A high-pressure diaphragm pump one-way valve structure multi-objective optimization method based on digital twinning

Abstract

The application discloses a high-pressure diaphragm pump one-way valve structure multi-objective optimization method based on digital twinning, and belongs to the technical field of pump valve optimization design; the application collects geometric size parameters and material properties of a high-pressure diaphragm pump one-way valve, and establishes a simulation model; an Isight and ANSYS joint simulation method is used to generate sample points and calculate response values; an agent model is constructed according to response values of each group of sample points, calculation efficiency is improved, and a digital twinning model is constructed; a one-way valve structure multi-objective optimization model is established by taking the maximum equivalent stress of a valve core and the minimum maximum flow velocity of a valve gap flow field as objective functions, a genetic algorithm is used for solving, the best structure parameter combination of the one-way valve impact resistance performance is obtained, and finally, result verification is carried out; the application can realize high-precision simulation of a one-way valve operating state and solving of optimal structure parameters of impact resistance performance, thereby guiding one-way valve structure design, reducing product research and development time cost, and improving service life.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pump valve optimization design, in particular to a high-pressure diaphragm pump one-way valve structure multi-objective optimization method based on digital twinning. BACKGROUND

[0002] As one of the key mechanical equipment for long-distance transportation of slurry pipeline, the reciprocating high-pressure diaphragm pump has the advantages of high lift, corrosion resistance, high pressure resistance, etc., and is suitable for transporting complex solid-liquid two-phase medium flow, and has a wide application prospect in the fields of coal, chemical industry, metallurgy and other industrial production. As the mechanical component that moves most frequently in the high-pressure diaphragm pump, the one-way valve has a good one-way flow function, which ensures the normal operation of the pump body liquid suction and liquid discharge. However, due to the influence of the cavity solid-liquid two-phase flow movement, the stress on the structure of the one-way valve is complex and variable, which leads to the occurrence of valve core sticking and breakdown. Since the structure damage is the main factor leading to the failure of the one-way valve, a good structure design can ensure better applicability in harsh working environment and prolong the service life. The traditional pump valve structure optimization design is completed by analyzing the optimal group from multiple experimental groups.

[0003] However, the traditional pump valve structure optimization design method not only has the disadvantages of long time consumption and high design cost, but also has certain safety hazards for such special industrial equipment. The existing solutions mostly use multiple simulation experiments to explore the influence of different structure parameter changes on the performance to propose an optimization scheme, ignoring the coupling effect between parameters and not finding the optimal structure parameter combination. Therefore, by constructing a high-fidelity digital twinning model integrating simulation, mechanism and agent model, considering the coupling effect between structure parameters, and obtaining the optimal structure parameter combination through a multi-objective optimization design method, a structure optimization scheme with guidance is provided, which can greatly improve the design efficiency and reduce the experimental test cost. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a high-pressure diaphragm pump one-way valve structure multi-objective optimization method based on digital twinning, which has the advantages of low cost and high efficiency, provides a guidance scheme for product structure optimization design, and solves the above technical problems.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a high-pressure diaphragm pump one-way valve structure multi-objective optimization method based on digital twinning, comprising the following steps:

[0006] S1, collecting the geometric structure size parameters and material properties of the high-pressure diaphragm pump one-way valve, and performing geometric modeling and mechanism modeling on the one-way valve to obtain a simulation model and a mechanism model;

[0007] The simulation model includes a geometric model and a numerical model.

[0008] The mechanism model comprises a slurry turbulent flow model, a fluid-structure coupling model and a spool movement model;

[0009] Further, the geometric model comprises a valve body, a spool, a limiter, a spring and a valve seat; the spool comprises a gland, a rubber pad and a valve cone;

[0010] Further, the numerical model comprises a grid model, a boundary condition and a material parameter attribute setting;

[0011] Further, the slurry turbulent flow model adopts an RNG K-ε model to construct, and an expression is as follows:

[0012]

[0013] In the formula, K represents turbulent kinetic energy; ρ represents liquid density; ε represents dissipation rate; G k represents turbulent kinetic energy caused by velocity gradient; G b represents turbulent kinetic energy caused by buoyancy; Y M represents contribution of wave fluctuation expansion number to total dissipation rate; α K and α ε respectively represent turbulent back-Prandtl number of K equation (first equation) and ε equation (second equation); represents partial derivative operation to x i ; represents partial derivative to x j ; x i and x j represent components in coordinate i, j directions; μ eff represents viscosity coefficient; S K and S ε represent source terms corresponding to K and ε; C 1ε , C 2ε and C 3ε respectively represent first, second and third empirical constants; R ε represents ratio of dissipation rate and turbulent kinetic energy K;

[0014] Further, an expression of the fluid-structure coupling model is as follows:

[0015]

[0016] In the formula, M S is a mass matrix; C S is a damping matrix; K S is a stiffness matrix; r s and r f respectively represent spool displacement and fluid displacement of a fluid-structure coupling surface; τ s and τ fThe valve core force and fluid force of the fluid-structure coupling surface respectively; n f And n s The fluid and the cosine of the vertical direction of the one-way valve contact surface respectively;

[0017] Further, the expression of the valve core motion model is as follows:

[0018]

[0019] In the formula, m s The valve core mass; p s , p are the valve inlet pressure and outlet pressure respectively; g is the acceleration of gravity; k and h0 are the spring stiffness and pre-tightening amount respectively; h is the valve core displacement; B is the fluid damping coefficient;

[0020] S2, the simulation model and the mechanism model are simulated by using the Isight and ANSYS joint simulation method, the experimental sample points are generated and the response values are calculated;

[0021] The joint simulation is that the DOE module of Isight performs experimental design on the design variables (the half cone angle α of the valve core, the small diameter d of the valve core, and the cone surface width h of the valve core) and the target values (the maximum equivalent stress S max of the valve core and the maximum flow velocity V max of the valve gap flow field) set by the one-way valve structure, the optimal Latin hypercube method is used to generate sample points, and the ANSYS software is used to numerically solve the model in step S1 to obtain the actual response values of each group of experimental sample points;

[0022] S3, according to the response values obtained in S2, a kriging surrogate model is constructed and the prediction accuracy evaluation index is used for feedback correction;

[0023] Specifically, the kriging surrogate model is a mathematical model representing the relationship between input and output by training experimental sample points, that is, a regression equation of the design variables (the half cone angle α of the valve core, the small diameter d of the valve core, and the cone surface width h of the valve core) and the target values (the maximum equivalent stress S max of the valve core and the maximum flow velocity V max of the valve gap flow field) is obtained, and the prediction accuracy evaluation index is used for feedback correction. The mathematical model can be expressed in the form of a sum of a polynomial regression model and a random process;

[0024] The expression of the kriging surrogate model is as follows:

[0025]

[0026] where num represents the number of polynomial functions; ω represents a polynomial coefficient vector; k(x) represents a base function of an input parameter x; S(x) represents a statistical variable satisfying a 0 expectation and a covariance of:

[0027]

[0028] where σ is a variance of a Gaussian process; R(θ,x i ,x j ) is a correlation function of a parameter θ, used to represent a spatial correlation relationship between sample points x i and x j , and its expression is:

[0029]

[0030] where m is the number of design variables; θ = {θ k : k = 1, 2,..., 3} is a model parameter to be determined, which can be a constant value or an optimized model parameter; d k is the Euclidean distance of the sample point k;

[0031] The evaluation index of the prediction accuracy is the determination coefficient R 2 , and when R 2 > 0.9, the accuracy requirement is met, and the calculation formula is as follows:

[0032]

[0033] where M is the number of test samples; is the predicted value of the test sample; is the mean value of the response value of the test sample; y p is the true response value of the test sample;

[0034] S4, a one-way valve structure multi-objective optimization mathematical model is established according to the result of S3, and the expression is as follows:

[0035]

[0036] where α, d and h represent the half-cone angle, small diameter and cone width of the valve core, and are set as the optimization design variables; S max and V max respectively represent the maximum equivalent stress and the maximum flow velocity of the valve gap flow field, and are set as the optimization objectives; according to the structural size relationship of each design variable and the single factor analysis of the optimization objectives, the constraint range of the design variable is finally determined;

[0037] S5, the NSGA-II multi-objective genetic algorithm is used for iterative solving of the single-way valve structure multi-objective optimization mathematical model to obtain optimal structure parameters;

[0038] S6, the optimal structure parameter result is simulated and verified by using a digital twin model;

[0039] Specifically, the optimal parameter combination of the obtained valve core half-cone angle alpha, small diameter d and cone surface width h and the target function value are used to construct the single-way valve optimization structure according to the parameters, and the digital twin model is used for simulation verification.

[0040] The beneficial effects of the application are:

[0041] The application constructs a digital twin model of the single-way valve, performs multi-objective optimization design on the model to obtain an optimization model, iteratively solves the optimization model by using a genetic algorithm, obtains an optimal structure parameter combination, realizes multi-objective optimization design of the single-way valve structure, reduces product maintenance cost and improves design efficiency, and provides important guidance for pump valve structure optimization design. DETAILED DESCRIPTION

[0042] Figure 1 The step flow chart of the application is shown in the figure;

[0043] Figure 2 The flow structure schematic diagram of the application is shown in the figure;

[0044] Figure 3 The single-way valve structure parameterization modeling schematic diagram of the application is shown in the figure;

[0045] Figure 4 The NSGA-II genetic algorithm flow chart of the application is shown in the figure;

[0046] Figure 5 The convergence curve diagram of the application is shown in the figure; part (a) is the convergence curve diagram of V max , and part (b) is the convergence curve diagram of S max ;

[0047] Figure 6 The single-way valve flow field velocity nephogram before and after optimization of the application is shown in the figure; part (a) is the single-way valve flow field velocity nephogram before optimization, and part (b) is the single-way valve flow field velocity nephogram after optimization;

[0048] Figure 7 The single-way valve equivalent stress nephogram before and after optimization of the application is shown in the figure; part (a) is the single-way valve equivalent stress nephogram before optimization, and part (b) is the single-way valve equivalent stress nephogram after optimization. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0050] As shown in Figure 1 and Figure 2 , a high-pressure diaphragm pump one-way valve structure multi-objective optimization method based on digital twinning includes the following steps:

[0051] S1, collecting the geometric structure size parameters and material properties of the high-pressure diaphragm pump one-way valve, and performing geometric modeling and mechanism modeling on the one-way valve to obtain a simulation model and a mechanism model;

[0052] The simulation model includes a geometric model and a numerical model.

[0053] The mechanism model includes a slurry turbulent flow model, a fluid-structure coupling model, and a valve core motion model.

[0054] Further, the geometric model includes a valve body, a valve core, a limiter, a spring, and a valve seat; as shown in Figure 3 ; wherein the valve core includes a gland, a rubber pad, and a valve cone.

[0055] Further, the numerical model includes a grid model, boundary conditions, and material parameter property settings.

[0056] In this embodiment, the geometric model is divided into a tetrahedral unit for discrete processing by grid division, and the valve core surface area is processed by grid encryption. The boundary condition is set as a velocity type inlet and a pressure type outlet, with specific values of v=2.6sin(4.54t) m / s and P=0.78 MPa. The rubber pad material is polyurethane, and the materials of the gland and the valve cone are 20CrNiMo alloy structural steel.

[0057] Further, the slurry turbulent flow model is constructed using the RNG K-ε model, and the expression is as follows:

[0058]

[0059] In the formula, K represents the turbulent kinetic energy; ρ represents the liquid density; ε represents the dissipation rate; G k represents the turbulent kinetic energy caused by the velocity gradient; G b represents the turbulent kinetic energy generated by buoyancy; Y M represents the contribution of the fluctuation expansion number to the total dissipation rate; α K and α εLet represent the Prandtl numbers of the turbulent reaction for the K equation (first equation) and the ε equation (second equation), respectively; Indicates x i Partial derivative operations; Indicates x j The partial derivative of x; i and x j μ represents the component along the coordinates i and j. eff S represents the viscosity coefficient; K and S ε Represents the source terms corresponding to K and ε; C 1ε C 2ε and C 3ε R represents the first empirical constant, the second empirical constant, and the third empirical constant, respectively; ε It represents the ratio of dissipation rate to turbulent kinetic energy K;

[0060] Furthermore, the expression for the fluid-structure interaction model is as follows:

[0061]

[0062] In the formula, M S C is the mass matrix; S K is the damping matrix; S r is the stiffness matrix; s and r f These represent the valve core displacement and fluid displacement at the fluid-structure interaction surface, respectively; τ s and τ f These represent the forces acting on the valve core and the fluid forces at the fluid-structure interaction surface, respectively; n f and n s These represent the cosines of the vertical direction of the fluid and the contact surface of the check valve, respectively.

[0063] Furthermore, the expression for the valve core motion model is as follows:

[0064]

[0065] In the formula, m s For valve core mass; p s p and p represent the valve inlet pressure and outlet pressure, respectively; g represents the acceleration due to gravity; k and h0 represent the spring stiffness and preload, respectively; h represents the valve core displacement; and B represents the fluid damping coefficient.

[0066] S2. Simulate the simulation model and mechanism model using the Isight and ANSYS joint simulation method, generate experimental sample points and calculate response values;

[0067] Co-simulation involves using Isight's DOE module to set design variables (valve core half-cone angle α, valve core minor diameter d, valve core cone width h) and target values ​​(valve core maximum equivalent stress S) for the one-way valve structure.max Maximum flow velocity V in the valve gap flow field max Experimental design was carried out, and the optimal Latin hypersolution method was used to generate sample points. At the same time, ANSYS software was used to numerically solve the model in step S1 to obtain the actual response values ​​of each group of experimental sample points; the process was completed automatically by computer software.

[0068] S3. Based on the response value obtained in S2, construct the Kriging surrogate model and use the evaluation index of prediction accuracy for feedback correction;

[0069] Specifically, the kriging surrogate model establishes a mathematical model representing the relationship between input and output by training experimental sample points, that is, obtaining the design variables (valve core half-cone angle α, valve core minor diameter d, valve core cone width h) and the target value (valve core maximum equivalent stress S). max and the maximum flow velocity V in the valve gap flow field max The regression equation is obtained, and feedback correction is performed using the evaluation index of prediction accuracy. The mathematical model can be expressed as the sum of a multinomial regression model and a stochastic process.

[0070] The kriging proxy model expression is as follows:

[0071]

[0072] In the formula, num represents the number of polynomial functions; ω represents the polynomial coefficient vector; k(x) represents the basis functions of the input parameter x; and S(x) represents the function that satisfies 0 expectation and The covariance of a statistical variable with variance is:

[0073]

[0074] In the formula, R(θ,x) represents the variance of the Gaussian process. i ,x j ) is the correlation function of parameter θ, used to characterize the sample point x. i With x j The spatial correlation between them is expressed as follows:

[0075]

[0076] In the formula, m is the number of design variables; θ = {θ k :k=1,2,…,3} are the model parameters that need to be determined. These parameters can be set as constants or used as model parameters for optimization; d k Let k be the Euclidean distance of the sample point k.

[0077] The evaluation index for prediction accuracy is the coefficient of determination R. 2 When R2 A value greater than 0.9 meets the accuracy requirement. The calculation formula is as follows:

[0078]

[0079] In the formula, M is the number of test samples; The predicted value for the test sample; y is the mean of the test sample response values; p This represents the true response value of the test sample;

[0080] S4. Based on the results of S3, establish a multi-objective optimization mathematical model for the one-way valve structure, as shown in the following expression:

[0081]

[0082] In the formula, α, d, and h represent the semi-cone angle, minor diameter, and cone surface width of the valve core, respectively, and are set as optimization design variables; S max and V max The maximum equivalent stress on the valve core and the maximum flow velocity in the valve gap flow field are respectively set as optimization targets. Based on the structural dimensional relationships of each design variable and combined with the single-factor analysis of the optimization targets, the constraint range of the design variables is finally determined.

[0083] S5. The optimal structural parameters are obtained by iteratively solving the multi-objective optimization mathematical model of the one-way valve structure using the NSGA-II multi-objective genetic algorithm.

[0084] Specifically, the NSGA-II multi-objective genetic algorithm iteratively solves the optimization model constructed in S4, and obtains the optimal combination of structure parameters after iteration; the algorithm flow is as follows: Figure 4 As shown, the population size is 20, the maximum number of iterations is 1000, the crossover probability is 0.9, and the mutation probability is 0.05. The optimal solution is obtained after 800 iterations. The result of the 800th iteration is selected as the optimization scheme. The convergence curve of the objective function is shown in the figure. Figure 5 As shown in parts (a) and (b);

[0085] S6. The results of the optimal structural parameters are verified by simulation using a digital twin model;

[0086] Specifically, the optimal parameter combination and objective function value of the valve core half-cone angle α, minor diameter d, and cone width h are shown in Table 1. Based on the parameters, an optimized one-way valve structure is constructed and verified by simulation using a digital twin model.

[0087] Table 1. Valve core structural parameters and comparison before and after target optimization.

[0088]

[0089] After completing all the above steps, the velocity contour plots and equivalent stress contour plots of the one-way valve flow field before and after optimization can be obtained, such as... Figures 6-7 As shown;

[0090] Depend on Figure 6 As shown in parts (a) and (b), the maximum velocity of the flow field inside the cavity before optimization and after optimization are 1.5 m / s and 1.3 m / s, respectively. The maximum velocity is distributed in the valve gap channel formed by the valve core and the valve seat. The optimized structure reduces the maximum velocity of the valve gap flow field. The fluid velocity is positively correlated with the erosion wear rate of the valve core. The erosion wear rate of the valve core structure can be reduced by reducing the maximum velocity.

[0091] Depend on Figure 7 As shown in sections (a) and (b), the maximum stress of the valve core structure before and after optimization is 5.5 MPa and 4.6 MPa, respectively. This reduces the maximum equivalent stress on the valve core structure, thereby weakening the impact of stress concentration on structural strength and extending the service life of the check valve.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for multi-objective optimization of a high-pressure diaphragm pump check valve structure based on digital twinning, characterized in that, The method comprises the following steps: S1, collecting geometric size parameters and material properties of a high-pressure diaphragm pump check valve, and performing geometric modeling and mechanism modeling on the check valve to obtain a simulation model and a mechanism model; The simulation model comprises: a geometric model, a numerical model; The mechanism model comprises: a slurry turbulent flow model, a fluid-structure coupling model, and a valve core motion model; S2, simulating the simulation model and the mechanism model by using an Isight and ANSYS joint simulation method, generating experimental sample points, and calculating response values; S3, constructing a kriging surrogate model according to the response values obtained in S2, and performing feedback correction by using an evaluation index of prediction accuracy; S4, establishing a check valve structure multi-objective optimization mathematical model according to the result of S3; S5, iteratively solving the check valve structure multi-objective optimization mathematical model by using an NSGA-II multi-objective genetic algorithm to obtain optimal structure parameters; S6, simulating and verifying the result of the optimal structure parameters by using a digital twin model.

2. The method according to claim 1, wherein the method is characterized in that: In the simulation of the simulation model and the mechanism model by the combined simulation method of Isight and ANSYS, the combined simulation is that the DOE module of Isight is used to design the experiment of the design variables and the target values of the structure of the one-way valve, the sample points are generated by using the optimal Latin hypercube method, and the model in step S1 is numerically solved by using the ANSYS software to obtain the actual response values of each group of experimental sample points, wherein the design variables include the half-cone angle a of the valve core, the small diameter d of the valve core, and the width h of the conical surface of the valve core, and the target values include the maximum equivalent stress S max of the valve core and the maximum flow velocity V max of the valve gap flow field. In the simulation of the simulation model and the mechanism model by the combined simulation method of Isight and ANSYS, the combined simulation is that the DOE module of Isight is used to design the experiment of the design variables and the target values of the structure of the one-way valve, the sample points are generated by using the optimal Latin hypercube method, and the model in step S1 is numerically solved by using the ANSYS software to obtain the actual response values of each group of experimental sample points, wherein the design variables include the half-cone angle a of the valve core, the small diameter d of the valve core, and the width h of the conical surface of the valve core, and the target values include the maximum equivalent stress S max of the valve core and the maximum flow velocity V max of 3. The method of claim 1, wherein the method is characterized by: In the kriging surrogate model expression is as follows: In the check valve structure multi-objective optimization mathematical model expression is as follows: In the formula, num represents the number of polynomial functions; ω represents a polynomial coefficient vector; k(x) represents a base function of an input parameter x; S(x) represents a statistical variable satisfying a 0 expectation and variance The evaluation index of prediction accuracy is the determination coefficient R 2 When R 2 >0.9, the accuracy requirement is met, and the calculation formula is as follows: where M is the number of test samples; is the predicted value for a test sample; is the mean of the response values for test samples; y p is the true response value for a test sample.

4. The method of claim 1, wherein the method is characterized by: ​ In the formula, α, d and h represent the half-cone angle, small diameter and cone width of the valve core, which are set as the design variables; S max and V max respectively represent the maximum equivalent stress and the maximum flow velocity of the valve gap flow field.

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