoming flow coupled with porous media, which balances simulation accuracy and computational efficiency under high-speed compressible flow conditions, thereby improving the accuracy of simulation of incoming flow coupled with porous media.

[0005] To address the aforementioned technical problems, this invention provides a method for simulating high-speed incoming flow coupled with porous media flow, comprising: Obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model. Based on the computational domain, the cylindrical array model is partitioned into a mesh to obtain computational mesh data; In the computational grid data, the cylindrical wall boundaries of each cylinder in the cylindrical array model are determined, and high-speed compressible inflow conditions and slip boundary conditions are set for the cylindrical wall boundaries to generate target grid data; Based on the preset fluid dynamics control algorithm and preset turbulence model, the target grid data is numerically iteratively solved to obtain the flow field distribution data of the porous medium to be simulated, thus completing the simulation of the coupled flow of high-speed incoming flow and porous medium.

[0006] This invention achieves a regularized representation of the geometry of complex porous media by acquiring the solids content and characteristic scale of the porous medium to be simulated and constructing a cylindrical array model, thus reducing the modeling difficulty. Subsequently, the computational domain is determined based on the cylindrical array model, ensuring the rationality and specificity of the computational range. Next, computational grid data is obtained by partitioning the cylindrical array model into sub-grids, balancing near-wall analytical accuracy and overall computational efficiency. The cylindrical wall boundary is then determined in the computational grid data, and high-speed compressible inflow conditions and slip boundary conditions are set to generate target grid data, enabling the simulation to accurately reflect the wall slip and temperature jump effects in the high-speed slip flow region. Finally, based on fluid dynamics control algorithms and turbulence models, numerical iteration is performed on the target grid data to obtain flow field distribution data, achieving a refined simulation of the pore scale of the coupled flow of high-speed inflow and porous media, while ensuring a balance between computational accuracy and efficiency.

[0007] Furthermore, the step of obtaining the solids content and characteristic scale of the porous medium to be simulated, constructing a cylindrical array model based on the solids content and characteristic scale, and determining the computational domain of the porous medium to be simulated based on the cylindrical array model includes: Obtain the solids content and characteristic scale of the porous medium to be simulated; Based on the solidity, the feature scale, and the fixed proportional relationship between the preset cylinder diameter and the feature scale, the number of cylinders and the unit cylinder diameter in the cylinder array model are determined. The cylindrical array model is constructed based on the preset rule arrangement, the number of cylinders, and the diameter of the unit cylinder, and the outer contour feature size of the cylindrical array model is equal to the feature scale. Obtain the spatial distribution of the cylindrical array model, and determine the computational domain of the porous medium to be simulated based on the spatial distribution.

[0008] This invention provides basic input parameters for model construction by obtaining the solids content and characteristic scale of the porous medium to be simulated. Furthermore, it determines the number of cylinders and the unit cylinder diameter based on the solids content, characteristic scale, and a pre-defined fixed ratio between the cylinder diameter and the characteristic scale, thus achieving a structured mapping of key physical property parameters of the porous medium to a regular geometric model. This allows for the construction of a cylindrical array model based on a regular arrangement, the number of cylinders, and the unit cylinder diameter, ensuring that the outer contour feature size equals the characteristic scale, guaranteeing an equivalent representation of the overall mechanical properties of the original porous medium. Finally, by obtaining the spatial distribution of the cylindrical array model and determining the computational domain based on this spatial distribution, the computational range of subsequent flow simulations is precisely matched to the model's geometric features, avoiding unnecessary computational waste.

[0009] Furthermore, obtaining the spatial distribution of the cylindrical array model and determining the computational domain of the porous medium to be simulated based on the spatial distribution includes: Along the direction of the incoming flow, determine the upstream and downstream boundary positions of the cylindrical array model; Along the direction perpendicular to the incoming flow, using the center line of the cylindrical array model as a reference, determine the positions of the two side boundaries respectively; The spatial distribution of the cylindrical array model is determined based on the upstream boundary position, downstream boundary position, and both side boundary positions. The spatial distribution is used to enclose the boundary of the flow computation domain, thereby determining the computation domain of the porous medium to be simulated.

[0010] This invention determines the upstream and downstream boundary positions of the cylindrical array model along the incoming flow direction, ensuring that the computational domain fully includes the disturbance and wake regions in the flow direction. Furthermore, by determining the boundary positions on both sides perpendicular to the incoming flow direction and using the centerline of the cylindrical array model as a reference, it ensures the lateral symmetry and sufficient width of the computational domain. Then, based on the upstream, downstream, and lateral boundary positions, it determines the spatial distribution of the cylindrical array model, achieving a complete description of the model's spatial position. Finally, by defining the computational domain boundary based on the spatial distribution, it ensures that the computational domain boundary matches the flow's physical characteristics, effectively reducing the impact of human interference such as boundary reflections on the simulation results.

[0011] Furthermore, the step of partitioning the cylindrical array model into a grid based on the computational domain to obtain computational grid data includes: Identify the wall positions of each cylinder in the cylindrical array model, and generate quadrilateral structure meshes at the wall positions of each cylinder to obtain the first mesh data; Identify the pore regions between the cylinders in the cylindrical array model, and generate triangular unstructured meshes in the pore regions to obtain second mesh data; Identify the far-field region from the outer side of the cylindrical array model to the boundary of the flow computation domain, and generate a structured quadrilateral mesh in the far-field region to obtain the third mesh data; The first grid data, the second grid data, and the third grid data are merged to obtain the computational grid data.

[0012] This invention obtains first grid data by identifying the wall positions of each cylinder and generating quadrilateral structured meshes at those positions, ensuring high-resolution analysis of the flow boundary layer near the cylinder walls. Then, by identifying the pore regions between cylinders and generating triangular unstructured meshes in these regions, second grid data is obtained, enabling flexible and adaptive filling of complex pore geometry. Next, by identifying the far-field region from the outer edge of the cylinder array model to the boundary of the flow computation domain and generating structured quadrilateral meshes, third grid data is obtained, ensuring high computational efficiency and good mesh orthogonality in the far-field region. Finally, by merging the first, second, and third grid data, a hybrid mesh system is formed that balances near-wall accuracy, pore adaptability, and far-field efficiency, laying a data foundation for subsequent high-precision solutions.

[0013] Furthermore, the step of determining the cylindrical wall boundaries of each cylinder in the cylindrical array model from the computational mesh data, and setting high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundaries to generate target mesh data includes: In the first grid data, the wall grid cells of each cylinder in the cylindrical array model are identified and the wall grid cells are marked as the cylindrical wall boundaries. Obtain preset high-speed compressible incoming flow parameters and associate the high-speed compressible incoming flow parameters with the third grid data to obtain target third grid data; Obtain preset sliding boundary condition parameters and associate the sliding boundary condition parameters with the cylindrical wall boundary to obtain target first mesh data; The target first grid data, the target second grid data, and the target third grid data are merged to generate target grid data.

[0014] This invention accurately defines the objects to which slip boundary conditions are applied by traversing and identifying the wall mesh elements of each cylinder in the cylindrical array model in the first mesh data and marking them as cylindrical wall boundaries. Subsequently, by acquiring the high-speed compressible incoming flow parameters and associating them with the third mesh data, the target third mesh data is obtained, ensuring that the far field and inlet boundaries correctly bear the incoming flow conditions. Next, by acquiring the slip boundary condition parameters and associating them with the cylindrical wall boundaries, the target first mesh data is obtained, enabling the cylindrical wall to simulate velocity slip and temperature jumps. Finally, the target first mesh data, second mesh data, and target third mesh data are merged to generate the target mesh data, achieving complete encapsulation of boundary conditions and mesh data, providing the solver with a directly usable input file, and avoiding processing errors caused by data fragmentation.

[0015] Furthermore, based on a preset fluid dynamics control algorithm and a preset turbulence model, the target mesh data is numerically iteratively solved to obtain the flow field distribution data of the porous medium to be simulated, thus completing the simulation of the coupled flow between high-speed incoming flow and porous medium, including: The solver is configured based on a preset fluid dynamics control algorithm and a preset turbulence model; The flow field is initialized, and the initial flow field state of the computational domain is determined based on the high-speed compressible incoming flow parameters. Numerical iterative solutions are then performed based on the initial flow field state and the solver. In each iteration, based on the current flow field state and the slip boundary condition parameters, the velocity slip and temperature jump at the cylindrical wall boundary are calculated, and the physical quantities on the cylindrical wall boundary are updated based on the velocity slip and temperature jump. Based on the physical quantities on the cylindrical wall boundary, the fluid dynamics control algorithm and the preset turbulence model are solved, and the flow field state is updated. The iteration stops when the preset conditions are met. The flow field distribution data of the porous medium to be simulated is obtained, and the simulation of the coupled flow of high-speed incoming flow and porous medium is completed.

[0016] This invention provides a suitable physical model foundation for simulating high-speed compressible flows by configuring a solver based on a preset fluid dynamics control algorithm and turbulence model. Furthermore, by initializing the flow field and determining the initial flow field state of the computational domain based on the parameters of the high-speed compressible inflow, numerical iterative solutions are performed based on this initial flow field state and the solver, ensuring the physical rationality and convergence starting point of the iterative process. In each iteration, the velocity slip and temperature jump at the cylindrical wall boundary are calculated based on the current flow field state and slip boundary condition parameters, and the velocity slip and temperature jump are then used as the basis for further calculations. The jump value updates the physical quantity values ​​on the wall boundary, and then the fluid dynamics control equations and turbulence model equations are solved based on the updated wall boundary physical quantity values ​​to update the flow field state until the preset conditions are met and the iteration stops. This coupled iteration process makes the wall slip effect evolve synchronously with the flow in the whole field, which truly reflects the interaction mechanism between the wall and the fluid in the high-speed slip flow region. By obtaining the flow field distribution data of the porous medium to be simulated, the simulation of the coupled flow of high-speed incoming flow and porous medium is completed, providing high-fidelity pore-scale data support for aerospace thermal protection and flow control design.

[0017] Secondly, the present invention provides a simulation device for high-speed incoming flow coupled with porous medium flow, comprising: a computational domain determination module, a mesh generation module, a condition determination module, and a simulation module; The computational domain determination module is used to obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model. The meshing module is used to partition the cylindrical array model into a mesh based on the computational domain to obtain computational mesh data. The condition determination module is used to determine the cylindrical wall boundary of each cylinder in the cylindrical array model from the computational grid data, and to set high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundary to generate target grid data. The simulation module is used to perform numerical iterative solutions on the target grid data based on a preset fluid dynamics control algorithm and a preset turbulence model, to obtain the flow field distribution data of the porous medium to be simulated, and to complete the simulation of the coupled flow of high-speed incoming flow and porous medium.

[0018] Furthermore, the computational domain determination module is used to obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model, including: Obtain the solids content and characteristic scale of the porous medium to be simulated; Based on the solidity, the feature scale, and the fixed proportional relationship between the preset cylinder diameter and the feature scale, the number of cylinders and the unit cylinder diameter in the cylinder array model are determined. The cylindrical array model is constructed based on the preset rule arrangement, the number of cylinders, and the diameter of the unit cylinder, and the outer contour feature size of the cylindrical array model is equal to the feature scale. Obtain the spatial distribution of the cylindrical array model, and determine the computational domain of the porous medium to be simulated based on the spatial distribution.

[0019] Furthermore, the computational domain determination module is used to obtain the spatial distribution of the cylindrical array model and determine the computational domain of the porous medium to be simulated based on the spatial distribution, including: Along the direction of the incoming flow, determine the upstream and downstream boundary positions of the cylindrical array model; Along the direction perpendicular to the incoming flow, using the center line of the cylindrical array model as a reference, determine the positions of the two side boundaries respectively; The spatial distribution of the cylindrical array model is determined based on the upstream boundary position, downstream boundary position, and both side boundary positions. The spatial distribution is used to enclose the boundary of the flow computation domain, thereby determining the computation domain of the porous medium to be simulated.

[0020] Furthermore, the meshing module is used to partition the cylindrical array model into a mesh based on the computational domain to obtain computational mesh data, including: Identify the wall positions of each cylinder in the cylindrical array model, and generate quadrilateral structure meshes at the wall positions of each cylinder to obtain the first mesh data; Identify the pore regions between the cylinders in the cylindrical array model, and generate triangular unstructured meshes in the pore regions to obtain second mesh data; Identify the far-field region from the outer side of the cylindrical array model to the boundary of the flow computation domain, and generate a structured quadrilateral mesh in the far-field region to obtain the third mesh data; The first grid data, the second grid data, and the third grid data are merged to obtain the computational grid data. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a simulation method for high-speed incoming flow coupled with porous medium flow, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a cylindrical array model provided in an embodiment of the present invention; Figure 3A schematic diagram of a flow computing domain provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a grid division provided in an embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] The terms "first" and "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] Example 1 See Figure 1 , Figure 1 This is a schematic flowchart illustrating a simulation method for high-speed incoming flow coupled with porous media, provided by an embodiment of the present invention. The embodiment of the present invention provides a simulation method for high-speed incoming flow coupled with porous media, including steps 101 to 104, as detailed below: Step 101: Obtain the solids ratio and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids ratio and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model; In this embodiment, obtaining the solids content and characteristic scale of the porous medium to be simulated, constructing a cylindrical array model based on the solids content and characteristic scale, and determining the computational domain of the porous medium to be simulated based on the cylindrical array model includes: Obtain the solids content and characteristic scale of the porous medium to be simulated; Based on the solidity, the feature scale, and the fixed proportional relationship between the preset cylinder diameter and the feature scale, the number of cylinders and the unit cylinder diameter in the cylinder array model are determined. The cylindrical array model is constructed based on the preset rule arrangement, the number of cylinders, and the diameter of the unit cylinder, and the outer contour feature size of the cylindrical array model is equal to the feature scale. Obtain the spatial distribution of the cylindrical array model, and determine the computational domain of the porous medium to be simulated based on the spatial distribution.

[0026] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a cylindrical array model provided in an embodiment of the present invention.

[0027] In this embodiment, the cylindrical array model consists of multiple regularly arranged cylinders. The diameter of a single cylinder is denoted by the lowercase letter 'd', and the outer contour feature size of the entire cylindrical array model is denoted by the uppercase letter 'D'. The preset fixed ratio is d = D / M, where M is a constant, set to 21 by default. After obtaining the solids content and feature size of the porous medium to be simulated, the feature size is first used as the outer contour feature size D of the cylindrical array model. Then, based on the solids content and the ratio, the required number of cylinders in the cylindrical array and the diameter of each cylinder are determined. Subsequently, cylinders with the specified number of cylinders are generated according to a regular periodic arrangement, such as a rectangular array or a regular hexagonal array, so that the outer contour diameter of the entire array is exactly equal to the feature size D. Finally, the spatial distribution of the cylindrical array model is obtained, and based on this spatial distribution, the feature size D is extended upstream, downstream, and to both sides by a preset multiple, thereby enclosing and forming the flow computation domain.

[0028] In this embodiment, the solids content and characteristic scale of the porous medium to be simulated are obtained, providing basic input parameters for model construction. Furthermore, the number of cylinders and the unit cylinder diameter are determined based on the solids content, characteristic scale, and a preset fixed ratio between the cylinder diameter and the characteristic scale, achieving a structured mapping of key physical property parameters of the porous medium to a regular geometric model. Thus, a cylindrical array model is constructed based on the regular arrangement, the number of cylinders, and the unit cylinder diameter, ensuring that the outer contour feature size equals the characteristic scale, guaranteeing an equivalent representation of the overall mechanical properties of the original porous medium. Finally, by obtaining the spatial distribution of the cylindrical array model and determining the computational domain based on this spatial distribution, the computational range of subsequent flow simulations is precisely matched with the model's geometric features, avoiding unnecessary computational waste.

[0029] In this embodiment, obtaining the spatial distribution of the cylindrical array model and determining the computational domain of the porous medium to be simulated based on the spatial distribution includes: Along the direction of the incoming flow, determine the upstream and downstream boundary positions of the cylindrical array model; Along the direction perpendicular to the incoming flow, using the center line of the cylindrical array model as a reference, determine the positions of the two side boundaries respectively; The spatial distribution of the cylindrical array model is determined based on the upstream boundary position, downstream boundary position, and both side boundary positions. The spatial distribution is used to enclose the boundary of the flow computation domain, thereby determining the computation domain of the porous medium to be simulated.

[0030] In this embodiment, the upstream endpoint of the cylindrical array model is used as the reference point, and a distance of a first preset multiple of the feature scale is extended upstream to determine the upstream boundary position. Similarly, the downstream endpoint of the cylindrical array model is used as the reference point, and a distance of a second preset multiple of the feature scale is extended downstream to determine the downstream boundary position. Perpendicular to the incoming flow direction, the centerline of the cylindrical array model is used as the reference line, and a distance of a third preset multiple of the feature scale is extended to both sides to determine the left and right boundary positions, respectively. Based on these upstream, downstream, left, and right boundary positions, the spatial distribution of the cylindrical array model in the computational domain is fully defined. Finally, a closed rectangular region is enclosed by these four boundary positions, serving as the flow computational domain for subsequent mesh generation and numerical solution.

[0031] Please refer to Figure 3 , Figure 3 This is a schematic diagram of a flow computing domain provided in an embodiment of the present invention.

[0032] In this embodiment, taking a cylindrical array model with a feature scale D of 21 mm as an example, its upstream endpoint is the leading edge of the first row of cylinders in the array, and its downstream endpoint is the trailing edge of the last row of cylinders. The upstream boundary is determined as follows: based on the upstream endpoint, extend upstream by 10 times D, i.e., 210 mm, and set this as the upstream boundary. The downstream boundary is determined as follows: based on the downstream endpoint, extend downstream by 50 times D, i.e., 1050 mm, and set this as the downstream boundary. The two side boundaries are determined as follows: based on the centerline of the cylindrical array model, extend left and right by 20 times D, i.e., 420 mm, respectively, and set as the left and right boundaries. The resulting upstream, downstream, left, and right boundaries together enclose a rectangular flow computation domain with a length of 1260 mm and a width of 840 mm. The upstream distance of this computational domain is sufficient to accommodate the bow shock wave generated by the high-speed incoming flow, and the downstream distance fully covers the wake development region behind the cylindrical array. The width on both sides effectively avoids the interference of the wall surface on the flow field, thus ensuring the physical accuracy of the simulation results.

[0033] In this embodiment, by determining the upstream and downstream boundary positions of the cylindrical array model along the incoming flow direction, the computational domain is ensured to fully encompass the disturbance and wake regions in the flow direction. Furthermore, by determining the two side boundary positions perpendicular to the incoming flow direction with the centerline of the cylindrical array model as the reference, the lateral symmetry and sufficient width of the computational domain are ensured. Subsequently, the spatial distribution of the cylindrical array model is determined based on the upstream, downstream, and side boundary positions, achieving a complete description of the model's spatial position. By defining the computational domain boundary based on the spatial distribution, the computational domain boundary matches the flow physical characteristics, effectively reducing the impact of human interference such as boundary reflections on the simulation results.

[0034] Step 102: Divide the cylindrical array model into partitioned meshes based on the computational domain to obtain computational mesh data; In this embodiment, the step of partitioning the cylindrical array model into a grid based on the computational domain to obtain computational grid data includes: Identify the wall positions of each cylinder in the cylindrical array model, and generate quadrilateral structure meshes at the wall positions of each cylinder to obtain the first mesh data; Identify the pore regions between the cylinders in the cylindrical array model, and generate triangular unstructured meshes in the pore regions to obtain second mesh data; Identify the far-field region from the outer side of the cylindrical array model to the boundary of the flow computation domain, and generate a structured quadrilateral mesh in the far-field region to obtain the third mesh data; The first grid data, the second grid data, and the third grid data are merged to obtain the computational grid data.

[0035] Please refer to Figure 4 , Figure 4 This is a schematic diagram of a grid division provided in an embodiment of the present invention.

[0036] In this embodiment, a partitioned hybrid mesh generation strategy is adopted based on the determined flow computation domain and the cylindrical array model. First, the wall position of each cylinder in the cylindrical array model is identified. A quadrilateral structured mesh is generated near the wall and locally refined to ensure the height of the first layer mesh meets the analytical requirements for wall viscous flow, thus obtaining the first mesh data. Second, the pore regions between cylinders are identified. Due to the complex geometry of these regions, triangular unstructured meshes are used to fill them to adapt to the geometric constraints of the pore boundaries, thus obtaining the second mesh data. Then, the far-field region from the outer edge of the cylindrical array model to the boundary of the flow computation domain is identified. A structured quadrilateral mesh is generated within this region to ensure mesh orthogonality and computational efficiency, thus obtaining the third mesh data. Finally, the first, second, and third mesh data are merged to form a complete computational mesh data covering the entire computation domain.

[0037] In this embodiment, a cylindrical array model with a solidity of 0.315, a feature size of 21 mm, and containing 139 cylinders with a diameter of 1 mm is used as an example. The computational domain is a rectangular area with a length of 1260 mm and a width of 840 mm. First, the wall position of each cylinder is identified, and a quadrilateral structured mesh is generated around the wall of each cylinder. Local refinement is performed near the wall to ensure that the height of the first layer of mesh closest to the wall is less than 0.01 mm, so as to meet the wall resolution requirement, that is, the dimensionless wall distance y+ is less than 1, thereby obtaining the first mesh data. Then, all the void regions formed between the 139 cylinders are identified, and these voids are filled with triangular unstructured meshes. The size of the triangular mesh cells is controlled between 0.02 mm and 0.05 mm to accommodate the narrow and curved geometric gaps between the cylinders, thereby obtaining the second mesh data. Next, the far-field region extending from the outer edge of the cylindrical array model to the boundary of the computational domain—that is, the area more than 210 mm from the center of the cylindrical array—is identified. Within this region, a uniform, structured quadrilateral mesh with a cell size of 0.1 mm is generated to ensure the orthogonality and computational efficiency of the far-field mesh, thus obtaining the third mesh data. Finally, the first, second, and third mesh data are combined to form a hybrid mesh system, which is then used as the computational mesh data output for subsequent numerical solutions.

[0038] As a specific example of an embodiment of the present invention, by identifying the wall positions of each cylinder and generating quadrilateral structured meshes at those positions, first mesh data is obtained, ensuring high-resolution analysis of the flow boundary layer near the cylinder wall. Subsequently, by identifying the pore regions between cylinders and generating triangular unstructured meshes in those regions, second mesh data is obtained, achieving flexible adaptive filling of complex pore geometry. Next, by identifying the far-field region from the outer side of the cylinder array model to the boundary of the flow computation domain and generating structured quadrilateral meshes, third mesh data is obtained, ensuring high computational efficiency and good mesh orthogonality in the far-field region. Furthermore, by merging the first, second, and third mesh data, computational mesh data is obtained, forming a hybrid mesh system that balances near-wall accuracy, pore adaptability, and far-field efficiency, laying a data foundation for subsequent high-precision solutions.

[0039] Step 103: Determine the cylindrical wall boundaries of each cylinder in the cylindrical array model from the computational mesh data, and set high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundaries to generate target mesh data; In this embodiment, the step of determining the cylindrical wall boundaries of each cylinder in the cylindrical array model from the computational mesh data, and setting high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundaries to generate target mesh data includes: In the first grid data, the wall grid cells of each cylinder in the cylindrical array model are identified and the wall grid cells are marked as the cylindrical wall boundaries. Obtain preset high-speed compressible incoming flow parameters and associate the high-speed compressible incoming flow parameters with the third grid data to obtain target third grid data; Obtain preset sliding boundary condition parameters and associate the sliding boundary condition parameters with the cylindrical wall boundary to obtain target first mesh data; The target first grid data, the target second grid data, and the target third grid data are merged to generate target grid data.

[0040] In this embodiment, after obtaining computational grid data containing first, second, and third grid data, a boundary identification operation is first performed on the first grid data. All grid cells in the first grid data are traversed, and grid cells located on the walls of each cylinder in the cylindrical array model are selected and marked as cylindrical wall boundaries, thus clarifying the objects to which subsequent slip boundary conditions are applied. Next, pre-set high-speed compressible inflow parameters, including inflow Mach number, inflow pressure, inflow temperature, and inflow direction, are obtained and associated with the third grid data. This means that inflow conditions are assigned to the inlet and far-field boundaries corresponding to the third grid data, forming target third grid data carrying inflow boundary conditions. Then, pre-set slip boundary condition parameters, including velocity slip coefficient and temperature jump coefficient, are obtained and associated with the marked cylindrical wall boundaries, forming target first grid data carrying slip boundary conditions.

[0041] Finally, the target first grid data, the unmodified second grid data, and the target third grid data are merged to generate a complete target grid data. This target grid data contains computational domain geometry information, incoming flow boundary conditions, and wall slip boundary conditions, and can be directly used in the numerical solver.

[0042] As a specific example of an embodiment of the present invention, taking a cylindrical array model with a solidity ratio of 0.315 and a feature size of 21 mm as an example, the first grid data contains 139 quadrilateral structure grids near the cylindrical wall. Each grid cell in the first grid data is traversed, and its location coordinates are used to determine whether it is located on the cylindrical surface: if the distance from the cell center to the cylindrical axis is equal to the cylindrical radius of 0.5 mm and the deviation is less than the grid size threshold, then the cell is marked as the cylindrical wall boundary. Approximately 8000 wall grid cells are marked. High-speed compressible inflow parameters are obtained: inflow Mach number is 8, inflow pressure is 1000 Pascals, inflow temperature is 300 Kelvin, and inflow direction is horizontal to the right. These parameters are assigned to the inlet boundary cell and far-field boundary cell in the third grid data. The inlet boundary is located 210 mm upstream, and the far-field boundary is located 420 mm to both sides and 1050 mm downstream, thus obtaining the target third grid data.

[0043] Subsequently, the slip boundary condition parameters were obtained. The velocity slip coefficient was adopted using the coefficient value corresponding to the second-order slip model, and the temperature jump coefficient was adopted using the coefficient value corresponding to the diffuse reflection assumption. These parameters were then associated with 8000 labeled cylindrical wall boundary elements, with each wall element recording these two coefficients to obtain the target first mesh data.

[0044] Finally, the target first grid data, the original second grid data (i.e., the pore triangle grid), and the target third grid data are merged to generate a complete target grid data file. This file has a total grid size of approximately 434,000, with approximately 120,000 elements in the first grid, approximately 200,000 elements in the second grid, and approximately 114,000 elements in the third grid. This target grid data can be directly input into the solver for numerical iterative solutions.

[0045] In this embodiment, by traversing and identifying the wall mesh elements of each cylinder in the cylindrical array model in the first mesh data and marking them as cylindrical wall boundaries, the object to which the slip boundary condition is applied is accurately defined. Subsequently, by acquiring the high-speed compressible incoming flow parameters and associating them with the third mesh data, the target third mesh data is obtained, enabling the far field and inlet boundaries to correctly bear the incoming flow conditions. Then, by acquiring the slip boundary condition parameters and associating them with the cylindrical wall boundaries, the target first mesh data is obtained, enabling the cylindrical wall to simulate velocity slip and temperature jumps. Finally, the target first mesh data, the second mesh data, and the target third mesh data are merged to generate the target mesh data, realizing the complete encapsulation of boundary conditions and mesh data, providing the solver with a directly usable input file, and avoiding processing errors caused by data fragmentation.

[0046] Step 104: Based on the preset fluid dynamics control algorithm and preset turbulence model, perform numerical iterative solution on the target grid data to obtain the flow field distribution data of the porous medium to be simulated, and complete the simulation of the coupled flow of high-speed incoming flow and porous medium.

[0047] In this embodiment, based on a preset fluid dynamics control algorithm and a preset turbulence model, the target mesh data is numerically iteratively solved to obtain the flow field distribution data of the porous medium to be simulated, thus completing the simulation of the coupled flow between high-speed incoming flow and porous medium, including: The solver is configured based on a preset fluid dynamics control algorithm and a preset turbulence model; The flow field is initialized, and the initial flow field state of the computational domain is determined based on the high-speed compressible incoming flow parameters. Numerical iterative solutions are then performed based on the initial flow field state and the solver. In each iteration, based on the current flow field state and the slip boundary condition parameters, the velocity slip and temperature jump at the cylindrical wall boundary are calculated, and the physical quantities on the cylindrical wall boundary are updated based on the velocity slip and temperature jump. Based on the physical quantities on the cylindrical wall boundary, the fluid dynamics control algorithm and the preset turbulence model are solved, and the flow field state is updated. The iteration stops when the preset conditions are met. The flow field distribution data of the porous medium to be simulated is obtained, and the simulation of the coupled flow of high-speed incoming flow and porous medium is completed.

[0048] In this embodiment, after obtaining the target mesh data, the physical model and numerical format of the solver are configured first according to the preset fluid dynamics control algorithm, namely the turbulent flow control equation obtained by statistical averaging of the Navier-Stokes equations, and the preset turbulence model, namely the two-equation turbulence model based on shear stress transport.

[0049] In this embodiment, the high-speed compressible inflow parameters carried in the target grid data are used as initial conditions. The physical quantities such as velocity, pressure, temperature and density in the entire computational domain are assigned the values ​​corresponding to the inflow parameters to form the initial flow field state, which is used as the starting point for iterative solution.

[0050] After entering the numerical iteration loop, each iteration performs a coupled update process including: calculating the velocity slip and temperature jump on the cylindrical wall boundary based on the velocity and temperature gradients near the wall in the current flow field state, combined with the slip boundary condition parameters; updating the velocity and temperature values ​​on the cylindrical wall boundary using the calculated velocity slip and temperature jump; solving the fluid dynamics control equations and turbulence model equations in the entire computational domain using the updated wall boundary physical quantities as boundary constraints to obtain the updated global field physical quantities; repeating the above process until the convergence preset condition is met, at which point the iteration stops. Finally, the converged flow field distribution data is output, completing the simulation of the coupled flow between high-speed incoming flow and porous media.

[0051] In this embodiment, the flow field distribution data includes velocity vectors, pressure, temperature, and density information on each grid cell within the computational domain. Based on this data, velocity contour maps, streamline maps, pressure contour maps, and temperature contour maps can be plotted, visually displaying the shock wave structure, shock wave boundary layer interference, vortex modes within the pores, and wake development characteristics generated when a high-speed incoming flow passes through the cylindrical array. Simultaneously, velocity and temperature gradients can be extracted from the wall boundaries, and integration can yield the wall shear stress and wall heat flux of each cylinder, thereby allowing for the statistical analysis of macroscopic aerodynamic parameters such as the total drag coefficient and average heat flux of the entire cylindrical array.

[0052] In an optional embodiment, the predicted distributions of wall pressure coefficient and heat flux coefficient are compared with the results of a high-precision direct simulation Monte Carlo method, thereby verifying the accuracy and reliability of this embodiment in the simulation of high-speed slip flow regions.

[0053] This completes the fine-scale simulation of high-speed incoming flow coupled with porous media at the pore scale. The output flow field distribution data can provide data support for the thermal protection design and flow control device development of aerospace vehicles.

[0054] In this embodiment, a suitable physical model foundation is provided for high-speed compressible flow simulation by configuring the solver based on a preset fluid dynamics control algorithm and turbulence model. Furthermore, by initializing the flow field and determining the initial flow field state of the computational domain based on the high-speed compressible inflow parameters, numerical iterative solutions are performed based on this initial flow field state and the solver, ensuring the physical rationality and convergence starting point of the iterative process. In each iteration, the velocity slip and temperature jump at the cylindrical wall boundary are calculated based on the current flow field state and slip boundary condition parameters, and the results are then used to calculate the velocity slip and temperature jump. Temperature jumps update the physical quantities on the wall boundary, and then the fluid dynamics control equations and turbulence model equations are solved based on the updated wall boundary physical quantities to update the flow field state until the preset conditions are met and the iteration stops. This coupled iteration process allows the wall slip effect to evolve synchronously with the flow in the whole field, which truly reflects the interaction mechanism between the wall and the fluid in the high-speed slip flow region. By obtaining the flow field distribution data of the porous medium to be simulated, the simulation of the coupled flow of high-speed incoming flow and porous medium is completed, providing high-fidelity pore-scale data support for aerospace thermal protection and flow control design.

[0055] In this embodiment, by acquiring the solids content and characteristic scale of the porous medium to be simulated and constructing a cylindrical array model, a regularized characterization of the geometry of complex porous media is achieved, reducing the modeling difficulty. Subsequently, the computational domain is determined based on the cylindrical array model, ensuring the rationality and specificity of the computational range. Next, computational grid data is obtained by partitioning the cylindrical array model into sub-grids, balancing near-wall analytical accuracy and overall computational efficiency. Thus, the cylindrical wall boundary is determined in the computational grid data, and high-speed compressible inflow conditions and slip boundary conditions are set to generate target grid data, enabling the simulation to accurately reflect the wall slip and temperature jump effects in the high-speed slip flow region. Furthermore, based on fluid dynamics control algorithms and turbulence models, numerical iteration is performed on the target grid data to obtain flow field distribution data, realizing a refined simulation of the pore scale of the coupled flow of high-speed inflow and porous media, while ensuring a balance between computational accuracy and efficiency.

[0056] This invention also provides a simulation device for the coupled flow of high-speed incoming flow and porous medium, comprising: a computational domain determination module, a mesh generation module, a condition determination module, and a simulation module; The computational domain determination module is used to obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model. The meshing module is used to partition the cylindrical array model into a mesh based on the computational domain to obtain computational mesh data. The condition determination module is used to determine the cylindrical wall boundary of each cylinder in the cylindrical array model from the computational grid data, and to set high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundary to generate target grid data. The simulation module is used to perform numerical iterative solutions on the target grid data based on a preset fluid dynamics control algorithm and a preset turbulence model, to obtain the flow field distribution data of the porous medium to be simulated, and to complete the simulation of the coupled flow of high-speed incoming flow and porous medium.

[0057] In this embodiment, the computational domain determination module is used to obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model, including: Obtain the solids content and characteristic scale of the porous medium to be simulated; Based on the solidity, the feature scale, and the fixed proportional relationship between the preset cylinder diameter and the feature scale, the number of cylinders and the unit cylinder diameter in the cylinder array model are determined. The cylindrical array model is constructed based on the preset rule arrangement, the number of cylinders, and the diameter of the unit cylinder, and the outer contour feature size of the cylindrical array model is equal to the feature scale. Obtain the spatial distribution of the cylindrical array model, and determine the computational domain of the porous medium to be simulated based on the spatial distribution.

[0058] In this embodiment, the computational domain determination module is used to obtain the spatial distribution of the cylindrical array model and determine the computational domain of the porous medium to be simulated based on the spatial distribution, including: Along the direction of the incoming flow, determine the upstream and downstream boundary positions of the cylindrical array model; Along the direction perpendicular to the incoming flow, using the center line of the cylindrical array model as a reference, determine the positions of the two side boundaries respectively; The spatial distribution of the cylindrical array model is determined based on the upstream boundary position, downstream boundary position, and both side boundary positions. The spatial distribution is used to enclose the boundary of the flow computation domain, thereby determining the computation domain of the porous medium to be simulated.

[0059] In this embodiment, the meshing module is used to partition the cylindrical array model into a regional mesh based on the computational domain to obtain computational mesh data, including: Identify the wall positions of each cylinder in the cylindrical array model, and generate quadrilateral structure meshes at the wall positions of each cylinder to obtain the first mesh data; Identify the pore regions between the cylinders in the cylindrical array model, and generate triangular unstructured meshes in the pore regions to obtain second mesh data; Identify the far-field region from the outer side of the cylindrical array model to the boundary of the flow computation domain, and generate a structured quadrilateral mesh in the far-field region to obtain the third mesh data; The first grid data, the second grid data, and the third grid data are merged to obtain the computational grid data.

[0060] In this embodiment, the condition determination module is used to determine the cylindrical wall boundaries of each cylinder in the cylindrical array model from the computational mesh data, and to set high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundaries to generate target mesh data, including: In the first grid data, the wall grid cells of each cylinder in the cylindrical array model are identified and the wall grid cells are marked as the cylindrical wall boundaries. Obtain preset high-speed compressible incoming flow parameters and associate the high-speed compressible incoming flow parameters with the third grid data to obtain target third grid data; Obtain preset sliding boundary condition parameters and associate the sliding boundary condition parameters with the cylindrical wall boundary to obtain target first mesh data; The target first grid data, the target second grid data, and the target third grid data are merged to generate target grid data.

[0061] In this embodiment, the simulation module is used to perform numerical iterative solutions on the target mesh data based on a preset fluid dynamics control algorithm and a preset turbulence model to obtain the flow field distribution data of the porous medium to be simulated, and to complete the simulation of the coupled flow of high-speed incoming flow and porous medium, including: The solver is configured based on a preset fluid dynamics control algorithm and a preset turbulence model; The flow field is initialized, and the initial flow field state of the computational domain is determined based on the high-speed compressible incoming flow parameters. Numerical iterative solutions are then performed based on the initial flow field state and the solver. In each iteration, based on the current flow field state and the slip boundary condition parameters, the velocity slip and temperature jump at the cylindrical wall boundary are calculated, and the physical quantities on the cylindrical wall boundary are updated based on the velocity slip and temperature jump. Based on the physical quantities on the cylindrical wall boundary, the fluid dynamics control algorithm and the preset turbulence model are solved, and the flow field state is updated. The iteration stops when the preset conditions are met. The flow field distribution data of the porous medium to be simulated is obtained, and the simulation of the coupled flow of high-speed incoming flow and porous medium is completed.

[0062] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A simulation method for high-speed incoming flow coupled with porous medium flow, characterized in that, include: Obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model. Based on the computational domain, the cylindrical array model is partitioned into a mesh to obtain computational mesh data; In the computational grid data, the cylindrical wall boundaries of each cylinder in the cylindrical array model are determined, and high-speed compressible inflow conditions and slip boundary conditions are set for the cylindrical wall boundaries to generate target grid data; Based on the preset fluid dynamics control algorithm and preset turbulence model, the target grid data is numerically iteratively solved to obtain the flow field distribution data of the porous medium to be simulated, thus completing the simulation of the coupled flow of high-speed incoming flow and porous medium.

2. The simulation method for high-speed incoming flow coupled with porous medium flow as described in claim 1, characterized in that, The process of obtaining the solids content and characteristic scale of the porous medium to be simulated, constructing a cylindrical array model based on the solids content and characteristic scale, and determining the computational domain of the porous medium to be simulated based on the cylindrical array model includes: Obtain the solids content and characteristic scale of the porous medium to be simulated; Based on the solidity, the feature scale, and the fixed proportional relationship between the preset cylinder diameter and the feature scale, the number of cylinders and the unit cylinder diameter in the cylinder array model are determined. The cylindrical array model is constructed based on the preset rule arrangement, the number of cylinders, and the diameter of the unit cylinder, and the outer contour feature size of the cylindrical array model is equal to the feature scale. Obtain the spatial distribution of the cylindrical array model, and determine the computational domain of the porous medium to be simulated based on the spatial distribution.

3. The simulation method for high-speed incoming flow coupled with porous medium flow as described in claim 2, characterized in that, The step of obtaining the spatial distribution of the cylindrical array model and determining the computational domain of the porous medium to be simulated based on the spatial distribution includes: Along the direction of the incoming flow, determine the upstream and downstream boundary positions of the cylindrical array model; Along the direction perpendicular to the incoming flow, using the center line of the cylindrical array model as a reference, determine the positions of the two side boundaries respectively; The spatial distribution of the cylindrical array model is determined based on the upstream boundary position, downstream boundary position, and both side boundary positions. The spatial distribution is used to enclose the boundary of the flow computation domain, thereby determining the computation domain of the porous medium to be simulated.

4. The simulation method for high-speed incoming flow coupled with porous medium flow as described in claim 3, characterized in that, The step of partitioning the cylindrical array model into a grid based on the computational domain to obtain computational grid data includes: Identify the wall positions of each cylinder in the cylindrical array model, and generate quadrilateral structure meshes at the wall positions of each cylinder to obtain the first mesh data; Identify the pore regions between the cylinders in the cylindrical array model, and generate triangular unstructured meshes in the pore regions to obtain second mesh data; Identify the far-field region from the outer side of the cylindrical array model to the boundary of the flow computation domain, and generate a structured quadrilateral mesh in the far-field region to obtain the third mesh data; The first grid data, the second grid data, and the third grid data are merged to obtain the computational grid data.

5. The simulation method for high-speed incoming flow coupled with porous medium flow as described in claim 4, characterized in that, The process of determining the cylindrical wall boundaries of each cylinder in the cylindrical array model from the computational mesh data, and setting high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundaries to generate target mesh data includes: In the first grid data, the wall grid cells of each cylinder in the cylindrical array model are identified and the wall grid cells are marked as the cylindrical wall boundaries. Obtain preset high-speed compressible incoming flow parameters and associate the high-speed compressible incoming flow parameters with the third grid data to obtain target third grid data; Obtain preset sliding boundary condition parameters and associate the sliding boundary condition parameters with the cylindrical wall boundary to obtain target first mesh data; The target first grid data, the target second grid data, and the target third grid data are merged to generate target grid data.

6. The simulation method for high-speed incoming flow coupled with porous medium flow as described in claim 5, characterized in that, The method, based on a preset fluid dynamics control algorithm and a preset turbulence model, performs numerical iterative solutions on the target mesh data to obtain the flow field distribution data of the porous medium to be simulated, thus completing the simulation of the coupled flow between high-speed incoming flow and porous medium, including: The solver is configured based on a preset fluid dynamics control algorithm and a preset turbulence model; The flow field is initialized, and the initial flow field state of the computational domain is determined based on the high-speed compressible incoming flow parameters. Numerical iterative solutions are then performed based on the initial flow field state and the solver. In each iteration, based on the current flow field state and the slip boundary condition parameters, the velocity slip and temperature jump at the cylindrical wall boundary are calculated, and the physical quantities on the cylindrical wall boundary are updated based on the velocity slip and temperature jump. Based on the physical quantities on the cylindrical wall boundary, the fluid dynamics control algorithm and the preset turbulence model are solved, and the flow field state is updated. The iteration stops when the preset conditions are met. The flow field distribution data of the porous medium to be simulated is obtained, and the simulation of the coupled flow of high-speed incoming flow and porous medium is completed.

7. A simulation device for high-speed incoming flow coupled with porous medium flow, characterized in that, include: The system includes modules for determining the computational domain, mesh generation, determining conditions, and simulation. The computational domain determination module is used to obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model. The meshing module is used to partition the cylindrical array model into a mesh based on the computational domain to obtain computational mesh data. The condition determination module is used to determine the cylindrical wall boundary of each cylinder in the cylindrical array model from the computational grid data, and to set high-speed compressible inflow conditions and slip boundary conditions for the cylindrical wall boundary to generate target grid data. The simulation module is used to perform numerical iterative solutions on the target grid data based on a preset fluid dynamics control algorithm and a preset turbulence model, to obtain the flow field distribution data of the porous medium to be simulated, and to complete the simulation of the coupled flow of high-speed incoming flow and porous medium.

8. The simulation device for high-speed incoming flow coupled with porous medium flow as described in claim 7, characterized in that, The computational domain determination module is used to obtain the solids content and characteristic scale of the porous medium to be simulated, construct a cylindrical array model based on the solids content and characteristic scale, and determine the computational domain of the porous medium to be simulated based on the cylindrical array model, including: Obtain the solids content and characteristic scale of the porous medium to be simulated; Based on the solidity, the feature scale, and the fixed proportional relationship between the preset cylinder diameter and the feature scale, the number of cylinders and the unit cylinder diameter in the cylinder array model are determined. The cylindrical array model is constructed based on the preset rule arrangement, the number of cylinders, and the diameter of the unit cylinder, and the outer contour feature size of the cylindrical array model is equal to the feature scale. Obtain the spatial distribution of the cylindrical array model, and determine the computational domain of the porous medium to be simulated based on the spatial distribution.

9. The simulation device for high-speed incoming flow coupled with porous medium flow as described in claim 8, characterized in that, The computational domain determination module is used to obtain the spatial distribution of the cylindrical array model and determine the computational domain of the porous medium to be simulated based on the spatial distribution, including: Along the direction of the incoming flow, determine the upstream and downstream boundary positions of the cylindrical array model; Along the direction perpendicular to the incoming flow, using the center line of the cylindrical array model as a reference, determine the positions of the two side boundaries respectively; The spatial distribution of the cylindrical array model is determined based on the upstream boundary position, downstream boundary position, and both side boundary positions. The spatial distribution is used to enclose the boundary of the flow computation domain, thereby determining the computation domain of the porous medium to be simulated.

10. The simulation device for high-speed incoming flow coupled with porous medium flow as described in claim 9, characterized in that, The meshing module is used to partition the cylindrical array model into a mesh based on the computational domain to obtain computational mesh data, including: Identify the wall positions of each cylinder in the cylindrical array model, and generate quadrilateral structure meshes at the wall positions of each cylinder to obtain the first mesh data; Identify the pore regions between the cylinders in the cylindrical array model, and generate triangular unstructured meshes in the pore regions to obtain second mesh data; Identify the far-field region from the outer side of the cylindrical array model to the boundary of the flow computation domain, and generate a structured quadrilateral mesh in the far-field region to obtain the third mesh data; The first grid data, the second grid data, and the third grid data are merged to obtain the computational grid data.

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