Flow Field Calculation Method, Device, Equipment and Storage Medium for Artificial Lung Blood Flow

Abstract

The present application provides a method, device, equipment and storage medium for calculating the flow field of artificial lung blood flow. The method includes: obtaining a three-dimensional model of an artificial lung containing a porous medium; establishing a fluid control equation for blood flow in the three-dimensional model; superimposing the momentum loss of blood flow in the porous medium region on the fluid control equation with a first momentum source term; and solving the fluid control equation to obtain the flow field of blood flow in the three-dimensional model. The embodiments of the present application can improve the accuracy of flow field calculation, so as to more accurately describe the flow characteristics of blood flow in the artificial lung.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to a flow field calculation method, device, equipment and storage medium for artificial pulmonary blood flow. Background Art

[0002] Artificial lungs, also known as oxygenators or gas exchangers, are artificial organs that replace human lungs to expel carbon dioxide and take in oxygen for gas exchange. How to calculate the flow field data of blood flow in artificial lungs is an important issue. However, the accuracy of flow field data calculation is currently poor, and it is impossible to accurately describe the flow characteristics of blood flow in artificial lungs. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a method, device, equipment and storage medium for calculating the flow field of artificial lung blood flow, which can improve the accuracy of calculating the flow field of blood flow in the artificial lung.

[0004] A first aspect of an embodiment of the present application provides a flow field calculation method for artificial pulmonary blood flow, comprising:

[0005] Obtain a 3D model of an artificial lung containing porous media.

[0006] The fluid governing equations for blood flow within the three-dimensional model were established.

[0007] The momentum loss of blood flow in the porous medium region is superimposed on the fluid control equation as the first momentum source term.

[0008] The fluid control equations are solved to obtain the flow field data of blood flow in the three-dimensional model.

[0009] In a first possible implementation manner of the first aspect, establishing a fluid control equation for blood flow in a three-dimensional model includes:

[0010] Based on the three-dimensional model, multiple sets of refined grids are generated, wherein each set of refined grids has a different degree of refinement for the three-dimensional model.

[0011] Based on each set of refined grids, data analysis of blood passing through the artificial lung is performed on the three-dimensional model.

[0012] Based on the data analysis results, a set of refined grids is selected from multiple sets of refined grids as the target grid.

[0013] Based on the target grid, the fluid control equations of blood flow in the three-dimensional model are established.

[0014] Based on the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the three-dimensional model further includes a blood flow channel.

[0015] In the data analysis of blood passing through the artificial lung for the three-dimensional model based on each refined grid, the operations for each refined grid include:

[0016] Blood simulation is input into the blood flow channel corresponding to the refined grid at different speeds respectively.

[0017] Calculate the pressure drop between the inlet and outlet of the blood flow in the blood flow channel, and obtain a simulation curve of velocity and pressure drop corresponding to the refined grid. The simulation curve is the data analysis result corresponding to the refined grid.

[0018] The refinement parameters of the refined grid include the total number of grid cells and the number of grid encryption layers. Based on the data analysis results, a set of refined grid is selected from multiple sets of refined grids as the target grid, including:

[0019] Take the simulation curve corresponding to the refined grid with the largest total number of grid cells and / or the largest number of grid encryption layers among multiple sets of refined grids as the reference curve, and calculate the deviation degree between the simulation curve corresponding to each set of refined grids and the reference curve respectively.

[0020] Select the refined grid with the deviation degree within the allowable error range and the smallest total number of grid cells and / or the smallest number of grid encryption layers as the target grid.

[0021] In the third possible implementation manner of the first aspect, the fluid control equation is the Navier-Stokes equation, and the momentum loss of the blood flow in the porous medium region is superimposed on the fluid control equation as the first momentum source term, including:

[0022] Based on Darcy's law, calculate the momentum loss of the blood flow in the porous medium region through the following formula.

[0023]

[0024]

[0025]

[0026] represents the momentum loss, μ represents the dynamic viscosity, C1 represents the permeability coefficient, v i represents the velocity vector, C2 represents the inertial loss coefficient, ρ represents the blood density, V mag represents the velocity magnitude, d0 represents the outer diameter of the porous medium, ∈ represents the porosity of the porous medium, and i represents the grid number.

[0027] Superimpose the momentum loss on the Navier-Stokes equation as the first momentum source term.

[0028] In the fourth possible implementation manner of the first aspect, after establishing the fluid control equation of the blood flow in the three-dimensional model, it further includes:

[0029] Introduce the turbulence model equation into the fluid control equation, and superimpose the Reynolds stress term in the turbulence model equation on the fluid control equation as the second momentum source term.

[0030] Based on the first possible implementation manner of the first aspect, in the fifth possible implementation manner of the first aspect, solve the fluid control equation to obtain the flow field data of the blood flow in the three-dimensional model, including:

[0031] Based on the target grid and the fluid control equation, partition the target grid to obtain a plurality of computational blocks.

[0032] Discretize each computational block using the finite volume method so that each computational block corresponds to a matrix equation.

[0033] Parallel-compute the matrix equations corresponding to each computational block to obtain the flow field data of the blood flow in the three-dimensional model.

[0034] In the sixth possible implementation manner of the first aspect, after solving the fluid control equation to obtain the flow field data of the blood flow in the three-dimensional model, it further includes:

[0035] Based on the flow field data, evaluate the hemolysis performance of the artificial lung through the following three-dimensional rapid hemolysis prediction model.

[0036] NIH = Hb × D × 100,

[0037]

[0038]

[0039]

[0040] where, NIH is the standard hemolysis parameter value, Hb is the total hemoglobin concentration, D is the hemolysis value, D i is the average linear hemolysis index, Q is the blood volume flow rate, σ is the hemolysis destruction rate per unit time, and τ is the shear stress of the blood flow.

[0041] The second aspect of the embodiments of the present application provides a flow field calculation device for the blood flow of an artificial lung, including:

[0042] An acquisition module for acquiring a three-dimensional model of an artificial lung containing a porous medium. A establishment module for establishing a fluid control equation for the blood flow in the three-dimensional model. A first calculation module for superimposing the momentum loss of the blood flow in the porous medium region on the fluid control equation as the first momentum source term. A second calculation module for solving the fluid control equation to obtain the flow field data of the blood flow in the three-dimensional model.

[0043] The third aspect of the embodiments of the present application provides a terminal device, including a memory and a processor. A computer program that can run on the processor is stored on the memory. When the processor executes the computer program, the steps of the above-mentioned flow field calculation method for artificial lung blood flow are implemented.

[0044] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by the processor, the steps of the above-mentioned flow field calculation method for artificial lung blood flow are implemented.

[0045] The beneficial effects of the embodiments of the present application compared with the prior art are as follows:

[0046] In the embodiments of the present application, a three-dimensional model of an artificial lung including a porous medium can be obtained. Since this three-dimensional model includes the model of the porous medium, the fluid control equation established therefrom can more accurately and completely describe the blood flow situation in the artificial lung. Therefore, by solving the fluid control equation superimposed with the momentum loss of blood flow in the porous medium region, the accuracy of flow field calculation can be improved, and thus the flow characteristics of blood flow in the artificial lung can be more accurately described.

[0047] In the embodiments of the present application, for the calculation result of the artificial lung flow field obtained, a fast three-dimensional numerical hemolysis prediction model is established to accurately evaluate whether the hemolysis performance of the artificial lung reaches the range allowed by normal human physiology. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 is a schematic flowchart of a flow field calculation method for artificial lung blood flow provided by an embodiment of the present application;

[0050] Figure 2 is a schematic structural diagram of an artificial lung provided by an embodiment of the present application;

[0051] Figure 3 is a schematic flowchart of a flow field calculation method for artificial lung blood flow provided by an embodiment of the present application;

[0052] Figure 4 is a schematic flowchart of a flow field calculation method for artificial lung blood flow provided by an embodiment of the present application;

[0053] Figure 5 is a schematic diagram of the simulation experiment results provided by an embodiment of the present application;

[0054] Figure 6 It is a schematic diagram of a refined grid provided by an embodiment of the present application;

[0055] Figure 7 It is a schematic flow diagram of a method for calculating the flow field of artificial lung blood flow provided by an embodiment of the present application;

[0056] Figure 8 It is a schematic diagram of the flow field calculation result provided by an embodiment of the present application;

[0057] Figure 9 It is a schematic diagram of the blood flow velocity distribution provided by an embodiment of the present application;

[0058] Figure 10 It is a schematic diagram of the blood pressure distribution provided by an embodiment of the present application;

[0059] Figure 11 It is a schematic diagram of the hemolytic performance of an artificial lung provided by an embodiment of the present application;

[0060] Figure 12 It is a schematic diagram of the parallel computing efficiency provided by an embodiment of the present application;

[0061] Figure 13 It is a schematic structural diagram of a flow field calculation device for artificial lung blood flow provided by an embodiment of the present application;

[0062] Figure 14 It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. Detailed implementation manners

[0063] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0064] An artificial lung, also known as an oxygenator or gas exchanger, is an artificial organ that replaces the human lungs in expelling carbon dioxide and taking in oxygen for gas exchange. As a type of artificial lung, the wearable artificial lung introduces venous blood from the blood flow inlet of the artificial lung during actual operation and passes it horizontally through a porous medium with a specific porosity. The porous medium can simulate the function of the alveoli, so that the introduced venous blood can exchange blood oxygen with the gas introduced into the porous medium, so that arterial blood with high oxygen saturation can flow out from the blood flow outlet of the artificial lung. The entire flow process is relatively complex, and the numerical solution process has high requirements for the mathematical description of the flow in the porous medium area, the discretization of the equations, and the solution accuracy.

[0065] The main problem in the numerical calculation of artificial lungs is the accuracy of flow field calculation. The flow field calculation can include the calculation of velocity field and pressure field.

[0066] In computational fluid dynamics, the calculation is divided into transient calculation and steady-state calculation according to whether the flow variables change over time. Steady-state calculation only calculates the flow variables in steady state and cannot show the complete process of flow. Transient calculation can fully show the details of the flow process and flow field.

[0067] Blood flow processing technology for artificial lungs can include microscale model description and porous media model description. Generally speaking, the microscale model description is independent of the fiber type and structure of the artificial lung and can effectively predict the blood flow process at the fiber level. However, it is still challenging to apply such technology to the overall flow calculation of artificial lungs because the total number of cells in the required grid is large, resulting in excessive calculation and long calculation time.

[0068] In the embodiment of the present application, the porous medium model description can model the fiber bundles, blood, and gas in the artificial lung as a lumped continuum, and introduce it into the momentum equation in the form of a source term for calculation. The description method of the porous medium model overcomes the huge number of cells required to model a single cylindrical fiber in the entire fiber bundle and the problem of micro-scale description accuracy, so that the blood flow inside the artificial lung can be more accurately displayed with a smaller number of grid cells.

[0069] Therefore, the embodiment of the present application provides a flow field calculation method for artificial lung blood flow, and applies the porous medium model to artificial lung hemodynamics calculation, which can describe the flow of blood inside the artificial lung from a holistic level, thereby improving the accuracy of flow field calculation.

[0070] In addition, this embodiment provides a numerical hemolysis prediction calculation method, which uses the accurate calculation results of the blood flow inside the artificial lung to accurately evaluate the hemolysis performance of the artificial lung at an overall level.

[0071] To illustrate the technical solution described in this application, the following will be described through specific embodiments.

[0072] Figure 1 The flowchart of the flow field calculation method for the artificial lung blood flow provided by an embodiment of this application is shown. The artificial lung includes a porous medium. The porous medium has pores and can simulate the alveoli of the human lung for the passage of gas during gas exchange. By way of example and not limitation, the porous medium is a fiber bundle. The artificial lung has a blood flow channel. When blood passes through this blood flow channel, gas exchange occurs through the pores of the fiber bundle. The flow field calculation method for the artificial lung blood flow is described in detail as follows.

[0073] S101. Obtain a three-dimensional model of an artificial lung containing a porous medium.

[0074] A three-dimensional model is a polygonal representation of an object and is usually displayed using a computer or other video device. The object displayed can be a real-world entity or a fictional object. Anything that exists in the physical nature can be represented by a three-dimensional model.

[0075] Therefore, the three-dimensional model of the artificial lung can define the flow range of blood in the artificial lung and provide a basis for calculating the flow field of blood in the artificial lung.

[0076] In this embodiment, the obtained three-dimensional model of the artificial lung further includes a model of the porous medium. The porous medium can simulate the alveoli for the passage of gas during gas exchange. The three-dimensional model of the artificial lung containing the porous medium can more accurately and completely describe the flow of blood in the artificial lung.

[0077] As an alternative implementation, the three-dimensional model containing the porous medium is as Figure 2 shown. Figure 2 Part (a) in it is a three-dimensional view, Figure 2 and part (b) of

[0078] is a sectional view.

[0079] This application embodiment does not specifically limit the data source of the three-dimensional model, and those skilled in the art can obtain it as needed. By way of example and not limitation, the data of the three-dimensional model can be obtained from the manufacturer of the artificial lung; or a three-dimensional model can be custom-built using a computer; or by scanning the artificial lung entity, a three-dimensional model corresponding to the artificial lung entity can be obtained.

[0080] It should be understood that after obtaining a three-dimensional model containing a porous medium, the blood flow field of the entire region of the three-dimensional model can be calculated, or the blood flow field of a partial region of the three-dimensional model can be calculated. The embodiments of the present application do not make specific limitations in this regard.

[0081] As an example rather than a limitation, in the embodiments of the present application, the case of calculating the blood flow field of the entire region of the three-dimensional model is taken as an example for description.

[0082] S102. Establish a fluid control equation for blood flow in the three-dimensional model.

[0083] After obtaining the three-dimensional modeling, a fluid control equation for blood flow in the three-dimensional model can be established.

[0084] It should be noted that a control equation refers to a mathematical equation that can accurately and completely describe a certain physical phenomenon or law. In problems of fluid flow and heat transfer, the control equation can be expressed as a mathematical expression that satisfies the conservation law, such as the continuity equation, momentum equation, and energy equation of viscous fluids and non-viscous fluids in fluid mechanics.

[0085] Therefore, in some embodiments of the present application, the established fluid control equation may include the continuity equation, momentum equation, and energy equation of blood flow.

[0086] Since the three-dimensional model includes a model of a porous medium. Therefore, the fluid control equation established therefrom can more accurately and completely describe the flow of blood in the artificial lung.

[0087] As an example rather than a limitation, the fluid control equation can be the Navier-Stokes equations (N-S). The N-S equation is a motion equation that describes the conservation of momentum of a viscous incompressible fluid. The equation is described as follows.

[0088]

[0089]

[0090] Among them, v refers to the blood flow velocity, p refers to the blood flow pressure, is the kinematic viscosity, μ is the dynamic viscosity, and ρ is the blood flow density.

[0091] According to the application characteristics of the artificial lung, the velocity and pressure of the blood flow at the inlet and outlet of the artificial lung should meet the corresponding conditions required by the human body. Therefore, as an optional implementation manner, the corresponding conditions can be used as the boundary conditions of the fluid control equation so as to solve the fluid control equation.

[0092] As an example rather than a limitation, the boundary condition at the inlet is:

[0093] Uinlet = 1.16 m / s (3)

[0094]

[0095] U inlet is the velocity of blood flow at the inlet, and p1 is the pressure of blood flow at the inlet.

[0096] As an example rather than a limitation, the boundary conditions of blood flow at the outlet are:

[0097]

[0098] p2 = 101325 Pa (6)

[0099] U outlet is the velocity of blood flow at the outlet, and p2 is the pressure of blood flow at the outlet.

[0100] S103. Superimpose the momentum loss of blood flow in the porous medium region on the fluid control equation as the first momentum source term.

[0101] The source term is a generalized quantity, which represents the sum of all other terms that cannot be included in the unsteady term, convective term, and diffusive term of the fluid control equation. The source term can include mass source term, momentum source term, energy source term, and turbulence source term.

[0102] In this embodiment, the momentum loss of blood flow in the porous medium region is superimposed on the fluid control equation as the first momentum source term. Therefore, the fluid control equation superimposed with this first momentum source term realizes the application of the porous medium model to the hemodynamic calculation of the artificial lung, and can more accurately and completely describe the blood flow situation inside the artificial lung, so as to improve the accuracy of the flow field calculation results.

[0103] As an alternative implementation, the embodiments of the present application can describe the momentum loss of blood flow in the porous medium region based on Darcy's law.

[0104] It should be noted that Darcy's law is a law that describes the linear relationship between the seepage velocity of water in saturated soil and the hydraulic gradient, also known as the linear seepage law. This law states that the velocity of water passing through the porous medium is proportional to the magnitude of the hydraulic gradient and the permeability of the medium.

[0105] According to the formula derivation of Darcy's law, Darcy's law describes the relationship that the seepage velocity is proportional to the head loss rate. It is also known that the permeability coefficient only depends on two factors, namely the characteristics of the seepage material system itself and the characteristics of the fluid itself. The former is only related to the composition structure of the porous medium and is the only content that can be changed. Since the permeability coefficient has the scale of velocity and is determined by the structure of the porous medium and the properties of the fluid. Therefore, solutions can be sought from this when analyzing and controlling seepage.

[0106] Therefore, based on Darcy's law, the expression for the momentum loss of blood flow in the porous medium region can be described as:

[0107]

[0108] The first term in Equation (1) represents the viscous loss, and the second term represents the inertial loss.

[0109] represents the momentum loss, μ represents the dynamic viscosity, C1 represents the permeability coefficient, v i represents the velocity vector, C2 represents the inertial loss coefficient, ρ represents the blood density, V mag represents the magnitude of the velocity, and i represents the grid number.

[0110]

[0111]

[0112] d0 represents the outer diameter of the porous medium, and ∈ represents the porosity of the porous medium.

[0113] Then the momentum loss can be superimposed as the first momentum source term in the N - S equation.

[0114] The embodiments of the present application can describe the momentum loss of blood flow in the porous medium region based on Darcy's law, realizing the application of the porous medium model in the calculation of artificial lung hemodynamics, and can describe the flow of blood inside the artificial lung from an overall level to improve the accuracy of the flow field calculation results.

[0115] S104. Solve the fluid control equation to obtain the flow field data of the blood flow in the three - dimensional model.

[0116] After constructing the above - mentioned fluid control equation superimposed with momentum loss, based on the above - mentioned boundary conditions, corresponding mathematical methods can be used to solve the fluid control equation, thereby calculating the flow field data of the blood flow in the three - dimensional model.

[0117] It should be noted that the embodiments of the present application do not specifically limit the corresponding mathematical methods, and those skilled in the art can select the corresponding mathematical methods for solution according to needs.

[0118] By way of example and not limitation, the corresponding mathematical methods may include: finite difference method, finite volume method, finite element method, discontinuous finite element method, spectral method, particle-based method, etc.

[0119] Based on the above description, the flow field calculation method of artificial lung blood flow provided by the embodiments of the present application can obtain a three-dimensional model including a porous medium. The fluid control equation established thereby can more accurately and completely describe the blood flow in the artificial lung. Therefore, solving the fluid control equation superimposed with the momentum loss of blood flow in the porous medium region can improve the accuracy of flow field calculation, and thus more accurately describe the flow characteristics of blood flow in the artificial lung.

[0120] As Figure 3 shown, in some embodiments, step S102 may include steps S301 to S304, which are described in detail as follows.

[0121] S301. Generate multiple sets of refined meshes based on the three-dimensional model, where the refinement degrees of the sets of refined meshes for the three-dimensional model are different.

[0122] In computational fluid dynamics, a set of discrete points distributed in the flow field according to certain rules is called a mesh. The process of generating these nodes is called mesh generation. Mesh generation is the link connecting the geometric model and the numerical algorithm. After the geometric model is divided into meshes of a certain standard, it can be numerically solved. Generally speaking, the denser the mesh division, the more accurate the result. The accuracy and efficiency of the numerical calculation results mainly depend on the mesh and the algorithm used in the division, and it and the solution of the fluid control equation are two important links in the flow field calculation.

[0123] It should be noted that in the embodiments of the present application, each cell on the mesh corresponds to a small area of the three-dimensional model, that is, a unit area. Therefore, in the process of establishing the fluid control equation, the equation can be established based on the divided mesh. The obtained fluid control equation has variables including the variables of each unit area in the three-dimensional model. By way of example and not limitation, the variables of each unit area may include velocity and pressure. After solving the velocity and pressure of each unit area, the flow field data of the blood flow can be obtained.

[0124] By way of example and not limitation, meshes can be divided into three categories: structured meshes, unstructured meshes, and hybrid meshes.

[0125] The advantage of structured meshes is that the relationship between nodes and adjacent points can be automatically obtained according to the rules of mesh numbers, and it is very easy to achieve boundary fitting of the region. The disadvantage of structured meshes is that the applicable range is relatively narrow and only applicable to regular-shaped figures.

[0126] The unstructured grid technology mainly makes up for the defect that structured grids cannot solve the grid division of arbitrary shapes and arbitrary connected regions. In such a grid, there is no fixed rule to follow for the node numbering, and the number of adjacent points for each node is not fixed. Therefore, the distribution controllability of nodes in the unstructured grid is good, it can handle boundaries well, and is suitable for the generation of grids for complex structure models in fluid machinery.

[0127] Hybrid grids include structured grids and unstructured grids.

[0128] Based on the above description of the grid, in some embodiments of the present application, the unstructured grid technology can be used to generate hexahedral grids.

[0129] Therefore, when generating a grid for a three-dimensional model, the three-dimensional model can be divided into a grid with several cells. For the same three-dimensional model, if the number of cells in different grids is different, then the refinement degrees of different grids for the three-dimensional model are also different.

[0130] It should be noted that when generating a grid, there can be multiple refinement parameters for the grid. The embodiments of the present application do not make specific limitations on this. By way of example and not limitation, the refinement parameters of the grid can include the number of cells in the grid and the number of grid encryption layers. The larger the number of cells and / or the number of grid encryption layers, the higher the refinement degree of the three-dimensional model. The fluid control equations established therefrom can more accurately and completely describe the blood flow situation in the artificial lung.

[0131] S302. Based on each set of refined grids, respectively perform data analysis on the blood passing through the artificial lung for the three-dimensional model.

[0132] Since the refinement degrees of each set of refined grids for the three-dimensional model are different, therefore, under the same other conditions, the flow characteristics of the blood passing through the artificial lung will change differently. For example, the blood flows into the inlet of the artificial lung at a specific speed and pressure. For different refined grids, the pressure of the blood at the outlet is different. Based on this characteristic, data analysis on the blood passing through the artificial lung for the three-dimensional model can be performed based on each set of refined grids.

[0133] It should be understood that the above speed and pressure are only for example, and the embodiments of the present application can also use the changes of other parameters as the object of data analysis. The embodiments of the present application do not make specific limitations on this. For example, other parameters can include physical quantities such as temperature, turbulent kinetic energy, and turbulent dissipation rate.

[0134] S303. Based on the data analysis results, select a set of refined grids from multiple sets of refined grids as the target grid.

[0135] Therefore, based on the above description, data analysis of blood flowing through the artificial lung for the three-dimensional model can be performed based on each set of refined meshes. Based on the data analysis results, a set of refined meshes is selected as the target mesh.

[0136] By way of example and not limitation, since the flow field of blood flow can include a pressure field and a velocity field. Therefore, the above data analysis can analyze the pressure drop and / or velocity difference between the inlet and outlet of the artificial lung for blood flow.

[0137] It should be noted that through mathematical relationship transformation, the pressure drop and / or velocity difference can be converted into other parameters, and these parameters are used as the data analysis results. The technical solutions obtained through this conversion should fall within the protection scope of the embodiments of the present application.

[0138] As Figure 4 shown, in some embodiments, the three-dimensional model further includes a blood flow channel. In step S302, the operations on each set of refined meshes may include the following steps:

[0139] S401. Input blood simulations into the blood flow channels corresponding to the refined meshes at different speeds respectively.

[0140] Since the diameter of the blood flow channel is fixed, the speed can be converted into flow rate for representation. By way of example and not limitation, different speeds can be characterized by a flow rate of 3 - 5 L / min.

[0141] By way of example and not limitation, the refinement parameters of multiple sets of refined meshes are shown in Table 1 below. Table 1 shows 5 sets of refined meshes, denoted by A, B, C, D, and E respectively.

[0142] Table 1

[0143] Refined grid Total number of cells in the grid <![CDATA[Average grid size (m 3 )]]> Number of grid encryption layers A 1903949 <![CDATA[5.73 E -4 > 5 B 4700780 <![CDATA[4.24 E -4 > 6 C 8741078 <![CDATA[3.45 E -4 > 8-9 D 3365301 <![CDATA[4.74 E -4 > 5-6 E 11885543 <![CDATA[3.11 E -4 > 11-12

[0144] S402. Calculate the pressure drop between the inlet and outlet of the blood flow in the blood flow channel to obtain a simulation curve of velocity and pressure drop corresponding to the refined mesh, and the simulation curve is the data analysis result corresponding to the refined mesh.

[0145] By way of example and not limitation, Figure 5 shows the simulation curve graphs corresponding to 5 sets of refined meshes.

[0146] In some embodiments, step S303 may include the following steps:

[0147] S403. Take the simulation curve corresponding to the refined mesh with the largest total number of cells and / or the largest number of mesh refinement layers among multiple sets of refined meshes as the reference curve, and calculate the deviation degrees of the simulation curves corresponding to each set of refined meshes from the reference curve respectively.

[0148] S404. Select the refined grid with the deviation within the allowable error range and the smallest total number of cells and / or the smallest number of grid encryption layers as the target grid.

[0149] In an ideal situation, the larger the values of the total number of cells in the grid and the number of grid encryption layers, the higher the accuracy of numerical calculation. However, in actual application scenarios, limited by the maximum computing power of the computer and the required maximum allowable time consumption, the values of the total number of cells in the grid and the number of grid encryption layers cannot be increased indefinitely.

[0150] Therefore, based on the maximum computing power of the computer and the required maximum allowable time consumption, the threshold ranges of the total number of cells in the grid and the number of grid encryption layers can be determined. For different refined grids, the values of the total number of cells in the grid and / or the number of grid encryption layers are inconsistent.

[0151] Based on the above analysis, the larger the values of the total number of cells in the grid and the number of grid encryption layers, the higher the accuracy of numerical calculation. Therefore, in step S403, the simulation curve corresponding to the refined grid E with the largest total number of cells can be used as the reference curve. Then, the deviation degrees between the simulation curves corresponding to the refined grids A, B, C, D, and E and the reference curve can be calculated respectively.

[0152] The deviation degrees of the simulation curves corresponding to the refined grids A, B, C, D, and E can be denoted as f1, f2, f3, f4, and f5 respectively. Among them, f5 is equal to 0. When f1, f2, f3, and f4 all exceed the allowable error range, only the refined grid E can be selected as the generated grid. When at least one of f1, f2, f3, and f4 is within the allowable error range, select the refined grid with the smallest total number of cells and / or the smallest number of grid encryption layers as the target grid.

[0153] As an example rather than a limitation, in Figure 5 shown, if only the deviation degrees of the simulation curves corresponding to the refined grids B and C are within the allowable error range, then select the refined grid B as the target grid. When this refined grid B is applied to the three-dimensional model, its effect is as Figure 6 shown. Figure 6 The (a) part and (b) part in Figure 2 correspond to the (a) part and (b) part in

[0154] Therefore, among the refined grids corresponding to the deviation degrees within the allowable error range, select the refined grid with the smallest total number of cells and / or the smallest number of grid encryption layers as the target grid.

[0155] Therefore, one function of steps S403 and S404 is to balance the calculation accuracy of the flow field and the calculation time consumption, and select a relatively reasonable refined grid as the target grid.

[0156] Based on the above description, embodiments of the present application can screen out a more reasonable refined grid as the target grid by analyzing the data of blood flowing through the artificial lung in the three-dimensional model, so as to ensure higher calculation accuracy of the flow field and less calculation time consumption.

[0157] S304. Based on the target grid, establish a fluid control equation for blood flow in the three-dimensional model.

[0158] Therefore, based on the above-determined target grid, a fluid control equation that takes into account both the calculation accuracy of the flow field and the calculation time consumption can be obtained.

[0159] As Figure 7 shown, in some embodiments, step S104 may include the following steps:

[0160] S701. Based on the target grid and the fluid control equation, partition the target grid to obtain a plurality of computational blocks.

[0161] It should be noted that one function of partitioning to obtain a plurality of computational blocks is that subsequent data processing can be performed on different computational blocks in a parallel manner, that is, each computational block corresponds to a processor computing core.

[0162] As an example rather than a limitation, during partitioning, matrix allocation of different cores can be performed according to the amount of calculation and the number of grid cells to obtain higher parallel efficiency.

[0163] S702. Discretize each computational block using the finite volume method, so that each computational block corresponds to a matrix equation.

[0164] In this embodiment, discretizing the computational block using the finite volume method can better describe and discretize the field variables of the corresponding cells and the cell centers on the computational block, making the entire calculation easy to converge, thereby improving the efficiency of parallel computing.

[0165] S703. Parallel-compute the matrix equations corresponding to each computational block to obtain the flow field data of blood flow in the three-dimensional model.

[0166] During calculation, for different matrix equations, corresponding matrix solution methods can be used for solution, so as to solve the blood flow pressure and blood flow velocity corresponding to each cell on each computational block.

[0167] As an example rather than a limitation, for each matrix equation, the multi-grid method can be used to solve the pressure, and a smoother can be used to calculate the predicted value of the velocity. After reaching the predetermined convergence condition, as the calculation time increases, the overall calculated flow field gradually stabilizes, and finally a flow field diagram of the flow field change and streamline change of the entire three-dimensional model in a certain time period can be obtained.

[0168] In some embodiments, based on the characteristics of blood flow, a corresponding turbulence model equation is introduced into the N-S equation. By way of example and not limitation, the calculation methods of turbulence can be divided into three categories: Reynolds-averaged simulation, scale-resolving simulation, and direct numerical simulation.

[0169] The Reynolds-averaged simulation method (Reynolds Average Navier-Stokes, RANS) assumes that the flow field variables in turbulence consist of a time-averaged quantity and a fluctuating quantity, and based on the Boussinesq hypothesis, that is, it is considered that the turbulent Reynolds stress is proportional to the strain. Thus, the calculation of turbulence is reduced to the calculation of the proportionality coefficient (i.e., the eddy viscosity coefficient) between the Reynolds stress and the strain, so as to close the time-averaged N-S equation.

[0170] The scale-resolving simulation method refers to directly solving a part of the turbulence in the flow field, and calculating the rest through a mathematical model.

[0171] The direct numerical simulation method (Direct numerical simulation, DNS) directly calculates turbulence using the transient Navier-Stokes equation. In theory, accurate calculation results can be obtained, but it will also cause a great increase in the calculation cost.

[0172] In order to close the Reynolds-averaged N-S equation, it is necessary to solve the eddy viscosity coefficient in the equation. The embodiments of the present application introduce the transport equations of two scalars, namely the turbulent kinetic energy k and the turbulent kinetic energy dissipation rate ε, using the corresponding standard k-ε model, and establish the relationship between the eddy viscosity coefficient and the two variables. The form of the equations is as follows:

[0173]

[0174]

[0175] In the formula, α means phase, and the value here is 1; ρ is the blood density; k is the turbulent kinetic energy; u is the velocity component; D k is the effective diffusivity of k; G is the generation rate of turbulent kinetic energy caused by the anisotropic part of the Reynolds stress tensor; ε is the turbulent kinetic energy diffusivity; S k is the internal source term of k; S fvoptions is the added source term; D ε is the effective diffusivity of ε; C4 is the model coefficient, with the unit of seconds; C 3,RDT is the coefficient of the rapid distortion theory compression term, dimensionless; C5 is the dimensionless parameter of the model; S ε is the internal source term of ε.

[0176] For the initial values of the k and ε variables in Equations (10) and (11), the following system of equations can be used for calculation. For isotropic turbulence:

[0177]

[0178]

[0179] In Equation (13), I is the turbulence intensity, U ref is the reference velocity, C μ is an empirical constant, and L is the characteristic length of the three-dimensional model.

[0180] By way of example and not limitation, in combination with Figure 1 、 Figure 3 、 Figure 4 and Figure 7 shown in the embodiments, the flow field changes and streamline changes of the three-dimensional model over a certain period of time can be calculated to obtain a flow field diagram as shown in Figure 8 . Figure 8 The (a) part and (b) part in Figure 6 correspond to the (a) part and (b) part in

[0181] In some embodiments, after solving the fluid control equations and calculating the flow field data of the blood flow in the three-dimensional model, the method for calculating the flow field of the artificial lung blood flow further includes the following steps:

[0182] Based on the flow field data calculated from the above embodiments, the hemolysis performance of the artificial lung is evaluated through the following three-dimensional rapid hemolysis prediction model.

[0183] NIH = Hb × D × 100 (14)

[0184]

[0185]

[0186]

[0187] where NIH is the standard hemolysis parameter value, Hb is the total hemoglobin concentration, D is the hemolysis value, D i is the average linear hemolysis index, Q is the blood volume flow rate, σ is the hemolysis destruction rate per unit time, and τ is the shear stress of the blood flow.

[0188] In the embodiments of the present application, the evaluation model of hemolytic performance takes into account the overall average effect of the flow field and calculates by performing a linear field integral on the velocity domain and shear stress domain of the entire stable flow field, which can quickly evaluate whether the hemolytic performance of the artificial lung reaches the normal physiological allowable range of the human body. Using the evaluation results, the product quality of the artificial lung can be verified, and a data basis is provided for improving the design of the artificial lung in the later stage.

[0189] As an example but not a limitation, as Figure 9 shown. Based on the flow field calculation method of the artificial lung blood flow provided in the above embodiments, the blood flow velocity distribution of the artificial lung is well-behaved and has extremely high resolution, including the overall blood flow velocity distribution and the details of the blood flow velocity at the inlet and outlet.

[0190] As an example but not a limitation, as Figure 10 shown. The blood pressure distribution of the artificial lung is well-behaved. As an example but not a limitation, as Figure 11 shown. The hemolytic performance of the artificial lung is well-behaved. As an example but not a limitation, as Figure 12 shown. The above embodiments show excellent parallel efficiency when tested on the supercomputer "Tianhe-2".

[0191] Based on the flow field calculation method of the artificial lung blood flow provided in the above embodiments, the following advantages can be achieved:

[0192] (1) Applying the porous medium model to the calculation of artificial lung hemodynamics can describe the blood flow inside the artificial lung from an overall level, comprehensively display the flow details, and improve the accuracy of the calculation results.

[0193] (2) Solving the incompressible N - S equation based on Reynolds-averaged simulation, and determining a set of refined grids through data analysis, can reduce the influence of the number of grid cells on the calculation results, and finally high-resolution calculation results can also be obtained.

[0194] (3) Based on the above high-resolution calculation results, the distribution of regions with higher shear stress in the artificial lung and the specific shear stress values are predicted, providing a reference for the future design and optimization of the artificial lung.

[0195] (4) A three-dimensional numerical hemolysis prediction model is established, which can evaluate whether the hemolytic performance of the artificial lung model is normal to verify the product quality of the artificial lung.

[0196] (5) Partitioning the target grid can perform matrix allocation of different cores according to the calculation amount and the number of grid cells to obtain higher parallel efficiency.

[0197] It should be understood that, on the premise of no logical conflict, the above-mentioned various application embodiments can be combined and implemented with each other to meet the actual application requirements. The specific embodiments or implementation schemes obtained after these combinations still fall within the protection scope of this application.

[0198] Corresponding to the method for calculating the flow field of artificial lung blood flow described in the above embodiments, Figure 13 The structural schematic diagram of the device for calculating the flow field of artificial lung blood flow provided by an embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0199] Please refer to Figure 13 As shown, the device for calculating the flow field of artificial lung blood flow includes:

[0200] An acquisition module 131, configured to acquire a three-dimensional model of an artificial lung containing a porous medium.

[0201] A building module 132, configured to establish a fluid control equation of blood flow in the three-dimensional model.

[0202] A first calculation module 133, configured to superimpose the momentum loss of blood flow in the porous medium region on the fluid control equation as a first momentum source term.

[0203] A second calculation module 134, configured to solve the fluid control equation to obtain the flow field data of blood flow in the three-dimensional model.

[0204] In some embodiments, the building module 132 includes: a generating unit, configured to generate multiple sets of refined meshes based on the three-dimensional model, where the refinement degrees of each set of refined meshes for the three-dimensional model are different. An analyzing unit, configured to respectively perform data analysis of blood passing through the artificial lung on the three-dimensional model based on each set of refined meshes. A screening unit, configured to screen out a set of refined meshes as the target mesh from the multiple sets of refined meshes based on the data analysis results. A building unit, configured to establish a fluid control equation of blood flow in the three-dimensional model based on the target mesh.

[0205] In some embodiments, the three-dimensional model further includes a blood flow channel. The analyzing unit includes: a simulation unit, configured to respectively simulate and input blood into the blood flow channel corresponding to this set of refined meshes at different speeds. A first calculation unit, configured to calculate the pressure drop between the inlet and the outlet of the blood flow in the blood flow channel, obtain a simulation curve of speed and pressure drop corresponding to this set of refined meshes, and use the obtained simulation curve as the data analysis result corresponding to this set of refined meshes.

[0206] In some embodiments, the refinement parameters for refining the grid include the total number of cells in the grid and the number of grid encryption layers. The screening unit includes: a second calculation unit for using the simulation curve corresponding to the refined grid with the largest total number of cells and / or the largest number of grid encryption layers among multiple sets of refined grids as the reference curve, and respectively calculating the deviation degrees of the simulation curves corresponding to each set of refined grids from the reference curve. A selection unit for screening out the refined grid with the deviation degree within the allowable error range and the smallest total number of cells and / or the smallest number of grid encryption layers as the target grid.

[0207] In some embodiments, the fluid control equation is the Navier - Stokes equation. The momentum loss of blood flow in the porous medium region can be described based on Darcy's law. And the momentum loss is superimposed on the Navier - Stokes equation as the first momentum source term.

[0208] In some embodiments, the flow field calculation device for artificial lung blood flow further includes:

[0209] An introduction module for introducing a turbulence model equation into the fluid control equation, and superimposing the Reynolds stress term in the turbulence model equation on the Navier - Stokes equation as the second momentum source term.

[0210] In some embodiments, the second calculation module 134 includes: a partitioning unit for partitioning the target grid based on the target grid and the fluid control equation to obtain a plurality of calculation blocks. A discretization unit for discretizing each calculation block using the finite volume method, such that each calculation block corresponds to a matrix equation.

[0211] A parallel calculation unit for parallelly calculating the matrix equations corresponding to each calculation block to obtain the flow field data of the blood flow in the three - dimensional model.

[0212] In some embodiments, after solving the fluid control equation to obtain the flow field data of the blood flow in the three - dimensional model, the flow field data can be used to establish a fast three - dimensional numerical hemolysis prediction model to accurately evaluate whether the hemolysis performance of the artificial lung reaches the normal physiological allowable range of the human body.

[0213] In the flow field calculation device for artificial lung blood flow provided by the embodiments of the present application, the process of each module realizing its respective functions can be specifically referred to the description of the foregoing Figure 1 illustrated embodiments and other related method embodiments, and will not be elaborated herein.

[0214] It should be noted that the information interaction, execution process, etc. between the above - mentioned devices / units, due to being based on the same concept as the method embodiments of the present application, for their specific functions and the technical effects brought, can be specifically referred to the method embodiment part, and will not be elaborated herein.

[0215] It should be understood that the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0216] It should be understood that when used in the specification and appended claims of the present application, the terms "comprising", "including", etc. indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0217] In addition, in the description of the specification and appended claims of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions, and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in some embodiments of the present application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first calculation module may be named the second calculation module, and similarly, the second calculation module may be named the first calculation module, without departing from the scope of the various described embodiments. The first calculation module and the second calculation module are both modules, but they are not the same module.

[0218] Reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the present application.

[0219] The method for calculating the flow field of artificial lung blood flow provided by the embodiments of the present application can be applied to terminals such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, etc. The embodiments of the present application do not impose any restrictions on the specific types of terminals.

[0220] For example, the terminal may be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing devices connected to a wireless modem, a vehicle-mounted device, a vehicle-to-everything (V2X) terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set top box (STB), a customer premise equipment (CPE), and / or other devices for communicating on a wireless system, as well as next-generation communication systems. For example, a terminal in a 5G network or a terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0221] Figure 14 is a schematic structural diagram of a terminal device provided by an embodiment of the present application. As Figure 14 shown, the terminal device 14 in this embodiment includes: at least one processor 140 ( Figure 14 only one is shown in the figure), a memory 141, and a computer program 142 that can run on the processor 140 is stored in the memory 141. When the processor 140 executes the computer program 142, the steps in the above embodiments of the flow field calculation method for each artificial lung blood flow are implemented, such as Figure 1 the steps S101 to S104 shown in the figure. Alternatively, when the processor 140 executes the computer program 142, the functions of each module / unit in the above device embodiments are implemented, such as Figure 13 the functions of the modules 131 to 134 shown in the figure.

[0222] The terminal device 14 may include, but is not limited to: a processor 140 and a memory 141. Those skilled in the art can understand that Figure 14 this is only an example of the terminal device 14, and does not constitute a limitation on the terminal device 14. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the terminal device 14 may further include an input and transmission device, a network access device, a bus, etc.

[0223] The processor 140 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0224] In some embodiments, the memory 141 may be an internal storage unit of the terminal device 14, such as the hard disk or memory of the terminal device 14. The memory 141 may also be an external storage device of the terminal device 14, such as a plug-in hard disk equipped on the terminal device 14, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. The memory 141 may also include both the internal storage unit of the terminal device 14 and the external storage device. The memory 141 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program 142. The memory 141 may also be used to temporarily store data that has been sent or will be sent.

[0225] In addition, those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The above units / modules can be implemented in the form of hardware or in the form of software functional units.

[0226] The embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments can be implemented.

[0227] The embodiment of the present application provides a computer program product, and when the computer program product runs on a terminal device, the terminal device can execute the steps in the above method embodiments when executed.

[0228] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0229] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0230] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0231] The unit described as a separated component may or may not be physically separated, and the component shown as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0232] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for calculating the flow field of artificial lung blood flow, characterized in that, Comprising: Obtaining a three-dimensional model of the artificial lung including a porous medium; Establishing a fluid control equation for blood flow within the three-dimensional model; Superimposing the momentum loss of blood flow in the region of the porous medium on the fluid control equation as a first momentum source term; Solving the fluid control equation to obtain the flow field data of blood flow within the three-dimensional model; The establishing of the fluid control equation for blood flow within the three-dimensional model includes: Generating multiple sets of refined grids based on the three-dimensional model, where the refinement degrees of each set of refined grids for the three-dimensional model are different; Respectively performing data analysis of blood passing through the artificial lung on the three-dimensional model based on each set of refined grids; Based on the data analysis results, screening out one set of refined grids from the multiple sets of refined grids as the target grid; Based on the target grid, establishing the fluid control equation for blood flow within the three-dimensional model; The three-dimensional model further includes a blood flow channel; In the respectively performing data analysis of blood passing through the artificial lung on the three-dimensional model based on each set of refined grids, the operations on each set of refined grids include: Respectively inputting blood simulations into the blood flow channels corresponding to the refined grids at different speeds; Calculating the pressure drop between the inlet and outlet of the blood flow in the blood flow channel to obtain a simulation curve of velocity and pressure drop corresponding to the refined grid, and the simulation curve is the data analysis result corresponding to the refined grid; The refinement parameters of the refined grid include the total number of grid cells and the number of grid encryption layers. The screening out one set of refined grids from the multiple sets of refined grids as the target grid based on the data analysis results includes: Taking the simulation curve corresponding to the refined grid with the largest total number of grid cells and / or the largest number of grid encryption layers among the multiple sets of refined grids as the reference curve, and respectively calculating the deviation degrees of the simulation curves corresponding to each set of refined grids from the reference curve; Screening out the refined grid with the deviation degree within the allowable error range and the smallest total number of grid cells and / or the smallest number of grid encryption layers as the target grid.

2. The flow field calculation method of artificial lung blood flow according to claim 1, characterized in that The fluid control equation is the Navier-Stokes equation. The superimposing the momentum loss of blood flow in the region of the porous medium on the fluid control equation as a first momentum source term includes: Based on Darcy's law, calculating the momentum loss of blood flow in the region of the porous medium through the following formula; Among them, S r_i represents the momentum loss, μ represents the dynamic viscosity, C1 represents the permeability coefficient, v i represents the velocity vector, C2 represents the inertial loss coefficient, ρ represents the blood density, V mag represents the velocity magnitude, d0 represents the outer diameter of the porous medium, ∈ represents the porosity of the porous medium, and i represents the grid number; Superimposing the momentum loss on the Navier-Stokes equation as the first momentum source term.

3. The method for calculating the flow field of artificial lung blood flow according to claim 1, wherein, After establishing the fluid control equation for blood flow within the three-dimensional model, it further includes: Introducing a turbulence model equation into the fluid control equation and superimposing the Reynolds stress term in the turbulence model equation on the fluid control equation as a second momentum source term.

4. The method for calculating the flow field of artificial lung blood flow according to claim 1, wherein The solving of the fluid control equation to obtain the flow field data of blood flow within the three-dimensional model includes: Based on the target grid and the fluid control equation, partitioning the target grid to obtain multiple computational blocks; Discretizing each of the computational blocks using the finite volume method such that each computational block corresponds to a matrix equation; Parallelly calculate the corresponding matrix equations on each of the calculation blocks to obtain the flow field data of the blood flow within the three-dimensional model.

5. The method for calculating the flow field of artificial lung blood flow according to claim 1, characterized in that After solving the fluid control equation to obtain the flow field data of the blood flow within the three-dimensional model, it further includes: Based on the flow field data, evaluate the hemolytic performance of the artificial lung through the following three-dimensional rapid hemolysis prediction model; NIH = Hb × D × 100, Among them, NIH is the standard hemolysis parameter value, Hb is the total hemoglobin concentration, D is the hemolysis value, and D i is the linear hemolysis index, is the average linear hemolysis index, Q is the blood volume flow rate, σ is the hemolysis destruction rate per unit time, and τ is the shear stress of the blood flow.

6. Flow field calculation device for artificial lung blood flow, characterized in that including: An acquisition module for acquiring the three-dimensional model of the artificial lung including the porous medium; A construction module for constructing the fluid control equation of the blood flow within the three-dimensional model; A first calculation module for superposing the momentum loss of the blood flow in the region of the porous medium on the fluid control equation as a first momentum source term; A second calculation module for solving the fluid control equation to obtain the flow field data of the blood flow within the three-dimensional model; The construction module is further used for: Based on the three-dimensional model, generate multiple sets of refined meshes, where the refinement degrees of each set of the refined meshes for the three-dimensional model are different; the three-dimensional model also includes a blood flow channel; Based on each set of the refined meshes, respectively perform data analysis on the blood passing through the artificial lung for the three-dimensional model; the refinement parameters of the refined meshes include the total number of grid cells and the number of grid encryption layers; Based on the data analysis results, screen out a set of the refined meshes from the multiple sets of the refined meshes as the target mesh; Based on the target mesh, construct the fluid control equation of the blood flow within the three-dimensional model; The construction module is further used for: Input the blood simulation into the blood flow channel corresponding to the refined mesh at different speeds respectively; Calculate the pressure drop between the inlet and the outlet of the blood flow in the blood flow channel to obtain a simulation curve of velocity and pressure drop corresponding to the refined mesh, and the simulation curve is the data analysis result corresponding to the refined mesh; Take the simulation curve corresponding to the refined mesh with the largest total number of grid cells and / or the largest number of grid encryption layers among the multiple sets of the refined meshes as the reference curve, and calculate the deviation degrees of the simulation curves corresponding to each set of the refined meshes from the reference curve respectively; Screen out the refined mesh with the deviation degree within the allowable error range and the smallest total number of grid cells and / or the smallest number of grid encryption layers as the target mesh.

7. The terminal device is characterized in that, It includes a memory and a processor, and a computer program that can run on the processor is stored on the memory. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 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 steps of the method according to any one of claims 1 to 5.

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