Three-dimensional model order reduction method for special valve and electronic equipment
By constructing a three-dimensional model and performing modal decomposition and fitting, the problems of long time consumption and high cost in special valve flow field simulation are solved, and rapid and accurate prediction of unknown working conditions is achieved, improving design and management efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-10
- Publication Date
- 2026-03-10
AI Technical Summary
Traditional computational fluid dynamics methods are time-consuming and costly when simulating the internal flow field of special valves, and cannot effectively predict unknown operating conditions, resulting in low efficiency.
By constructing a three-dimensional model, performing modal decomposition and fitting modal coefficients, the flow field inside the valve is reconstructed, enabling rapid prediction of unknown operating conditions.
Significantly reduce computational workload and time costs, improve the efficiency of special valve design and operation management, and ensure forecast accuracy.
Smart Images
Figure CN121637918A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the field of special valves, and particularly relates to a three-dimensional model order reduction method for special valves and an electronic device. BACKGROUND
[0002] In the design, operation and optimization process of a special valve, accurate understanding of the internal flow field is crucial for performance evaluation. Although the traditional computational fluid dynamics method can accurately simulate the flow field and obtain the internal flow field information of the valve, the method is time-consuming and costly in calculation when facing numerous working conditions due to the complex internal structure of the special valve and the large number of simulations required. The method cannot predict unknown working conditions and is inefficient.
[0003] Therefore, there is an urgent need to provide a method for quickly predicting the internal flow field of a special valve under unknown working conditions to improve the performance optimization and operation management efficiency of the special valve. SUMMARY
[0004] The purpose of the present application is to overcome the defects in the prior art and provide a three-dimensional model order reduction method for special valves and an electronic device. The present application realizes the rapid prediction of the internal flow field of a special valve under unknown working conditions by constructing a three-dimensional model, performing modal decomposition, fitting modal coefficients and reconstructing the internal flow field of the valve, aiming to solve the calculation efficiency and cost problems of the traditional method under multiple complex working conditions.
[0005] The specific technical solutions adopted by the present application are as follows: In a first aspect, the present application provides a three-dimensional model order reduction method for special valves, which is as follows: S1: According to the design file of the special valve, a three-dimensional structure model is constructed by a three-dimensional modeling software; then according to the internal flow passage of the three-dimensional structure model, an internal flow passage model of the special valve is extracted; the internal flow passage model is discretized into an internal flow passage grid model by a meshing software; according to the working condition change range in the design file, an experiment table is designed; the inlet and outlet boundary conditions of the internal flow passage grid model are set by a computational fluid dynamics software according to the experiment table; S2: A corresponding turbulence model is selected by a computational fluid dynamics software; in the selected turbulence model, calculation is performed according to the inlet and outlet boundary conditions until the flow field under all working conditions in the experiment table is calculated, and then the calculation is stopped, obtaining a sample data set of the internal flow passage grid model obtained by flow field calculation; the sample data set is a matrix form of physical quantity distribution information data of the special valve; S3: modal decomposition is performed on the sample data set to obtain spatial modes and modal coefficients; the spatial modes are matrices with the same size as the sample data set; the modal coefficients are square matrices with the same number of rows and columns as the number of working conditions in the experiment table n ; the number of modes is selected according to the modal energy, and unimportant modes are discarded; S4: fitting is performed on the modal coefficients to establish a relationship between the inlet and outlet boundary conditions and the modal coefficients, and a fitting model is obtained; the internal flow field of the special valve under unknown working conditions is predicted according to the fitting model and the spatial modes.
[0006] As a preferred, in the S1, the design file includes drawings of the special valve and working condition parameters of the special valve in actual engineering; the working condition parameters include pressure, temperature and flow.
[0007] As a preferred, in the S1, the three-dimensional modeling software adopts one of Inventor, Solidworks, Creo, UG / NX, CATIA, ANSYS Workbench DesignModeler or ANSYS Workbench SpaceClaim; the mesh generation software adopts one of ICEM CFD, HyperMesh, TGrid, PointWise, ANSA, GridPro or ANSYS Workbench Mesh; the computational fluid dynamics software adopts one of ANSYS CFX, ANSYS Fluent, STAR-CD, STAR-CCM, NUMECA or OpenFOAM.
[0008] As a preferred, in the S1, the design method of the experiment table adopts one of orthogonal experimental design method, Box Behnken design method, Latin hypercube experimental design method or central composite design method.
[0009] As a preferred, in the S2, the turbulence model adopts one of Standard k-ε model, Spalart-Allmaras model, RNG kε model, Realizable k-ε model, Standard k-ω model, BSL k-ω model or SST k-ω model.
[0010] As a preferred, the S3 is specifically as follows: S31: singular value decomposition is performed on the sample data set to obtain spatial modes, modal coefficients and singular values of each order mode, the number of orders being the same as the number of working conditions in the experiment table n . S32: the size of each order modal singular value is the energy of the modal; if the sum of the energy of the current m order modal is greater than 90% of the total energy, that is, it is preliminarily judged that the flow condition in the special valve is represented by the m order modal, and the remaining modal contains little flow field information and is ignored and discarded; S33: for the spatial modal with the number of rows n and the number of columns being the number of internal flow channel grid model units, only the first m row is retained, and the subsequent m +1 to n row is discarded to obtain the matrix of the trimmed spatial modal; for the modal coefficient of n × n , it is trimmed to a matrix of n × m , that is, only the first m column is retained, and the subsequent m +1 to n column is discarded; S34: the flow field in the special valve is reconstructed by using the spatial modal and the modal coefficient trimmed in S33, and compared with the sample data set described in S2 to calculate the relative error; if the relative error is greater than 10%, it means that too many modes are discarded in S33, then m 1 is added and the S33 step is repeated, and the reconstruction and the relative error calculation are performed again, until the sum of the energy of the first m order modal is greater than 99% of the total energy or the relative error is less than or equal to 10%.
[0011] As preferred, S4 is specifically as follows: S41: the inlet and outlet boundary conditions corresponding to the n working conditions in the experimental table are fitted with the modal coefficients processed in S3 to obtain a fitting model; the fitting method adopts one of Kriging, support vector machine regression, Gaussian process regression, neural network regression, Galerkin projection, linear regression, polynomial fitting or ridge regression; S42: the working condition parameters to be predicted are input into the fitting model to obtain new modal coefficients under the working condition, the new modal coefficients are vectors with a length of m , and the new modal coefficients are multiplied with the corresponding spatial modal matrix in sequence and accumulated to reconstruct the internal flow field information of the special valve at this time in combination with the spatial modal processed in S3.
[0012] In the second aspect, the application provides a three-dimensional model reduction electronic device for a special valve, comprising a processor and a memory and a storage medium connected with the processor; The memory is used to store computer programs, including program code; The storage medium is used to store computational data and software, including sample datasets obtained from flow field numerical simulations, software required for running the program code, and computational fluid dynamics software. The processor is used to call and execute the computer program in the memory to perform the steps of the method for reducing the order of a special valve using a three-dimensional model as described in any of the first aspects.
[0013] Preferably, the memory uses one of DDR4, DDR5, LPDDR5, GDDR6 or HBM memory chips; the storage medium uses one of solid-state drive, hard disk drive, tape library or optical disk storage device; and the processor uses one of general-purpose central processing unit, microprocessor, graphics processor, digital signal processor, field-programmable gate array, application-specific integrated circuit, complex programmable logic device, application-specific processor or microcontroller.
[0014] Compared with the prior art, the present invention has the following advantages: This invention significantly reduces computational load and time costs through order reduction processing and modal coefficient fitting, enabling rapid prediction of flow field information for special valves under unknown operating conditions, thereby improving the design and operation management efficiency of special valves. Simultaneously, while discarding secondary modal information, this invention retains the primary modes, possessing the key features and main trends of the original model, ensuring the accuracy of the reduced-order model's prediction of the flow field characteristics of special valves. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of a method for reducing the order of a three-dimensional model for a special valve, provided in one embodiment of the present invention. Figure 2 This is a schematic diagram of the connection relationship of a three-dimensional model of a special valve for electronic devices, provided as an embodiment of the present invention. Detailed Implementation
[0016] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment in the present invention can be combined accordingly without conflict. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] like Figure 1 As shown, this invention provides a method for reducing the order of a three-dimensional model for special valves. The method is as follows: S1: According to the design file of the special valve, a three-dimensional structure model of the special valve is constructed by using a three-dimensional modeling software. Then, according to the internal flow passage of the obtained three-dimensional structure model, an internal flow passage model of the special valve is extracted. The obtained internal flow passage model is discretized into an internal flow passage grid model by using a meshing software. According to the working condition change range in the design file, an experiment table is designed. The inlet and outlet boundary conditions of the internal flow passage grid model are set according to the obtained experiment table by using a computational fluid dynamics software.
[0018] As a preferred embodiment of the present application, the three-dimensional modeling software can adopt one of Inventor, Solidworks, Creo, UG / NX, CATIA, ANSYS Workbench DesignModeler or ANSYS Workbench SpaceClaim, the meshing software can adopt one of ICEM CFD, HyperMesh, TGrid, PointWise, ANSA, GridPro or ANSYS Workbench Mesh, and the computational fluid dynamics software can adopt one of ANSYS CFX, ANSYS Fluent, STAR-CD, STAR-CCM, NUMECA or OpenFOAM.
[0019] As a preferred embodiment of the present application, the design file mainly includes drawings of the special valve and working condition parameters (including pressure, temperature and flow rate, etc.) of the special valve in actual engineering.
[0020] As a preferred embodiment of the present application, in the process of extracting the internal flow passage model, when the structure of the internal flow passage is geometrically symmetrical relative to its longitudinal section, the internal flow passage model is a complete model or a 1 / 2 model relative to the longitudinal section, and when the structure of the internal flow passage is geometrically asymmetrical relative to its longitudinal section, the internal flow passage model is a complete model.
[0021] As a preferred embodiment of the present application, the special valve can be any valve in the field of special valves, such as a high-temperature steam pressure reducing valve, a main steam isolation valve, etc.
[0022] As a preferred embodiment of the present application, the design method of the experiment table can adopt one of orthogonal experimental design method, Box Behnken design method, Latin hypercube experimental design method or central composite design method.
[0023] In the embodiment, a high-temperature steam pressure reducing valve is taken as an example. The valve needs to work under different inlet pressure, outlet pressure, flow rate, and inlet temperature conditions, and the four working condition parameters will fluctuate in actual engineering applications. In the specific implementation, a 3-level experiment table is designed by using a Box Behnken experiment design method. The experiment table obtained needs to calculate 27 groups of working condition parameter combinations.
[0024] The inlet and outlet boundary conditions of the internal flow channel grid model are set by using a computational fluid dynamics software according to the experiment table. In the embodiment, the inlet and outlet boundary conditions include the numerical values of the inlet pressure, outlet pressure, flow rate, and inlet temperature. If a complete model is used when the flow channel model is extracted, the inner wall surface of the internal flow channel grid model is set as a no-slip boundary condition. If a 1 / 2 model with a longitudinal section as a boundary is used when the flow channel model is extracted, a symmetric boundary condition is set at the longitudinal section, and the inner wall surface of the internal flow channel grid model is set as a no-slip boundary condition.
[0025] S2: The corresponding turbulent flow model is selected by using a computational fluid dynamics software. In the selected turbulent flow model, the flow field is calculated under the inlet and outlet boundary conditions (i.e., under the conditions of the given pressure, temperature, and flow rate of the working condition) until the flow field of all working conditions in the experiment table is calculated and the calculation is stopped, to obtain a sample data set of the internal flow channel grid model obtained by the flow field calculation. The sample data set is data that presents the distribution information of the internal physical quantities of the special valve in the form of a matrix.
[0026] As a preferred embodiment of the present application, the turbulent flow model adopts one of a Standard k-ε model, a Spalart-Allmaras model, an RNG k-ε model, a Realizable k-ε model, a Standard k-ω model, a BSL k-ω model, or an SST k-ω model.
[0027] S3: The obtained sample data set is subjected to modal decomposition to obtain a spatial mode and a modal coefficient. The spatial mode is a matrix with the same size as the sample data set. The modal coefficient is a square matrix with the same number of rows and columns as the number of working conditions in the experiment table. n The number of modes is selected according to the modal energy, and the unimportant modes are discarded.
[0028] As a preferred embodiment of the present application, the step is specifically as follows: S31: The obtained sample data set is subjected to singular value decomposition to obtain each order spatial mode, modal coefficient, and singular value of each order mode, and the number of orders is the same as the number of working conditions in the experiment table. S32: The size of the singular value of each order mode is the energy of the mode. If the current mThe sum of the energy of the first modes is greater than 90% of the total energy, i.e., the first modes are preliminarily determined to be sufficient to represent the flow condition inside the special valve, and the remaining modes contain little flow field information and can be neglected and discarded. m The first modes are sufficient to represent the flow condition inside the special valve, and the remaining modes contain little flow field information and can be neglected and discarded. S33: For the spatial modes with the number of rows being n and the number of columns being the number of units of the internal flow channel grid model, only the first m rows are retained, and the subsequent m +1 to n rows are discarded, to obtain a matrix of the trimmed spatial modes; for the modal coefficients with the size of n n , the modal coefficients are trimmed into a matrix with the size of n m , i.e., only the first m columns are retained, and the subsequent m +1 to n columns are discarded. S34: The flow field inside the special valve is reconstructed by using the spatial modes and the modal coefficients trimmed in S33, and compared with the sample data set described in S2, to calculate the relative error; if the relative error is greater than 10%, it is indicated that too many modes are discarded in S33, and then m 1 is added to repeat the step S33, and the reconstruction and the calculation of the relative error are performed again, until the sum of the energy of the first m modes is greater than 99% of the total energy or the relative error is less than or equal to 10%.
[0029] In this embodiment, the matrix of the sample data set is 27x8032030, where 27 is the number of working conditions, and 8032030 is the number of units of the internal flow channel grid model. Specifically, the sample data set is subjected to modal decomposition by using the singular value decomposition method, to obtain spatial modes and modal coefficients. The spatial modes are a matrix with the same size as the original sample data, and the size is 27x8032030. The modal coefficients are a square matrix with the size of 27x27, and the number of rows is the same as the number of columns. A singular value matrix is also obtained. The values on the diagonal of the singular value matrix are the energies of the modes. According to the rule of singular value decomposition, the modal energies are sorted in descending order from 1 to 27. The number of modes is selected according to the modal energy and the relative error. When the total energy of the first m modes is greater than 99%, or the relative error with the original data is less than 10%, it is determined that the first m modes are sufficient to represent the flow condition inside the special valve, and the remaining n - m First, the energy threshold is set to 90%, and the first 6 modes are selected. The spatial mode is pruned into a 6×8032030 matrix, and the modal coefficients are pruned into a 27×6 matrix. The number of rows in the spatial mode matrix is equal to the number of columns in the modal coefficients. Each column of the modal coefficients is multiplied sequentially by the corresponding row of the spatial mode, and the results are accumulated to obtain a 27×8032030 reconstructed flow field data matrix. The relative error between this matrix and the original sample data is calculated. If the error is greater than 10%, one more mode is selected, and the pruning, reconstruction, and relative error calculation process is repeated until the total energy of the selected modes exceeds 99%, or the relative error with the original sample data is less than 10%. In this embodiment, the final selected mode is... m =12, that is, the final spatial mode matrix is 12×8032030, and the modal coefficient is 27×12.
[0030] S4: Fit the modal coefficients to establish the relationship between the inlet and outlet boundary conditions and the modal coefficients, and obtain the fitted model; based on the fitted model and the spatial modes, quickly predict the internal flow field of the special valve under unknown working conditions.
[0031] In a preferred embodiment of the present invention, the steps are as follows: S41: [The experimental table is missing] n The inlet and outlet boundary conditions corresponding to each working condition are fitted with the modal coefficients after S3 processing to obtain the fitted model. The fitting method can be one of Kriging, support vector machine regression, Gaussian process regression, neural network regression, Galerkin projection, linear regression, multinomial fitting or ridge regression. S42: Input the operating condition parameters to be predicted into the fitting model to obtain the new modal coefficients under the operating condition. The new modal coefficients are of length [missing information]. m The vector, combined with the processed spatial modes of S3 (i.e., the clipped spatial mode matrix), is used to reconstruct the internal flow field information of the special valve by multiplying each number in the new mode coefficients with the corresponding spatial mode matrix in turn and accumulating them.
[0032] In the embodiment, the Kriging model is used for fitting, the working condition parameters are 27 groups of combinations of inlet pressure, outlet pressure, inlet temperature and flow rate in the experiment table, which is a matrix with a size of 27x4, and the modal coefficient is a matrix with a size of 27x12. According to the fitting model and the spatial modal obtained by fitting, the internal flow field of the special valve under an unknown working condition is quickly predicted. For example, when the flow field information of the special valve under a specific working condition of inlet pressure, outlet pressure, inlet temperature and flow rate is needed, and the combination of the working condition parameters is not in the experiment table, first, the combination of the working condition parameters is input into the fitting model in the form of a vector with a length of 4, to obtain the modal coefficient corresponding to the working condition, the modal coefficient is a vector with a length of 12, each number in the vector is multiplied by each row of the spatial modal in turn, and is accumulated to obtain a vector with a length of 8032030, which is the internal flow field information of the special valve reconstructed. In the embodiment, the time required for numerical simulation of the internal flow channel grid model of the special valve to obtain the flow field information is 11665s, and the time required for the three-dimensional model reduction method to obtain the flow field information is only 4.80s. Therefore, the method can greatly improve the calculation efficiency of the internal flow field of the special valve.
[0033] As shown in Figure 2 The application also provides a three-dimensional model reduction electronic device for a special valve, which mainly comprises a processor, a memory and a storage medium, wherein the memory and the storage medium are connected with the processor.
[0034] The memory is used for storing a computer program, including program codes. In actual use, the memory can adopt one of DDR4, DDR5, LPDDR5, GDDR6 or HBM memory particles.
[0035] The storage medium is used for storing calculation data and software, including sample data sets obtained by flow field numerical simulation, software required for running the program codes and computational fluid dynamics software. In actual use, the storage medium can adopt one of solid state disk (SSD), mechanical hard disk (HDD), tape library or optical disk storage device.
[0036] The processor is used for calling and executing the computer program in the memory to execute each step of the three-dimensional model reduction method for a special valve of the application. In actual use, the processor can adopt one of general central processing unit (CPU), microprocessor, graphics processing unit (GPU), digital signal processor (DSP), field programmable gate array (FPGA), application specific integrated circuit (ASIC), complex programmable logic device (CPLD), special application processor (TAP) or microcontroller.
[0037] In the embodiment, the computational fluid dynamics software used is Ansys Fluent, the software required for code running is Matlab, and in the operation process of the computational fluid dynamics software and the code program, data is stored in the memory; and after the program operation is completed, the spatial mode, the modal coefficient and the valve internal flow field data obtained are stored in a storage medium.
[0038] The method of the present application effectively reduces the operation amount while ensuring the operation precision, greatly improves the calculation efficiency compared with the numerical simulation method, and can meet the requirements of users for efficiently and highly timely obtaining the internal flow field information of the special valve.
[0039] The above-described embodiment is only a preferred scheme of the present application, and is not intended to limit the present application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, any technical scheme obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present application.
Claims
1. A reduced order method for three-dimensional modeling of specialty valves, characterized by, Specifically as follows: S1: according to the design file of the special valve, a three-dimensional structure model is constructed through a three-dimensional modeling software; then according to the internal flow channel of the three-dimensional structure model, an internal flow channel model of the special valve is extracted; through a meshing software, the internal flow channel model is discretized into an internal flow channel grid model; according to the working condition change range in the design file, an experiment table is designed; through a computational fluid dynamics software, the inlet and outlet boundary conditions of the internal flow channel grid model are set according to the experiment table; S2: through the computational fluid dynamics software, a corresponding turbulence model is selected; in the selected turbulence model, calculation is carried out according to the inlet and outlet boundary conditions until the flow field of all working conditions in the experiment table is calculated, and then the calculation is stopped, and a sample data set of the internal flow channel grid model obtained by the flow field calculation is obtained; the sample data set is a matrix form presenting the physical quantity distribution information data of the special valve internal flow channel; S3: modal decomposition of the sample data set, to obtain spatial modes and modal coefficients; the spatial modes are matrices with the same size as the sample data set; the modal coefficients are square matrices with the same number of rows and columns as the number of working conditions in the experimental table n ; selecting the number of modes according to the modal energy, and discarding unimportant modes; S4: the modal coefficient is fitted, the relationship between the inlet and outlet boundary conditions and the modal coefficient is established, and a fitting model is obtained; according to the fitting model and the space modal, the internal flow field of the special valve under unknown working conditions is predicted.
2. The reduced order modeling method for special valves according to claim 1, wherein In the S1, the design file includes drawings of the special valve and working condition parameters of the special valve in actual engineering; the working condition parameters include pressure, temperature and flow.
3. The reduced order modeling method for special valves of claim 1, wherein In the S1, the three-dimensional modeling software adopts one of Inventor, Solidworks, Creo, UG / NX, CATIA, ANSYS Workbench DesignModeler or ANSYS Workbench SpaceClaim; the meshing software adopts one of ICEMCFD, HyperMesh, TGrid, PointWise, ANSA, GridPro or ANSYS Workbench Mesh; the computational fluid dynamics software adopts one of ANSYS CFX, ANSYS Fluent, STAR-CD, STAR-CCM, NUMECA or OpenFOAM.
4. The reduced order modeling method for special valves of claim 1, wherein In the S1, the design method of the experiment table adopts one of orthogonal experiment design method, Box Behnken design method, Latin hypercube experiment design method or central composite design method.
5. The reduced order modeling method for special valves of claim 1, wherein In the S2, the turbulence model adopts one of Standard k-ε model, Spalart-Allmaras model, RNG k-ε model, Realizable k-ε model, Standard k-ω model, BSL k-ω model or SST k-ω model.
6. The reduced order modeling method for special valves of claim 1, wherein The S3 is specifically as follows: S31: singular value decomposition is performed on the sample data set to obtain spatial modes of each order, mode coefficients and singular values of each order, the number of orders being equal to the number of working conditions in the experiment table n same; S32: The size of each order modal singular value is the energy of the modal; if the sum of the energy of the current order modal is greater than 90% of the total energy, it is preliminarily judged that the order modal is sufficient to represent the flow condition inside the special valve, and the remaining modal contains little flow field information and is ignored and discarded. m m The size of each order modal singular value is the energy of the modal; if the sum of the energy of the current order modal is greater than 90% of the total energy, it is preliminarily judged that the order modal is sufficient to represent the flow condition inside the special valve, and the remaining modal contains little flow field information and is ignored and discarded. S33: For the space mode with the row number of n and the column number of the internal flow channel grid model unit number, only the first m row is kept, and the subsequent m +1 to n row is discarded, to obtain the matrix of the cut space mode. For the modal coefficients of n × n , they are cropped to a matrix of n × m , i.e. only the first m columns are kept, while the subsequent m +1 to n columns are discarded; S34: Reconstruct the flow field in the special valve using the spatial modes and modal coefficients after the pruning in S33, and compare with the sample data set in S2 to calculate the relative error; if the relative error is greater than 10%, it means that too many modes are discarded in S33, then m add 1 and repeat S33, and again reconstruct and calculate the relative error until the sum of the energy of the first-order modes is greater than 99% of the total energy or the relative error is less than or equal to 10%. m add 1 and repeat S33, and again reconstruct and calculate the relative error until the sum of the energy of the first-order modes is greater than 99% of the total energy or the relative error is less than or equal to 10%.
7. The reduced order modeling method for special valves of claim 1, wherein The S4 is specifically as follows: S41: The experimental table is... n The inlet and outlet boundary conditions corresponding to each working condition are fitted with the modal coefficients after S3 processing to obtain a fitted model; the fitting method adopts one of Kriging, support vector machine regression, Gaussian process regression, neural network regression, Galerkin projection, linear regression, multinomial fitting or ridge regression. S42: input the working condition parameters that need to be predicted into the fitting model, obtain the new modal coefficients under the working condition, the new modal coefficients are vectors with a length of m , combine the processed spatial modes in S3, multiply each number in the new modal coefficients with the matrix of the corresponding spatial mode in turn and accumulate, and reconstruct the internal flow field information of the special valve at this time.
8. A three-dimensional model reduction electronic device for special valves, characterized by, The processor and the memory and the storage medium connected with the processor are included; The memory is used for storing a computer program including program codes; The storage medium is used for storing calculation data and software, including sample data set obtained by flow field numerical simulation, software required for running the program codes and computational fluid dynamics software; The processor is configured to invoke and execute a computer program in the memory to perform each step of the reduced order method of three-dimensional model for special valves according to any one of claims 1-7.
9. The three-dimensional model reduction electronic device for special valves according to claim 8, characterized in that, The memory is one of DDR4, DDR5, LPDDR5, GDDR6 or HBM memory particles; the storage medium is one of solid state disk, mechanical hard disk, tape library or optical disk storage device; and the processor is one of general central processing unit, microprocessor, graphics processor, digital signal processor, field programmable gate array, application specific integrated circuit, complex programmable logic device, special application processor or microcontroller.