A Planar Gate Valve Opening and Closing Capacity Prediction Method Based on Parametric Calculation

Abstract

This invention discloses a method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation. The method includes: acquiring the geometric parameter set and hydraulic state parameter set of the planar gate valve to be analyzed; calculating the total gravity of the gate valve system and the top vertical water pressure accordingly; within a preset operating opening range, solving for the extreme values ​​of a pre-configured parametric mapping model representing the relationship between the relative opening of the planar gate valve and the bottom dynamic water force to determine the most unfavorable extreme value of the bottom dynamic water force; and calculating the maximum opening force and determining the predicted capacity of the gate valve based on the total gravity of the gate valve system, the top vertical water pressure, the most unfavorable extreme value, and the total friction force determined by the hydraulic state parameter set, according to the principle of force balance. This invention can accurately identify the most unfavorable operating conditions, improving the accuracy and reliability of opening and closing capacity prediction.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy and hydropower engineering, and in particular, it is a method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation. Background Technology

[0002] Planar gate valves are key structures for controlling water flow in water conservancy and hydropower projects, and accurate prediction of their opening and closing capacity is the cornerstone of ensuring the safe and reliable operation of the project. The accuracy of opening and closing capacity prediction directly affects the rationality of the selection of gate hoisting equipment, impacting not only project investment but, more importantly, inadequate estimation of the gate opening force under the most unfavorable operating conditions may lead to the gate failing to open at critical moments. Therefore, developing a high-precision method for predicting opening and closing capacity is of significant technical importance for improving the reliability and safety of water conservancy project design.

[0003] Currently, the prediction methods for the opening and closing capacity of planar gate valves mainly rely on physical model experiments or computational fluid dynamics (CFD) simulations. In engineering design, a lookup method based on a table of hydrodynamic force coefficients is commonly used. This method involves conducting preliminary experiments or simulations on several standardized bottom edge geometries and specific opening points (e.g., relative openings of 0.05, 0.1, 0.2, etc.), and compiling the obtained hydrodynamic force coefficients into charts or databases. Designers, based on the geometric parameters of the gate to be analyzed, find the closest operating condition in the existing charts, obtain the corresponding hydrodynamic force coefficients, and then perform subsequent mechanical equilibrium calculations accordingly.

[0004] However, the aforementioned existing technologies still have several technical problems in terms of prediction accuracy, model generalization ability, and decision reliability. Specifically, since the hydrodynamic force coefficient table is based on discrete opening points, discretization may lead to the omission of the true mechanical extreme points between two measuring points, posing a technical risk of missed extreme value detection. In addition, existing coefficient tables are usually for standardized bottom edge configurations. When the actual gate bottom edge has unique chamfers, rounded corners, and other detailed structural features, the model cannot accurately reflect the impact of these subtle changes on hydrodynamics, resulting in insufficient model generalization ability. Moreover, the traditional prediction process is a deterministic calculation, and its output does not contain uncertainty information, making it impossible to quantify the confidence level of the predicted values, making it difficult for designers to make risk-based robust decisions. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation, so as to solve the above-mentioned problems existing in the prior art.

[0006] The technical solution, a method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation, includes:

[0007] Obtain the geometric parameter set and hydraulic state parameter set of the plane gate valve to be analyzed;

[0008] The total gravity of the gate valve system is calculated based on the set of geometric parameters, and the vertical water pressure at the top is calculated based on the set of geometric parameters and the set of hydraulic state parameters.

[0009] Within the preset operating opening range, the extreme value of the pre-configured parametric mapping model representing the relationship between the relative opening of the planar gate valve and the bottom hydrodynamic force is solved to determine the most unfavorable extreme value of the bottom hydrodynamic force.

[0010] Based on the total gravity of the gate valve system, the top vertical water pressure, the most unfavorable extreme value, and the total friction force determined by the hydraulic state parameter set, the maximum opening force is calculated according to the principle of force balance.

[0011] The predicted capacity of the gate hoist is determined based on the maximum gate opening force.

[0012] According to one aspect of this application, the total weight of the gate valve system includes the self-weight of the planar gate valve, the counterweight, and the weight of the gate valve lifting rod system;

[0013] The total gravity of the gate valve system is calculated based on the set of geometric parameters using the following formula:

[0014] G Z =G1+G2+G3;

[0015] Among them G Z G1 is the total weight of the gate valve system, G2 is the self-weight of the plane gate valve, G3 is the counterweight, and G4 is the weight of the gate valve lifting rod system.

[0016] According to one aspect of this application, the top vertical water pressure is calculated using the following formula:

[0017] F u =ρ×g×(Z j0 -Z d0 )×B×d;

[0018] Where F u The vertical water pressure at the top, ρ is the density of water, g is the acceleration due to gravity, and Z is the acceleration due to gravity. j0 Z represents the initial water level of the valve well before operation. d0 B represents the top elevation of the valve when it is fully closed, B represents the width of the flat gate valve, and d represents the thickness of the flat gate valve.

[0019] According to one aspect of this application, the parametric mapping model specifies that the bottom hydrodynamic force is positive upwards and negative downwards, and selects the calculation logic based on the bottom edge dip angle type defined in the geometric parameter set:

[0020] When the bottom edge is an upward-sloping bottom edge: F d (n)=ρ×g×h×B×d×k up (n, α);

[0021] When the bottom edge is a downward-sloping bottom edge: F d (n)=ρ×g×h×B×d×k down (n, α);

[0022] Where F d (n) represents the bottom dynamic water force at a relative opening of n, ρ is the density of water, g is the acceleration due to gravity, B is the width of the plane gate valve, d is the thickness of the plane gate valve, h is the applied water head, α is the bottom edge inclination angle, and k up (n, α) and k down (n, α) are the hydrodynamic force coefficients for the corresponding opening degree and inclination angle, respectively.

[0023] According to one aspect of this application, the total frictional force includes water-stopping frictional resistance and roller frictional resistance;

[0024] The maximum door opening force is calculated based on the principle of force balance, specifically using the following formula:

[0025] T=G Z +F u -F dmin +f z ;

[0026] Where T is the maximum opening force, and G Z For the total weight of the gate valve system, F u F is the vertical water pressure at the top. dmin For the most unfavorable extreme value, f z This represents the total frictional force.

[0027] According to one aspect of this application, the parameterized mapping model is constructed using a continuous function model containing fractional rational terms to characterize the hydraulic surge characteristics at small opening degrees. The continuous function model is expressed as:

[0028] F d (n)=C hyd ×[(a0+a1×n) / (n+b)+a2×n];

[0029] Where n is the relative opening, F d (n) represents the dynamic force of the bottom water flow, C hyd The coefficients are related to the water head. a0, a1, a2 and b are undetermined model parameters obtained through identification. The (a0+a1×n) / (n+b) term is used to characterize the nonlinear surge behavior in the small opening range, and the a2×n term is used to characterize the linear change trend in the large opening range.

[0030] According to one aspect of this application, the undetermined model parameters are associated with the bottom edge detailed structural features defined in the geometric parameter set;

[0031] The determination of the parameters of the undetermined model includes:

[0032] Constructing the detailed feature vector X of the bottom edge g Bottom edge detail feature vector X g It should include at least the bottom edge inclination angle, bottom edge chamfer length, bottom edge fillet radius, bottom edge gap, and waterstop location category;

[0033] Constructing the hydraulic state vector X h Hydraulic state vector X h At least the effective head and the downstream velocity should be included;

[0034] Establish a feature vector X of the bottom edge detail g and hydraulic state vector X h Using the input as the regression mapping relationship with the undetermined model parameters as the output, the specific values ​​of a0, a1, a2 and b are determined.

[0035] According to one aspect of this application, the method further includes an online calibration step, specifically:

[0036] During the engineering trial operation or commissioning phase of a planar gate valve, obtain the measured opening and closing force data under the predetermined group of measured opening degrees;

[0037] Based on the measured opening and closing force data and the principle of force balance, the measured hydrodynamic force coefficients under the corresponding opening degree are obtained by inversion.

[0038] Calculate the deviation between the measured hydrodynamic force coefficient and the output value of the parameterized mapping model, and construct a deviation calibration term;

[0039] The parametric mapping model is corrected online using a deviation calibration term to obtain calibrated predicted values ​​of bottom hydrodynamic forces.

[0040] According to one aspect of this application, the parameterized mapping model is a continuous function of relative opening degree;

[0041] Determine the most unfavorable extreme value of the bottom hydrodynamic force, including:

[0042] The bottom dynamic water force is used as a continuous objective function of the relative opening.

[0043] Within the operating range, a global optimization operation is performed on the continuous objective function to identify the most unfavorable opening that causes the continuous objective function to reach its extreme value.

[0044] Substituting the most unfavorable opening value into the continuous objective function, the most unfavorable extreme value is calculated.

[0045] According to one aspect of this application, identifying the most unfavorable opening that causes a continuous objective function to reach an extremum includes:

[0046] The derivative function is obtained by taking the first derivative of the continuous objective function with respect to the relative opening.

[0047] Find the stationary points where the derivative function is equal to zero, and check whether the stationary points are located within the operating opening interval;

[0048] By comparing the function values ​​at the stagnation point and the boundary points of the operating opening range, the most unfavorable opening is determined.

[0049] Beneficial effects: This invention can accurately identify the most unfavorable operating conditions, improving the accuracy and reliability of opening and closing capacity prediction; it eliminates the risk of missing extreme values ​​due to interpolation or sparse data points, improving the accuracy of prediction; it accurately reflects the impact of subtle structural changes on hydrodynamics, improving the model's generalization ability and applicability. Attached Figure Description

[0050] Figure 1 The flowchart illustrates the steps of a method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation, as provided in this application embodiment.

[0051] Figure 2 A flowchart illustrating the steps for determining undetermined model parameters provided in this application embodiment.

[0052] Figure 3 This is a flowchart illustrating the steps for online calibration provided in an embodiment of this application.

[0053] Figure 4 A flowchart illustrating the steps for determining the most unfavorable extreme value of the bottom dynamic water force, provided in an embodiment of this application.

[0054] Figure 5 This is a schematic diagram of the gate chamber water level process during the opening of the drain valve, as provided in the embodiments of this application.

[0055] Figure 6 This is a schematic diagram of the water level process in the valve well during the opening of the drain valve, as provided in an embodiment of this application.

[0056] Figure 7 This is a schematic diagram of the measured valve opening and closing force process during the opening of the drain valve provided in the embodiment of this application.

[0057] Figure 8 A schematic diagram comparing and verifying the dynamic water force process line at the bottom edge of a planar valve provided in the embodiments of this application. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms include and have, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0060] like Figure 1 As shown, a method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation includes the following steps:

[0061] Obtain the geometric parameter set and hydraulic state parameter set of the plane gate valve to be analyzed.

[0062] In this embodiment, obtaining the geometric parameter set and hydraulic state parameter set of the planar gate valve to be analyzed is the initialization stage of the entire prediction process. The geometric parameter set and hydraulic state parameter set are the foundation for all subsequent physical calculations and model construction. Specifically, the geometric parameter set can be organized into structured data objects, such as JSON objects or dictionary structures in programming languages, for easy access by computer programs. The geometric parameter set includes at least: main structural parameters, such as gate width, thickness, height, and self-weight; detailed structural features of the bottom edge, such as bottom edge type (e.g., upslope or downslope), specific values ​​of the bottom edge inclination angle, bottom edge chamfer length, bottom edge fillet radius, etc.; and auxiliary structural parameters, such as the type of water-stopping device, roller parameters, and weight of the lifting rod system, etc. Correspondingly, the hydraulic state parameter set defines the external environmental conditions for the operation of the gate valve. The hydraulic state parameter set includes at least: upstream water level, downstream water level, initial flow velocity below the gate, water density, and kinematic viscosity coefficient of water, etc. These parameters collectively determine the magnitude of various water pressures and hydrodynamic forces acting on the gate.

[0063] Optionally, after acquiring the geometric parameter set and hydraulic state parameter set, the system performs validity verification on the input data. Verification rules include: geometric parameters such as gate width B, thickness d, and height H should be positive real numbers and within a preset reasonable range, for example, B∈[1 m, 6 m], d∈[0.2 m, 1.5 m]; the applied head h should be a non-negative real number and not exceed the maximum allowable value of the gate's design head; the bottom edge inclination angle α should be within the range of [-60 degrees, +60 degrees], where a positive value represents an upward inclination angle and a negative value represents a downward inclination angle; the fillet radius r and chamfer length l should be non-negative real numbers. If the input parameters do not meet the verification rules, the system will output an error message and terminate subsequent calculations to avoid generating physically meaningless prediction results.

[0064] The total gravity of the gate valve system is calculated based on the set of geometric parameters, and the top vertical water pressure is calculated based on the set of geometric parameters and the set of hydraulic state parameters.

[0065] Specifically, a basic static analysis is performed on the gate to calculate several relatively stable vertical forces that exist throughout the opening and closing process. The total gravity of the gate valve system is the total force acting on the gate and its auxiliary components in the gravitational field, and it is the fundamental component for calculating the gate opening force. The top vertical water pressure is the hydrostatic pressure acting on the top sealing plate of the gate, and its magnitude is directly related to the submersion depth of the top of the gate. Understandably, the calculation of these forces usually follows the standard principles of fluid statics and solid mechanics.

[0066] Within the preset operating opening range, the extreme value of the parametric mapping model representing the relationship between the relative opening of the plane gate valve and the bottom hydrodynamic force is solved to determine the most unfavorable extreme value of the bottom hydrodynamic force.

[0067] In this embodiment, the most unfavorable hydrodynamic force on the hoist is identified throughout the entire gate opening process, i.e., the interval from 0 to 1 relative opening. The most unfavorable extreme value usually refers to the maximum downward suction force (i.e., the negative force), because both of these situations place the highest demands on the opening and closing force. The method for solving the extreme value depends on the specific form of the model. For discrete models, the extreme value solution process involves ergodic comparison; while for continuous function models, the extreme value solution process is transformed into a standard function optimization problem, which can be solved using efficient methods such as analytical differentiation or numerical optimization.

[0068] Based on the total gravity of the gate valve system, the vertical water pressure at the top, the most unfavorable extreme value, and the total friction force determined by the set of hydraulic state parameters, the maximum opening force is calculated according to the principle of force balance.

[0069] Specifically, a force analysis is performed on the gate in the vertical direction, and a force balance equation is established. The maximum opening force is the maximum pulling force required by the hoist to overcome all downward forces (including gravity, downward suction, friction, etc.) and lift the gate upward under the most unfavorable operating conditions. Total friction is another important resistance term, typically including water-stop friction resistance and roller friction resistance, the magnitude of which is closely related to water pressure and gate structure. By algebraically summing all force components, the predicted value of the maximum opening force can be obtained based on the principle of force balance.

[0070] The predicted capacity of the gate hoist is determined based on the maximum gate opening force.

[0071] In this embodiment, after calculating the maximum gate opening force, considering the safety redundancy in the engineering design, it needs to be multiplied by a preset safety factor, such as a value between 1.1 and 1.2, to obtain the design capacity or verification capacity of the gate hoist. The predicted capacity of the gate hoist can ultimately serve as a key design or verification basis to guide engineering practice. The output can be a prediction report containing detailed calculation processes and final capacity values.

[0072] Optionally, a parametric mapping model can be constructed based on the bottom edge detail structural features defined in the geometric parameter set to characterize the relationship between the relative opening of the planar gate valve and the bottom dynamic water force.

[0073] Specifically, by constructing a parameterized mathematical model, the continuous relationship between the dynamic water force at the bottom of the gate and the relative opening degree is accurately described. The construction of the parameterized mapping model transforms discrete, empirical data points into continuous functions or high-precision mappers that reflect the inherent physical laws and can be mathematically analyzed. The input to the parameterized mapping model includes not only the relative opening degree as the independent variable, but more importantly, the model's intrinsic parameters or structure itself are directly determined by the detailed structural features of the bottom edge (such as inclination angle and chamfer). This gives the model good generalization ability, enabling prediction for gates with different bottom edge designs. The parameterized mapping model can be implemented as a discrete model or a preferred continuous function model. This solves the problems of low accuracy and inability to reflect the influence of bottom edge details in traditional lookup table methods.

[0074] It should be noted that the planar gate valve opening and closing capacity prediction method based on parametric calculation provided in this application can run on general-purpose computing devices. As an example, the computing device can be a server or personal computer configured with a central processing unit (CPU), sufficient memory (RAM), and hard disk storage. At the software level, the planar gate valve opening and closing capacity prediction method based on parametric calculation can be implemented on mainstream operating systems, such as Windows Server or Linux systems, and deployed using high-level programming languages, such as Python or MATLAB, to facilitate complex numerical calculations and model building.

[0075] In one possible implementation, the total weight of the gate valve system includes the self-weight of the planar gate valve, the counterweight, and the weight of the gate valve lifting rod system.

[0076] In this embodiment, the total gravity of the gate valve system is a constant downward force, the value of which is unaffected by changes in gate opening or water flow state. The total gravity of the gate valve system consists of three parts. For example, the total gravity of the gate valve system is calculated based on a set of geometric parameters using the following formula:

[0077] G Z =G1+G2+G3;

[0078] Among them G Z G1 is the total weight of the gate valve system; G2 is the self-weight of the flat gate valve, i.e., the weight of the gate leaf itself; G3 is the weight of the counterweight, i.e., the weight of the counterweight block used to balance the weight of the gate; G4 is the weight of the gate valve lifting rod system, i.e., the weight of the lifting rod, lifting lugs, and other components connecting the hoist and the gate leaf. These parameters are usually provided by the gate valve design drawings or the manufacturer and are known geometric parameters.

[0079] According to one aspect of this application, the top vertical water pressure is calculated using the following formula:

[0080] F u =ρ×g×(Z j0 -Z d0 )×B×d;

[0081] Where F u ρ is the vertical water pressure at the top; ρ is the density of water, typically 1000 kg / m³; g is the acceleration due to gravity, typically 9.8 m / s²; Z j0 Z represents the initial water level elevation in the valve well before the gate is opened. d0 The elevation of the top of the valve when it is fully closed is therefore (Z) j0 -Z d0) represents the effective submersion depth at the top of the gate; B is the width of the planar gate valve, and d is the thickness of the planar gate valve. The vertical water pressure at the top is the total vertical static water pressure exerted on the top of the gate due to its submersion depth in still water. The calculation of the vertical water pressure at the top follows the basic principles of fluid statics.

[0082] In one exemplary embodiment, the parametric mapping model specifies that the bottom hydrodynamic force is positive upwards and negative downwards, and selects the calculation logic based on the bottom edge dip angle type defined in the geometric parameter set:

[0083] In this embodiment, the parameterized mapping model is specifically implemented as a calculation method based on a discrete coefficient table. Specifically, the hydrodynamic force coefficients under different operating conditions are obtained in advance through physical model experiments or a series of computational fluid dynamics (CFD) simulations, and these coefficients are compiled into a data table. During calculation, the corresponding calculation branch is selected according to the geometric configuration of the gate bottom edge.

[0084] When the bottom edge is an upward-sloping bottom edge, the dynamic water force is usually manifested as an upward lift force, and the calculation formula is:

[0085] F d (n)=ρ×g×h×B×d×k up (n, α);

[0086] When the bottom edge is a downward-sloping bottom edge, its dynamic water force usually manifests as a downward suction force at a small opening. The calculation formula is as follows:

[0087] F d (n)=ρ×g×h×B×d×k down (n, α);

[0088] Where F d (n) represents the bottom dynamic water force at a relative opening of n; h represents the effective head, usually referring to the water level difference before and after the gate; α represents the bottom edge inclination angle; k up (n, α) and k down (n, α) represent the hydrodynamic force coefficients for the corresponding relative opening n and bottom edge inclination α, respectively, which can be obtained by consulting a pre-made coefficient table. It is understandable that the extreme value solution process involves finding the maximum or minimum coefficient value in the coefficient table. As an optional implementation, the hydrodynamic force coefficients can also be approximated using semi-empirical formulas based on physical principles. For example, the hydrodynamic force coefficient for a downsloping bottom edge can be approximately expressed as a form related to the sine function of the bottom edge inclination angle.

[0089] In one possible embodiment, the total frictional force includes the water-stop frictional resistance and the roller frictional resistance; based on the total weight of the gate valve system, the top vertical water pressure, the most unfavorable extreme value, and the total frictional force determined by the hydraulic state parameter set, the maximum opening force is calculated according to the principle of force balance, specifically using the following formula:

[0090] T=G Z +F u -F dmin +f z ;

[0091] Where T is the maximum opening force, and G Z For the total weight of the gate valve system, F u F is the vertical water pressure at the top. dmin For the most unfavorable extreme value, f z This represents the total frictional force.

[0092] Specifically, total friction is one of the main resistances preventing the gate from moving upwards. For example, the total friction force f... z It consists of two parts:

[0093] f z =f seal +f roller ;

[0094] Where f seal To stop the water friction resistance, f roller This represents the frictional resistance of the roller.

[0095] Furthermore, the water-stopping friction resistance f seal This is the frictional force generated by water pressure pressing the rubber seal firmly against the door groove embedded part. Its magnitude can be estimated using the following formula: f seal =μ seal ×P seal ×L seal ;where μ seal The coefficient of hydrostatic friction is related to the material and the condition of the contact surface; P seal The average pressure on the stopline is related to the effective head h; L seal This is the total length of the stopway. Correspondingly, the roller friction resistance f... roller This is the resistance generated when the gate rollers roll on the track. Its magnitude can be estimated using the following formula: f roller =μ roller ×P h ;where μ roller P is the overall coefficient of friction of the roller. h This represents the total horizontal still water pressure acting on the entire gate leaf. After calculating all force components, the maximum opening force T can be calculated based on the principle of force balance in the vertical direction, where F... dminFor the most unfavorable extreme value, in the calculation logic of this embodiment, it specifically refers to the maximum hydrodynamic suction force within the entire opening range, calculated by looking up a coefficient table or empirical formula. Since downward is defined as negative, F dmin It is a negative value.

[0096] As an example, the complete calculation process of this embodiment will be illustrated below through a specific numerical calculation case.

[0097] Assume the geometric parameter set and hydraulic state parameter set of a planar gate valve are as follows: gate width B is 5 meters, thickness d is 0.8 meters; total gravity G of the gate valve system. Z 1500 kN; Top elevation Z d0 The depth is 100 meters, and the water level in the valve well before opening is Z. j0 The effective head is 102 meters; the effective head h is 30 meters; the total frictional force f z The calculated value is 200 kN. The gate has a downward-sloping bottom edge. Consulting the corresponding hydrodynamic force coefficient table, it was found that the most unfavorable hydrodynamic force coefficient k occurs when the relative opening n is 0.4-0.5. down Its value is -0.1. Calculate the vertical water pressure F at the top. u :F u =1000×9.8×(102-100)×5×0.8=78400N=78.4kN; Calculate the most unfavorable bottom hydrodynamic force F. dmin :F dmin =1000×9.8×30×5×0.8×(-0.1)=-117600 N=-117.6 kN; Calculate the maximum opening force T according to the force balance formula: T=1500-78.4-(-117.6)+200=1500-78.4+117.6+200=1739.2 kN. The maximum opening force required under specific working conditions is calculated to be 1739.2 kN.

[0098] In another embodiment of this application, the parameterized mapping model is constructed using a continuous function model containing fractional rational terms to characterize the hydraulic surge characteristics at small opening degrees. The continuous function model is expressed as:

[0099] F d (n)=C hyd ×[(a0+a1×n) / (n+b)+a2×n];

[0100] Where n is the relative opening, and its value ranges from 0 to 1; F d (n) represents the bottom dynamic water force that varies with the relative opening n; C hyd The coefficients are related to hydraulic conditions; a0, a1, a2 and b are undetermined model parameters that together determine the specific shape of the hydrodynamic force curve.

[0101] In this embodiment, to accurately capture the nonlinear behavior of the hydrodynamic force as a function of the gate opening, especially the drastic changes in the small opening region, a physically meaningful continuous function model is proposed. Specifically, the design of the continuous function structure incorporates physical insights. The rational fractional term (a0 + a1 × n) / (n + b) is used to specifically characterize the nonlinear surge or drop in behavior caused by the turbulent flow due to the rapid change in the gate bottom gap when the relative opening n approaches zero. The linear term a2 × n is used to characterize the trend of the hydrodynamic force changing approximately linearly with the opening in the larger opening range. By superimposing these two parts, the model can take into account both the nonlinear characteristics in the small opening region and the linear trend in the large opening region, achieving high-precision fitting across the entire opening range. For example, the coefficient term C related to hydraulic conditions... hyd =ρ×g×h×B×d; where ρ is the density of water, g is the gravitational acceleration, h is the applied head, B is the gate width, and d is the gate thickness.

[0102] like Figure 2 As shown, in a further embodiment, the undetermined model parameters are associated with the bottom edge detail structural features defined in the geometric parameter set;

[0103] Constructing a parametric mapping model characterizing the relationship between the relative opening of a planar gate valve and the bottom hydrodynamic force, including determining the parameters of the undetermined model, specifically:

[0104] Constructing the detailed feature vector X of the bottom edge g Bottom edge detail feature vector X g It should include at least the bottom edge inclination angle, bottom edge chamfer length, bottom edge fillet radius, bottom edge gap, and waterstop location category;

[0105] Constructing the hydraulic state vector X h Hydraulic state vector X h At least the effective head and the downstream velocity should be included;

[0106] Establish a feature vector X of the bottom edge detail g and hydraulic state vector X h Using the input as the regression mapping relationship with the undetermined model parameters as the output, the specific values ​​of a0, a1, a2 and b are determined.

[0107] In this embodiment, a meta-model is constructed, that is, a regression model that can automatically predict the optimal parameters (a0, a1, a2, b) of the function model based on the specific geometry and hydraulic conditions of the gate. Specifically, it is necessary to quantify the physical factors affecting the hydrodynamic forces, that is, to construct feature vectors. Bottom edge detailed feature vector X gThis is used to quantify the geometry of the gate bottom. For example, the bottom edge inclination angle can be directly used as its angle value, and the bottom edge chamfer length and fillet radius can be used as their length values. For the water-stopping location category, one-hot encoding can be used; for example, the vector [1, 0] represents bottom-mounted water-stopping, and [0, 1] represents side-mounted water-stopping. A hydraulic state vector X is constructed. h This is used to quantify the water flow environment during gate operation. The hydraulic state vector contains values ​​such as the applied head and downstream velocity. A system is established based on the input feature vector [X]. g X h The regression mapping relationship from the output parameters [a0, a1, a2, b] to the output parameters [a0, a1, a2, b].

[0108] In a preferred implementation, a feature vector X of the bottom edge detail is established. g and hydraulic state vector X h The regression mapping relationship, with the input being the undetermined model parameters and the output being the undetermined model parameters, can be implemented using any of the following methods:

[0109] Construct a multilayer perceptron neural network with X g and X h The input layer is a0, a1, a2, and b, which are used as the output layers for training; or, a Gaussian process regression model is constructed to learn the nonlinear mapping pattern in the pre-stored sample set, and the output parameters are the predicted mean and predicted variance.

[0110] Specifically, two preferred schemes for constructing regression mapping relationships are provided. The first scheme is to construct a multilayer perceptron (MLP) neural network. The number of input layer nodes in this network is equal to the feature vector X. g With X h The sum of the dimensions of the input and output layers is used to construct the neural network. The output layer contains four nodes, corresponding to the four model parameters a0, a1, a2, and b. One or more hidden layers can be placed between the input and output layers; for example, two hidden layers can be used, each containing 16 neurons and employing the Corrected Linear Unit (ReLU) as the activation function. By training on a large number of condition-parameter sample pairs, the neural network can learn complex nonlinear mapping patterns.

[0111] The second approach is to construct a Gaussian process regression (GPR) model. Unlike neural networks that output deterministic predictions, the advantage of the GPR model is that it not only provides the predicted mean of the model parameters but also the confidence level of the prediction results, i.e., the prediction variance. The prediction variance quantifies the degree of uncertainty of the model under specific input conditions and is a key foundation for subsequent risk assessment and robustness optimization.

[0112] In a detailed numerical example, suppose the bottom edge feature vector X of a planar gate is... gThe following parameters are specified: bottom edge inclination angle α = 30 degrees, bottom edge chamfer length l = 0 mm, bottom edge fillet radius r = 20 mm, bottom edge gap g = 5 mm, and the water-stopping position type is bottom-mounted (coded as [1, 0]). Hydraulic state vector X h The design features are: a head h = 30 meters and a downstream velocity v = 2 meters per second. Inputting these feature vectors into a pre-trained multilayer perceptron neural network or Gaussian process regression model yields the following undetermined model parameters: a0 = -0.08, a1 = 0.15, a2 = 0.02, b = 0.03. Substituting these parameters into a continuous function model yields the gate's hydrodynamic force-opening relationship curve. Furthermore, if a Gaussian process regression model is used, in addition to outputting the predicted mean of the parameters, it can also output the predicted variance, such as σ. a0 =0.01, σ a1 =0.02, σ a2 =0.005, σ b =0.008. Variance information can be used to construct a conservative upper bound objective function.

[0113] In some alternative implementations, other machine learning models capable of achieving high-dimensional nonlinear mappings can also be used to construct the meta-model. For example, algorithms such as Support Vector Regression (SVR) or Gradient Boosting Decision Tree (GBDT) can be used to establish a regression mapping from feature vectors to model parameters.

[0114] In an optional embodiment, the parameters of the model to be determined can be identified in advance through the following steps:

[0115] Obtain an offline sample dataset containing the correspondence between the relative opening degree and the dynamic water force of a predetermined group; construct a weighted least squares objective function with physical constraints, including at least the sign constraints of the parameters and the boundedness constraints of the function values ​​within the domain; assign different weight coefficients to the sample data of different relative opening degree intervals, with the weight coefficients set higher for the smaller opening degree interval than for the larger opening degree interval; solve for the minimum value of the weighted least squares objective function using a numerical optimization algorithm to determine the optimal parameter combination of a0, a1, a2, and b.

[0116] In this embodiment, a single specific operating condition (i.e., a fixed set of X) is described. g and X hThe optimal model parameters (a0, a1, a2, b) are determined, providing training samples for the meta-model. Specifically, the obtained offline sample dataset can come from a series of high-precision computational fluid dynamics (CFD) simulations or accurate physical hydraulic model experiments. A weighted least squares (WLS) objective function is constructed for parameter identification. In engineering, the most important concern is the extreme downforce that may occur in the small opening region; therefore, the model needs to achieve the highest fitting accuracy in this region, and a weighted mechanism is preferred. For example, the weight coefficient w can be determined by an exponential decay function with respect to the relative opening n, such as w(n) = exp(-γ*n) + w0; where γ is a decay coefficient greater than 0, and w0 is the base weight. The exponential decay function ensures that sample points with n approaching 0 receive a much greater weight than sample points with n approaching 1.

[0117] Furthermore, introducing physical constraints into the objective function can prevent the model from producing solutions that violate physical laws. For example, parameter b must be positive to avoid singularities in the function's physical domain; also, the sign range of parameters a0, a1, and a2 can be constrained based on prior knowledge, or bounded constraints can be imposed on the function values ​​at specific openings. Numerical optimization algorithms, such as the Levenberg-Marquardt (LM) algorithm, can be used to solve the weighted and constrained nonlinear least squares problem to obtain the optimal parameter combination.

[0118] like Figure 3 As shown, in a further embodiment, the method further includes an online calibration step, specifically:

[0119] During the engineering trial operation or commissioning phase of a planar gate valve, obtain the measured opening and closing force data under the predetermined group of measured opening degrees;

[0120] Based on the measured opening and closing force data and the principle of force balance, the measured hydrodynamic force coefficients under the corresponding opening degree are obtained by inversion.

[0121] Calculate the deviation between the measured hydrodynamic force coefficient and the output value of the parameterized mapping model, and construct a deviation calibration term;

[0122] The parametric mapping model is corrected online using a deviation calibration term to obtain calibrated predicted values ​​of bottom hydrodynamic forces.

[0123] In this embodiment, a digital twin application is provided to eliminate the discrepancy between the pre-trained model and the actual physical entity. Due to factors such as manufacturing errors, installation deviations, or long-term operational wear, the pre-trained theoretical model may differ from the actual field conditions. The calibration process utilizes a small amount of high-precision measured opening and closing force data from the field, and performs inversion calculations using force balance equations to obtain the dynamic water force under real operating conditions. The actual values ​​are compared with the model predictions to obtain a series of deviation data points. Based on this, a deviation calibration function δ(n) can be constructed, for example, by performing polynomial fitting or spline interpolation on the deviation data points. The model output F after online calibration is... d_calibrated (n) is:

[0124] F d_calibrated (n)=F d_model (n)+δ(n);

[0125] Where F d_model (n) represents the model's predicted value. This ensures that the model's predictions closely match the actual situation on-site, enabling personalized and precise model correction.

[0126] This embodiment solves the problems of limited accuracy, inability to interpolate working conditions not included in the table, and weak generalization ability of the method based on discrete coefficient table by constructing a continuous parameterized mapping model constrained by physical laws, thus achieving higher accuracy and higher generalization ability in predicting dynamic water forces.

[0127] like Figure 4 As shown, in another embodiment of this application, the parameterized mapping model is a continuous function of relative opening degree; within a preset operating opening degree range, the parameterized mapping model is subjected to extreme value solving to determine the most unfavorable extreme value of the bottom hydrodynamic force, including:

[0128] The bottom dynamic water force is used as a continuous objective function of the relative opening.

[0129] Within the operating range, a global optimization operation is performed on the continuous objective function to identify the most unfavorable opening that causes the continuous objective function to reach its extreme value.

[0130] Substituting the most unfavorable opening value into the continuous objective function, the most unfavorable extreme value is calculated.

[0131] In this embodiment, due to the bottom dynamic water force F, d (n) is a continuous function expression with respect to the relative opening n. Therefore, the problem of finding the most unfavorable extremum is transformed from a problem of comparing discrete data points into a global optimization problem of a standard continuous function on a given closed interval, i.e., [0, 1]. Mathematicalizing the physical problem, that is, F... dF(n) is defined as the objective function for optimization, with the relative aperture n as its independent variable. The goal of the global optimization operation is to find one or more aperture positions such that the objective function F... d (n) The global maximum or minimum value is obtained within this interval. Once the most unfavorable opening degree n* is determined, the corresponding most unfavorable extreme value, F, can be obtained by a simple function. d (n*).

[0132] In a preferred embodiment, a global optimization operation is performed on the continuous objective function to identify the most unfavorable aperture that causes the continuous objective function to reach its extreme value, including analytical differentiation, specifically:

[0133] The derivative function is obtained by taking the first derivative of the continuous objective function with respect to the relative opening.

[0134] Find the stationary points where the derivative function is equal to zero, and check whether the stationary points are located within the operating opening interval;

[0135] By comparing the function values ​​at the stagnation point and the boundary points of the operating opening range, the most unfavorable opening is determined.

[0136] Specifically, for function F d (n) Take the first derivative with respect to the relative opening n to obtain the derivative function dF. d / dn. Let the derivative function equal to zero, i.e., dF. d With dn=0, solve the equation to obtain all stationary points. Select all stationary points located within the operating opening interval [0, 1]. Substitute the valid stationary points and the two boundary points of the interval, n=0 and n=1, into the original function F. d The function value is calculated in (n). The function values ​​at all candidate points are compared, and the maximum and minimum values ​​are the global extrema of the function within the operating degree interval. This embodiment can accurately find the extremum point, but it is based on the premise that the model has no uncertainty and is applicable to continuous objective functions, i.e., F. d When the form of the (n) function is relatively simple and easy to differentiate.

[0137] In an optional embodiment, a global optimization operation is performed on the continuous objective function to identify the most unfavorable opening degree that causes the continuous objective function to reach an extreme value. Specifically, this includes an adaptive search method, which is used to solve for the conservative upper bound extreme value of the door opening force.

[0138] Construct a conservative upper bound objective function T for the door opening force ub (n):

[0139] T ub (n)=T(n)+λ×σ T (n);

[0140] Where T(n) is the door opening force predicted by the parametric mapping model and varying with the relative opening degree n; λ is the preset confidence coefficient; σ T (n) is the predicted standard deviation of the door opening force T(n), the magnitude of which is determined by the prediction variance of the parameterized mapping model.

[0141] In this embodiment, considering the potential prediction errors in the constructed parameterized mapping model, an adaptive search method that considers model uncertainty is preferred to obtain a safer and more conservative decision result in engineering. Instead of directly optimizing the predicted door opening force, a conservative upper bound objective function for the door opening force is constructed and optimized, where the calculation method of the door opening force T(n) is consistent with the force balance equation. The construction of the conservative upper bound objective function embodies a risk-based decision-making approach, considering not only the predicted mean T(n) of the door opening force but also the second term λ×σ. T (n) An increased safety margin was added. The safety margin is related to the model's uncertainty σ at that point. T (n) is directly proportional. Therefore, the goal of optimization is no longer to find the extreme value of the predicted value, but to find the extreme value of the combination of the predicted value and the safety margin, so that the optimization result can fully take into account the possible prediction bias of the model and the result is safer and more reliable.

[0142] It should be noted that the confidence coefficient λ is a dimensionless parameter, and its value reflects the decision-maker's risk preference. A larger λ value, such as λ = 3, corresponds to an upper bound of approximately 99.7% confidence interval, indicating a more conservative approach to uncertainty. This will cause the optimization results to favor regions with high model variance, achieving a higher safety margin, and is suitable for high-risk or critical gate valve projects. A smaller λ value, such as λ = 1, indicates greater confidence in the model's predicted mean.

[0143] In a preferred implementation, the prediction variance of the parameterized mapping model is obtained as follows:

[0144] The model residual distribution during the offline identification process is statistically analyzed, and the standard deviation of the residuals is calculated as a fixed prediction variance.

[0145] Alternatively, when constructing a parameterized mapping relationship using a Gaussian process regression model, the posterior variance of the Gaussian process regression output can be directly obtained as the prediction variance.

[0146] Specifically, the uncertainty term σ T The source of (n) provides two specific paths. The first approach is to statistically analyze the residuals between the model's predicted values ​​and the actual values ​​for all sample points during the offline parameter identification process, and then calculate the population standard deviation of the residuals. The square of the population standard deviation can be used as a fixed prediction variance, representing the average uncertainty level of the model.

[0147] In the preferred second approach, when a Gaussian Process Regression (GPR) model is used to construct the meta-model, the GPR model itself can output the corresponding posterior variance for each new input condition. The posterior variance varies with the input and reflects that the model has higher uncertainty in regions with sparse training data and lower uncertainty in regions with dense data. Directly using the posterior variance as the model's prediction variance allows for a more precise quantification of uncertainty.

[0148] For example, suppose the parametric mapping model obtained through the Gaussian process regression model predicts a door opening force of mean T(0.05) = 1500 kN at a relative opening of n = 0.05, and the prediction standard deviation σ T (0.05) = 80 kN. When using a relatively conservative confidence coefficient λ = 1, the conservative upper bound objective function value is: T ub (0.05) = 1500 + 1 × 80 = 1580 kN. When a relatively conservative confidence coefficient λ = 3 is used, the conservative upper bound objective function value is: T ub (0.05) = 1500 + 3 × 80 = 1740 kN. By adjusting T throughout the entire operating opening interval [0, 1]... ub (n) An adaptive search is performed. Assuming that when λ=1, the most unfavorable opening n=0.06, the corresponding predicted maximum opening force is 1620 kN; when λ=3, the most unfavorable opening n=0.04, the corresponding predicted maximum opening force (conservative upper bound) is 1850 kN. It is evident that a larger confidence coefficient leads to a more conservative prediction result, suitable for engineering scenarios with high safety requirements. It should be noted that this example is only used to demonstrate the conservative upper bound and most unfavorable load search method of Gaussian process regression. The most unfavorable opening appearing in the small opening range is merely an illustrative effect; in actual planar valve engineering, the most unfavorable load usually appears in the relative opening range of 0.4~0.5.

[0149] An adaptive grid search algorithm or interval splitting algorithm is employed to search within the operating interval for a conservative upper bound objective function T. ub (n) is the position of maximum opening, and the position of opening is taken as the most unfavorable opening n*.

[0150] As a preferred implementation, the adaptive search process can be implemented using a Bayesian optimization algorithm. Bayesian optimization intelligently selects the next evaluation point with the goal of maximizing the acquisition function, and in this embodiment, T... ub The (n) function is precisely the acquisition function of the upper confidence bound (UCB). The adaptive grid search algorithm can find the objective function T that satisfies the conservative upper bound globally with very high efficiency and the fewest function evaluations. ub (n) The position n* where the opening is maximized.

[0151] This embodiment can more accurately and reliably locate the most unfavorable point globally, effectively avoiding the problem of missed judgments caused by sparse data points.

[0152] According to another aspect of this application, the construction of the continuous function model is based on the jet contraction theory, including:

[0153] A model is established to show the relationship between the jet contraction coefficient and the relative opening. The jet contraction coefficient is defined as the ratio of the actual jet cross-sectional area to the geometric opening area.

[0154] In this embodiment, when the planar gate valve is partially open, upstream water is ejected from the gap at the bottom of the gate, forming a contracting jet. The jet contraction coefficient is a core physical quantity characterizing the degree of jet contraction. The variation law of the jet contraction coefficient with relative opening can be obtained from the asymptotic analysis of potential flow theory. At the smallest opening, due to the sharp transition at the boundary, the jet contraction is most intense, and the contraction coefficient approaches a certain limit value; as the opening increases, the boundary transition effect weakens, the jet contraction tends to disappear, and the contraction coefficient asymptotically approaches 1. In a preferred implementation, a relationship model between the jet contraction coefficient and the relative opening is established, specifically as follows:

[0155] C c (n)=1-(1-C c0 )×exp(-β×n / n ref );

[0156] Where C c (n) is the jet contraction coefficient at a relative opening n; C c0 β is the zero-aperture limiting shrinkage coefficient, typically ranging from 0.61 to 0.85, with the specific value determined by the bottom edge geometry; β is the shrinkage characteristic index, characterizing how quickly the shrinkage effect decays with opening degree, with a typical value ranging from 2 to 5; n ref The reference opening is defined as the ratio of the bottom edge characteristic length to the net height of the gate opening, used to achieve dimensionless measurement; exp is the natural exponential function.

[0157] The zero-opening limit shrinkage coefficient in the relational model is determined based on the pre-stored bottom edge geometric parameters, which include the bottom edge inclination angle, fillet radius, and chamfer length.

[0158] In this embodiment, a relationship model is established between the zero-opening limit shrinkage coefficient and the bottom edge geometric parameters. The zero-opening limit shrinkage coefficient C... c0 It is determined by the detailed geometry of the bottom edge. For example, the zero-aperture limit contraction coefficient in the relational model is determined based on the bottom edge geometric parameters, specifically as follows:

[0159] C c0 =C c_base ×[1+Δ α +Δr +Δ l ];

[0160] Where C c0 C is the zero-opening limit shrinkage coefficient; c_base Using the baseline shrinkage coefficient, for the bottom edge of a right-angled acute edge, according to Borda's shrinkage theory, its theoretical value is approximately π / (π+2) = 0.611; Δ α This is the bottom edge inclination correction term, where α is the bottom edge inclination angle; Δ r This is the bottom edge fillet correction term, where r is the bottom edge fillet radius; Δ l This is the bottom edge chamfer correction item, where l is the bottom edge chamfer length;

[0161] Bottom edge angle correction term is:

[0162] For the bottom edge of the upward dip angle, i.e., the dip angle is tilted upstream: Δ α =k α_up ×sin(α)×(1+cos(α));

[0163] For the bottom edge of the downslope angle, i.e., the case where the slope is tilted downstream: Δ α =k α_down ×sin(α)×(1-cos(α));

[0164] Where α is the bottom edge inclination angle; k α_up k is the influence coefficient of the uptilt angle. α_down is the downslope angle influence coefficient, sin is the sine function, and cos is the cosine function.

[0165] In this embodiment, the specific form of the bottom edge tilt angle correction term is determined according to the tilt angle type. For an upward tilting bottom edge, the tilt angle correction term is positive, indicating that the upward tilt angle can slow down jet contraction and increase the contraction coefficient; for a downward tilting bottom edge, the tilt angle correction term is negative, indicating that the downward tilt angle will exacerbate jet contraction. The upward tilt angle influence coefficient k... α_up The typical value is 0.3, and the downtilt angle influence coefficient k α_down The typical value is 0.2.

[0166] Bottom edge fillet correction term Δ r for:

[0167] Δ r =k r ×(r / d) 0.5 ×[1-exp(-(r / d) / 0.1)];

[0168] Where r is the radius of the bottom edge fillet, d is the gate thickness, and k r The fillet radius is the influence coefficient, typically 0.4; the presence of fillets can guide the water flow to transition smoothly and slow down jet contraction.

[0169] Bottom edge chamfer correction term Δ l for:

[0170] Δ l =k l ×(l / d) / (1+l / d);

[0171] Where l is the bottom edge chamfer length, k l This is the chamfering effect coefficient, typically set to 0.15. Chamfering functions similarly to filleting, but with a relatively weaker effect.

[0172] It should be noted that the typical values ​​of each influence coefficient (k) are... α_up =0.3, k α_down =0.2, k r =0.4, k l =0.15) is an empirical value obtained based on statistical analysis of a large amount of hydraulic model test data. In practical applications, these coefficients can be adjusted within a certain range: k α_up The value of k typically ranges from 0.2 to 0.4, with larger values ​​suitable for situations where the tilt angle is larger and the streamline bending effect is more significant; α_down The value of k typically ranges from 0.1 to 0.3, with larger values ​​suitable for situations where the downstream flow is more turbulent; r The value range of k is typically from 0.3 to 0.5; the larger the fillet radius, the more significant the effect of the fillet correction term. l The value range is typically from 0.1 to 0.2; the larger the chamfer length, the more significant the effect of the chamfer correction term. Sensitivity analysis of the parameters shows that the fillet correction coefficient k... r The limiting shrinkage coefficient C at zero opening c0 The impact is most significant, and the accurate measurement of the bottom edge fillet radius should be given priority in engineering design.

[0173] Based on the momentum theorem, the analytical relationship between the dimensionless hydrodynamic force coefficient and the relative opening is derived using the jet contraction coefficient, resulting in a continuous function model.

[0174] In this embodiment, after obtaining the jet contraction coefficient, the hydrodynamic force acting on the bottom of the gate can be derived according to the momentum theorem of fluid mechanics. The gap region at the bottom of the gate is selected as the control volume. Considering the momentum flux and pressure distribution at the inlet and outlet of the control volume, a momentum balance equation is established according to Newton's second law. After derivation, the hydrodynamic force at the bottom can be expressed as:

[0175] F d (n)=ρ×g×h×B×d×K d (n);

[0176] Where F d(n) represents the bottom dynamic water force at a relative opening of n, ρ is the density of water, g is the acceleration due to gravity, h is the head of water in front of the gate, B is the gate width, d is the gate thickness, and K d (n) is the dimensionless coefficient of dynamic water force.

[0177] In a preferred implementation, the analytical relationship between the dimensionless hydrodynamic force coefficient and the relative opening is derived based on the momentum theorem, specifically as follows:

[0178] K d (n)=(1-C c (n)) / C c (n)-[(1-C c (n)) 2 / (2×C c (n) 2 )]×[1 / (n+ε) 2 ]×Φ(n);

[0179] Where K d (n) is the dimensionless hydrodynamic force coefficient at a relative opening n, C c (n) is the jet contraction coefficient at a relative aperture n, where n is the relative aperture and ε is the regularization parameter, typically 0.01, used to avoid numerical singularities at n = 0. The first term in the formula (1-C c (n)) / C c (n) reflects the static pressure difference effect caused by jet contraction. When the jet contracts, the average pressure at the jet cross-section is lower than the pressure at the bottom of the gate, generating an upward thrust or reducing the downward suction. The second term in the formula reflects the dynamic pressure effect generated by jet acceleration. The water flow accelerates from the low-velocity zone in front of the gate into the high-velocity jet zone at the bottom of the gate. According to Bernoulli's equation, the pressure decreases with the increase in velocity. The dynamic pressure effect generates local negative pressure in the jet contraction zone, which is the main source of downward suction. Φ(n) is the boundary layer correction function, used to correct the deviation caused by the limited thickness of the boundary layer. Its expression is:

[0180] Φ(n)=1-exp(-n / n δ );

[0181] Where n δ is the boundary layer feature opening, typically taking a value of 0.02, and exp is the natural exponential function.

[0182] In a specific numerical case, assume a planar gate valve has a bottom edge designed with a downward inclination angle α equal to 30 degrees, a fillet radius r equal to 20 mm, a chamfer length l equal to 0, and a gate thickness d equal to 800 mm. Calculate the correction terms. The inclination angle correction term is: Δ α=0.2×sin(30°)×(1-cos(30°))=0.2×0.5×(1-0.866)=0.0134; The fillet correction term is: Δ r =0.4×(0.02 / 0.8) 0.5 ×[1-exp(-0.25)]=0.4×0.158×0.221=0.0140; the chamfer correction term is: Δ l =0; the zero-opening limit shrinkage coefficient is: C c0 =0.611×(1+0.0134+0.0140+0)=0.611×1.0274=0.628. Taking the contraction characteristic index β as equal to 3, and the reference opening degree n... ref If the relative opening n equals 0.1, then when the relative opening n equals 0.05, the jet contraction coefficient is: C c (0.05) = 1 - (1 - 0.628) × exp(-3 × 0.05 / 0.1) = 1 - 0.372 × exp(-1.5) = 1 - 0.372 × 0.223 = 0.917. Substituting into the hydrodynamic formula, and taking ε = 0.01, n δ The boundary layer correction function is Φ(0.05) = 1 - exp(-0.05 / 0.02) = 1 - exp(-2.5) = 1 - 0.082 = 0.918. The dimensionless hydrodynamic force coefficient is K. d (0.05) = (1 - 0.917) / 0.917 - [(1 - 0.917)] 2 / (2×0.917 2 )]×[1 / (0.05+0.01) 2 × 0.918 = 0.0905 - 1.045 = -0.955. A negative value indicates that the direction of the dynamic water force at this opening is downward, which is the downward suction force.

[0183] In an optional embodiment, the continuous function model is constructed using a piecewise coupled approach, including:

[0184] The jet velocity is calculated based on the jet contraction coefficient, and the local Froude number is determined by the ratio of the jet velocity to the local water depth.

[0185] In this embodiment, a flow regime identification criterion is defined. The flow regime characteristics differ fundamentally at different opening degrees, and a single model is insufficient to accurately describe the hydrodynamic characteristics across the entire opening range. Therefore, a local Froude number can be introduced as a flow regime discrimination criterion:

[0186] Fr local (n)=V jet (n) / sqrt(g×h local (n));

[0187] Where Frlocal (n) is the local Froude number at a relative opening of n, V jet (n) is the jet velocity, g is the acceleration due to gravity, and h is the acceleration due to gravity. local (n) represents the local water depth in the jet region, and sqrt is the square root function. The jet velocity V jet (n) can be obtained from the continuity equation:

[0188] V jet (n)=Q / (B×e×n×C c (n))=μ×sqrt(2×g×h) / (n×C c (n));

[0189] Where V jet (n) represents the jet velocity, Q represents the flow rate through the gate, B represents the gate width, e represents the gate orifice height, n represents the relative opening, and C represents the gate opening. c (n) is the jet contraction coefficient, μ is the gate orifice flow coefficient, g is the gravitational acceleration, h is the head upstream of the gate, and sqrt is the square root function. For submerged outflow conditions, the local water depth h local (n) can be approximated by the downstream water depth h. d .

[0190] Based on the comparison between the local Froude number and the preset critical Froude number, the first critical opening and the second critical opening for the flow regime transition are determined.

[0191] Specifically, the critical aperture for flow regime transition is determined by defining two critical Froude numbers to distinguish different flow regime intervals. The first critical Froude number is Fr. cr1 Used to distinguish the boundary between the jet-dominant region and the transition region, typically taken as 2.5. Second critical Froude number Fr cr2 The boundary used to distinguish the transition zone from the pressure differential-dominated zone is typically taken as 1.0. The first critical aperture n1* for the flow regime transition is determined by solving the following equation:

[0192] Fr local (n1*)=Fr cr1 ;

[0193] Where n1* is the critical aperture between the jet-dominant region and the transition region, i.e., the first critical aperture; Fr local (n1*) is the local Froude number at the first critical opening, Fr cr1 It is the first critical Froude number.

[0194] The second critical aperture n2* for flow regime transition is determined by solving the following equation:

[0195] Fr local (n2*)=Fr cr2 ;

[0196] Where n2* is the critical opening degree between the transition zone and the pressure difference-dominated zone, i.e., the second critical opening degree; Fr local (n2*) is the local Froude number at the second critical aperture, Fr cr2 This is the second critical Froude number. Because Fr... local (n) is a monotonically decreasing function of n, meaning that the larger the opening, the lower the jet velocity and thus the smaller the Froude number. Therefore, the above equation has a unique physical solution. The critical opening can be solved using the bisection method or Newton's iteration method.

[0197] For the interval less than or equal to the first critical opening, a jet-dominated model based on the jet contraction coefficient is adopted; for the interval greater than or equal to the second critical opening, a quasi-static pressure difference-dominated model is adopted; for the transition interval between the two critical openings, a smooth transition function is used to weight and mix the two models to obtain a piecewise coupled model that is continuously differentiable across the full opening interval.

[0198] In this embodiment, a segmented coupling model is established. Based on the flow regime identification results, targeted physical models are adopted for different intervals. For the jet-dominant region, i.e., the interval where the relative aperture n satisfies 0 < n ≤ n1*, a complete model based on jet contraction theory is adopted:

[0199] F d_I (n)=ρ×g×h×B×d×K d (n);

[0200] Where F d_I (n) represents the bottom hydrodynamic force in the jet-dominant zone, ρ is the water density, g is the gravitational acceleration, h is the head of water upstream of the gate, B is the gate width, d is the gate thickness, and K... d (n) is the dimensionless coefficient of dynamic water force.

[0201] For the pressure differential-dominated zone, i.e., the interval where the relative opening n satisfies n ≥ n², the jet contraction effect essentially disappears when the opening is large, and the hydrodynamic force is mainly determined by the static pressure difference before and after the gate. In this case, a simplified quasi-static model can be used, and the expression for the pressure differential-dominated model is:

[0202] F d_III (n) = ρ × g × h × B × d × K³ × (1 - n) m ;

[0203] Where F d_III (n) represents the bottom dynamic water force in the pressure differential dominance zone, ρ represents the density of water, g represents the gravitational acceleration, h represents the head in front of the gate, B represents the gate width, d represents the gate thickness, K3 represents the pressure differential dominance zone coefficient, with a typical value range of 0.1 to 0.3; n represents the relative opening degree, and m represents the opening degree index, with a typical value of 1.2.

[0204] For the transition region, i.e., the interval where the relative opening n satisfies n1* < n < n2*, a differentiable smooth transition function is used to achieve a hybrid of the two models:

[0205] F d_II (n)=σ(n)×F d_I (n)+[1-σ(n)]×F d_III (n);

[0206] Where F d_II (n) represents the bottom hydrodynamic force in the transition zone, σ(n) is the smooth transition function, and F d_I (n) is the calculated value of the jet-dominant region model, F d_III (n) represents the calculated value from the pressure differential dominance model. The smooth transition function σ(n) adopts the hyperbolic tangent function form:

[0207] σ(n)=(1 / 2)×[1-tanh((nn m ) / Δn)];

[0208] Where σ(n) is the transition weight at relative opening n; tanh is the hyperbolic tangent function, n is the relative opening, and n m Δn represents the center opening of the transition zone, and Δn is the width parameter of the transition zone.

[0209] The center opening of the transition zone and the width of the transition band are determined based on the first critical opening and the second critical opening:

[0210] n m =(n1*+n2*) / 2;

[0211] Where n m n1* represents the center opening of the transition zone, n2* represents the first critical opening, and n2* represents the second critical opening.

[0212] Δn = (n2* - n1*) / 4;

[0213] Where Δn is the transition band width parameter. The smoothing function satisfies the following properties: when n approaches n1*, σ(n) approaches 1, and the model output approaches the jet model; when n approaches n2*, σ(n) approaches 0, and the model output approaches the pressure difference model; σ(n) is a continuously differentiable function, ensuring the continuity and differentiability of the overall model.

[0214] Furthermore, with the adoption of a piecewise coupled model, the extremum solution process needs corresponding adjustments. The overall hydrodynamic force function F d (n) is defined in each interval as:

[0215] F d (n)=F d_I (n), when 0 < n < ≤ n1*;

[0216] F d (n)=F d_II (n), when n1* < n < n2*;

[0217] F d (n)=F d_III (n), when n≥n2*.

[0218] Since the piecewise model is continuously differentiable within each interval, and continuity is guaranteed by a smoothing function at the interval boundaries, the extreme values ​​can still be solved using analytical differentiation or adaptive search methods. Candidate extreme values ​​include: stagnation points where the derivative is zero in the jet-dominated region, stagnation points where the derivative is zero in the transition region, stagnation points where the derivative is zero in the pressure-dominated region, function values ​​at boundary points where n equals 0, and function values ​​at boundary points where n equals 1. It should be noted that the function behavior near the critical openings n1* and n2* of the flow regime transition should be examined, as abrupt changes in hydrodynamic characteristics may occur at the flow regime transition points.

[0219] In the detailed numerical example, it is assumed that the gate flow coefficient μ is equal to 0.62, the upstream head h is equal to 10 meters, and the downstream water depth h is... d Given a relative opening of 3 meters and a gravitational acceleration g of 9.81 m / s², calculate the jet velocity and local Froude number at various opening degrees. At a relative opening n of 0.05: V jet (0.05) = 0.62 × sqrt(2 × 9.81 × 10) / (0.05 × 0.917) = 0.62 × 14.01 / 0.0459 = 189.3 meters per second; Fr local (0.05) = 189.3 / sqrt(9.81×3) = 189.3 / 5.42 = 34.9; This Froude number is much greater than Fr cr1 (2.5), belonging to the jet-dominant region. Assuming C = 0.3 relative aperture n, ... c (0.3) equals 0.985: V jet (0.3) = 0.62 × 14.01 / (0.3 × 0.985) = 8.69 / 0.296 = 29.4 meters per second; Fr local (0.3) = 29.4 / 5.42 = 5.4; This Froude number is greater than Fr. cr1 (2.5), still within the jet-dominated region. When the relative aperture n equals 0.6, assuming C... c (0.6) equals 0.998: V jet (0.6) = 0.62 × 14.01 / (0.6 × 0.998) = 8.69 / 0.599 = 14.5 meters per second; Fr local (0.6) = 14.5 / 5.42 = 2.7; This Froude number is slightly greater than Fr. cr1(2.5), located near the boundary between the jet-dominant region and the transition region. Interpolation yields the first critical aperture n1* to be approximately 0.62, and the second critical aperture n2* to be approximately 0.85. The center aperture n of the transition region... m The value is 0.735, and the transition band width parameter Δn is 0.0575.

[0220] According to another aspect of this application, the method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation can also be:

[0221] S1: Obtain the total gravity G of the gate valve system based on the self-weight of the plane gate valve, its counterweight, and the self-weight of the lifting rod. Z .

[0222] Specifically, the total gravity G of the gate valve system Z The weight should include the weight of the gate valve itself (G1) and its counterweight (G2), as well as the weight of the gate valve's lifting rod system (G3). Z = G1 + G2 + G3.

[0223] S2: Obtain the pressure F at the top of the gate valve based on the water level in the gate well before the gate valve opens. u .

[0224] In this embodiment, the pressure F at the top of the gate valve u It can be obtained by the following formula: F u =ρg(Z j0 - Z d0 Bd; where ρ is the density of water, in kg / m³ 3 g is the acceleration due to gravity, with units of m / s². 2 Z j0 Z represents the initial water level of the valve well before operation, in meters (m). d0 B is the top elevation of the valve in the fully closed state, in meters; B is the width of the flat gate valve, in meters; d is the thickness of the flat gate valve, in meters.

[0225] S3: Based on the bottom flange form of the flat gate valve, select the corresponding dynamic water force prediction formula for different bottom flange shapes, and calculate the downward suction force or upward support force F at the bottom of the valve under each typical opening degree. d Extract the worst-case value F dmin .

[0226] Specifically, the force F of the bottom water flow in a gate valve under different opening conditions d The most unfavorable hydrodynamic force F is obtained from calculations based on hydraulic and geometric parameters. dmin Here, it is defined that the force of dynamic water is positive when it is upward and negative when it is downward;

[0227] Top slant bottom edge: F d =(3.91n2 -0.0004α 2 +0.0022 nα-4.57n+0.053α-0.05)ρghBd+F f ;

[0228] Bottom edge with downward slope: F d =(1.83n 2 -0.000011α 2 +0.000034nα-2.06n+0.0009α+0.093)ρghBd+F f ;

[0229] Where n is the opening degree of the plane gate valve, i.e., the ratio of the height of the flow basin to the height of the water conveyance corridor; when the valve is fully open, n=1; α is the bottom edge inclination angle; h is the effective head, in meters; F f This refers to the buoyancy of a planar gate valve.

[0230] S4: Calculate the total friction force f according to relevant standards and tables. z .

[0231] S5: The maximum opening force T of the planar gate valve is obtained based on the principle of force balance.

[0232] In this embodiment, the maximum opening force T of the planar gate valve is calculated using the following formula: T=G Z +F u -F dmin +f z .

[0233] S6: Based on the maximum opening force of the plane gate valve, round up to the standard series to obtain the predicted capacity of the hoist.

[0234] The prediction method in this embodiment incorporates the comprehensive influence of key parameters such as the weight, hydraulic parameters, and geometric parameters of the planar gate valve and its boom system on the gate valve's hoisting capacity. The hydraulic parameters include the applied head and downstream flow velocity, while the geometric parameters include the basic dimensions of the planar gate valve, such as length, width, and height, as well as the detailed structural dimensions of the bottom edge. A high-precision bottom edge dynamic water load is used as the core algorithm and embedded into the technical system to accurately design the valve and select the hoisting capacity.

[0235] According to another aspect of this application, a planar gate valve opening and closing capacity prediction system based on parametric calculation is also provided, comprising: a planar gate valve basic data acquisition unit, used to acquire the operating water level and elevation of the project, the self-weight and counterweight of the planar gate valve, structural dimensions, and design data including waterstop and roller materials; a planar gate valve and hanger system total weight calculation unit; a planar gate valve top water pressure calculation unit; a planar gate valve bottom dynamic water force calculation unit; a planar gate valve total friction force calculation unit; a planar gate valve maximum opening force calculation unit; and a hoist capacity selection prediction processing unit.

[0236] In a detailed embodiment, a lock project is designed with a maximum head of 17.8m. The water conveyance system uses planar gate valves with a sill elevation of -6.65m and gate leaf dimensions of 5.46m × 5.22m. The gate thickness is 0.92m, and the waterstop dimensions are 4.42m × 5.12m. The valve bottom edge is designed as a sharp-flange type, with the bottom edge inclined upstream at a 42° angle to the horizontal plane. The total gravity G of the gate valve system is obtained based on the self-weight and counterweight of the planar gate valve, and the weight of the lifting rod. Z G Z = G1 + G2 + G3 = 211.8 + 0 + 50 = 261.8 kN; where G1 is the self-weight of the flat gate valve, which is 211.8 kN; G2 is the counterweight, which is 0 kN; and G3 is the weight of the lifting rod, which is 50 kN. The pressure F at the top of the gate valve is obtained based on the water level in the gate well before the flat gate valve opens. u ;F u =ρg(Z j0 - Z d0 Bd = 839 kN; where ρ is the density of water, which is 1000 kg / m³. 3 g is the acceleration due to gravity, which is 9.81 m / s². 2 Z j0 The initial water level in the valve well before operation was 20.6m; Z d0 B represents the top elevation of the valve in its fully closed state, which is -0.52m; B is the width of the flat gate valve, which is 4.4m; and d is the thickness of the flat gate valve, which is 0.92m. The bottom edge of the flat gate valve slopes upstream. Using the hydrodynamic force prediction formula, the downward suction force or upward support force F acting on the bottom of the valve at various typical opening degrees is calculated. d Extract the worst-case value F dmin ;F d =(3.91n 2 -0.0004α 2 +0.0022 nα-4.57n+0.053α-0.05)ρghBd+F f ;F dmin=274.20 kN. The calculated dynamic water force at the bottom edge is as follows: When the opening degree n of the plane gate valve is 0.10, the dimensionless correction factor k of the dynamic water force at the bottom edge is 1.03, and the dynamic water load at the bottom edge is 890.14 kN; when the opening degree n of the plane gate valve is 0.20, the dimensionless correction factor k of the dynamic water force at the bottom edge is 0.70, and the dynamic water load at the bottom edge is 656.46 kN; when the opening degree n of the plane gate valve is 0.30, the dimensionless correction factor k of the dynamic water force at the bottom edge is 0.45, and the dynamic water load at the bottom edge is 478.02 kN; when the opening degree n of the plane gate valve is 0.40, the dimensionless correction factor k of the dynamic water force at the bottom edge is 0.28, and the dynamic water load at the bottom edge is 35 kN. 4.84 kN; When the opening degree n of the plane gate valve is 0.50, the dimensionless correction factor k of the dynamic water force on the bottom edge is 0.18, and the dynamic water load on the bottom edge is 286.90 kN; When the opening degree n of the plane gate valve is 0.60, the dimensionless correction factor k of the dynamic water force on the bottom edge is 0.16, and the dynamic water load on the bottom edge is 274.20 kN; When the opening degree n of the plane gate valve is 0.70, the dimensionless correction factor k of the dynamic water force on the bottom edge is 0.22, and the dynamic water load on the bottom edge is 316.75 kN; When the opening degree n of the plane gate valve is 0.80, the dimensionless correction factor k of the dynamic water force on the bottom edge is 0.36, and the dynamic water load on the bottom edge is 414.55 kN. Where α is the bottom edge inclination angle, which is 42°; h is the design maximum head, which is 17.8 m; F f The buoyancy of the planar gate valve is 160 kN; the dimensionless correction factor for the dynamic water force at the bottom edge is k = 3.91n. 2 -0.0004α 2 +0.0022 nα-4.57n+0.053α-0.05. Obtain the sliding friction resistance and rolling friction resistance coefficients from relevant standards and tables, then calculate the total friction force f. z ;f z =μ1 K1 K2ρgh(2H zs +B zs Δ + P / R(μ2r + l) = 26.3 + 92 = 118.3 kN; where μ1 is the coefficient of friction between rubber and stainless steel, which is 0.2; K1 is the correction coefficient for the friction force of the rubber water seal, which is 1.8; K2 is the correction coefficient for the width of the rubber water seal, which is 0.6; H zs The valve stop height is 3.95m; B zs Let Δ be the valve stop width (3.74m), Δ be the width of the rubber water seal under water pressure (0.6m), P be the horizontal pressure (3609.5kN), R be the roller radius (450mm), μ2 be the bearing sliding friction coefficient (0.14), r be the roller shaft radius (75mm), and l be the rolling friction arm (1mm). Based on the principle of force balance, the maximum opening force T of the planar gate valve is obtained: T = G Z +Fu -F dmin +f z =261.8 + 839 - 274.20 + 118.3 = 944.9 kN. Based on the maximum opening force of the plane gate valve, rounding up to the standard series gives the predicted capacity of the hoist. The hoist capacity for the water supply valves in this lock project can be selected as 1000 kN.

[0237] like Figures 5 to 7 As shown, the pressure change curve during valve opening in engineering is illustrated, and the water level and pressure at each opening degree are extracted. The opening and closing forces at each opening degree are calculated using the method proposed in this embodiment, and the results match the calculated results of the measured opening and closing forces, demonstrating the reliability of the prediction method. According to... Figure 8 The measured maximum valve opening force is 850 kN, while the predicted maximum opening force in this embodiment is 944.9 kN. The hoist capacity selected by rounding upwards is 1000 kN. Considering the effective operating efficiency of the hoist, the hoist capacity selection is reasonable and has a certain safety margin. Simultaneously, the prediction of the hydrodynamic force at the valve's bottom edge in this embodiment is calculated using the measured water level process line during the opening of the lock project's spillway valve. This calculation is then compared with the empirical values ​​recommended in current lock valve design specifications. It can be seen that the hydrodynamic force at the bottom edge in this embodiment matches the trend of the measured process item, demonstrating high prediction accuracy.

[0238] This invention constructs a continuous function model containing rational fractional terms to accurately describe the complete and continuous curve of bottom hydrodynamic force as a function of gate opening. The problem of finding the most unfavorable operating condition is transformed from comparing discrete points to solving a global optimization problem of the continuous function. By employing mathematical optimization algorithms such as analytical differentiation or adaptive search, the true global most unfavorable point can be accurately located throughout the entire operating range, eliminating the risk of missed extreme values ​​due to interpolation or sparse data points, thus improving prediction accuracy. Detailed geometric features such as bottom edge inclination, chamfers, and fillets are vectorized, and a nonlinear mapping relationship is established between these geometric features and key parameters of the hydrodynamic model. This allows the model to customize the optimal hydrodynamic curve for any given gate geometry, accurately reflecting the impact of subtle structural changes on hydrodynamics, and improving the model's generalization ability and applicability. Uncertainty quantification is introduced; by employing techniques such as Gaussian process regression, the model can output not only the predicted mean but also the variance of the predicted values ​​simultaneously. Building upon this, an optimization is performed by constructing a conservative upper bound objective function that incorporates the prediction variance, ensuring that the final decision incorporates consideration of model uncertainty. This provides designers with a tool to quantify risk, enabling more robust and reliable gate capacity decisions based on the project's safety level.

[0239] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for predicting the opening and closing capacity of a planar gate valve based on parametric calculation, characterized in that, include: Obtain the geometric parameter set and hydraulic state parameter set of the plane gate valve to be analyzed; The total gravity of the gate valve system is calculated based on the set of geometric parameters, and the vertical water pressure at the top is calculated based on the set of geometric parameters and the set of hydraulic state parameters. Within the preset operating opening range, the extreme value of the pre-configured parametric mapping model representing the relationship between the relative opening of the planar gate valve and the bottom hydrodynamic force is solved to determine the most unfavorable extreme value of the bottom hydrodynamic force. Based on the total gravity of the gate valve system, the top vertical water pressure, the most unfavorable extreme value, and the total friction force determined by the hydraulic state parameter set, the maximum opening force is calculated according to the principle of force balance. Determine the predicted capacity of the hoist based on the maximum opening force; The parameterized mapping model is constructed using a continuous function model containing fractional rational terms to characterize the hydraulic surge characteristics at small openings. The continuous function model is expressed as: F d (n)=C hyd ×[(a0+a1×n) / (n+b)+a2×n]; Where n is the relative opening, F d (n) represents the dynamic force of the bottom water flow, C hyd The coefficients are related to the water head. a0, a1, a2 and b are undetermined model parameters obtained through identification. The (a0+a1×n) / (n+b) term is used to characterize the nonlinear surge behavior in the small opening range, and the a2×n term is used to characterize the linear change trend in the large opening range. The undetermined model parameters are associated with the bottom edge detailed structural features defined in the geometric parameter set; The determination of the parameters of the undetermined model includes: Constructing the detailed feature vector X of the bottom edge g Bottom edge detail feature vector X g It should include at least the bottom edge inclination angle, bottom edge chamfer length, bottom edge fillet radius, bottom edge gap, and waterstop location category; Constructing the hydraulic state vector X h Hydraulic state vector X h At least the effective head and the downstream velocity should be included; Establish a feature vector X of the bottom edge detail g and hydraulic state vector X h Using the input as the regression mapping relationship and the undetermined model parameters as the output, determine the specific values ​​of a0, a1, a2, and b; The method further includes an online calibration step, specifically: During the engineering trial operation or commissioning phase of a planar gate valve, obtain the measured opening and closing force data under the predetermined group of measured opening degrees; Based on the measured opening and closing force data and the principle of force balance, the measured hydrodynamic force coefficients under the corresponding opening degree are obtained by inversion. Calculate the deviation between the measured hydrodynamic force coefficient and the output value of the parameterized mapping model, and construct a deviation calibration term; The parametric mapping model is corrected online using a deviation calibration term to obtain calibrated predicted values ​​of bottom hydrodynamic forces.

2. The method according to claim 1, characterized in that, The total weight of the gate valve system includes the self-weight of the flat gate valve, the counterweight, and the weight of the gate valve lifting rod system; The total gravity of the gate valve system is calculated based on the set of geometric parameters using the following formula: G Z =G1+G2+G3; Among them G Z G1 is the total weight of the gate valve system, G2 is the self-weight of the plane gate valve, G3 is the counterweight, and G4 is the weight of the gate valve lifting rod system.

3. The method according to claim 1, characterized in that, The vertical water pressure at the top is calculated using the following formula: F u =ρ×g×(Z j0 -WITH d0 )×B×d; Where F u The vertical water pressure at the top, ρ is the density of water, g is the acceleration due to gravity, and Z is the acceleration due to gravity. j0 Z represents the initial water level of the valve well before operation. d0 B represents the top elevation of the valve when it is fully closed, B represents the width of the flat gate valve, and d represents the thickness of the flat gate valve.

4. The method according to claim 1, characterized in that, The parametric mapping model specifies that the dynamic water force at the bottom is positive upwards and negative downwards, and selects the calculation logic based on the bottom edge dip angle type defined in the geometric parameter set: When the bottom edge is an upward-sloping bottom edge: F d (n)=ρ×g×h×B×d×k up (n, α); When the bottom edge is a downward-sloping bottom edge: F d (n)=ρ×g×h×B×d×k down (n, α); Where F d (n) represents the bottom dynamic water force at a relative opening of n, ρ is the density of water, g is the acceleration due to gravity, B is the width of the plane gate valve, d is the thickness of the plane gate valve, h is the applied water head, α is the bottom edge inclination angle, and k up (n, α) and k down (n, α) are the hydrodynamic force coefficients for the corresponding opening degree and inclination angle, respectively.

5. The method according to claim 1, characterized in that, The total frictional force includes the water-stop frictional resistance and the roller frictional resistance; The maximum door opening force is calculated based on the principle of force balance, specifically using the following formula: T=G Z +F u -F dmin +f z ; Where T is the maximum opening force, and G Z For the total weight of the gate valve system, F u F is the vertical water pressure at the top. dmin For the most unfavorable extreme value, f z This represents the total frictional force.

6. The method according to claim 1, characterized in that, The parameterized mapping model is a continuous function of relative opening degree; Determine the most unfavorable extreme value of the bottom hydrodynamic force, including: The bottom dynamic water force is used as a continuous objective function of the relative opening. Within the operating range, a global optimization operation is performed on the continuous objective function to identify the most unfavorable opening that causes the continuous objective function to reach its extreme value. Substituting the most unfavorable opening value into the continuous objective function, the most unfavorable extreme value is calculated.

7. The method according to claim 6, characterized in that, Identify the most unfavorable opening that causes a continuous objective function to reach an extremum, including: The derivative function is obtained by taking the first derivative of the continuous objective function with respect to the relative opening. Find the stationary points where the derivative function is equal to zero, and check whether the stationary points are located within the operating opening interval; By comparing the function values ​​at the stagnation point and the boundary points of the operating opening range, the most unfavorable opening is determined.

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