Flexible ball valve flow characteristic model establishing method and device

Abstract

The invention belongs to the field of model establishment and optimization, and particularly discloses a flexible ball valve flow characteristic model establishment method and device, and the method comprises the steps: collecting the monitoring data of a field flexible ball valve; establishing a self-excited vibration model based on a characteristic line method and the characteristics of the flexible valve, and performing pressure data simulation through the self-excited vibration model to obtain simulation data; substituting the self-excited vibration model into a particle swarm algorithm, and carrying out particle iteration updating based on an established fitness function until a particle optimal solution is obtained; the fitness function is established based on the monitoring data and the simulation data, and the fitness function is used for reflecting the excellence degree of the flow characteristic parameters of the flexible ball valve; and establishing a flexible ball valve flow characteristic model by using parameters corresponding to the particle optimal solution. According to the invention, the accuracy of the flow characteristic model of the flexible ball valve can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of model establishment and optimization, and more specifically, to a method and device for establishing a flow characteristic model of a flexible ball valve. Background Art

[0002] With the increasing use of renewable energy today, pumped storage power stations can effectively solve the intermittent and instability problems of renewable energy. In this context, maintaining the normal operation of pumped storage power stations is essential to ensure the stable, flexible and efficient operation of the power system. Only by maintaining the normal operation of pumped storage power stations can they better play their role in the power system and provide support for energy transformation and sustainable development.

[0003] As an important part of pumped storage power stations, ball valves are mainly used to control flow and stabilize pressure. When the ball valve seal deteriorates and leaks, it will become a flexible valve and cause self-excited vibration, which will seriously affect the safe and stable operation of the unit.

[0004] However, in the current ball valve modeling process, it is difficult to obtain the characteristic parameters of the flexible valve because the valve leakage cannot be accurately measured. It is impossible to accurately simulate the self-excited vibration process of the ball valve of the pumped storage power station, which leads to the problem of low model accuracy. Summary of the invention

[0005] In view of the defects of the prior art, the purpose of the present application is to provide a method and device for establishing a flow characteristic model of a flexible ball valve, aiming to solve the problem of low accuracy of the flow characteristic model of a flexible ball valve.

[0006] In a first aspect, the present application provides a method for establishing a flow characteristic model of a flexible ball valve, comprising:

[0007] Collect monitoring data of flexible ball valves on site;

[0008] A self-excited vibration model is established based on the characteristic line method and the flexible valve characteristics, and pressure data simulation is performed through the self-excited vibration model to obtain simulation data;

[0009] Substitute the self-excited vibration model into the particle swarm algorithm, and perform particle iterative update based on the established fitness function until the optimal particle solution is obtained; the fitness function is established based on the monitoring data and the simulation data, and the fitness function is used to reflect the excellence of the flow characteristic parameters of the flexible ball valve;

[0010] The flow characteristic model of the flexible ball valve is established using the parameters corresponding to the optimal solution of the particles.

[0011] Optionally, after simulating the pressure data by using the self-excited vibration model to obtain the simulation data, the method further includes:

[0012] Determine a maximum monitoring value and a minimum monitoring value of the monitoring data at the self-excited vibration time t, use the maximum monitoring value as an upper monitoring envelope point of the monitoring data, and use the minimum monitoring value as a lower monitoring envelope point;

[0013] Determining a monitoring data envelope curve according to the monitoring upper envelope point and the monitoring lower envelope point, and fitting the monitoring data envelope curve to obtain an optimization curve of the monitoring data;

[0014] Determine a simulation maximum value and a simulation minimum value of the simulation data at the self-excited vibration time t, use the simulation maximum value as a simulation upper envelope point of the simulation data, and use the simulation minimum value as a simulation lower envelope point;

[0015] A simulation data envelope curve is determined according to the simulation upper envelope point and the simulation lower envelope point, and the simulation data envelope curve is fitted to obtain an optimization curve of the simulation data.

[0016] Optionally, the method for establishing the fitness function includes:

[0017] Comparing the optimized monitoring data with the simulation data to obtain a consistency function between the optimized monitoring data and the simulation data;

[0018] Determining characteristic parameters and initial water leakage of the flexible valve, and determining limiting conditions of the initial water leakage in the simulation according to the characteristic parameters and the initial water leakage;

[0019] A penalty function is determined according to the restriction condition, and the fitness function is obtained by adding the consistency degree function and the penalty function.

[0020] Optionally, the self-excited vibration model is substituted into a particle swarm algorithm for parameter identification, and particles are iteratively updated based on an established fitness function until an optimal particle solution is obtained, including:

[0021] Determine system parameters and limit the range of particles to randomly generate particles;

[0022] Substitute each group of particles into the characteristic line method program to obtain simulation results, obtain monitoring data through the on-site status monitoring device, and calculate the initial fitness of each group of particles through the fitness function;

[0023] The initial fitness is taken as the local optimal solution of each particle, and the global optimal solution of the entire particle swarm is determined;

[0024] Iteratively update the velocity and position of particles based on the velocity and position update formulas, and process particles out of range according to boundary conditions;

[0025] Iteratively obtain the fitness of each new particle, compare the fitness of each new particle with the local optimal solution, determine an updated local optimal solution, and determine an updated global optimal solution from all updated local optimal solutions.

[0026] Optionally, the speed and position update formulas are as follows:

[0027]

[0028] Among them, x j is the particle position, v j is the particle update speed, w is the inertia factor, c1 and c2 are acceleration constants; pbest j is the local optimal position, gbest jj is the global optimal position.

[0029] Optionally, establishing a self-excited vibration model based on a characteristic line method and flexible valve characteristics includes:

[0030] Determine boundary point parameters by combining characteristic line equation with boundary conditions, and calculate other node parameters except boundary points by using the characteristic line equation;

[0031] Determine the flexible valve characteristics according to the relationship between the water leakage of the flexible valve and the pressure difference before and after the valve, and establish the self-excited vibration model based on the acquired boundary point parameters, other node parameters and the flexible valve characteristics;

[0032] The characteristic line equation is shown in the following formula:

[0033] C + :H Pi =H i-1 +BQ i-1 -RQ i-1 |Q i-1 |-BQ Pi

[0034] C - :H Pl =H i+1 -BQ l+1 +RQ i+1 |Q i+1 |+BQ pi

[0035] Among them, H Pi is the water head at the i-th node at time P, B is the pipeline parameter, R is the pipeline characteristic coefficient, Q Pi is the flow rate of the i-th node at time P;

[0036] The relationship between the water leakage of the flexible valve and the pressure difference before and after the valve is shown in the following formula:

[0037]

[0038] Where Q is the water leakage, H is the pressure difference before and after the valve, H1 is the pressure difference between the closed cavity and the open cavity of the working seal, k1 and k2 are the flow characteristic parameters of the valve, D1 is the valve diameter, x0 is the sealing gap when the pressure difference before and after the valve is 0, ρ is the fluid density, g is the acceleration of gravity, A f is the flexible leakage area of ​​the valve, and K is the flexibility coefficient of the valve.

[0039] Optionally, the consistency function is as shown in the following formula:

[0040] F1(t)=∫0 600 t(|H m2 (t)-H m1 (t)|+|H n2 (t)-H n1 (t)|)dt

[0041] The penalty function is shown in the following formula:

[0042]

[0043] Among them, H m2 (t) is the upper envelope of the simulation results, H n2 (t) is the lower envelope of the simulation results; H m1 (t) is the package route of monitoring data, H n1 (t) is the lower envelope of the monitoring data, Q0 is the initial water leakage of the valve, H0 is the initial water head of the valve, and g is the acceleration of gravity.

[0044] In a second aspect, the present application also provides a device for establishing a flow characteristic model of a flexible ball valve, comprising:

[0045] Data acquisition module, used to collect monitoring data of on-site flexible ball valves;

[0046] A simulation module, used for establishing a self-excited vibration model based on a characteristic line method and flexible valve characteristics, and performing pressure data simulation through the self-excited vibration model to obtain simulation data;

[0047] An optimization module, used for substituting the self-excited vibration model into a particle swarm algorithm for parameter identification, and performing particle iterative update based on an established fitness function until an optimal particle solution is obtained; the fitness function is established based on monitoring data and the simulation data, and the fitness function is used to reflect the excellence of the flow characteristic parameters of the flexible ball valve;

[0048] The model building module is used to build a flow characteristic model of a flexible ball valve using parameters corresponding to the particle optimal solution.

[0049] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0051] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0052] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0053] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0054] (1) The simulation data generated by the self-excited vibration model established by the characteristic line method in this application is combined and optimized with the actual pressure data collected on site, so as to establish a flexible valve model that is closer to the actual on-site model to improve the model accuracy. The self-excited vibration model is optimized through continuous iteration of model parameters. The particle swarm algorithm is used in the iterative process to gradually find the optimal solution by continuously adjusting parameters and evaluating fitness, thereby ensuring that the selection of model parameters is most suitable for the actual situation and further improving the accuracy of the model.

[0055] (2) By combining the self-excited vibration model and the particle swarm algorithm, the ball valve characteristics under different working conditions can be processed and the model's adaptability to different working conditions can be enhanced, that is, the adaptability of the model can be improved, which can provide assistance for subsequent self-excited vibration simulation and suppression measures research, and has strong practical value and wide promotion value.

[0056] (3) The model iteratively optimized by the particle swarm algorithm can better correct the deviation caused by the imperfect initial model or data collection error, making the prediction under various working conditions more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a water diversion structure diagram of a pumped storage power station provided in an embodiment of the present application;

[0058] Figure 2This is one of the flow charts of the method for establishing a flow characteristic model of a flexible ball valve provided in an embodiment of the present application;

[0059] Figure 3 is an actual self-excited vibration pressure diagram provided by an embodiment of the present application;

[0060] Figure 4 is a parameter identification flow chart provided in an embodiment of the present application;

[0061] Figure 5 is a comparison chart of the simulation results after identification and the monitoring data provided in the embodiment of the present application;

[0062] Figure 6 This is a rectangular calculation network diagram of the characteristic line method provided in the embodiment of the present application

[0063] Figure 7 This is the second flow chart of the method for establishing a flow characteristic model of a flexible ball valve provided in an embodiment of the present application;

[0064] Figure 8 It is a structural schematic diagram of a device for establishing a flow characteristic model of a flexible ball valve provided in an embodiment of the present application;

[0065] Fig. 9 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0068] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0069] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0070] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0071] Reference Figure 1 , Figure 1 This is a diagram of the water diversion structure of a pumped storage power station in an embodiment of the present application. Water is diverted between the upstream reservoir and the downstream reservoir through multiple pipelines, valves, units and pressure regulating wells.

[0072] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0073] Reference Figure 2 The present application provides a method for establishing a flow characteristic model of a flexible ball valve, comprising:

[0074] S201. Collect monitoring data of the flexible ball valve on site;

[0075] S202. Establishing a self-excited vibration model based on the characteristic line method and the flexible valve characteristics, simulating the pressure data through the self-excited vibration model to obtain simulation data;

[0076] S203. Substitute the self-excited vibration model into the particle swarm algorithm, and perform iterative update of particles based on the established fitness function until the optimal particle solution is obtained; the fitness function is established based on monitoring data and simulation data;

[0077] S204. Establish a flow characteristic model of a flexible ball valve using the parameters corresponding to the particle optimal solution.

[0078] Specifically, we first need to select a suitable ball valve and related fluid system, and use sensors and data acquisition equipment to obtain on-site pressure data. Specifically, we use the pressure sensor on the upstream side of the on-site ball valve to collect the pressure data of the on-site ball valve, and plot the collected pressure data. Figure 3 The accuracy and reliability of field data are the basis for subsequent model establishment and optimization.

[0079] Secondly, the characteristic line method is used to analyze the flow characteristics of the ball valve, and the flexibility caused by the valve material and structure is considered. The characteristic line method can describe the flow of the fluid in the valve and its relationship with the valve vibration by establishing a mathematical model.

[0080] Through the above two steps, a preliminary self-excited vibration model is constructed, and this model is used to simulate the pressure data to generate corresponding simulation data.

[0081] Furthermore, the self-excited vibration model is substituted into the particle swarm algorithm for iterative optimization.

[0082] In this stage, the particle swarm algorithm is used to optimize the self-excited vibration model.

[0083] In this embodiment, it is necessary to define a fitness function, and use the fitness function to iteratively update particles. Specifically, a group of particles are initialized, each particle represents a set of model parameters. The particles move in the parameter space, and the fitness function is calculated to evaluate the pros and cons of the current solution, and the speed and position of the particles are updated according to the optimal solution. The particles are continuously iterated to find the global optimal solution. When the preset stop condition (such as the maximum number of iterations or the convergence of the fitness function) is reached, the algorithm ends and the optimal solution for the particles is obtained.

[0084] Finally, the parameters of the particle optimal solution are used to establish the flow characteristic model of the flexible ball valve. According to the parameters corresponding to the particle optimal solution, a new flow characteristic model is constructed, which can more accurately describe the flow performance of the valve under different working conditions.

[0085] The simulation data generated by the self-excited vibration model established by the characteristic line method in the embodiment of the present application is combined and optimized with the actual pressure data collected on site, so as to establish a flexible valve model that is closer to the actual field model to improve the model accuracy. The self-excited vibration model is optimized through continuous iteration of model parameters, and the particle swarm algorithm is used to gradually find the optimal solution by continuously adjusting parameters and evaluating fitness during the iteration process, thereby ensuring that the selection of model parameters is most suitable for the actual situation and further improving the accuracy of the model.

[0086] Optionally, after simulating the pressure data by using the self-excited vibration model to obtain the simulation data, the method further includes:

[0087] Determine a maximum monitoring value and a minimum monitoring value of the monitoring data at the self-excited vibration time t, use the maximum monitoring value as an upper monitoring envelope point of the monitoring data, and use the minimum monitoring value as a lower monitoring envelope point;

[0088] Determining a monitoring data envelope curve according to the monitoring upper envelope point and the monitoring lower envelope point, and fitting the monitoring data envelope curve to obtain an optimization curve of the monitoring data;

[0089] Determine a simulation maximum value and a simulation minimum value of the simulation data at the self-excited vibration time t, use the simulation maximum value as a simulation upper envelope point of the simulation data, and use the simulation minimum value as a simulation lower envelope point;

[0090] A simulation data envelope curve is determined according to the simulation upper envelope point and the simulation lower envelope point, and the simulation data envelope curve is fitted to obtain an optimization curve of the simulation data.

[0091] Specifically, due to the low sampling rate of the monitoring data, it is difficult to fully reflect the change process of pressure over time. If the consistency between the simulation results and the monitoring data is evaluated by comparing the data at each time point, a large error will occur. In order to make the comparison result more accurate, this application compares the upper and lower envelopes of the monitoring data and the simulation results to determine the consistency of the actual and simulated self-excited vibration development process. The points that form the envelope equation of the monitoring data are as follows:

[0092]

[0093] Among them, H P (t) is the pressure data; H m is the point of the upper envelope; H n is the point of the lower envelope; t is the self-excited vibration time.

[0094] In order to make the curve smoother, the curve is fitted. Since the function type of the curve has not been determined, the expression of the curve can be converted into a polynomial function through Taylor series expansion, and the least squares method finds the best function match for fitting the polynomial function by calculating the sum of squares of the minimum error. The function expressions of the upper and lower envelopes after fitting are:

[0095]

[0096] Among them, H m1 (t) is the upper envelope of the optimized monitoring data; H n1 (t) is the lower envelope of the monitoring data after optimization.

[0097] Similarly, the simulation data is processed using the same method as the monitoring data.

[0098] Optionally, the method for establishing the fitness function includes:

[0099] Comparing the optimized monitoring data with the simulation data to obtain a consistency function between the optimized monitoring data and the simulation data;

[0100] Determining characteristic parameters and initial water leakage of the flexible valve, and determining limiting conditions of the initial water leakage in the simulation according to the characteristic parameters and the initial water leakage;

[0101] A penalty function is determined according to the restriction condition, and the fitness function is obtained by adding the consistency degree function and the penalty function.

[0102] Specifically, the process of establishing the fitness function is as follows:

[0103] The processed monitoring data is compared with the simulation results, and the time multiplied by the absolute error integral criterion is used as the performance indicator. The function reflecting the consistency between the simulation results and the monitoring data is obtained as follows:

[0104]

[0105] Among them, H m2 (t) is the upper envelope of the simulation results, H n2 (t) is the lower envelope of the simulation results; H m1 (t) is the package route of monitoring data, H n1 (t) is the lower envelope of the monitoring data.

[0106] Since different flexible valve characteristic parameters will lead to different valve leakage, it is necessary to limit the leakage range to exclude characteristic parameters that do not meet the requirements. The restriction conditions for the initial leakage in the simulation are as follows:

[0107]

[0108] Where: g is the acceleration of gravity, k1 and k2 are the flow characteristic parameters of the valve, H0 is the initial water head of the valve, Q0 is the initial water leakage of the valve, A max is the maximum leakage area of ​​the valve, D1 is the valve diameter, and x0 is the sealing gap when the pressure difference before and after the valve is 0.

[0109] Furthermore, for the flexible valve characteristic parameters that do not satisfy formula (4), a penalty function is added to the performance index. The penalty function is as follows:

[0110]

[0111] Finally, the function penalty function reflecting the consistency between the simulation results and the monitoring data is added to obtain the final fitness function as follows:

[0112] y(j)=F1(t)+ε(t) (6)

[0113] Optionally, the self-excited vibration model is substituted into a particle swarm algorithm for parameter identification, and particles are iteratively updated based on an established fitness function until an optimal particle solution is obtained, including:

[0114] Determine system parameters and limit the range of particles to randomly generate particles;

[0115] Substitute each group of particles into the characteristic line method program to obtain simulation results, obtain monitoring data through the on-site status monitoring device, and calculate the initial fitness of each group of particles through the fitness function;

[0116] The initial fitness is taken as the local optimal solution of each particle, and the global optimal solution of the entire particle swarm is determined;

[0117] Iteratively update the velocity and position of particles based on the velocity and position update formulas, and process particles out of range according to boundary conditions;

[0118] Iteratively obtain the fitness of each new particle, compare the fitness of each new particle with the local optimal solution, determine an updated local optimal solution, and determine an updated global optimal solution from all updated local optimal solutions.

[0119] Specifically, the model iteration process is as follows:

[0120] Determine system parameters and limit the range of particles to randomly generate particles; first, it is necessary to determine the system parameters used to establish the model. These parameters include but are not limited to fluid characteristics, valve structure parameters, material properties, etc., which are used to define the position and speed update range of particles.

[0121] According to the range of system parameters, set the boundaries of particles in parameter space. If the model parameters have maximum and minimum limits, the positions of particles need to be generated within these ranges. Generate particles randomly within the set range. Each particle represents a set of model parameters. The initial position and speed of the particle need to be randomly determined. These initial particles will be used as the starting point of the algorithm for optimization.

[0122] The parameters of each group of particles are substituted into the characteristic line method program, which generates simulation results of fluid flow and valve response based on the parameters of the particles. Flow characteristics or other relevant data are obtained from the simulation program, and these simulation data will be compared with the field monitoring data to evaluate the fitness of the particles.

[0123] Furthermore, by defining a fitness function, the fitness value between the corresponding simulation result and the actual data is calculated, and this fitness value is used as the initial fitness of each particle, and the initial fitness is used to determine the local optimal solution and the global optimal solution.

[0124] Local optimal solution: Initialize the local optimal solution of each particle to the particle with the best initial fitness.

[0125] Global optimal solution: Among all particles, the particle with the best fitness is determined as the global optimal solution.

[0126] Boundary processing: To process particles out of range, the usual method is to limit the position of particles out of range to within the boundary or reinitialize their position to a random value within the boundary.

[0127] The speed and position of the particles are updated through the speed and position update formulas, the fitness of the new particles is iteratively obtained and the local and global optimal solutions are updated.

[0128] The iteration and update process is as follows: Calculate the fitness of each new particle and compare it with the local optimal solution. If the fitness of the new particle is better than the current local optimal solution, update the local optimal solution. : Determine the new global optimal solution from all updated local optimal solutions.

[0129] Repeat the above steps of speed and position update, fitness calculation, local optimal solution and global optimal solution update until the stopping condition is met (such as reaching the maximum number of iterations or fitness value convergence). When the stopping condition is met, the iterative process is terminated and the final global optimal solution is obtained.

[0130] Through the above steps, the particle swarm optimization algorithm can effectively optimize the parameters of the flexible ball valve flow characteristic model. The algorithm optimizes the position and speed of particles through continuous iterations, gradually approaches the optimal solution, and thus improves the accuracy and reliability of the model.

[0131] Optionally, the speed and position update formulas are as follows:

[0132]

[0133] Among them, x j is the particle position, v j is the particle update speed, w is the inertia factor, c1 and c2 are acceleration constants; pbest j is the local optimal position, gbest jj is the global optimal position.

[0134] The process of substituting the self-excited vibration model based on the characteristic line method into the particle swarm algorithm for parameter optimization is as follows: Figure 4 As shown, the main steps are as follows:

[0135] Step 1: Generate particles randomly by setting appropriate system parameters and limiting the range of particles.

[0136] Step 2: Substitute each group of particles into the MOC program to obtain the simulation results, obtain monitoring data through the on-site status monitoring device, and calculate the fitness of each group of particles through the fitness function.

[0137] Step 3: Take the initial fitness as the local optimal solution of each particle and find the global optimal solution.

[0138] Step 4: Update the velocity and position of the particle through the velocity and position update formula. Particles out of range will be processed according to the boundary conditions.

[0139] Step 5: Calculate the fitness of each new particle and compare it with the local optimal solution to find a new local optimal solution, and then find the global optimal solution from all new local optimal solutions.

[0140] Step 6: Repeat steps 4 and 5 until the optimal solution is found.

[0141] After substituting the data into calculation, the characteristic parameters of the flexible valve are obtained as k1=0.0398 and k2=3.19·10-5.

[0142] The flow characteristic model of the flexible ball valve is established through the identified parameters, and the self-excited vibration simulation is performed to compare with the actual situation. The comparison between the simulation results after identification and the monitoring data is as follows: Figure 5 As shown in the figure, it can be seen that there is a small error between the optimized simulation results and the actual self-excited vibration process before 400s, and the development process after 400s is basically the same. The flow characteristic model of the flexible ball valve established by particle swarm algorithm identification is consistent with the actual situation.

[0143] Optionally, establishing a self-excited vibration model based on a characteristic line method and flexible valve characteristics includes:

[0144] Determine boundary point parameters by combining characteristic line equation with boundary conditions, and calculate other node parameters except boundary points by using the characteristic line equation;

[0145] Determine the flexible valve characteristics according to the relationship between the water leakage of the flexible valve and the pressure difference before and after the valve, and establish the self-excited vibration model based on the acquired boundary point parameters, other node parameters and the flexible valve characteristics;

[0146] Specifically, the present application is based on the basic water hammer equation, and converts the basic water hammer equation into a finite difference equation for calculation through the characteristic line method. The rectangular calculation network of the characteristic line method is as follows: Figure 6 As shown. The pipeline is divided into sections of length Δx, and time is divided into each time period Δt = Δx / a. + is the positive characteristic line, C - is a negative characteristic line, and i represents a pipeline node. The calculation starts from t = 0. The parameters of other nodes except the boundary points can be obtained by calculating the characteristic line equation, and the boundary points can be calculated by combining the characteristic line equation with the boundary conditions.

[0147] The characteristic line equation is shown in the following formula:

[0148] C + :H Pi =H i-1 +BQ i-1-RQ i-1 |Q i-1 |-BQ Pi (8)

[0149] C - :H Pi =H i+1 -BQ i+1 +RQ i+1 |Q i+1 |+BQ pi (9)

[0150] Among them, H Pi is the water head at the i-th node at time P, B is the pipeline parameter, R is the pipeline characteristic coefficient, Q Pi is the flow rate of the i-th node at time P;

[0151] The relationship between the water leakage of the flexible valve and the pressure difference before and after the valve is as shown in the following formula:

[0152]

[0153] Where Q is the water leakage, H is the pressure difference before and after the valve, H1 is the pressure difference between the closed cavity and the open cavity of the working seal, k1 and k2 are the flow characteristic parameters of the valve, D1 is the valve diameter, x0 is the sealing gap when the pressure difference before and after the valve is 0, ρ is the fluid density, g is the acceleration of gravity, A f is the flexible leakage area of ​​the valve, and K is the flexibility coefficient of the valve.

[0154] Reference Figure 7 , Figure 7 The present invention is a method for establishing a flow characteristic model of a flexible ball valve and a complete schematic diagram of its implementation process, which includes model analysis, physical system, mathematical model and iterative update.

[0155] Reference Figure 8 The present application also provides a device for establishing a flow characteristic model of a flexible ball valve, comprising:

[0156] The data acquisition module 810 is used to collect monitoring data of the flexible ball valve on site;

[0157] A simulation module 820 is used to establish a self-excited vibration model based on a characteristic line method and flexible valve characteristics, and to simulate pressure data through the self-excited vibration model to obtain simulation data;

[0158] The optimization module 830 is used to substitute the self-excited vibration model into the particle swarm algorithm for parameter identification, and perform particle iterative update based on the established fitness function until the particle optimal solution is obtained; the fitness function is established based on the monitoring data and the simulation data, and the fitness function is used to reflect the excellence of the flow characteristic parameters of the flexible ball valve;

[0159] The model building module 840 is used to build a flow characteristic model of the flexible ball valve using parameters corresponding to the particle optimal solution.

[0160] Optionally, a data processing module is further included, for:

[0161] Determine a maximum monitoring value and a minimum monitoring value of the monitoring data at the self-excited vibration time t, use the maximum monitoring value as an upper monitoring envelope point of the monitoring data, and use the minimum monitoring value as a lower monitoring envelope point;

[0162] Determining a monitoring data envelope curve according to the monitoring upper envelope point and the monitoring lower envelope point, and fitting the monitoring data envelope curve to obtain an optimization curve of the monitoring data;

[0163] Determine a simulation maximum value and a simulation minimum value of the simulation data at the self-excited vibration time t, use the simulation maximum value as a simulation upper envelope point of the simulation data, and use the simulation minimum value as a simulation lower envelope point;

[0164] A simulation data envelope curve is determined according to the simulation upper envelope point and the simulation lower envelope point, and the simulation data envelope curve is fitted to obtain an optimization curve of the simulation data.

[0165] Optionally, the method for establishing the fitness function includes:

[0166] Comparing the optimized monitoring data with the simulation data to obtain a consistency function between the optimized monitoring data and the simulation data;

[0167] Determining characteristic parameters and initial water leakage of the flexible valve, and determining limiting conditions of the initial water leakage in the simulation according to the characteristic parameters and the initial water leakage;

[0168] A penalty function is determined according to the restriction condition, and the fitness function is obtained by adding the consistency degree function and the penalty function.

[0169] Optionally, the self-excited vibration model is substituted into a particle swarm algorithm for parameter identification, and particles are iteratively updated based on an established fitness function until an optimal particle solution is obtained, including:

[0170] Determine system parameters and limit the range of particles to randomly generate particles;

[0171] Substitute each group of particles into the characteristic line method program to obtain simulation results, obtain monitoring data through the on-site status monitoring device, and calculate the initial fitness of each group of particles through the fitness function;

[0172] The initial fitness is taken as the local optimal solution of each particle, and the global optimal solution of the entire particle swarm is determined;

[0173] Iteratively update the velocity and position of particles based on the velocity and position update formulas, and process particles out of range according to boundary conditions;

[0174] Iteratively obtain the fitness of each new particle, compare the fitness of each new particle with the local optimal solution, determine an updated local optimal solution, and determine an updated global optimal solution from all updated local optimal solutions.

[0175] Optionally, the speed and position update formulas are as follows:

[0176]

[0177] Among them, x j is the particle position, v j is the particle update speed, w is the inertia factor, c1 and c2 are acceleration constants; pbest j is the local optimal position, gbest jj is the global optimal position.

[0178] Optionally, establishing a self-excited vibration model based on a characteristic line method and flexible valve characteristics includes:

[0179] Determine boundary point parameters by combining characteristic line equation with boundary conditions, and calculate other node parameters except boundary points by using the characteristic line equation;

[0180] Determine the flexible valve characteristics according to the relationship between the water leakage of the flexible valve and the pressure difference before and after the valve, and establish the self-excited vibration model based on the acquired boundary point parameters, other node parameters and the flexible valve characteristics;

[0181] The characteristic line equation is shown in the following formula:

[0182] C + :H Pi =H i-1 +BQ i-1 -RQ i-1 |Q i-1 |-BQ Pl

[0183] C - :H Pl =H i+1 -BQ l+1 +RQ i+1 |Q i+1 |+BQ pi

[0184] Among them, HPi is the water head at the i-th node at time P, B is the pipeline parameter, R is the pipeline characteristic coefficient, Q Pi is the flow rate of the i-th node at time P;

[0185] The relationship between the water leakage of the flexible valve and the pressure difference before and after the valve is shown in the following formula:

[0186]

[0187] Where Q is the water leakage, H is the pressure difference before and after the valve, H1 is the pressure difference between the closed cavity and the open cavity of the working seal, k1 and k2 are the flow characteristic parameters of the valve, D1 is the valve diameter, x0 is the sealing gap when the pressure difference before and after the valve is 0, ρ is the fluid density, g is the acceleration of gravity, A f is the flexible leakage area of ​​the valve, and K is the flexibility coefficient of the valve.

[0188] Optionally, the consistency function is as shown in the following formula:

[0189] F1(t)=∫0 600 t(|H m2 (t)-H m1 (t)|+|H n2 (t)-H n1 (t)|)dt

[0190] The penalty function is shown in the following formula:

[0191]

[0192] Among them, H m2 (t) is the upper envelope of the simulation results, H n2 (t) is the lower envelope of the simulation results; H m1 (t) is the package route of monitoring data, H n1 (t) is the lower envelope of the monitoring data, Q0 is the initial water leakage of the valve, H0 is the initial water head of the valve, and g is the acceleration of gravity.

[0193] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0194] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0195] Reference Fig. 9Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, which may include: a processor (processor) 910, a communication interface (Communications Interface) 920, a memory (memory) 930 and a communication bus 940, wherein the processor 910, the communication interface 920, and the memory 930 communicate with each other through the communication bus 940. The processor 910 can call the logic instructions in the memory 930 to execute the method in the above embodiment.

[0196] In addition, the logic instructions in the above-mentioned memory 930 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0197] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0198] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0199] It is understandable that the processor in the embodiment of the present application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0200] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0201] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0202] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0203] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for establishing a flow characteristic model of a flexible ball valve, characterized in that: include: Collect monitoring data of flexible ball valves on site; A self-excited vibration model is established based on the characteristic line method and the flexible valve characteristics, and pressure data simulation is performed through the self-excited vibration model to obtain simulation data; Substituting the self-excited vibration model into the particle swarm algorithm, and performing iterative particle updates based on the established fitness function until the optimal particle solution is obtained; The fitness function is established based on the monitoring data and the simulation data, and the fitness function is used to reflect the excellence of the flow characteristic parameters of the flexible ball valve; The flow characteristic model of the flexible ball valve is established using the parameters corresponding to the optimal solution of the particles.

2. The method for establishing a flow characteristic model of a flexible ball valve according to claim 1, characterized in that: After simulating the pressure data by the self-excited vibration model and obtaining the simulation data, the method further includes: Determine a maximum monitoring value and a minimum monitoring value of the monitoring data at the self-excited vibration time t, use the maximum monitoring value as an upper monitoring envelope point of the monitoring data, and use the minimum monitoring value as a lower monitoring envelope point; Determining a monitoring data envelope curve according to the monitoring upper envelope point and the monitoring lower envelope point, and fitting the monitoring data envelope curve to obtain an optimization curve of the monitoring data; Determine a simulation maximum value and a simulation minimum value of the simulation data at the self-excited vibration time t, use the simulation maximum value as a simulation upper envelope point of the simulation data, and use the simulation minimum value as a simulation lower envelope point; A simulation data envelope curve is determined according to the simulation upper envelope point and the simulation lower envelope point, and the simulation data envelope curve is fitted to obtain an optimization curve of the simulation data.

3. The method for establishing a flow characteristic model of a flexible ball valve according to claim 2, characterized in that: The method for establishing the fitness function comprises: Comparing the optimized monitoring data with the simulation data to obtain a consistency function between the optimized monitoring data and the simulation data; Determining characteristic parameters and initial water leakage of the flexible valve, and determining limiting conditions of the initial water leakage in the simulation according to the characteristic parameters and the initial water leakage; A penalty function is determined according to the restriction condition, and the fitness function is obtained by adding the consistency degree function and the penalty function.

4. The method for establishing a flow characteristic model of a flexible ball valve according to claim 1, characterized in that: Substituting the self-excited vibration model into the particle swarm algorithm for parameter identification, and performing particle iterative updating based on the established fitness function until the optimal particle solution is obtained, includes: Determine system parameters and limit the range of particles to randomly generate particles; Substitute each group of particles into the characteristic line method program to obtain simulation results, obtain monitoring data through the on-site status monitoring device, and calculate the initial fitness of each group of particles through the fitness function; The initial fitness is taken as the local optimal solution of each particle, and the global optimal solution of the entire particle swarm is determined; Iteratively update the velocity and position of particles based on the velocity and position update formulas, and process particles out of range according to boundary conditions; Iteratively obtain the fitness of each new particle, compare the fitness of each new particle with the local optimal solution, determine an updated local optimal solution, and determine an updated global optimal solution from all updated local optimal solutions.

5. The method for establishing a flow characteristic model of a flexible ball valve according to claim 4, characterized in that: The velocity and position update formulas are as follows: Among them, x j is the particle position, v j is the particle update speed, w is the inertia factor, c1 and c2 are acceleration constants; pbest j is the local optimal position, gbest jj is the global optimal position.

6. The method for establishing a flow characteristic model of a flexible ball valve according to claim 1, characterized in that: The self-excited vibration model is established based on the characteristic line method and the flexible valve characteristics, including: Determine boundary point parameters by combining characteristic line equation with boundary conditions, and calculate other node parameters except boundary points by using the characteristic line equation; Determine the flexible valve characteristics according to the relationship between the water leakage of the flexible valve and the pressure difference before and after the valve, and establish the self-excited vibration model based on the acquired boundary point parameters, other node parameters and the flexible valve characteristics; The characteristic line equation is shown in the following formula: C + :H Pi =H i-1 +BQ i-1 -RQ i-1 |Q i-1 |-BQ Pi C - :H Pi =H i+1 -BQ i+1 +RQ i+1 |Q i+1 |+BQ pi Among them, HP i is the water head at the i-th node at time P, B is the pipeline parameter, R is the pipeline characteristic coefficient, Q Pi is the flow rate of the i-th node at time P; The relationship between the water leakage of the flexible valve and the pressure difference before and after the valve is as shown in the following formula: Where Q is the water leakage, H is the pressure difference before and after the valve, H1 is the pressure difference between the closed cavity and the open cavity of the working seal, k1 and k2 are the flow characteristic parameters of the valve, D1 is the valve diameter, x0 is the sealing gap when the pressure difference before and after the valve is 0, ρ is the fluid density, g is the acceleration of gravity, A f is the flexible leakage area of ​​the valve, and K is the flexibility coefficient of the valve.

7. The method for establishing a flow characteristic model of a flexible ball valve according to claim 3, characterized in that: The consistency function is shown in the following formula: The penalty function is shown in the following formula: Among them, H m2 (t) is the upper envelope of the simulation results, H n2 (t) is the lower envelope of the simulation results; H m1 (t) is the package route of monitoring data, H n1 (t) is the lower envelope of the monitoring data, Q0 is the initial water leakage of the valve, H0 is the initial water head of the valve, and g is the acceleration of gravity.

8. A device for establishing a flow characteristic model of a flexible ball valve, characterized in that: include: Data acquisition module, used to collect monitoring data of on-site flexible ball valves; A simulation module, used for establishing a self-excited vibration model based on a characteristic line method and flexible valve characteristics, and performing pressure data simulation through the self-excited vibration model to obtain simulation data; An optimization module, used for substituting the self-excited vibration model into a particle swarm algorithm for parameter identification, and performing particle iterative update based on an established fitness function until an optimal particle solution is obtained; the fitness function is established based on monitoring data and the simulation data, and the fitness function is used to reflect the excellence of the flow characteristic parameters of the flexible ball valve; The model building module is used to build a flow characteristic model of a flexible ball valve using parameters corresponding to the particle optimal solution.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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