Digital twinborn data base plate construction method for mixed flow pump station blade angle difference flow field information

Abstract

The invention relates to a digital twinborn data base plate construction method for mixed flow pump station blade angle difference flow field information. The method comprises the steps of construction of different blade angle difference three-dimensional models, flow field information acquisition of the different blade angle difference models, response surface analysis (data base plate construction) of the flow field information, flow field information acquisition of the different blade angle difference models and database construction. The method is advanced and scientific, a data base plate of different blade angle difference flow field information is constructed in a pump station digital twin frame, data is the foundation of digital twin pump station construction, and foundation support is provided for data, upper-layer application and intelligent analysis. Through a Latin hypercube sampling method, reasonable sampling can be performed from a plurality of parameter combinations, the parameter space of the angular difference is effectively covered, and the representativeness and integrity of data samples are ensured, so that the blade angular difference combinations neglected in the research process are reduced, and the globality and precision of the model are ensured.

Description

Technical Field

[0001] The present invention relates to a method for constructing a digital twin data baseboard of blade angle difference flow field information of a mixed flow pump station, and belongs to the field of impeller machinery design optimization. Background Art

[0002] In the design and adjustment process of water pumps, blade angle difference is an important parameter that needs to be reasonably selected and optimized. Generally, adjustments are made through experiments, numerical simulations, or experience accumulation to obtain the best blade angle difference value to achieve the best water pump performance and work efficiency. Therefore, it is necessary to establish a data base construction method for flow field information with different blade angle differences. The prior art does not have public information on the construction of data bases for flow field information with different blade angle differences. A digital twin data base construction method for blade angle difference flow field information of a mixed flow pump station uses Latin hypercube to extract angle difference models of different blades, obtain flow field information through numerical simulation after meshing, and construct a data base through response surface analysis of the flow field information. Summary of the invention

[0003] Considering that blade angle difference is an important parameter in the design and adjustment process of water pumps and needs to be reasonably selected and optimized, the present invention provides a method for constructing a digital twin data baseboard of blade angle difference flow field information of a mixed flow pump station.

[0004] The present invention provides the following technical solution: a method for constructing a digital twin data base of blade angle difference flow field information of a mixed flow pump station, including constructing a three-dimensional model of different blade angle differences, acquiring flow field information of different blade angle difference models, response surface analysis of flow field information (data base construction), acquiring flow field information of different blade angle difference models and constructing a database. When used, the method includes the following steps: Step S1, construct different blade angle models, sample and extract the angle difference range selected according to the number of impeller blades, and construct three-dimensional models with different blade angle differences; specifically: first determine the number of impeller blades of the mixed flow pump station, select the angle difference range according to the number of blades (for example, the blade placement angle of 0° can be in the angle difference range of -4°~+4°), perform Latin hypercube sampling on the blade angle difference combination in the range, and use UG software to construct a three-dimensional model of the sampled combination; Step S2, acquiring flow field information of different blade angle difference models, meshing the three-dimensional model established in step S1 and then performing numerical simulation to obtain flow field information; specifically: Meshing the three-dimensional model with different blade angle differences established in step S1, and performing numerical simulation) to obtain flow field information, including: head H, efficiency η (performance curve) at different flow rates Q, flow field data (streamline, velocity, pressure, etc.) at different flow rates Q, required NPSH (cavitation curve) under design conditions, and flow field data (streamline, velocity, pressure, cavitation distribution, etc.) under different NPSH under design conditions; Step S3, response surface analysis of flow field information (data baseboard construction), the flow field information obtained in step S2 is subjected to response surface analysis to obtain flow field information of all angle difference combinations; Step S4, the flow field information of different blade angle difference models is obtained and a database is constructed. Through response surface analysis of the flow field information, all angle difference combinations are obtained as the data base to construct the database.

[0005] The construction of different blade angle difference models includes preparing a three-dimensional model of a pump station, determining the number of blades of a water pump impeller in the pump station, selecting an angle difference range according to the number of blades (for example, a blade placement angle of 0° can be in an angle difference range of -4° to +4°), performing Latin hypercube sampling on blade angle difference combinations, and constructing three-dimensional models of different blade angle differences; Latin hypercube sampling is a method of approximating random sampling from a multivariate parameter distribution. It is a stratified sampling technique and is often used in computer experiments or Monte Carlo integration. The sampling units are divided into different layers, and then samples are independently and randomly drawn from different layers. This ensures that the structure of the sample is similar to that of the population, thereby improving the accuracy of the estimate.

[0006] The flow field information acquisition of different blade angle difference models adopts a grid generation technology to grid the three-dimensional models of different blade angle differences, and the grid adopts a combination of hexahedron and tetrahedron structured grids; the model numerical simulation obtains the flow field information, including the head H and efficiency η (performance curve) under different flow rates Q, the flow field data (streamline, velocity, pressure, etc.) under different flow rates Q, the required cavitation margin (cavitation curve) under the design conditions, and the flow field data (streamline, velocity, pressure, cavitation distribution, etc.) under different cavitation margins NPSH under the design conditions.

[0007] The response surface analysis of the flow field information (construction of the data base) is to select each (different) cross-model flow field information obtained by sampling as a test point for the response surface analysis, and select each flow field information as the objective function to iteratively obtain the response surface to obtain the flow field information of all angular difference combinations except the sampled angular difference model.

[0008] The flow field information of different blade angle difference models is acquired and a database is constructed. All angle difference combinations obtained by response surface analysis of flow field information are used as a data base to construct a database.

[0009] The method of the present invention is advanced and scientific. It constructs a data base for different blade angle difference flow field information in the pump station digital twin framework. Data is the basis for the construction of digital twin pump stations. With data, upper-level applications and intelligent analysis have basic support. Through the present invention, a method for constructing a digital twin data base for blade angle difference flow field information can be provided.

[0010] The method provided by the present invention has several significant advantages: First, through the Latin hypercube sampling method, reasonable sampling can be carried out from multiple parameter combinations to effectively cover the parameter space of angle differences and ensure the representativeness and completeness of the data samples, thereby reducing the blade angle difference combinations that are ignored in the research process and ensuring the globality and accuracy of the model.

[0011] Secondly, by constructing a three-dimensional model of different blade angle differences and performing numerical simulation, it is possible to efficiently obtain important flow field information related to blade angle differences (such as head, efficiency, flow field, cavitation, etc.), and obtain accurate fluid dynamic characteristics through meshing and numerical simulation technology, laying a solid foundation for subsequent analysis. This method also introduces response surface analysis, which uses a small amount of sampled data to iteratively solve the flow field information and derive the flow field information under all blade angle difference combinations, thereby reducing the number of models for direct simulation, saving computing resources and time, and improving the ability to capture complex nonlinear relationships, optimizing data utilization efficiency. The flow field information obtained through response surface analysis is used to build a database, which provides important data support for the optimization of blade angle differences and performance prediction of mixed flow pump stations, and can quickly and accurately provide a reference for performance analysis and improvement under different working conditions.

[0012] In addition, the combination of Latin hypercube sampling and response surface analysis improves the accuracy of simulation and analysis. Compared with the traditional random sampling method, this method can better reflect the globality of the parameter space, reduce sample bias, and improve the credibility of the results.

[0013] Finally, by systematically analyzing the flow field characteristics under different blade angle difference combinations, this method provides an important basis for the design and angle optimization of mixed flow pump blades, which can improve the overall performance of the pump station (such as efficiency, cavitation performance, etc.) and help reduce noise and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Construct a flow chart for the blade angle difference flow field data base.

[0015] Figure 2 Schematic diagram of the three-dimensional model construction for different blade angle differences.

[0016] Figure 3 Schematic diagram of obtaining flow field information of blade angle difference model. DETAILED DESCRIPTION

[0017] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0018] like Figures 1 to 3 As shown, the present invention provides a digital twin data baseboard construction method for blade angle difference flow field information of a mixed flow pump station, which includes constructing a three-dimensional model of different blade angle differences, acquiring flow field information of different blade angle difference models, response surface analysis of flow field information (data baseboard construction), acquiring flow field information of different blade angle difference models and constructing a database; The construction of different blade angle models is to sample and extract the angle difference range selected according to the number of impeller blades, and construct three-dimensional models with different blade angle differences; the flow field information of the different blade angle difference models is obtained, and the constructed three-dimensional model is meshed and then numerically simulated to obtain flow field information; the response surface analysis of the flow field information (data baseboard construction) selects the flow field information for response surface analysis to obtain the flow field information of all angle difference combinations; The flow field information of different blade angle difference models is acquired and a database is constructed, that is, all angle difference combinations obtained by response surface analysis of flow field information are used as a data base to construct a database; The construction of different blade angle difference models includes the preparation of a three-dimensional model of a pump station, determining the number of blades of a water pump impeller in the pump station, selecting an angle difference range according to the number of blades (for example, a blade placement angle of 0° can be within an angle difference range of -4° to +4°), performing Latin hypercube sampling on blade angle difference combinations, and constructing three-dimensional models with different blade angle differences; the flow field information of the different blade angle difference models is acquired and a database is constructed, using all angle difference combinations obtained by response surface analysis of the flow field information as a data base to construct a database.

[0019] Among them, Latin hypercube sampling (English: Latin hypercube sampling, abbreviated LHS) is a method of approximate random sampling from a multivariate parameter distribution. It belongs to a stratified sampling technique and is often used in computer experiments or Monte Carlo integration. In statistical sampling, a Latin square refers to a square matrix that contains only one sample in each row and column. Latin hypercube is a generalization of the Latin square in multiple dimensions, and each hyperplane perpendicular to the axis contains at most one sample. Assuming there are N variables (dimensions), each variable can be divided into M intervals with the same probability. At this time, M sample points that meet the Latin hypercube conditions can be selected. It should be noted that Latin hypercube sampling requires that the number of partitions M for each variable is the same, and this method does not require that the number of samples M increase as the variable increases.

[0020] The meshing technology is used to mesh the three-dimensional models of different blade angle difference models for obtaining flow field information. The mesh adopts a combination of hexahedron and tetrahedron structured grids. The model is numerically simulated to obtain flow field information, including head H and efficiency η (performance curve) under different flow rates Q, flow field data (streamline, velocity, pressure, etc.) under different flow rates Q, required NPSH under design conditions (cavitation curve), and flow field data (streamline, velocity, pressure, cavitation distribution, etc.) under different NPSH under design conditions.

[0021] The response surface analysis of flow field information (construction of data base) is to select each (different) cross-model flow field information obtained by sampling as the test point of response surface analysis, and select each flow field information as the objective function to iteratively obtain the response surface to obtain the flow field information of all angular difference combinations except the sampled angular difference model.

[0022] Finally, the flow field information of different blade angle difference models is acquired and a database is constructed. All angle difference combinations obtained by the response surface analysis of the flow field information are used as the data base to construct a database.

[0023] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0024] Table 1 Results of response surface analysis and data base construction Serial number Blade1 Blade2 Blade3 Blade4 Lift(m) efficiency(%) NPSH(m) 1 -0.08 0.08 2.32 -0.72 4.25 75.34% 4.52 2 3.44 -1.52 1.04 2.16 4.56 77.12% 4.61 3 -2.48 2.8 0.4 -2.8 4.18 72.45% 4.33 4 -3.92 2.48 -1.04 3.44 4.4 74.29% 4.44 5 3.92 3.76 -0.56 1.68 4.8 78.58% 4.75 6 2.16 -2.16 -0.72 -0.56 4.32 73.87% 4.51 7 0.56 0.72 3.28 -3.44 4.5 76.22% 4.67 8 -1.2 -0.24 0.24 1.2 4.22 71.92% 4.21 9 -1.84 -3.44 -1.52 3.76 4.35 75.64% 4.39 10 3.76 0.88 3.6 0.08 4.67 78.21% 4.84 11 2.96 2.64 -2 -2.48 4.49 72.86% 4.36 12 0.4 0.56 2 3.12 4.58 77.45% 4.62 13 -1.36 0.24 -1.68 0.24 4.21 71.51% 4.24 14 -3.12 -2.48 -2.16 -1.04 4.75 79.03% 4.71 15 2.8 3.92 1.2 2.8 4.4 74.06% 4.48 16 2.32 -1.84 -3.12 -1.52 4.3 73.25% 4.38 17 1.2 -0.88 2.16 -3.12 4.5 76.45% 4.59 18 -2 1.36 0.72 0.56 4.25 75.12% 4.3 19 -0.56 2.16 -0.4 -1.2 4.68 78.77% 4.72 20 2 -1.04 0.56 1.36 4.6 77.31% 4.54 21 -1.04 -2.64 0.88 -0.88 4.39 72.69% 4.42 22 1.68 -3.28 1.84 2.32 4.55 76.91% 4.61 23 0.72 -3.92 1.52 -2.32 4.18 71.27% 4.25 24 -3.6 1.04 -3.28 1.52 4.72 79.52% 4.76 25 -0.88 2.32 -1.36 -0.4 4.24 75.84% 4.34 26 -2.8 -2.96 3.92 -1.36 4.38 73.54% 4.43 27 -3.76 -3.76 -1.84 -0.08 4.62 78.02% 4.66 28 1.84 2 0.08 -2.96 4.47 72.29% 4.47 29 2.48 -3.12 -2.48 -3.28 4.5 76.21% 4.41 30 0.08 -0.08 -0.88 2.48 4.36 74.11% 4.35 31 -0.4 3.12 -3.92 2.96 4.55 77.93% 4.64 32 3.6 -0.72 -2.96 -2.16 4.8 79.25% 4.79 33 -2.16 -2.8 -1.2 -2 4.2 71.67% 4.22 34 -3.44 1.68 2.64 -1.68 4.48 75.45% 4.49 35 3.12 -3.6 3.76 2 4.66 78.66% 4.73 36 -2.32 -0.4 -2.64 -0.24 4.39 72.18% 4.41 37 1.52 -1.36 -3.44 0.72 4.54 76.71% 4.5 38 -1.52 1.2 1.36 3.6 4.29 73.98% 4.32 39 -0.24 -1.68 -3.76 2.64 4.7 78.39% 4.65 40 -2.64 -2.32 3.12 -2.64 4.18 71.83% 4.2 41 3.28 -0.56 2.8 0.88 4.65 77.97% 4.69 42 1.04 3.28 2.48 0.4 4.5 75.05% 4.52 43 -1.68 2.96 -0.24 -3.92 4.32 72.42% 4.4 44 0.88 -2 -2.32 3.28 4.56 76.15% 4.6 45 1.36 -1.2 -0.08 1.84 4.22 71.46% 4.29 46 -3.28 0.4 1.68 -3.76 4.75 79.11% 4.74 47 -2.96 1.52 2.96 3.92 4.34 74.52% 4.49 48 -0.72 3.44 3.44 1.04 4.6 77.22% 4.61 49 0.24 3.6 -2.8 -1.84 4.4 74.93% 4.53 50 2.64 1.84 -3.6 -3.6 4.5 76.59% 4.72

Claims

1. A method for constructing a digital twin data baseboard for blade angle difference flow field information of a mixed flow pump station, characterized in that: Including building three-dimensional models with different blade angle differences, acquiring flow field information with different blade angle difference models, response surface analysis of flow field information, acquiring flow field information with different blade angle difference models and building a database; The following steps are involved in use: Step S1, construct different blade angle models. Sampling and extracting the angle difference range selected according to the number of impeller blades, constructing a three-dimensional model with different blade angle differences; specifically: first determine the number of impeller blades of the mixed flow pump station, select the angle difference range according to the number of blades, perform Latin hypercube sampling on the blade angle difference combination in the range, and use UG software to construct a three-dimensional model of the sampling combination; Step S2, acquiring flow field information of different blade angle difference models, meshing the three-dimensional model established in step S1 and then performing numerical simulation to obtain flow field information; specifically: The three-dimensional model with different blade angle differences established in step S1 is meshed, and numerical simulation is performed to obtain flow field information, including: head H, efficiency η and flow field data under different flow rates Q, required NPSH under design conditions, and flow field data under different NPSH under design conditions; wherein, the head H and efficiency η under different flow rates Q constitute a performance curve; the flow field data under different flow rates Q include streamlines, velocities, and pressures; the required NPSH under design conditions is a cavitation curve; the flow field data under different NPSH under design conditions include streamlines, velocities, pressures, and cavitation distributions; Step S3, response surface analysis of flow field information, that is, constructing a data base, performing response surface analysis on the flow field information obtained in step S2 to obtain flow field information of all angle difference combinations; Step S4, the flow field information of different blade angle difference models is obtained and a database is constructed. Through response surface analysis of the flow field information, all angle difference combinations are obtained as the data base to construct the database.

2. According to claim 1, a method for constructing a digital twin data baseboard of blade angle difference flow field information of a mixed flow pump station is characterized in that: The construction of the three-dimensional model of different blade angle differences includes preparing the three-dimensional model of the pump station, determining the number of blades, selecting the angle difference range according to the number of blades, performing Latin hypercube sampling on the blade angle difference combination, and constructing the three-dimensional model of different blade angle differences; The Latin hypercube sampling is a method of approximate random sampling from a multivariate parameter distribution, which belongs to the stratified sampling technique; first, the sampling units are divided into different layers, and then samples are independently and randomly drawn from the different layers to ensure that the structure of the samples is relatively close to the structure of the population, thereby improving the accuracy of the estimation.

3. The method for constructing a digital twin data baseboard of blade angle difference flow field information of a mixed flow pump station according to claim 1 is characterized in that: The flow field information acquisition of the different blade angle difference models adopts a meshing technology to mesh the different blade angle difference three-dimensional models, and the mesh adopts a combination of hexahedron and tetrahedron structured grids; the model numerical simulation acquires the flow field information, including the head H, efficiency η, flow field data under different flow rates Q, required NPSH under design conditions, and flow field data under different NPSH under design conditions; Among them, the head H and efficiency η under different flow rates Q constitute the performance curve; the flow field data under different flow rates Q include streamlines, velocities, and pressures; the required NPSH under design conditions is the cavitation curve; the flow field data under different NPSH under design conditions include streamlines, velocities, pressures, and cavitation distributions.

4. The method for constructing a digital twin data baseboard of blade angle difference flow field information of a mixed flow pump station according to claim 1, characterized in that: The response surface analysis of the flow field information is the construction of the data base, which is to select each different cross-model flow field information obtained by sampling as a test point for the response surface analysis, and select each flow field information as the objective function to iteratively obtain the response surface to obtain the flow field information of all angular difference combinations except the sampled angular difference model.

5. The method for constructing a digital twin data baseboard of blade angle difference flow field information of a mixed flow pump station according to claim 1, characterized in that: The flow field information of different blade angle difference models is acquired and a database is constructed. All angle difference combinations obtained by response surface analysis of flow field information are used as a data base to construct a database.

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