A digital twin data baseboard construction method for blade angle difference flow field information of mixed flow pump station

Abstract

The present invention relates to a method for constructing a digital twin data base for the flow field information of blade angle differences in a mixed flow pump station, comprising constructing three-dimensional models of different blade angle differences, acquiring flow field information of different blade angle difference models, performing response surface analysis of the flow field information (data base construction), acquiring flow field information of different blade angle difference models, and constructing a database. The method of the present invention is advanced and scientific, and constructs a data base for the flow field information of different blade angle differences in the digital twin framework of the pump station. Data is the foundation for the construction of digital twin pump stations. With data, upper-level applications and intelligent analysis have a basic support. Through the Latin hypercube sampling method, reasonable sampling can be performed from multiple parameter combinations, effectively covering the parameter space of angle differences, ensuring the representativeness and integrity of the data samples, thereby reducing the blade angle difference combinations that are ignored during the research process and ensuring the globality and accuracy of the model.

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 existing technology 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 is to use Latin hypercube to extract the angle difference model of different blades, obtain the flow field information through numerical simulation after meshing, and construct the 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 mixed flow pumping stations.

[0004] The present invention provides the following technical solution: a method for constructing a digital twin data base for blade angle difference flow field information in a mixed flow pump station, comprising constructing a three-dimensional model of different blade angle differences, acquiring flow field information from the different blade angle difference models, performing response surface analysis of the flow field information (data base construction), and acquiring and constructing a database for the flow field information from the different blade angle difference models. The method comprises the following steps:

[0005] Step S1, constructing different blade angle models, sampling and extracting the angle difference range selected according to the number of impeller blades, and constructing three-dimensional models of different blade angle differences; specifically, first determining the number of blades of the mixed flow pump station impeller, selecting an angle difference range based on 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 the blade angle difference combinations within the range, and using UG software to construct a three-dimensional model of the sampled combinations;

[0006] 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:

[0007] 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 (streamlines, velocities, pressures, etc.) at different flow rates Q, required NPSH under design conditions (cavitation curve), and flow field data (streamlines, velocities, pressures, cavitation distribution, etc.) at different NPSH under design conditions;

[0008] 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;

[0009] Step S4: Acquire flow field information of different blade angle difference models and construct a database. Through response surface analysis of the flow field information, all angle difference combinations obtained are used as a data base to construct the database.

[0010] The construction of different blade angle difference models includes preparing a three-dimensional model of the pump station, determining the number of blades of the water pump impeller in the pump station, selecting an angle difference range based on 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 the blade angle difference combinations, and constructing three-dimensional models of different blade angle differences;

[0011] Latin hypercube sampling is a method for approximating random sampling from a multivariate parametric distribution. It is a stratified sampling technique commonly used in computer experiments and Monte Carlo integration. The sampling unit is divided into strata, and samples are then independently and randomly drawn from each stratum. This ensures that the structure of the sample is close to that of the population, thereby improving the accuracy of the estimate.

[0012] The flow field information of the different blade angle difference models is obtained by using a grid partitioning technology to grid the three-dimensional models of different blade angle differences, and the grid adopts a combination of hexahedral and tetrahedral 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 NPSH under design conditions (cavitation curve), and the flow field data (streamline, velocity, pressure, cavitation distribution, etc.) under different NPSH under design conditions.

[0013] 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 of 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.

[0014] 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.

[0015] This method is advanced and scientific. It constructs a data base for flow field information at different blade angle differences within the pump station digital twin framework. Data is the foundation for building a digital twin pump station. With data, upper-level applications and intelligent analysis have a fundamental foundation. This method provides a method for constructing a digital twin data base for flow field information at different blade angle differences.

[0016] The method provided by the present invention has several significant advantages:

[0017] First, through the Latin hypercube sampling method, reasonable sampling can be carried out from multiple parameter combinations, effectively covering the parameter space of angle differences, ensuring the representativeness and integrity of the data samples, thereby reducing the blade angle difference combinations that are ignored during the research process and ensuring the globality and accuracy of the model.

[0018] Secondly, by constructing three-dimensional models of different blade angle differences and performing numerical simulations, important flow field information related to blade angle differences (such as head, efficiency, flow field, cavitation, etc.) can be efficiently obtained. Meshing and numerical simulation techniques are then used to obtain accurate fluid dynamic characteristics, laying a solid foundation for subsequent analysis. This method also introduces response surface analysis, which iteratively solves the flow field information using a small amount of sampled data to derive flow field information for all blade angle difference combinations. This reduces the number of models required for direct simulation, saves computing resources and time, improves the ability to capture complex nonlinear relationships, and optimizes data utilization efficiency. The flow field information obtained through response surface analysis is used to construct a database, providing important data support for blade angle difference optimization and performance prediction in mixed flow pump stations, and can quickly and accurately provide a reference for performance analysis and improvement under different operating conditions.

[0019] In addition, the combination of Latin hypercube sampling and response surface analysis improves the accuracy of simulation and analysis. Compared with traditional random sampling methods, this method can better reflect the globality of the parameter space, reduce sample bias, and improve the credibility of the results.

[0020] 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 pumping station (such as efficiency, cavitation performance, etc.) and help reduce noise and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Construct a flow chart for the blade angle difference flow field data base.

[0022] Figure 2 Schematic diagram of the three-dimensional model construction for different blade angle differences.

[0023] Figure 3 Schematic diagram of obtaining flow field information of the blade angle difference model. DETAILED DESCRIPTION

[0024] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0025] like Figures 1 to 3 As shown, the present invention provides a digital twin data base 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 the flow field information (data base construction), acquiring flow field information of different blade angle difference models and constructing a database;

[0026] The different blade angle models are constructed by sampling and extracting the angle difference range selected according to the number of impeller blades to construct three-dimensional models with different blade angle differences; the flow field information of the different blade angle difference models is obtained by meshing the constructed three-dimensional model and then performing numerical simulation 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 flow field information of all angle difference combinations;

[0027] The flow field information of the different blade angle difference models is acquired and a database is constructed, that is, all angle difference combinations obtained by the response surface analysis of the flow field information are used as a data base to construct a database;

[0028] The construction of different blade angle difference models includes preparing a three-dimensional model of the pump station, determining the number of blades of the water pump impeller in the pump station, selecting an angle difference range based on 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 obtained 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 the database.

[0029] Among them, Latin hypercube sampling (English: Latin hypercube sampling, abbreviated LHS) is a method for approximating random sampling from a multivariate parametric distribution. It is 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 per row and column. The Latin hypercube is a generalization of the Latin square in multiple dimensions, where 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 equal probability. In this case, 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. This method does not require that the number of samples M increase as the number of variables increases.

[0030] The flow field information of different blade angle difference models is obtained by meshing the three-dimensional models with different blade angle difference using meshing technology, and the mesh adopts a combination of hexahedral and tetrahedral 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 NPSH under design conditions (cavitation curve), and the flow field data (streamline, velocity, pressure, cavitation distribution, etc.) under different NPSH under design conditions.

[0031] 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.

[0032] Finally, the flow field information of different blade angle difference models is obtained 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 the database.

[0033] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

[0034] Table 1 Response surface analysis and data base construction results

[0035] 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 by: 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 its use: Step S1: construct different blade angle models, perform sampling extraction based on the angle difference range selected according to the number of impeller blades, and construct three-dimensional models of different blade angle differences; specifically, first determine the number of blades of the mixed flow pump station impeller, select an angle difference range based on the number of blades, perform Latin hypercube sampling on the blade angle difference combinations within the range, and use UG software to construct a three-dimensional model from the sampled combinations; 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 of 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: Acquire flow field information of different blade angle difference models and construct a database. Through response surface analysis of the flow field information, all angle difference combinations obtained are used as a data base to construct the database.

2. The method for constructing a digital twin data baseboard for blade angle difference flow field information of a mixed flow pump station according to claim 1 is characterized in that: The construction of three-dimensional models with different blade angle differences includes preparing a three-dimensional model of a pump station, determining the number of blades, selecting an angle difference range according to the number of blades, performing Latin hypercube sampling on blade angle difference combinations, and constructing three-dimensional models with different blade angle differences; The Latin hypercube sampling is a method for approximately random sampling from a multivariate parameter distribution and belongs to a stratified sampling technique. The sampling units are first divided into different strata, and then samples are independently and randomly drawn from the different strata 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 for blade angle difference flow field information of a mixed flow pump station according to claim 1 is characterized in that: The flow field information of the different blade angle difference models is obtained by meshing the three-dimensional models of different blade angle differences using a meshing technique, wherein the mesh is a combination of hexahedron and tetrahedron structured grids; the model is numerically simulated to obtain flow field information, including 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 distribution.

4. The method for constructing a digital twin data baseboard for blade angle difference flow field information of a mixed flow pump station according to claim 1 is 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 for 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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