Valve flow path optimization method based on simulation model

Abstract

The present invention belongs to the field of valve manufacturing technology, and specifically discloses a valve flow channel optimization method based on a simulation model, including: obtaining flow channel optimization parameters and determining optimization thresholds, constructing a parameterized geometric model, simulating flow performance parameters through a dynamic simulation model, and screening the optimal flow channel. Among them, the flow channel optimization parameters include curvature radius and cross-sectional area change rate. The curvature radius is optimized by setting a key curvature radius variable threshold, and using the curvature radius optimization model combined with the four-quadrant method to analyze the fluid pressure drop and turbulence intensity to compensate for the optimization parameters; the cross-sectional area change rate is optimized by adding a time attribute to the flow parameter mutation point, constructing a neural network model to train the mapping relationship between flow parameters and cross-sectional area change rate and dynamically adjusting it. The present invention solves the problems of insufficient flow performance optimization, poor adaptability to dynamic working conditions, low design efficiency, etc. in traditional designs, realizes the coordinated optimization of multiple flow channel parameters, and improves the stability and reliability of the valve under complex working conditions.

Description

Technical Field

[0001] The invention belongs to the technical field of valve manufacturing, and in particular relates to a valve flow channel optimization method based on a simulation model. Background Art

[0002] In valve manufacturing, flow channel structural design is a key factor in determining a valve's fluid control performance. The geometric parameters of the flow channel directly affect the fluid's flow resistance, turbulence distribution, and energy loss. The adaptability of the flow channel under dynamic conditions determines the valve's stability in complex scenarios. As industrial fluid systems evolve toward high-pressure, high-frequency dynamic regulation, higher requirements are placed on the refined design of valve flow channels and the coordinated optimization of multiple parameters.

[0003] In the existing technology, traditional flow channel design mainly relies on the experience of engineers to set parameters and adjust the structure. It is difficult to quantitatively analyze the mapping relationship between parameters such as curvature radius and cross-sectional area change rate and flow performance, resulting in flow channel structure optimization remaining at the empirical level, which is prone to problems such as excessive pressure drop or flow field turbulence. In addition, the multi-parameter optimization process relies on physical trial and error, resulting in a long design cycle and high cost, and easily causing parameter optimization conflicts.

[0004] Therefore, it is urgent to propose a valve flow channel optimization method based on simulation model to solve the above technical problems. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects in the prior art and provide a valve flow channel optimization method based on a simulation model.

[0006] The present invention provides a valve flow channel optimization method based on a simulation model, comprising:

[0007] Acquiring flow channel optimization parameters, determining flow channel optimization thresholds based on the flow channel optimization parameters, constructing a parameterized geometric model, and generating multiple groups of flow channel solutions based on the flow channel optimization thresholds;

[0008] The flow performance parameters of different flow channels are simulated by dynamic simulation models, and the valve flow channel with the optimal flow performance parameters is selected.

[0009] A further solution is that the flow channel optimization parameters include at least the curvature radius and the cross-sectional area change rate;

[0010] The curvature radius optimization process is as follows: setting the key curvature radius as a variable and determining the variable threshold of each key curvature radius based on design requirements, and outputting a curvature radius compensation optimization strategy through a curvature radius optimization model;

[0011] The cross-sectional area change rate optimization process is as follows: measuring the flow parameters of the fluid at several cross-sections of the flow channel as input of the dynamic simulation model to obtain the instantaneous process of the fluid flow in the flow channel; and measuring the cross-sectional area change rate of the cross-section corresponding to the flow parameters to obtain the mapping relationship between the flow parameters and the cross-sectional area change rate, and dynamically adjusting the cross-sectional area change rate based on the mapping relationship and the preset flow parameters; the cross-sectional area change rate is the ratio of the cross-sectional area of ​​the flow channel to the cross-sectional area at the inlet of the valve body at any moment when the fluid flows through the flow channel.

[0012] A further solution is that the key curvature radii are: the inlet transition curvature radius, where the valve front pipe connects to the valve body; the throat contraction curvature radius, which affects the peak flow rate; and the outlet diffusion curvature radius, which affects the pressure drop recovery.

[0013] A further solution is that the curvature radius optimization model includes an input layer, a curvature radius library, a conditional selection gate, a fluid simulation compensation unit and an output layer;

[0014] The input layer includes at least three independent input paths, which are used to input the variable threshold of the inlet transition curvature radius, the variable threshold of the throat contraction curvature radius, and the variable threshold of the outlet diffusion curvature radius respectively;

[0015] The curvature radius library is respectively connected to the three independent input paths and is used for temporarily storing variable thresholds;

[0016] The conditional selection gate is connected to the curvature radius library and serves as the only data transmission channel between the curvature radius library and the fluid simulation compensation unit. The conditional selection gate selects the optimal variable threshold of the key curvature radius in the curvature radius library based on the application scenario and parameter requirements of the valve, and clears the curvature radius library;

[0017] The fluid simulation compensation unit performs fluid dynamics simulation based on the optimal variable threshold, and compensates the optimal variable threshold based on the fluid dynamics simulation result;

[0018] The output layer is used to output the compensated optimal inlet transition curvature radius, the optimal throat contraction curvature radius, and the optimal outlet diffusion curvature radius.

[0019] A further solution is that the compensation process of the fluid simulation compensation unit for the optimal variable threshold is:

[0020] Quantifying the flow performance parameters into fluid pressure drop variables and fluid turbulence intensity variables and setting pressure drop thresholds and turbulence intensity thresholds;

[0021] Obtain the flow performance parameters of the fluid dynamic simulation at the connection between the valve front pipe and the valve body, the throat contraction, and the connection between the valve rear pipe and the valve body, and compare them with the pressure drop threshold and turbulence intensity threshold;

[0022] The four-quadrant method is used to analyze the fluid pressure drop and fluid turbulence intensity. If the fluid pressure drop and fluid turbulence intensity are located in the first quadrant, the optimal variable threshold is increased proportionally based on the increase ratio of the fluid pressure drop and fluid turbulence intensity; if the fluid pressure drop and fluid turbulence intensity are located in the third quadrant, the optimal variable threshold is decreased proportionally based on the decrease ratio of the fluid pressure drop and fluid turbulence intensity; if the fluid pressure drop and fluid turbulence intensity are located in the second quadrant or the fourth quadrant, it is determined to be abnormal, and the condition selection gate reselects the optimal variable threshold.

[0023] A further solution is to increase or decrease the optimal variable threshold in geometric proportion. The calculation formula is:

[0024] ;

[0025] in, is the increase or decrease of the optimal variable threshold, is the fluid pressure drop, is the voltage drop threshold, is the fluid turbulence intensity, is the turbulence intensity threshold, is the optimal critical curvature radius.

[0026] A further solution is to add a time attribute to the mutation point of the flow parameter and synchronize the time attribute to the cross-sectional area change rate so that the cross-sectional area change rate with different time attributes corresponds to the flow parameter mutation point of the time attribute.

[0027] A further solution is that the preset flow parameters are set based on the application scenarios and parameter requirements of the conditional selection gate; and the time interval of the preset flow parameters is set based on the time attribute of the mutation point, so that any time attribute of the cross-sectional area change rate is within the time interval and at this time attribute, the cross-sectional area change rate uniquely corresponds to the preset flow parameter.

[0028] A further solution is that the process of dynamically adjusting the cross-sectional area change rate is as follows: collecting a large amount of pressure data and flow rate data with time tags, calculating the cross-sectional area change rate of the flow channel at the time of the time tags, and having human experts mark the sudden change time attributes of the pressure data and flow rate data;

[0029] The pressure data , flow rate data , cross-sectional area change rate And the mutation point time attribute Perform manual expert labeling to form several groups of mapping pairs with time labels As a training set;

[0030] The mapping number Input into the neural network unit for iterative training to obtain the pressure data based on , flow rate data , mutation point time attribute Output cross-sectional area change rate Optimization model of cross-sectional area change rate;

[0031] Dynamically adjust the cross-sectional area change rate based on the cross-sectional area change rate optimization model .

[0032] A further approach is to use the pressure drop threshold and turbulence intensity threshold as global constraints for the cross-sectional area change rate optimization model. The fluid pressure drop and fluid turbulence intensity of the cross section after the cross-sectional area change rate is adjusted are compared with the pressure drop threshold and turbulence intensity threshold, respectively, and fine-tuning measurements are triggered based on the comparison results:

[0033] like , , reduce the cross-sectional area change rate according to the following formula:

[0034] ;

[0035] like , , increase the cross-sectional area change rate according to the following formula:

[0036] ;

[0037] in, is the rate of change of cross-sectional area after reduction or increase.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention uses parametric geometric models and dynamic simulation analysis to quantitatively analyze the impact of flow channel optimization parameters on flow performance parameters. It can simultaneously compare multiple groups of flow channel solutions, improve optimization efficiency, ensure that the optimal flow channel solution is screened out, reduce fluid pressure drop and turbulence intensity, and improve energy utilization efficiency.

[0040] The present invention focuses on the curvature radius and the rate of change of cross-sectional area. Through variable threshold setting and dynamic optimization, it accurately controls the flow characteristics of the fluid at key positions such as the pre-valve connection, throat contraction, and post-valve diffusion, reduces local resistance loss, and realizes the refined design of the flow channel structure by distinguishing the differentiated effects of different curvature radii on flow performance, avoiding performance conflicts caused by parameter coupling in traditional empirical design.

[0041] The present invention constructs a curvature radius optimization model, adopts the four-quadrant method to analyze the pressure drop and turbulence intensity, and dynamically compensates the curvature radius threshold through a calculation formula. The condition selection gate automatically matches the curvature radius threshold based on the working condition requirements, and combines the fluid simulation compensation unit to realize intelligent iteration of parameters, thereby solving the problem of insufficient adaptability to multiple working conditions in traditional designs and enabling the flow channel structure to quickly adapt to complex scenarios such as high pressure and high frequency regulation. The four-quadrant method and the formulaic compensation mechanism ( The calculation formula quantifies the parameter adjustment logic to avoid the subjectivity of manual experience, ensure the scientific nature and repeatability of the curvature radius optimization process, and improve the reliability of the optimization results.

[0042] This invention overcomes the traditional design's neglect of the time dimension by marking the flow parameter mutation points with time attributes, accurately synchronizing the cross-sectional area change rate with the dynamic operating conditions. This solves the problem of delayed flow field response during transient valve operations and improves stability during sudden operating condition changes. The neural network model of this invention uses big data training to establish a nonlinear mapping relationship between pressure, flow rate, time, and cross-sectional area change rate, enabling adaptive adjustment of the flow channel structure. It has a stronger fitting capability, especially for flow parameters within complex time intervals (such as periodic load fluctuations).

[0043] The present invention uses the pressure drop threshold and turbulence intensity threshold as global constraints of the cross-sectional area change rate optimization model to ensure that the cross-sectional area change rate adjustment does not exceed the performance bottom line. Combined with the formulated fine-tuning mechanism, the balance of flow channel performance is maintained during dynamic optimization, avoiding performance exceeding the limit caused by single parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The following drawings are merely provided for illustrative purposes only and are not intended to limit the scope of the present invention.

[0045] Figure 1 This is a flow chart of the valve flow channel optimization method of the present invention;

[0046] Figure 2 This is the principle diagram of the curvature radius optimization model;

[0047] Figure 3 Schematic diagram of the distribution of fluid pressure drop and fluid turbulence intensity in the four quadrants. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution, design method and advantages of the present invention more clear, the present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] like Figure 1 As shown, the present invention provides a valve flow channel optimization method based on a simulation model, comprising the following steps:

[0050] Step 1: Obtain the flow channel optimization parameters. In this embodiment, the flow channel optimization parameters are determined to be the curvature radius and the cross-sectional area change rate. The curvature radius includes three key parameters: the inlet transition curvature radius : The curvature of the connection between the valve front pipe and the valve body affects the smoothness of the fluid entering the valve body; the curvature radius of the throat contraction : The contraction curvature of the valve throat (peak velocity area) determines the velocity distribution; the outlet diffusion curvature radius : The diffusion curvature of the connection between the valve back pipe and the valve body affects the pressure drop recovery. Cross-sectional area change rate : Defined as the cross-sectional area of ​​the fluid flowing through the flow channel at any time and valve body inlet cross-sectional area The ratio of is used to characterize the dynamic changes of the flow channel cross section.

[0051] Step 2: Determine the flow channel optimization threshold based on the flow channel optimization parameters, and construct a parameterized geometric model using 3D software. 、 、 and Set it as an adjustable variable and set the initial variable threshold range. Generate multiple groups of flow channel schemes through parametric modeling. Each group of schemes corresponds to a different combination of curvature radius and cross-sectional area change rate sequence.

[0052] The flow performance parameters of different flow channels are simulated by dynamic simulation models, and the valve flow channel with the optimal flow performance parameters is selected.

[0053] Step 3: The curvature radius optimization process is as follows: setting the key curvature radius as a variable and determining the variable threshold of each key curvature radius based on the design requirements, and outputting the curvature radius compensation optimization strategy through the curvature radius optimization model; wherein, Figure 2 As shown, the curvature radius optimization model includes an input layer, a curvature radius library, a conditional selection gate, a fluid simulation compensation unit and an output layer;

[0054] The input layer includes at least three independent input paths, which are respectively input through the three independent paths. 、 、 Variable threshold ranges can be manually entered or automatically loaded with default thresholds based on industry standards such as ASME B16.34;

[0055] The curvature radius library is connected to the three independent input paths respectively, and is used to temporarily store the input threshold range to form a discrete parameter combination (such as 、 、 Each is divided into 5 intervals, generating 125 combinations);

[0056] The condition selection gate is connected to the curvature radius library and serves as the only data transmission channel between the curvature radius library and the fluid simulation compensation unit. The condition selection gate calls the preset rules according to the valve application scenario (such as high pressure working condition, high frequency adjustment working condition) to select the appropriate curvature radius combination. For example, the high pressure working condition gives priority to the smaller curvature radius combination. To increase the flow rate, the high frequency adjustment condition gives priority to the larger 、 To reduce turbulence.

[0057] Delete obviously unreasonable parameter combinations (such as or ), and clear the non-preferred combinations in the curvature radius library;

[0058] The fluid simulation compensation unit performs fluid dynamics simulation based on the optimal variable threshold, and performs fluid dynamics simulation on the screened curvature radius combination based on CFD simulation software (such as ANSYS Fluent) to obtain the fluid pressure drop at the valve front connection, throat, and valve rear connection. and turbulence intensity , and compensate the optimal variable threshold based on the fluid dynamics simulation results;

[0059] The output layer is used to output the compensated 、 、 , used to update the geometric model.

[0060] In the above, the compensation process of the fluid simulation compensation unit for the optimal variable threshold is:

[0061] Quantifying the flow performance parameters into fluid pressure drop variables and fluid turbulence intensity variables and setting pressure drop thresholds and turbulence intensity thresholds;

[0062] Obtain the flow performance parameters of the fluid dynamic simulation at the connection between the valve front pipe and the valve body, the throat contraction, and the connection between the valve rear pipe and the valve body, and compare them with the pressure drop threshold and turbulence intensity threshold;

[0063] like Figure 3 As shown in the figure, the four-quadrant method is used to analyze the fluid pressure drop and fluid turbulence intensity. If the fluid pressure drop and fluid turbulence intensity are in the first quadrant, it means that both the pressure drop and turbulence exceed the standard. Based on the increase ratio of the fluid pressure drop and fluid turbulence intensity, the optimal variable threshold is increased proportionally to increase the flow channel smoothness. The calculation formula is:

[0064] ;

[0065] If the fluid pressure drop and fluid turbulence intensity are in the third quadrant, it means that both the pressure drop and turbulence are insufficient. Based on the reduction ratio of the fluid pressure drop and fluid turbulence intensity, the optimal variable threshold is proportionally reduced to enhance the fluid constraint. The calculation formula is:

[0066] ;

[0067] in, is the increase or decrease of the optimal variable threshold, is the fluid pressure drop, is the voltage drop threshold, is the fluid turbulence intensity, is the turbulence intensity threshold, is the optimal critical curvature radius.

[0068] If the fluid pressure drop and fluid turbulence intensity are in the second or fourth quadrant, it is considered that the parameters are in conflict (for example, the pressure drop meets the standard but the turbulence exceeds the standard), and the trigger condition selection gate is used to re-screen the combination.

[0069] Step 4, cross-sectional area change rate optimization: Measure the fluid flow parameters at several sections of the flow channel as input to the dynamic simulation model to obtain the instantaneous process of fluid flow in the flow channel; measure the cross-sectional area change rate of the section corresponding to the flow parameters to obtain a mapping relationship between the flow parameters and the cross-sectional area change rate, and dynamically adjust the cross-sectional area change rate based on the mapping relationship and preset flow parameters; the cross-sectional area change rate is the ratio of the cross-sectional area of ​​the flow channel to the cross-sectional area at the valve body inlet at any moment when the fluid flows through the flow channel. In this embodiment, the specific implementation process of step 4 includes:

[0070] Associating flow parameters with time attributes: adding time attributes to the mutation points of the above flow parameters, and synchronizing the time attributes to the cross-sectional area change rate, so that the cross-sectional area change rate of different time attributes corresponds to the flow parameter mutation point of the time attribute, and the preset flow parameters are set based on the application scenario and parameter requirements of the conditional selection gate; setting the time interval of the preset flow parameters based on the time attribute of the mutation point of the flow parameters, so that any time attribute of the cross-sectional area change rate is within the time interval and the cross-sectional area change rate corresponding to any time attribute uniquely corresponds to the preset flow parameters. Specifically, the pressure data during valve operation is collected in real time by a pressure sensor and a flow meter. , flow rate data , cross-sectional area change rate And the mutation point time attribute Perform manual expert labeling to form several groups of mapping pairs with time labels As a training set;

[0071] The mapping number Input into the neural network unit for iterative training to obtain the pressure data based on , flow rate data , mutation point time attribute Output cross-sectional area change rate Optimization model of cross-sectional area change rate;

[0072] Dynamically adjust the cross-sectional area change rate based on the cross-sectional area change rate optimization model .

[0073] In the above, the value of the preset flow parameter needs to be directly bound to the actual application scenario and functional requirements of the valve, and is dynamically determined by the conditional selection gate according to the specific scenario to achieve the accuracy of flow channel optimization and adaptability to working conditions; in this embodiment, the preset flow parameter refers to the performance indicator threshold pre-set during the flow channel optimization process, which is used to measure the rationality of the flow channel structure. For example: pressure drop threshold : Maximum allowable pressure drop; turbulence intensity threshold : The maximum turbulence intensity allowed; Flow rate peak threshold: The expected flow rate range under specific working conditions; The conditional selection gate is the core decision-making unit in the curvature radius optimization model. Its functions include: obtaining the application scenarios of the valve (such as high-pressure pipelines, chemical fluid regulation, high-frequency start-stop scenarios, etc.), selecting the initial variable threshold that matches the working conditions from the curvature radius library, and triggering the subsequent simulation compensation process.

[0074] Therefore, when determining the value of the preset flow parameters, the value range needs to be determined according to the application scenario. For example, under high-pressure conditions, the fluid pressure is high, and the flow resistance needs to be reduced to reduce energy loss. The condition selection gate gives priority to a larger inlet transition curvature radius. and the outlet diffusion curvature radius , to reduce fluid shock; pressure drop threshold Set to a lower value (such as ), mandatory requirements for flow channel optimization to reduce pressure drop; turbulence intensity threshold Can be appropriately relaxed (such as ), a certain degree of turbulence is allowed to ensure the flow capacity. Under high-frequency regulation conditions, the valve needs to be opened or closed frequently, and the transient response stability needs to be improved. The conditional selection door gives priority to a smaller throat contraction curvature radius. , to quickly change the flow rate;

[0075] Voltage drop threshold Can be appropriately increased (such as ), a certain pressure drop is allowed in exchange for flow rate adjustment sensitivity;

[0076] Turbulence intensity threshold Strictly limited (such as ), to avoid high-frequency turbulence causing component wear.

[0077] In the above, the neural network unit uses LSTM (long short-term memory network) to build a cross-sectional area change rate optimization model, and the input layer is 、 、 , the output layer is The training process is as follows: input the training set into the model, optimize the network weights through the back propagation algorithm, and the goal is to minimize the prediction The mean square error with the actual value, introducing the voltage drop threshold and turbulence intensity threshold As a global constraint, ensure that the model output The corresponding simulation results do not exceed the performance boundary, and the current working conditions are obtained in real time. 、 、 , input the trained model and output the real-time cross-sectional area change rate If the simulation results show , , reduce the cross-sectional area change rate according to the following formula:

[0078] ;

[0079] like , , increase the cross-sectional area change rate according to the following formula:

[0080] ;

[0081] in, is the rate of change of cross-sectional area after reduction or increase.

[0082] Step 5: Iteratively optimize the flow channel scheme, combining the curvature radius optimization results with the dynamic adjustment strategy of the cross-sectional area change rate to generate the final flow channel structure scheme; perform full-condition simulation (including steady-state and transient conditions) on all schemes to screen out the scheme with the best flow performance parameters as the final design scheme.

[0083] While various embodiments of the present invention have been described above, the above descriptions are intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A valve flow channel optimization method based on a simulation model, characterized in that: include: Acquiring flow channel optimization parameters, determining flow channel optimization thresholds based on the flow channel optimization parameters, constructing a parameterized geometric model, and generating multiple groups of flow channel solutions based on the flow channel optimization thresholds; Through the dynamic simulation model, the flow performance parameters of different flow channels are simulated to select the valve flow channel with the best flow performance parameters; The flow channel optimization parameters include at least the curvature radius and the cross-sectional area change rate; The curvature radius optimization process is as follows: setting the key curvature radius as a variable and determining the variable threshold of each key curvature radius based on design requirements, and outputting a curvature radius compensation optimization strategy through a curvature radius optimization model; The cross-sectional area change rate optimization process is as follows: measuring the flow parameters of the fluid at several cross-sections of the flow channel as input of the dynamic simulation model to obtain the instantaneous process of the fluid flow in the flow channel; and measuring the cross-sectional area change rate of the cross-section corresponding to the flow parameters to obtain the mapping relationship between the flow parameters and the cross-sectional area change rate, and dynamically adjusting the cross-sectional area change rate based on the mapping relationship and the preset flow parameters; the cross-sectional area change rate is the ratio of the cross-sectional area of ​​the flow channel to the cross-sectional area at the inlet of the valve body at any moment when the fluid flows through the flow channel.

2. The valve flow channel optimization method based on the simulation model according to claim 1, characterized in that: The key curvature radii are: inlet transition curvature radius, throat contraction curvature radius, and outlet divergence curvature radius.

3. The valve flow channel optimization method based on simulation model according to claim 2, characterized in that: The curvature radius optimization model includes an input layer, a curvature radius library, a conditional selection gate, a fluid simulation compensation unit and an output layer; The input layer includes at least three independent input paths, which are used to input the variable threshold of the inlet transition curvature radius, the variable threshold of the throat contraction curvature radius, and the variable threshold of the outlet diffusion curvature radius respectively; The curvature radius library is respectively connected to the three independent input paths and is used for temporarily storing variable thresholds; The conditional selection gate is connected to the curvature radius library and serves as the only data transmission channel between the curvature radius library and the fluid simulation unit. The conditional selection gate selects the optimal variable threshold of the key curvature radius in the curvature radius library based on the application scenario and parameter requirements of the valve, and clears the curvature radius library; The fluid simulation compensation unit performs fluid dynamics simulation based on the optimal variable threshold, and compensates the optimal variable threshold based on the fluid dynamics simulation result; The output layer is used to output the compensated optimal inlet transition curvature radius, the optimal throat contraction curvature radius, and the optimal outlet diffusion curvature radius.

4. The valve flow channel optimization method based on the simulation model according to claim 3, characterized in that: The compensation process of the fluid simulation compensation unit for the optimal variable threshold is: Quantifying the flow performance parameters into fluid pressure drop variables and fluid turbulence intensity variables and setting pressure drop thresholds and turbulence intensity thresholds; Obtain the flow performance parameters of the fluid dynamic simulation at the connection between the valve front pipe and the valve body, the throat contraction, and the connection between the valve rear pipe and the valve body, and compare them with the pressure drop threshold and turbulence intensity threshold; The four - quadrant method is adopted to analyze the fluid pressure drop and the fluid turbulence intensity. If the fluid pressure drop and the fluid turbulence intensity are in the first quadrant, the optimal variable threshold is increased geometrically based on the increasing ratio of the fluid pressure drop and the fluid turbulence intensity; if the fluid pressure drop and the fluid turbulence intensity are in the third quadrant, the optimal variable threshold is decreased geometrically based on the decreasing ratio of the fluid pressure drop and the fluid turbulence intensity; if the fluid pressure drop and the fluid turbulence intensity are in the second quadrant or the fourth quadrant, it is determined as abnormal, and the condition selection gate re - selects the optimal variable threshold.

5. The valve flow channel optimization method based on simulation model according to claim 4, characterized in that: The calculation formula for increasing or decreasing the optimal variable threshold geometrically is: where, ΔR is the increase or decrease amount of the optimal variable threshold, P1 is the fluid pressure drop, P is the pressure drop threshold, I1 is the fluid turbulence intensity, I is the turbulence intensity threshold, and R is the optimal critical curvature radius.

6. The valve flow channel optimization method based on simulation model according to claim 5, characterized in that: Add a time attribute to the mutation point of the flow parameter and synchronize the time attribute to the cross - sectional area change rate, so that the cross - sectional area change rates with different time attributes correspond to the mutation points of the flow parameter with the same time attribute.

7. The valve flow channel optimization method based on simulation model according to claim 6, characterized in that: The preset flow parameter is set based on the application scenario and parameter requirements of the condition selection gate; the time interval of the preset flow parameter is set based on the time attribute of the mutation point of the flow parameter, so that any time attribute of the cross - sectional area change rate is within the time interval and the cross - sectional area change rate corresponding to any time attribute is uniquely corresponding to the preset flow parameter.

8. The valve flow channel optimization method based on simulation model according to claim 7, characterized in that: The process of dynamically adjusting the cross - sectional area change rate is as follows: collect a large amount of pressure data and flow velocity data with time tags, calculate the cross - sectional area change rate of the flow channel at the moment of the time tag, and manually mark the mutation time attributes of the pressure data and the flow velocity data; The pressure data p t , flow rate data V t , cross-sectional area change rate μ t And the mutation point time attribute T t Perform manual expert labeling to form several groups of mapping pairs with time labels [(p t 、V t 、T t )μ t ] as the training set; The mapping number [(p t 、V t 、T t )μ t ] is input into the neural network unit for iterative training to obtain the pressure data p t , flow rate data V t , mutation point time attribute T t Output cross-sectional area change rate μ t Optimization model of cross-sectional area change rate; Dynamically adjust the cross-sectional area change rate μ based on the cross-sectional area change rate optimization model t .

9. The valve flow channel optimization method based on simulation model according to claim 8, characterized in that: Take the pressure drop threshold and the turbulence intensity threshold as the global constraint conditions of the cross - sectional area change rate optimization model, compare the fluid pressure drop and the fluid turbulence intensity of the cross - section after adjusting the cross - sectional area change rate with the pressure drop threshold and the turbulence intensity threshold respectively, and trigger fine - tuning measurement based on the comparison results: If P1 > P, I1 > I, the cross - sectional area change rate is decreased according to the following formula: If P1 < P, I1 < I, the cross - sectional area change rate is increased according to the following formula: Among them, μ' t is the rate of change of cross-sectional area after reduction or increase.

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