Optimization method of joint simulation platform of blood pump, storage medium and processor

Abstract

The invention is applicable to the technical field of computer simulation, and provides an optimization method of a joint simulation platform of a blood pump, a storage medium and a processor, the method comprises the following steps: establishing a Solidworks and Workbench joint simulation environment to realize secondary flow channel parametric modeling, and importing optimization variables into Workbench as input parameters; the method comprises the following steps: establishing a Cfturbo and Workbench joint simulation environment to realize parametric modeling of a blood pump impeller, and integrating the parametric modeling into Workbench; the method comprises the following steps: through a Workbench optimization platform, integrating Spaceclaim, ansys meying and CFX to realize automatic hydraulic performance calculation of the blood pump, and matching geometric parameters at interfaces among parts forming the blood pump; according to the platform, the whole process automation is realized, the simulation difficulty is simplified, the simulation time is greatly saved, and the updating iteration of a new product is accelerated. The storage medium for storing the method and the processor for operating the method also have the same technical effects.

Description

Technical Field

[0001] This application belongs to the field of computer simulation technology, and particularly relates to an optimization method, a storage medium, and a processor for a combined simulation platform of a blood pump. Background Art

[0002] The blood pump is the core component of extracorporeal membrane oxygenation (ECOM), providing power for the blood circulation of patients with cardiopulmonary failure and the ECMO system, and is also the main cause of blood damage. As a special impeller machine, the initial design parameters of its impeller follow the traditional design experience formula of hydraulic machinery, and there is a large room for optimization. At the same time, in order to obtain better blood flushing performance, reduce the flow channel stress, and avoid blood stasis, the magnetic levitation blood pump adopts the structure of front and rear secondary flow channels. However, too large a secondary flow channel gap will not only cause too high a turbulence intensity in the impeller flow channel, resulting in blood damage, but also reduce the efficiency of the blood pump and affect the use stability of the blood pump.

[0003] In the prior art, during the optimization process of the blood pump based on the simulation program, most consider the optimization based on the main parameters of the pump blade to match the flow channel characteristics, such as (CN 112784375 A). Or consider the optimization design of the vortex pump from the matching characteristics of the impeller and the flow channel, but the actual optimization is very difficult and the utilization efficiency of the simulation computing resources is not considered, such as (CN117150774A). It can be seen that the existing simulation optimization schemes of blood pumps are difficult to meet the simulation process of parametric modeling of the secondary flow channel and the impeller simultaneously. There are deficiencies in the prior art. Summary of the Invention

[0004] The purpose of this application is to provide an optimization method, a storage medium, and a processor for a combined simulation platform of a blood pump, aiming to solve the technical problem that when the simulation process simultaneously considers the geometric parameters of the secondary flow channel and the geometric parameters of the impeller, interference or mismatch will occur during the parametric update of the model parameters between its components.

[0005] On the one hand, this application provides an optimization method for a combined simulation platform of a blood pump, and the method includes the following steps:

[0006] s1. Establish a fluid domain model of the front and rear secondary flow channels of the blood pump in Solidworks, and write the variable parameters to be optimized with equations; including the inlet width of the front cover plate: qg1 = SW_D15@qg1; the outlet width of the front cover plate: qg2 = SW_D16@qg2; the inlet width of the rear cover plate: hg1 = SW_D13@hg1; the outlet width of the rear cover plate: hg2 = SW_D14@hg2; the eye diameter: S5 = SW_D7@S5, and the gap between the impeller and the volute: R5 = SW_D11@r5;

[0007] Among them, SW is the variable prefix set when importing the geometric structure in Workbench. Workbench can identify the parameters input by Solidworks through this prefix; D15 following SW is the name given by SolidWorks to the line segment; @ + parameter is the name of this line segment;

[0008] S2. Build the combined environment of Solidworks and Workbench;

[0009] S3. Import the fluid domain models of the front and rear secondary flow channels established in step S1 into Workbench, and extract the characteristic parameters established in step S1 in Designmodel to achieve the integration and data interaction between Solidworks and Workbench; The characteristic parameters include: the inlet width (qg1) of the front cover plate, the outlet width (qg2) of the front cover plate, the inlet width (hg1) of the rear cover plate, the outlet width (hg2) of the rear cover plate, the wheel eye diameter (S5), and the impeller - volute clearance (R5);

[0010] S4. Build the impeller model in Cfturbo; Build the combined environment of Cfturbo and Workbench; Among them, through the Cfturbo.wbex plug - in, achieve the integration between Cfturbo and workbench; Extract the main dimension parameters of the impeller to achieve data interaction between Cfturbo and workbench;

[0011] In the impeller model, the main dimension parameters are: the blade inlet diameter (D1), the blade outlet width, the blade wrap angle, the number of blades, the blade inlet angle, and the blade outlet angle;

[0012] S5. Import the volute and the inlet and outlet fluid domains by SpaceClaim; And take the inlet section diameter (Dj) of the interface between the inlet flow channel and the secondary flow channel and the volute inlet diameter (D3) as variable input parameters in Spaceclaim;

[0013] S6. Match the geometric parameters at the interfaces between the components constituting the blood pump;

[0014] S7. Define the width change direction of the inlet and outlet flow channels of the front and rear secondary flow channels as changing towards the outside of the secondary flow channel.

[0015] On the other hand, the present application also provides a storage medium, and the storage medium stores a program file capable of implementing the optimization method of the combined simulation platform of the above - mentioned blood pump.

[0016] On the other hand, the present application also provides a processor, characterized in that the processor is used to run a program, wherein when the program runs, it executes the optimization method of the above-mentioned combined simulation platform for the blood pump.

[0017] The present application builds a combined simulation environment of Solidworks and Workbench to realize parametric modeling of the secondary flow channel, and imports the optimization variables into Workbench as input parameters; builds a combined simulation environment of Cfturbo and Workbench to realize parametric modeling of the blood pump impeller and integrates it into Workbench; through the Workbench optimization platform, integrates Spaceclaim, ansys meshing, and CFX to realize automatic hydraulic performance calculation of the blood pump, and matches the geometric parameters at the interface between the components constituting the blood pump; at the same time, the width change direction of the inlet and outlet channels of the front and rear secondary flow channels is defined as changing towards the outside of the secondary flow channel to avoid model interference. Finally, the head, power consumption, and efficiency of the blood pump are obtained. This platform realizes full automation, simplifies the simulation difficulty, greatly saves the simulation time, and accelerates the update and iteration of new products. Description of the Drawings

[0018] Figure 1 is a schematic diagram of the architecture of the combined simulation optimization platform for the blood pump based on Solidworks, Cfturbo, and Workbench provided by the present application;

[0019] Figure 2 is a simulation process framework diagram of the optimization method of the combined simulation platform for the blood pump provided by the present application;

[0020] Figure 3 is a schematic sectional view of the front and rear secondary flow channels of the blood pump;

[0021] Figure 4 is a schematic diagram of the partial disassembly structure of the impeller of the blood pump;

[0022] Figure 5 is a schematic sectional view of the partial structure of the blood pump;

[0023] Figure 6 is a schematic diagram of the main parameter design of the blood pump impeller;

[0024] Figure 7 is the basic flow chart of the optimization method of the combined simulation platform for the blood pump provided by the present application. Detailed Embodiments

[0025] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] The following describes the specific implementation of the present application in detail in conjunction with specific embodiments:

[0027] Example 1:

[0028] Figure 7 The implementation process of the optimization method for the co-simulation platform of the blood pump provided in the first 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 and are described in detail as follows:

[0029] On the one hand, as shown in the attached Figures 1-7 The present application provides an optimization method for the co-simulation platform of a blood pump, and the method includes the following steps:

[0030] S1. Establish a fluid domain model of the front and rear secondary flow channels of the blood pump in Solidworks, and write the variable parameters to be optimized using equations; (SW is the parameter for Workbench to identify variables, and the SW suffix is the name of each parameter) including the inlet width of the front cover plate: qg1 = SW_D15@qg1; the outlet width of the front cover plate: qg2 = SW_D16@qg2; the inlet width of the rear cover plate: hg1 = SW_D13@hg1; the outlet width of the rear cover plate: hg2 = SW_D14@hg2; the impeller eye diameter: S5 = SW_D7@S5, and the gap between the impeller and the volute: R5 = SW_D11@r5;

[0031] Among them, SW is the variable prefix set when importing the geometric structure in Workbench, and Workbench can identify the parameters input from Solidworks through this prefix; D15 followed by SW is the naming of the line segment by SolidWorks; @ + parameter is the naming of this line segment;

[0032] S2. Build a combined environment of Solidworks and Workbench;

[0033] S3. Import the fluid domain model of the front and rear secondary flow channels established in step S1 into Workbench, and extract the characteristic parameters established in step S1 in Designmodel to achieve the integration and data interaction between Solidworks and Workbench; the characteristic parameters include: the inlet width qg1 of the front cover plate, the outlet width qg2 of the front cover plate, the inlet width hg1 of the rear cover plate, the outlet width hg2 of the rear cover plate, the impeller eye diameter S5, and the gap R5 between the impeller and the volute;

[0034] In specific implementation, since the secondary flow channel model usually has a complex geometric shape, the modeling ability of Solidworks can effectively handle these complex geometric shapes to complete the model construction. At the same time, the parametric modeling function of Solidworks enables the design of the secondary flow channel to quickly adjust the model structure according to specific parameter requirements, which is conducive to seamless data connection between SolidWork and workbench.

[0035] S4. Build the impeller model in Cfturbo; set up the combined environment of Cfturbo and Workbench; among them, through the Cfturbo.wbex plug-in, realize the integration between Cfturbo and workbench; extract the main dimension parameters of the impeller to achieve data interaction between Cfturbo and workbench;

[0036] In the impeller model, the main dimension parameters are: blade inlet diameter D1, blade outlet width, blade wrap angle, number of blades, blade inlet angle, blade outlet angle;

[0037] In specific implementation, using Cfturbo to build the impeller model can effectively utilize the parameter matching of Cfturbo in the design of fluid machinery for impeller machinery, achieve precise modeling, and realize seamless data connection with Workbench.

[0038] S5. Import the volute and inlet and outlet fluid domains by SpaceClaim; and take the inlet section diameter Dj of the interface between the inlet flow channel and the secondary flow channel and the volute inlet diameter D3 as variable parameters in Spaceclaim; this helps to shorten the model update time.

[0039] In specific implementation, the design of the volute and the inlet and outlet fluid domains generally does not involve parameter optimization, so it is only necessary to import the pre-built model by SpaceClaim. Since the optimization does not involve this part of the structure, it avoids parametric modeling of all parameters and saves modeling time.

[0040] This application uses different software to model different parts of the model, and mainly optimizes the modeling process of the co-simulation platform from the following aspects:

[0041] Although Cfturbo can build the secondary flow channel model, the secondary flow channel modeling of Cftubo uses Bezier curve modeling and cannot fully restore the complex secondary flow channel model. At the same time, in the lower version of the software, Cfturbo does not have the secondary flow channel parameter optimization function, while the parametric modeling of SolidWorks can make up for these shortcomings;

[0042] The volute and the inlet and outlet flow channels are imported using Spaceclaim. The main consideration is that the fluid in this part does not participate in the model update, so the mesh file is imported separately using a third-party software. If the structure that does not participate in the update is modeled in Cfturbo, it will cause the model update time to become longer each time, and consequently the entire optimization simulation time will become longer.

[0043] Based on the above considerations, a strategy of jointly modeling different regions using three software respectively is selected to optimize the blood pump simulation.

[0044] Appendix Figure 5 shows the volute and its sectional structure;

[0045] S6. Match the geometric parameters at the interface between the components constituting the blood pump;

[0046] S7. Define the width change direction of the inlet and outlet flow channels of the front and rear secondary flow channels as changing towards the outside of the secondary flow channels.

[0047] In specific implementation, as a special fluid machinery, the blood pump is applied in the field of artificial hearts. Due to reducing the stagnation in blood flow, it is designed with front and rear secondary flow channels. In this application, not only the influence of impeller parameters on the performance of the blood pump is considered, but also the geometric parameters of the front and rear secondary flow channels and the linkage between various parameters during the component update process are considered. The optimized simulation platform can provide certain guiding significance for obtaining the performance of high-performance pumps of the same type.

[0048] Further, the step S6 specifically includes:

[0049] Keep the diameter Dj of the inlet section equal to the diameter D4 of the inlet of the secondary flow channel;

[0050] Keep the diameter Dj of the inlet section equal to the diameter D1 of the inlet of the impeller;

[0051] Keep the diameter D5 of the inlet of the secondary flow channel equal to the outlet diameter of the impeller;

[0052] Obtain the diameter D3 of the inlet of the volute by adding 2 times the clearance R5 between the impeller and the volute to the diameter D5 of the inlet of the secondary flow channel.

[0053] As shown in the appendix Figure 4 shown, appendix Figure 4 shows the anatomical structure of the impeller of the blood pump.

[0054] As shown in the appendix Figure 6 shown, which shows the parametric model of the blade, and the main variable parameters include the inlet diameter D1 of the blade and the outlet diameter D2 of the impeller.

[0055] Further, the step S7 specifically includes:

[0056] Set the width change directions of the inlet width qg1 and the outlet width qg2 of the front cover plate to change towards the outside of the secondary flow channel, and fix the inner wall surface parameters of the front cover plate;

[0057] Set the width change directions of the inlet width hg1 and the outlet width hg2 of the rear cover plate to change towards the outside of the secondary flow channel, and fix the inner wall surface parameters of the rear cover plate.

[0058] As shown in the appendix Figures 3-7 shown: appendix Figure 3 is a model of the secondary flow channel of the blood pump constructed by Solidworks, where the key geometric parameters are the inlet diameter D4 of the secondary flow channel, the inlet and outlet widths qg1 and hg1 of the front secondary flow channel, the inlet and outlet widths hg1 and hg2 of the rear secondary flow channel, the eye diameter S5, and the inner diameter D5 of the secondary flow channel.

[0059] Furthermore, the following steps are also included:

[0060] s8. Use Ansys meshing to establish tetrahedral meshes for the impeller, front and rear secondary flow channels, volute, and inlet and outlet water channels.

[0061] Furthermore, the following steps are also included:

[0062] s9. Import the mesh constructed in step S8 into CFX-pre and solve the external characteristics of the blood pump based on CFX.

[0063] Furthermore, the following steps are also included:

[0064] s10. Establish a Plackett-Burman (p-b) screening experiment based on Minitab to determine the designs of the secondary flow channel and the impeller that have a significant impact on the performance of the blood pump.

[0065] As shown in the appendix Figure 2 shown, the simulation process also includes the following steps:

[0066] s11. The parameters obtained through the p-b screening test include: the impeller parameters are the number of blades, the blade wrap angle, the blade outlet setting angle, the blade outlet width, and the secondary flow channel parameters include the inlet widths of the front and rear secondary flow channels and the eye diameter (S5).

[0067] Furthermore, the following steps are also included:

[0068] S12. Use the optimal Latin hypercube test method to construct a test matrix for the design variables screened in step S10. The variable factors of this matrix are the geometric parameters of the 4 impellers and 3 secondary flow channels screened in step S10. Through the blood pump automatic optimization platform built in steps S1 - S7, calculate the test matrix, set the non-variable parameters as constants, and finally obtain the database for subsequent machine learning and optimization of the blood pump.

[0069] Further, a total of 192 groups of sample points are generated in step S11; a total of 192 groups of sample test matrices are constructed in step S12.

[0070] Further, the following steps are also included:

[0071] S13. Use an intelligent optimization algorithm combined with machine learning to optimize the blood pump sample database constructed in step S12.

[0072] Among them, steps S10 to S13 are based on the optimized simulation platform, and use the p-b screening test to analyze the sensitivity factors affecting the performance of the blood pump, reduce the optimization variables, and use the optimal Latin hypercube test design to design n groups of test matrices. The results of these n groups of sample points are automatically calculated by the blood pump optimization platform, and the power consumption, head, and efficiency corresponding to each group of test matrices are output to obtain the optimization database of the blood pump; and use the RBF neural network to construct the non-linear relationship between the optimization variables and the objective function, and use the proxy model with a qualified fitting accuracy as the input model for the subsequent genetic algorithm. In this embodiment, the NSGA-II genetic algorithm is used to optimize the proxy model to obtain the parato front of the blood pump, and a group of models with better performance is selected from it for modeling and verification of the subsequent process, effectively improving the accuracy of the simulation results.

[0073] This application uses the modular design concept for joint simulation modeling, separates each component part of the blood pump, so that when a certain part of the model is updated, it does not affect the functions of other parts, reducing the interference risk. In particular, the impeller part is modeled and updated using special impeller software, the secondary flow channel is modeled and updated using Solidworks, and the parts that do not participate in model optimization (volute, inlet and outlet flow channels) are separately imported into workbench using Spaceclaim, which can greatly reduce the model update time and speed up the simulation progress.

[0074] During specific joint simulation, the origin coordinates of all components need to be kept consistent. Among them, the origin of the impeller center of cfturbo is used as the base point, and the origin of the secondary flow channel center is kept consistent with the impeller origin.

[0075] Meanwhile, control the parameter relationships at the interfaces of each component. First, to ensure the matching of the inlet section, impeller, and secondary flow path, the inlet diameter Dj of the inlet fluid domain, the inlet diameter D1 of the impeller, and the inlet diameter D4 of the secondary flow path are kept equal in value. Among them, the inlet diameter Dj of the inlet fluid domain and the inlet diameter of the secondary flow path change following the inlet diameter of the impeller. This part of the function can be completed based on the parameter set management interface of Workbench.

[0076] During optimization, the outlet diameter of the impeller is kept equal to the inlet diameter of the secondary flow path. Among them, the inlet diameter of the secondary flow path changes following the outlet diameter of the impeller, which can avoid model interference caused by structural mismatch after parameter optimization.

[0077] To further ensure no interference when the secondary flow path and the impeller model are updated, the changing directions of the inlet and outlet widths of the front and rear secondary flow paths are fixed on the outside.

[0078] To ensure the matching of the secondary flow path and the volute, it is necessary to control the outlet diameter of the secondary flow path to be equal to the inlet diameter of the volute. Among them, the inlet diameter of the volute changes following the outlet diameter of the secondary flow path, and the influence of the dimension R5 between the outlet diameter of the impeller and the volute clearance on the performance of the blood pump can be realized in the P-b test stage. This step can be completed by the Workbench parameter set management interface.

[0079] Through the construction of the combined simulation environment of SolidWorks and Workbench in this application, the parametric modeling of the secondary flow path is coupled into the Workbench platform. By combining Cfturbo and Workbench, the parametric modeling of the impeller is seamlessly connected to the Workbench platform, and the model matching problem during the parameter change process between the secondary flow path and the impeller is considered, realizing the full automation of the simulation of the blood pump with front and rear secondary flow paths without interference. It effectively saves labor costs, improves the simulation efficiency and the utilization rate of computing resources, and greatly shortens the R & D time of this type of blood pump.

[0080] Example 2:

[0081] This application also provides a storage medium, and the storage medium stores a program file capable of implementing the optimization method of the combined simulation platform of the blood pump described in any one of the above.

[0082] Those of ordinary skill in the art can understand that all or part of the steps in implementing the method of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, optical disc, etc.

[0083] Example 3:

[0084] The present application also provides a processor, characterized in that the processor is used to run a program, wherein when the program runs, it executes the optimization method of the co-simulation platform of the blood pump described in any one of the above.

[0085] In the embodiments of the present application, the optimization method of the co-simulation platform of the blood pump can be implemented by corresponding hardware or software units. Each unit can be an independent software or hardware unit, or can be integrated into a software or hardware unit, which is not used to limit the present application herein. The specific implementation manners of each unit can refer to the description of Embodiment 1 and will not be elaborated herein.

[0086] The foregoing are only the preferred embodiments of the present application and are not used to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for optimizing a joint simulation platform of a blood pump, characterized in that: The method comprises the following steps: s1. Establish the fluid domain model of the front and rear secondary flow channels of the blood pump in Solidworks, and write the variable parameters that need to be optimized with equations; including the front cover plate inlet width: qg1 = SW_D15@qg1; the front cover plate outlet width: qg2 = SW_D16@qg2; the rear cover plate inlet width: hg1 = SW_D13@hg1; the rear cover plate outlet width: hg2 = SW_D14@hg2; the wheel eye diameter: S5 = SW_D7@S5, the gap between the impeller and the volute: R5 = SW_D11@r5; Among them, SW is the variable prefix set when the geometry structure is imported into Workbench. Workbench can recognize the parameters input by Solidworks through this prefix; D15 following SW is the name of the line segment in Solidworks; @+parameter is the name of the line segment; s2. Build a joint environment of Solidworks and Workbench; s3. Import the fluid domain model of the front and rear secondary flow channels established in step s1 into Workbench, and extract the characteristic parameters established in step s1 in Designmodel to achieve integration and data interaction between Solidworks and Workbench; the characteristic parameters include: front cover plate inlet width (qg1), front cover plate outlet width (qg2), rear cover plate inlet width (hg1), rear cover plate outlet width (hg2), wheel eye diameter (S5), and impeller and volute clearance (R5); s4. Build the impeller model in Cfturbo; build the joint environment of Cfturbo and Workbench; realize the integration between Cfturbo and Workbench through Cfturbo.wbex plug-in; extract the main size parameters of the impeller to realize data interaction between Cfturbo and Workbench; In the impeller model, the main size parameters are: blade inlet diameter (D1), blade outlet width (b2), blade wrap angle (φ), number of blades (Z), blade inlet angle (β1), blade outlet angle (β2); s5. Import the volute and inlet and outlet fluid domains from SpaceClaim; and in Spaceclaim, set the inlet section diameter (Dj) of the interface between the inlet flow channel and the secondary flow channel as a variable parameter, and the volute inlet diameter (D3) as a variable input parameter; s6. Matching geometric parameters at the interface between components constituting the blood pump; s7. The width change direction of the inlet and outlet channels of the front and rear secondary channels is defined as changing toward the outside of the secondary channels.

2. The method according to claim 1, characterized in that The step s6 specifically includes: Keep the inlet section diameter (Dj) equal to the secondary flow channel inlet diameter (D4); Keep the secondary flow channel inlet diameter (D5) equal to the impeller outlet diameter; The volute inlet diameter (D3) is obtained by adding twice the impeller-volute clearance (R5) to the secondary flow passage inlet diameter (D5).

3. The method according to claim 2, characterized in that The step s7 specifically includes: The width change direction of the front cover plate inlet width (qg1) and the front cover plate outlet width (qg2) is set to change toward the outside of the secondary flow channel, and the inner wall parameters of the front cover plate are fixed; The width change directions of the rear cover plate inlet width (hg1) and the rear cover plate outlet width (hg2) are set to change toward the outside of the secondary flow channel, and the inner wall parameters of the rear cover plate are fixed.

4. The method according to claim 3, characterized in that The following steps are also included: s8. Use Ansys meshing to create tetrahedral meshes for the impeller, front and rear secondary flow passages, volute, and inlet and outlet water flow passages.

5. The method according to claim 4, characterized in that The following steps are also included: s9. Import the mesh constructed in step S8 into CFX-pre, and solve the external characteristics of the blood pump based on CFX.

6. The method according to claim 5, characterized in that The following steps are also included: s10. Establish the Plackett-Burman (pb) screening experiment based on Minitab to determine the design of the secondary flow channel and impeller that have a significant impact on the performance of the blood pump.

7. The method according to claim 6, characterized in that The following steps are also included: s11. The parameters obtained through the PB screening test include: impeller parameters are the number of blades, blade wrap angle, blade outlet placement angle, blade outlet width, and secondary flow channel parameters include the front and rear secondary flow channel inlet width and wheel eye diameter (S5).

8. The method according to claim 7, characterized in that The following steps are also included: s12. Use the optimal Latin hypercube test method to construct a test matrix for the design variables screened out in step s10. The variable factors of the matrix use the geometric parameters of the four impellers and three secondary flow channels screened out in step s10. The test matrix is ​​calculated through the blood pump automatic optimization platform built in steps s1-s7, and the non-variable parameters are set to constants, finally obtaining a database for subsequent machine learning and optimization of the blood pump.

9. The method according to claim 8, characterized in that The step s11 generates a total of 192 groups of sample points; the step s12 constructs a total of 192 groups of sample test matrices.

10. The method according to claim 9, characterized in that The following steps are also included: s13. Use intelligent optimization algorithm combined with machine learning to optimize the blood pump sample database constructed in step s12.

11. A storage medium, characterized in that: The storage medium stores a program file capable of implementing the optimization method of the joint simulation platform of the blood pump according to any one of claims 1 to 10.

12. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the optimization method of the blood pump joint simulation platform according to any one of claims 1 to 10.

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