Vane pump transient process performance optimization design method

Abstract

Disclosed in the present invention is a vane pump transient process performance optimization design method, comprising: respectively creating an initial design model of a vane pump, an initial design model of an impeller, and a vane pump starting process full-flow-field simulation model, selecting impeller structure parameters as design variables, and on the basis of the initial design models and the vane pump starting process full-flow-field simulation model, performing starting process transient numerical calculation by using CFD simulation software, wherein starting process transient external characteristics comprise a transient lift and transient efficiency; performing data processing on the starting process transient lift and transient efficiency to respectively obtain a weighted average lift and weighted average efficiency; taking the design variables as input parameters, and taking the weighted average lift and the weighted average efficiency as response values; establishing a transient process performance optimization model on the basis of a response surface function; and optimizing vane pump transient process performance by using the established transient process performance optimization model. The vane pump transient process performance optimization design method of the present invention can ensure the calculation accuracy, accelerate the optimization process and shorten the optimization time.

Description

A method for optimizing the transient performance of vane pumps Technical Field

[0001] The present invention relates to the technical field of fluid machinery, and in particular to a method for optimizing the transient performance of a vane pump. Background Art

[0002] In recent years, my country has proposed a novel approach to using vane pumps as torpedo propulsion systems in its defense equipment, which has been incorporated into its new generation of submarine weapon launch systems. During the launch process, the vane pump's speed must increase from zero to its rated speed in a very short time, while also achieving rated head and flow rate to meet the high-speed flow required for the torpedo to exit the chamber and provide sufficient initial kinetic energy. After the torpedo exits the chamber, the vane pump undergoes a deceleration and shutdown process, awaiting the next torpedo launch. During this process, the vane pump's external work is transient, significantly different from the typical steady-state external work process. During the startup process, the head, flow rate, and speed all change rapidly, and the flow regime also rapidly transitions, transitioning from laminar flow to turbulent flow in a very short period of time. The startup process presents numerous complex flow phenomena within the vane pump, including backflow at the inlet and outlet, leakage vortices generated within the impeller flow path, separation vortices, and other secondary flow structures. The interaction of these phenomena significantly impacts the pump's transient efficiency during startup, resulting in low energy conversion efficiency and insufficient kinetic energy for torpedo exit. Therefore, in order to improve the launch effect and hitting accuracy of torpedoes and enhance the national security and development level, it is necessary to further improve the hydraulic performance and global efficiency of the vane pump in the transient process.

[0003] Internationally, research on the transient characteristics of pumps has yielded considerable theoretical and experimental results. Initially, researchers used quasi-steady-state methods to predict pump transient performance. However, during transient testing, significant discrepancies between the transient performance and the quasi-steady-state predictions were observed in some cases. This is due to pressure and flow fluctuations during rapid pump startup and shutdown. These fluctuations cause the pulse pressure around the blades and the circulation around the blades to lag behind the quasi-steady-state values, resulting in discrepancies between the dynamic and quasi-steady-state characteristics of the pump startup and shutdown process. With the continued advancement of theoretical research on transient processes, researchers have proposed formulas for predicting the transient head and efficiency of vane pumps. By optimizing numerical models, they have achieved accurate simulation and prediction of the pump startup and shutdown process. With the development of computational fluid dynamics (CFD), it is now possible to optimize the transient performance of vane pumps using a high-precision platform using full-flow modeling. Designers can use CFD results to analyze the internal flow characteristics and adjust the pump's structural parameters to improve its transient hydraulic performance. However, this requires extensive experience in transient research. At present, there are few research results on the transient performance of vane pumps at home and abroad. The patent number CN115199583A, "A blade design method for optimizing startup transient performance and a vane pump designed therewith", effectively reduces the blade inlet loss and improves the pressure distribution on the blade inlet edge by optimizing the attack angle on the blade inlet edge, thereby suppressing flow separation and increasing the stable flow rate at the end of startup. However, this patent does not consider the optimization of other profile parameters, nor the optimization of the speed change process, and the performance improvement is limited.

[0004] At the same time, based on CFD calculation results, scholars have proposed an optimization design scheme that combines approximate models with intelligent optimization algorithms. Patent number CN103646297A, "A Dual-channel Pump Optimization Method Based on a Multi-Objective Genetic Algorithm," combines an artificial neural network model with a multi-objective genetic algorithm. Using impeller inlet and outlet diameters, impeller outlet width, channel centerline angle, volute base diameter, and volute inlet diameter as design variables, and optimizing head, efficiency, and maximum particle size under rated operating conditions, the scheme improves the design quality of dual-channel pumps. Patent number CN112784375B, "A High-Efficiency, Low-Pulsation Vane Pump Optimization Method Based on a Discrete Genetic Algorithm," uses the impeller outlet diameter, impeller outlet width, blade outlet chamfer radius, blade inlet and outlet placement angle, and blade wrap angle as optimization parameters, and pump efficiency and the amplitude of the main frequency of pressure pulsation at the monitoring point at the diaphragm as optimization targets. Combined with a discrete genetic algorithm, it optimizes the unsteady characteristics of the vane pump. Both patents use the steady-state hydraulic characteristics of the pump as optimization targets, and the optimization process and complexity are relatively simple. However, in the CFD transient numerical calculations of the startup process, a set of transient head and transient efficiency values ​​are output after each time step is calculated. Thousands of sets of output results are obtained throughout the startup process, resulting in a large amount of data and no systematic solution has yet been proposed.

[0005] Summary of the Invention

[0006] In order to address the deficiencies in the prior art, this application proposes a method for optimizing the transient performance of a vane pump. The method is based on the transient performance optimization design of a vane pump that combines CFD transient numerical calculations, a response surface prediction model, and a multi-objective optimization algorithm. Improvement measures are proposed for problems such as excessive energy loss, low efficiency, and insufficient head during the startup of the vane pump.

[0007] The technical solutions adopted in the present invention are as follows:

[0008] A method for optimizing the transient performance of a vane pump comprises the following steps:

[0009] Step 1: Create initial design models of the vane pump and impeller, and a full flow field simulation model of the vane pump startup process, and select impeller structural parameters as design variables. The design variables include blade inlet placement angle, blade outlet placement angle, blade wrap angle, blade thickness, and blade thickness coefficient.

[0010] Step 2: Based on the initial design model created in step 1 and the full flow field simulation model of the vane pump startup process, CFD simulation software is used to perform transient numerical calculations of the startup process, wherein the transient numerical values ​​of the startup process include transient head and transient efficiency;

[0011] Step 3: Process the calculated transient values ​​of the startup process to obtain the weighted average lift and weighted average efficiency;

[0012] Step 4: Using the design variables as input parameters and the weighted average head and weighted average efficiency as response values, a transient process performance optimization model is established based on the response surface function;

[0013] Step 5: Optimize the transient process performance of the vane pump using the established transient process performance optimization model.

[0014] Furthermore, the method for establishing the transient process performance optimization model in step 4 is:

[0015] Step 4.1, set the range corresponding to each design variable;

[0016] Step 4.2: Using the design variables as input parameters and the monitored transient head and transient efficiency as output parameters, a sample collection platform is constructed to obtain sample points; the output parameters are processed to obtain the weighted average head and weighted average efficiency;

[0017] Step 4.3: Using the input parameters of the sample points as design variables and the weighted average head and weighted average efficiency as response values, a response surface function is selected to establish a transient process performance optimization model;

[0018] Step 4.4, evaluate the prediction accuracy of the transient process performance optimization model;

[0019] Step 4.5, the first stage is G z The constraint condition is that the weighted average head of the original pump is greater than that of the original pump. z Take the maximum value as the objective function, and obtain several prediction points by building a point search on the determined transient process performance optimization model; in the second stage, H(t)≥H0(t), η(t)≥η0(t) are used as constraints, F z The maximum value is the objective function, and the prediction points obtained in the first stage are searched in turn to obtain the final optimization result;

[0020] Step 4.6: Use CFX to simulate and verify the impeller model after the vane pump parameters are optimized.

[0021] Furthermore, the range corresponding to each design variable is selected based on ±15% of the initial value of each design variable.

[0022] Furthermore, the weighted average lift is expressed as:

[0023] The weighted average efficiency is expressed as:

[0024] Where: t is the start time; T tolis the total startup time when the flow rate is stable; Δt is the time step; H is the transient head of the designed pump; H0 is the transient head of the original pump; η is the transient efficiency of the designed pump; η0 is the transient efficiency of the original pump; F z is the weighted average of the transient efficiency.

[0025] Furthermore, in step 4.2, the sample points are established using the advanced Latin hypercube sampling method.

[0026] Furthermore, in step 4.4, the sample variance is analyzed and the design variables are tested for significance according to the f criterion, and the parameters with a greater impact on the response value are selected as optimization variables; the sample residuals are analyzed and the performance, adaptability, and prediction accuracy of the response surface model are evaluated according to the R criterion.

[0027] Furthermore, the expression of the f criterion is:

[0028] Where S a is the sum of squares of deviations of each variable, S n is the sum of squares of deviations caused by experimental errors, E a and E n is the number of degrees of freedom for the design variables and deviations;

[0029] R criteria are determined by R 2 Criteria, R adj 2 Criteria and R pred 2 The guidelines together.

[0030] Furthermore, the response surface function is selected from polynomial, nonlinear function, power function, BP neural network model, radial basis function or multivariate adaptive regression spline function.

[0031] Furthermore, the modeling method in step 1 is as follows:

[0032] Step 1.1: Use UG to create the initial design model of the impeller of the vane pump;

[0033] Step 1.2: Import the initial impeller design model created in step 1.1 into ANSYS CFturbo through ANSYS Workbench for reverse modeling, and parameterize the design variables that need to be changed;

[0034] Step 1.3: Import the impeller reverse-modeled in step 1.2 into ANSYS TurboGrid through ANSYS Workbench to generate a structured grid.

[0035] Step 1.4: Use UG to create models of the vane pump guide vanes, volute chamber, test pipeline, gate valve, and surge tank;

[0036] Step 1.5: Import the model created in step 1.4 into ANSYS ICEM to generate a structured mesh.

[0037] Furthermore, the thickness distribution between the blade hub and the rim is defined as the thickness coefficient θ. When θ<0, the blade thickness will develop towards the rim area, and when θ>0, the blade thickness will develop towards the hub area.

[0038] Beneficial effects of the present invention:

[0039] (1) The present invention combines the full flow field calculation of the vane pump startup process with the optimization algorithm, and finds the optimal solution of the vane pump structural parameters through the optimization algorithm. It can provide a reliable design basis for workers who lack rich design experience and improve the quality of design.

[0040] (2) The training samples of the response surface prediction model in the optimization model of the present invention are obtained through CFD analysis, and the hydraulic performance of a large number of prediction points generated by the optimization process is predicted by the approximate model. This not only ensures the calculation accuracy, but also greatly speeds up the optimization process and shortens the optimization time. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] FIG1 is a flow chart of a method for optimizing the transient performance of a vane pump according to the present invention.

[0042] Figure 2 is the impeller meridian view and the impeller three-dimensional view.

[0043] Figure 3 is a diagram of blade thickness distribution.

[0044] Figure 4 is a full flow field simulation model diagram of the vane pump startup process.

[0045] Figure 5 shows the weighted average lift G z The response surface of .

[0046] Figure 6 shows the weighted average efficiency F z The response surface of .

[0047] FIG. 7 is an external characteristic curve of the mixed flow pump during startup before and after optimization in this embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention.

[0049] As shown in FIG1 , the present invention proposes a vane pump transient performance optimization design method, comprising the following steps:

[0050] Step 1: Use simulation software to create the initial design models of the vane pump and impeller, and the full flow field simulation model of the vane pump startup process.

[0051] In this embodiment, the methods for creating the vane pump and impeller initial design models and the vane pump startup process full flow field simulation model are as follows:

[0052] Step 1.1: Use UG to create the initial design model of the impeller of the vane pump;

[0053] Step 1.2: Import the initial impeller design model created in step 1.1 into ANSYS CFturbo through ANSYS Workbench for reverse modeling, and parameterize the design variables that need to be changed.

[0054] Step 1.3: Import the impeller reverse-modeled in step 1.2 into ANSYS TurboGrid through ANSYS Workbench to generate a structured grid.

[0055] Step 1.4: Use UG to create models of the vane pump guide vanes, volute chamber, test pipeline, gate valve, and surge tank. The full flow field simulation model of the vane pump startup process is shown in Figure 4.

[0056] Step 1.5, import the model created in step 1.4 into ANSYS ICEM to divide the structured mesh;

[0057] In this embodiment, based on the geometric characteristics of the vane pump impeller, impeller structural parameters that have a greater impact on the hydraulic performance of the vane pump are selected as design variables, including the blade inlet placement angle, outlet placement angle, blade wrap angle, blade thickness and blade thickness coefficient, and their value range is determined based on design experience, generally within ±15% of the initial value.

[0058] As shown in Figure 2, the blade is divided into four equal parts along the span direction, and five spline curves are established at span = 0, span = 0.25, span = 0.5, span = 0.75 and span = 1.0. The parameters on the spline curves are selected as design variables. Let the blade inlet placement angle α1-α5, the blade outlet placement angle β1-β5 and the blade wrap angle A total of 11 parameters control the blade profile. The blade's leading edge is measured at two points near the hub and rim, with thicknesses defined as ζ1 and ζ2. The trailing edge is measured at two points near the hub and rim, with thicknesses defined as ζ3 and ζ4. The thickness distribution between the hub and rim is also defined as the thickness coefficient θ. Figure 3 shows the blade's thickness distribution when θ = 0. When θ < 0, the blade's thickness extends toward the rim, while when θ > 0, the blade's thickness extends toward the hub.

[0059] Step 2: Based on the initial design model created in step 1 and the full flow field simulation model of the vane pump startup process, CFD simulation software is used to perform transient numerical calculations of the startup process; by analyzing the hydraulic characteristics of the vane pump startup process, the most representative external characteristic parameters are selected as optimization targets, namely transient head and transient efficiency.

[0060] More specifically, the meshes divided in steps 1.3 and 1.5 are imported into ANSYS CFX to complete the construction of the full-flow field simulation model of the vane pump startup process. In the full-flow field closed-loop model, the system can simulate the flow rate change process by itself. It only needs to input the fitting formula of the speed to carry out the numerical calculation.

[0061] The control equation for the transient simulation calculation of the vane pump startup process is established, where the mass conservation equation is:

[0062] The momentum conservation equation is:

[0063] Where ρ is the medium density, t is the time, μ is the dynamic viscosity, p is the fluid microelement pressure, and f i is the volume force.

[0064] In view of the flow conditions of the fluid in the startup process, which undergoes laminar flow, transition and turbulent stages in a very short time, in order to more accurately simulate the boundary layer flow separation phenomenon in the transition flow process, the DES model is selected as the turbulence model for this transient calculation. In the near-wall region, the limiting formula of the turbulence length scale forces the model to switch between RANS and LES. When the region is close to the solid boundary and the turbulence scale is smaller than the maximum grid size, the RANS method will be used to solve the region. When the turbulence length scale exceeds the grid size, the LES method will be used to solve the region. The expression of the DES turbulence scale l based on SST k-ω is: l=min(L t ,C DES Δ max ), where L t is the turbulence length scale, Δ max is the maximum grid size; C DES is an adaptive parameter.

[0065] Using design variables from ANSYS CFturbo as input parameters and transient head and efficiency monitored in CFD-Post as output parameters, a sample collection platform was established in conjunction with ANSYS optiSLang. Advanced Latin Hypercube sampling was used to establish 150 sample design points, and transient numerical calculations of the startup process were completed for each sample point.

[0066] Step 3: Process the calculated transient values ​​of the startup process to obtain the weighted average lift and weighted average efficiency, which are expressed as:

[0067] Weighted average head:

[0068] Weighted average efficiency:

[0069] Where: t is the start time, unit is s; T tol is the total startup time when the flow rate is stable, in seconds; Δt is the time step, in seconds; H is the transient head of the designed pump, in meters; H0 is the transient head of the original pump, in meters; η is the transient efficiency of the designed pump; η0 is the transient efficiency of the original pump; F z is the weighted average of transient efficiency;

[0070] Step 4: Using the design variables as input parameters and the weighted average head and weighted average efficiency as response values, a transient process performance optimization model is established based on the response surface function. Establishing the transient process performance optimization model includes the following steps:

[0071] Step 4.1: Set the range of input parameters.

[0072] The design variables are used as input parameters, and their range is generally ±15% of the initial value of each design variable.

[0073] Step 4.2: Sampling generates sample space.

[0074] Using design variables from ANSYS CFturbo as input parameters and transient head and efficiency monitored in CFD-Post as output parameters, a sample collection platform was established in conjunction with ANSYS optiSLang. Advanced Latin Hypercube sampling was used to establish 150 sample points. Data processing of the output parameters was performed to obtain the weighted average head and weighted average efficiency, completing the transient numerical calculation of the startup process at each sample point.

[0075] Step 4.3: Establish a transient process performance optimization model

[0076] The input parameters of 150 sample points are used as design variables, and the weighted average head and weighted average efficiency are used as response values. In the Design-Expert software, the response surface function is selected to establish a transient process performance optimization model. The response surface function can be a polynomial, nonlinear function, power function, BP neural network model, radial basis function or multivariate adaptive regression spline function.

[0077] Step 4.4: Evaluate the prediction accuracy of the transient process performance optimization model

[0078] Analyze the sample variance and perform significance tests on the design variables according to the f criterion, and select the parameters that have a greater impact on the response value as the optimization variables. The expression of the f criterion is:

[0079] Where S a is the sum of squares of deviations of each variable, S n is the sum of squares of deviations caused by experimental errors, E a and E n is the number of degrees of freedom of the design variables and deviations. The critical value of the significance level is set to 0.05. When f≥f 1-0.05 (E a , E n ), that is, P≤0.05, the influence of the variable is significant; when f≤f 1-0.05 (E a , E n ), that is, P ≥ 0.05, then the effect of the variable is said to be insignificant.

[0080] Analyze the sample residuals and comprehensively evaluate the performance, adaptability and prediction accuracy of the response surface model according to the R criterion. The R criterion is a model selection criterion. 2 Criteria, R adj 2 Criteria and R pred 2 The criteria together form a framework for determining which of multiple linear regression models is more suitable for describing a given data set.

[0081] Where, SS E is the variance of the design variable; SS T is the total variance of the model; d E is the degree of freedom of the design variable, d T is the total degrees of freedom of the model; PRESS is the sum of squares of the prediction errors.

[0082] When the response surface satisfies R 2 and R adj 2 The value of is greater than 0.9 and R pred 2 and R adj 2 When the difference is less than 0.2, it indicates that the response surface fitting accuracy is high and can well express the intrinsic relationship between the input parameters and the response values. The response surface model can be used for subsequent optimization solutions.

[0083] Step 4.5: Rapid optimization based on transient process performance optimization model

[0084] The objective function and constraints of the optimization scheme are proposed: H(t)≥H0(t) (7) η(t)≥η0(t) (8)

[0085] The first stage is G z The constraint condition is that the weighted average head of the original pump is greater than that of the original pump. z Take the maximum value as the objective function, and find the optimal point on the determined transient process performance optimization model to obtain 100 prediction points that meet the constraints. The prediction points are calculated according to the prediction F z The values ​​are arranged in descending order;

[0086] The second stage takes H(t)≥H0(t) and η(t)≥η0(t) as constraints, and F z The maximum is the objective function, and the optimal solution is found in the 100 prediction points to obtain the final optimization result.

[0087] Step 4.6, CFX simulation verification

[0088] The results of intelligent optimization were verified. The vane pump parameters derived from the optimization were used to build an optimized impeller model, which was then imported into the CFX full-flow model for numerical calculations of the startup process. The numerical calculation results of the optimized model were compared with those predicted by the response surface model to verify the model's prediction accuracy. The numerical calculation results of the optimized model were also compared with the initial model to verify the effectiveness of the optimization method.

[0089] Step 5: Optimize the transient process performance of the vane pump using the established transient process performance optimization model.

[0090] In order to more clearly illustrate the technical solution of the present invention, the following is a rated speed n = 1450r / min, rated flow Q = 480m 3 Taking a mixed flow pump with a rated head of H = 6 m and a blade number z = 4 as an example, its axial projection is shown in Figure 2. The method of the present invention maintains the impeller inlet width b1, impeller outlet width b2, impeller inlet diameter D1, and blade number z unchanged. The transient performance optimization design process for this pump is described in detail as follows:

[0091] Table 1 shows the determined optimized design parameters and their ranges of variation. Using the original pump's structural parameters, an initial model was established using UG. A full flow field model of the mixed-flow pump startup process was constructed, as shown in Figure 3. The original pump model was imported into CFturbo for reverse modeling, and the resulting model was imported into TurboGrid to generate a high-quality structured mesh, as shown in Figure 4. The resulting impeller mesh and meshes of other flow components were imported into ANSYS CFX via ANSYS Workbench for numerical simulation of the mixed-flow pump startup process. The transient efficiency η and transient head H during the startup process were monitored. In this example, the mixed-flow pump was started linearly with constant acceleration. The speed reached 1450 rpm in 1.35 s. The total startup time, with a set flow rate stability, was 2.5 s, with a time step of 0.001 s. During the transient calculation of the startup process, a set of transient head and transient efficiency values ​​was output at each time step, resulting in a total of 2500 data sets after the 2.5 s calculation.

[0092] Table 1

[0093] Using the 16 design variables shown in Table 1 as input parameters and the transient head H and transient efficiency η monitored in CFD-Post as output parameters, 150 sample design points were established using the advanced Latin hypercube sampling method in conjunction with ANSYS optiSLang. The startup transients were then numerically calculated for these sample points. A weighted average formula for head and efficiency was established in Excel to preprocess the transient head and efficiency data.

[0094] In Design-Expert, a central composite design (CCD) with 10 factors is established and two response values ​​G are set. z 、F z Then, the data processed in Excel were input into the generated design table one by one. After the significance test, it was found that the blade inlet placement angle α2, α3 and blade thickness had a low impact on the weighted average head and weighted average efficiency. Therefore, after excluding these parameters from the optimization variables, the response surface model was established with the remaining 10 design variables and 2 response values, as shown in Figures 5 and 6. The accuracy test of the response surface model was carried out to obtain Table 2. It can be seen from the table that the R 2 and R adj 2 The values ​​of are all greater than 0.9, and R pred 2 and R adj 2 The differences are all less than 0.2. This shows that the response surface fitting accuracy is high and it can well express the intrinsic relationship between the input parameters and the response values. The response surface model can be used for subsequent optimization solutions.

[0095] Table 2

[0096] The first stage optimization conditions: G z The constraint condition is that the weighted average head of the original pump is greater than that of the original pump. z Take the maximum value as the objective function; the second stage optimization conditions: take H(t)≥H0(t), η(t)≥η0(t) as constraints, F z The maximum is the objective function. After two stages of optimization, the final optimization results are shown in Table 3.

[0097] Table 3

[0098] The optimized mixed-flow pump was validated through CFX simulation, resulting in Table 4. This table shows that the optimized mixed-flow pump's weighted average efficiency during transient startup increased by 8.91%, its weighted average head increased by 2.97%, its rated head increased by 3.51%, and its rated efficiency increased by 5.08%. Figure 7 shows the external characteristic curves of the mixed-flow pump's startup process before and after optimization. The figure shows that the optimized pump's transient head and efficiency at all times are higher than those of the original pump, indicating that the optimization achieved its intended goal.

[0099] Table 4

[0100] The above embodiments are intended only to illustrate the design concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. The scope of protection of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design concepts disclosed in the present invention are within the scope of protection of the present invention.

Claims

1. A method for optimizing the transient performance of a vane pump, characterized in that: The steps include: Step 1, respectively create the initial design models of the vane pump and the impeller and the full flow field simulation model of the vane pump startup process, and select the impeller structural parameters as design variables, the design variables including the blade inlet placement angle, the blade outlet placement angle, the blade wrap angle, the blade thickness and the blade thickness coefficient; Step 2: Based on the initial design model created in step 1 and the full flow field simulation model of the vane pump startup process, the transient external characteristics of the startup process are calculated using CFD simulation software, where the transient external characteristics of the startup process include transient head and transient efficiency; Step 3, performing data processing on the calculated transient external characteristics of the startup process to obtain the weighted average head and weighted average efficiency respectively; Step 4: Taking the design variables as input parameters, and the weighted average head and weighted average efficiency as response values; Establish a transient process performance optimization model based on response surface function; Step 5: Optimize the transient process performance of the vane pump using the established transient process performance optimization model.

2. A vane pump transient performance optimization design method according to claim 1, characterized in that: The method for establishing the transient process performance optimization model in step 4 is: Step 4.1, set the range corresponding to each design variable; Step 4.2, using the design variables as input parameters and the monitored transient head and transient efficiency as output parameters, a sample collection platform is built to obtain sample points; Perform data processing on the output parameters to obtain the weighted average lift and weighted average efficiency; Step 4.3, taking the input parameters of the sample points as design variables, and the weighted average head and weighted average efficiency as response values; Select response surface function to establish transient process performance optimization model; Step 4.4, evaluating the prediction accuracy of the transient process performance optimization model; Step 4.5: The first stage is based on G z The constraint condition is that the weighted average head of the original pump is greater than that of the original pump. z The maximum value is taken as the objective function, and several prediction points are obtained by building points on the determined transient process performance optimization model for optimization. In the second stage, H(t)≥H0(t) and η(t)≥η0(t) are used as constraints, and F z The maximum value is the objective function, and the prediction points obtained in the first stage are searched in turn to obtain the final optimization result; step 4.6, use CFX to simulate and verify the impeller model after the vane pump parameters are optimized.

3. A vane pump transient performance optimization design method according to claim 2, characterized in that: The range corresponding to each design variable is selected based on ±15% of the initial value of each design variable.

4. A vane pump transient performance optimization design method according to claim 2, characterized in that: The weighted average head is expressed as: The weighted average efficiency is expressed as: Where: t is the start time; T tol is the total startup time when the flow rate is stable; Δt is the time step; H is the transient head of the designed pump; H0 is the transient head of the original pump; η is the transient efficiency of the designed pump; η0 is the transient efficiency of the original pump; F z is the weighted average of the transient efficiency.

5. The method for optimizing the transient performance of a vane pump according to claim 2, characterized in that: Step 4.2 uses the advanced Latin hypercube sampling method to establish sample points.

6. A vane pump transient performance optimization design method according to claim 2, characterized in that: In step 4.4, the sample variance is analyzed and the significance test of the design variables is performed according to the f criterion, and the parameters with greater influence on the response value are selected as optimization variables; the sample residual is analyzed and the performance, adaptability and prediction accuracy of the response surface model are evaluated according to the R criterion.

7. A vane pump transient performance optimization design method according to claim 6, characterized in that: The expression of the f criterion is: In the formula, S a is the sum of squares of deviations of each variable, S n is the sum of squares of deviations caused by experimental errors, E a and E n is the number of degrees of freedom for the design variables and deviations; R criteria are determined by R 2 Criteria, R adj 2 Criteria and R pred 2 The guidelines together.

8. A method for optimizing the transient performance of a vane pump according to any one of claims 1 to 7, characterized in that: The response surface function can be selected from polynomial, nonlinear function, power function, BP neural network model, radial basis function or multivariate adaptive regression spline function.

9. A vane pump transient performance optimization design method according to claim 8, characterized in that: The modeling method in step 1 is as follows: Step 1.1, use UG to create the initial design model of the impeller of the vane pump; Step 1.2, import the initial design model of the impeller created in step 1.1 into ANSYS CFturbo through ANSYS Workbench for reverse modeling, and parameterize the design variables that need to be changed; Step 1.3, import the impeller reverse modeled in step 1.2 into ANSYS TurboGrid through ANSYS Workbench to divide the structured grid; Step 1.4, use UG to create models of vane pump guide vanes, volute chambers, test pipelines, gate valves and pressure-surge tanks; Step 1.5, import the model created in step 1.4 into ANSYS ICEM to divide the structured grid.

10. The method for optimizing the transient performance of a vane pump according to claim 8, characterized in that: The thickness distribution between the blade hub and the rim is defined as the thickness coefficient θ. When θ<0, the blade thickness will develop toward the rim area, and when θ>0, the blade thickness will develop toward the hub area.

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