A method for determining a pipe leakage point based on dimensional analysis and CFD simulation

Abstract

The application discloses a method for determining a pipeline leakage point based on dimension analysis and CFD simulation, relates to the technical field of pipeline detection, and can solve the problems of high pipeline detection cost and long time consumption. The specific scheme comprises the following steps: obtaining a plurality of physical variables affecting pipeline leakage behavior, the physical variables comprising influence factor variables and result variables, performing dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group; obtaining a historical data set through CFD simulation, the historical data set storing a mapping relationship between parameter values corresponding to the influence factor variables and parameter values corresponding to the result variables; generating a prediction model by using a training data set and the dimensionless relationship group; obtaining acquisition data of a current pipeline, the acquisition data comprising a pipeline leakage rate value, a pipeline fluid density value, a pipeline fluid flow rate value, a pipeline diameter value, and a pressure change value between a pipeline inlet and an outlet; and inputting the acquisition data into the prediction model to predict a pipeline leakage position.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline detection, and in particular to a method for determining a pipeline leakage point based on dimensional analysis and CFD simulation. BACKGROUND

[0002] Pipeline transportation is an important delivery method for combustible gas in a coal-to-olefin production process. During the transmission process, some old pipelines may leak due to design and construction defects, long-term operation, aging, and quality changes, causing damage to the environment. If the combustible gas encounters high-temperature flames, it may even burn or explode, seriously threatening the safety of personnel during the production process. Therefore, it is necessary to monitor pipeline leakage, promptly identify leakage points and solve hidden dangers, which not only reduces product loss, but also helps improve process safety. Traditional pipeline leakage technology relies on physical detection methods based on sensor detection, but such physical detection methods have the problems of high cost and long time consumption. SUMMARY

[0003] The present application provides a method for determining a pipeline leakage point based on dimensional analysis and CFD simulation, which can solve the problems of high cost and long time consumption in pipeline detection.

[0004] To achieve the above purpose, the present application adopts the following technical solutions:

[0005] In a first aspect, the present application provides a method for determining a pipeline leakage point based on dimensional analysis and CFD simulation, which comprises:

[0006] Obtaining a plurality of physical variables affecting pipeline leakage behavior, the physical variables including influence factor variables and result variables, the influence factor variables including pipeline leakage rate, pipeline fluid density, pipeline diameter, pipeline fluid flow rate, and distance between the leakage point and the pipeline outlet, and the result variables including pressure change between the pipeline inlet and outlet;

[0007] Non-dimensionalizing the plurality of physical variables to obtain a non-dimensional relationship group;

[0008] Obtaining a historical data set, the historical data set storing a mapping relationship between parameter values corresponding to the influence factor variables and parameter values corresponding to the result variables, the historical data set being divided into a training data set and a validation data set;

[0009] Generating a prediction model using the training data set and the non-dimensional relationship group;

[0010] Obtaining collection data of the current pipeline, the collection data including pipeline leakage rate value, pipeline fluid density value, pipeline fluid flow rate value, pipeline diameter value, and pressure change value between the pipeline inlet and outlet;

[0011] inputting the collected data into the prediction model to predict a leakage position of the pipeline.

[0012] As a possible implementation manner, the dimensionless processing on the plurality of physical variables to obtain the dimensionless relation group comprises:

[0013] selecting a target variable for dimensionless processing from the plurality of physical variables;

[0014] determining a basic variable from the plurality of physical variables;

[0015] constructing the dimensionless relation group according to the target variable and the basic variable.

[0016] As a possible implementation manner, the target variable comprises the pipeline leakage rate, the distance and the pressure change; the basic variable comprises the fluid density, the fluid flow rate and the pipeline diameter; and the constructing the dimensionless relation group according to the target variable and the basic variable comprises:

[0017] dimensionless processing on the pipeline leakage rate to form a first dimensionless relation based on the pipeline leakage rate, the fluid density, the fluid flow rate and the pipeline diameter;

[0018] dimensionless processing on the distance to form a second dimensionless relation based on the distance and the pipeline diameter;

[0019] dimensionless processing on the pressure change to form a third dimensionless relation based on the pressure change, the fluid density and the fluid flow rate.

[0020] As a possible implementation manner, the generating a prediction model by combining the training data set and the dimensionless relation group comprises:

[0021] inputting the pressure change value under different influence factor variable values in the training data set into a corresponding dimensionless relation to obtain a plurality of corresponding first conversion values, second conversion values and third conversion values;

[0022] determining the prediction model by using a plurality of corresponding first conversion values, second conversion values and third conversion values.

[0023] As a possible implementation manner, after the generating a prediction model by combining the training data set and the dimensionless relation group, the method further comprises:

[0024] Parameter optimization is performed on the prediction model by using the verification data set until the prediction accuracy of the prediction model reaches a preset accuracy, and a target prediction model is obtained;

[0025] The inputting of the collected data into the prediction model includes:

[0026] The collected data is input into the target prediction model.

[0027] As a possible implementation manner, the historical data set includes:

[0028] Simulation models of multiple different pipeline leakage scenarios are established, including different leakage hole diameters and leakage points;

[0029] The simulation models of the multiple different pipeline leakage scenarios are simulated by using a CFD calculation method, and pressure change values of each pipeline under different influence factor variable values are obtained.

[0030] The pressure change values of each pipeline under different influence factor variable values are mapped and stored, and the historical data set is obtained.

[0031] As a possible implementation manner, the historical technical set includes:

[0032] Pressure change values under different influence factor variable values obtained by using multiple sensors to test multiple different pipelines under different leakage scenarios at a historical time are obtained.

[0033] The pressure change values under different influence factor variable values are mapped and stored, and the historical data set is obtained.

[0034] In a second aspect, the application provides a device for determining a pipeline leakage point based on dimensional analysis and CFD simulation, which comprises:

[0035] A first obtaining module is configured to obtain multiple physical variables affecting pipeline leakage behavior, wherein the physical variables include influence factor variables and result variables, the influence factor variables include a pipeline leakage rate, a pipeline fluid density, a pipeline diameter, a pipeline fluid flow rate, and a distance between a leakage point and a pipeline outlet, and the result variables include a pressure change between a pipeline inlet and an outlet;

[0036] A first processing module is configured to perform dimensionless processing on the multiple physical variables to obtain a dimensionless relationship group.

[0037] A second obtaining module is configured to obtain a historical data set, wherein the historical data set stores a mapping relationship between parameter values of the influence factor variables and parameter values of the result variables, and the historical data set is divided into a training data set and a verification data set.

[0038] a generating module configured to generate a prediction model by using the training data set and the dimensionless relation set;

[0039] a third obtaining module configured to obtain acquisition data of a current pipeline, the acquisition data comprising a pipeline leakage rate value, a fluid density value in the pipeline, a fluid flow rate value in the pipeline, a pipeline diameter value, and a pressure change value between a pipeline inlet and an outlet;

[0040] a prediction module configured to input the acquisition data into the prediction model to predict a leakage position of the pipeline.

[0041] In a third aspect, an electronic device is provided, which comprises a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the method for determining a pipeline leakage position based on dimension analysis and CFD simulation in the first aspect is implemented.

[0042] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the method for determining a pipeline leakage position based on dimension analysis and CFD simulation in the first aspect is implemented.

[0043] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0044] The method for determining a pipeline leakage point based on dimensional analysis and CFD simulation provided by the embodiment of the application comprises the following steps: obtaining a plurality of physical variables affecting pipeline leakage behavior, wherein the physical variables comprise influence factor variables and result variables, the influence factor variables comprise a pipeline leakage rate, a pipeline fluid density, a pipeline diameter, a pipeline fluid flow rate, and a distance between a leakage point and a pipeline outlet, and the result variables comprise a pressure change between a pipeline inlet and an outlet; performing dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group; obtaining a historical data set, wherein the historical data set stores a mapping relationship between parameter values of the influence factor variables and parameter values of the result variables, and the historical data set is divided into a training data set and a verification data set; generating a prediction model by using the training data set and the dimensionless relationship group; obtaining collected data of a current pipeline, wherein the collected data comprises a pipeline leakage rate value, a pipeline fluid density value, a pipeline fluid flow rate value, a pipeline diameter value, and a pressure change value between a pipeline inlet and an outlet; and inputting the collected data into the prediction model to predict a leakage position of the pipeline. In the process of predicting the leakage point by using the dimensional analysis and the CFD simulation method, the dimensionless processing can convert a plurality of physical variables into a few key dimensionless variables, so that the generation efficiency of the prediction model is improved, and the training data set and the key dimensionless relationship group of the prediction model are generated by using the CFD simulation, so that the leakage point is predicted. This method not only improves the prediction accuracy, but also shortens the calculation time, so that a quick response can be made in an emergency. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A flowchart of the method for determining a pipeline leakage point based on dimensional analysis and CFD simulation provided by the embodiment of the application is shown in the figure.

[0046] Figure 2 A structural diagram of the device for determining a pipeline leakage point based on dimensional analysis and CFD simulation provided by the embodiment of the application is shown in the figure.

[0047] Figure 3 A structural diagram of an electronic device provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0049] Hereinafter, the terms "first", "second", "third", etc. are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implying the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0050] In addition, the use of "based on" or "according to" means open and inclusive, because the process, step, calculation or other action "based on" or "according to" one or more conditions or values can be based on additional conditions or values in practice.

[0051] The embodiments of the present application provide a method for determining a pipeline leakage point based on dimensional analysis and CFD simulation, which comprises:

[0052] Step 101, obtaining a plurality of physical variables affecting the pipeline leakage behavior.

[0053] Among them, the physical variables include influence factor variables and result variables, the influence factor variables include pipeline leakage rate, pipeline fluid density, pipeline diameter, pipeline fluid flow rate, and the distance between the leakage point and the pipeline outlet, and the result variables include the pressure change between the pipeline inlet and the outlet.

[0054] Among them, the pressure change between the pipeline inlet and the outlet can be the pressure difference between the first detection point at the pipeline inlet and the second detection point at the pipeline outlet.

[0055] It should be noted that the physical variables obtained here only determine the physical variables affecting the pipeline leakage behavior, rather than obtaining the numerical values corresponding to these physical variables.

[0056] Step 102, dimensionless processing is performed on the plurality of physical variables to obtain a dimensionless relationship group.

[0057] Through dimensionless processing, a plurality of physical variables can be converted into a few key dimensionless variables, which can improve the generation efficiency of the prediction model.

[0058] Step 103, obtaining a historical data set, the historical data set stores a mapping relationship between the parameter values corresponding to the influence factor variables and the parameter values corresponding to the result variables, and the historical data set is divided into a training data set and a verification data set.

[0059] It should be noted that the historical data set stores the mapping relationship between the parameter values corresponding to the influence factor variables and the parameter values corresponding to the result variables collected at the historical time. The mapping relationship stored here is the variable and the data value corresponding to the variable, rather than only the variable.

[0060] Step 104, generating a prediction model by using the training data set and the dimensionless relationship set.

[0061] Step 105, acquiring acquisition data of the current pipeline, the acquisition data including: a pipeline leakage rate value, a pipeline fluid density value, a pipeline fluid flow rate value, a pipeline diameter value, and a pressure change value between a pipeline inlet and an outlet.

[0062] The pipeline diameter value can be directly measured. Taking the fluid in the pipeline as gas as an example, the pipeline fluid density can be calculated according to the gas pressure, temperature and molar mass of the gas in the pipeline, wherein the gas pressure can be detected by using a pressure detector.

[0063] The pipeline fluid flow rate value can be detected by using a gas detector. In addition, the gas detector can also detect the gas flow rate and gas concentration in the pipeline, and in the case of knowing the approximate diameter range of the leakage hole, the pipeline leakage rate can be calculated.

[0064] Step 106, inputting the acquisition data into the prediction model to predict the leakage position of the pipeline.

[0065] It should be noted that the present application can execute steps 101 and 102 first and then execute step 103, or the step 103 of the present application can be executed in parallel with the process of steps 101 and 102, and the present application does not make specific limitations thereto.

[0066] The method for determining a pipeline leakage point based on dimensional analysis and CFD simulation provided by the embodiment of the present application comprises the following steps: obtaining a plurality of physical variables affecting pipeline leakage behavior, wherein the physical variables comprise influencing factor variables and result variables, the influencing factor variables comprise a pipeline leakage rate, a fluid density in a pipeline, a pipeline diameter, a fluid flow rate in the pipeline, and a distance between a leakage point and a pipeline outlet, and the result variables comprise a pressure change between a pipeline inlet and an outlet; performing dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group; obtaining a historical data set, wherein the historical data set stores a mapping relationship between parameter values corresponding to the influencing factor variables and parameter values corresponding to the result variables, and the historical data set is divided into a training data set and a verification data set; generating a prediction model by using the training data set and the dimensionless relationship group; obtaining collected data of a current pipeline, wherein the collected data comprises a pipeline leakage rate value, a fluid density value in the pipeline, a fluid flow rate value in the pipeline, a pipeline diameter value, and a pressure change value between the pipeline inlet and the outlet; and inputting the collected data into the prediction model to predict a leakage position of the pipeline. The method for predicting a leakage point position by using dimensional analysis and combining CFD simulation, the dimensionless processing can convert a plurality of physical variables into a few key dimensionless variables, which can improve the generation efficiency of the prediction model, and the training data set and the key dimensionless relationship group of the prediction model are generated by using CFD simulation, so as to predict the leakage point position. The method can not only improve the prediction accuracy, but also shorten the calculation time, so that the method can be quickly responded in an emergency situation.

[0067] Optionally, the process of performing dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group in the step 102 can be:

[0068] selecting a target variable for dimensionless processing from the plurality of physical variables; determining a basic variable from the plurality of physical variables; and constructing the dimensionless relationship group according to the target variable and the basic variable.

[0069] Optionally, the target variable can comprise the pipeline leakage rate, the distance, and the pressure change; the basic variable can comprise the fluid density, the fluid flow rate, and the pipeline diameter; and the process of constructing the dimensionless relationship group according to the target variable and the basic variable can be:

[0070] performing dimensionless processing on the pipeline leakage rate to form a first dimensionless relationship based on the pipeline leakage rate, the fluid density, the fluid flow rate, and the pipeline diameter;

[0071] performing dimensionless processing on the distance to form a second dimensionless relationship based on the distance and the pipeline diameter;

[0072] The pressure change is dimensionless processed to form a third dimensionless relationship based on the pressure change, the fluid density, and the fluid flow rate.

[0073] Specifically, after obtaining the target variables and the basic variables, a dimensionless group can be constructed using the Buckingham Pi theorem.

[0074] The first dimensionless relationship can be:

[0075] The second dimensionless relationship can be:

[0076] The third dimensionless relationship can be:

[0077] The above, is the pipeline leakage rate, p is the fluid density in the pipeline, u is the fluid flow rate in the pipeline, D is the pipeline diameter, L is the distance between the leakage point and the pipeline outlet, and P is the pressure change between two different detection points on the pipeline.

[0078] It can be understood that the dimensionless processing can convert a plurality of physical variables into a few key dimensionless variables, namely pi1, pi2, and pi3.

[0079] Optionally, the process of generating a prediction model using the training data set and the dimensionless relationship group in step 104 can be:

[0080] The pressure change values in the training data set under different influence factor variable values are input into the corresponding dimensionless relationship to obtain a plurality of corresponding first conversion values, second conversion values, and third conversion values; and the prediction model is determined using a plurality of corresponding first conversion values, second conversion values, and third conversion values.

[0081] The first conversion value is pi1, the second conversion value is pi2, and the third conversion value is pi3. In actual execution, the data in the training data set can be input into the first dimensionless relationship, the second dimensionless relationship, and the third dimensionless relationship to obtain corresponding first conversion values, second conversion values, and third conversion values.

[0082] It should be noted that the prediction model is a prediction function fitted based on a plurality of pi1, pi2, and pi3 data.

[0083] Optionally, after the step 104 of generating the prediction model, the method further comprises: performing parameter optimization on the prediction model by using the verification data set until the prediction accuracy of the prediction model reaches a preset accuracy, to obtain a target prediction model; and the inputting the collected data into the prediction model comprises: inputting the collected data into the target prediction model.

[0084] In actual execution, the process of performing parameter optimization on the prediction model by using the verification data set can be: using the verification data set to determine and optimize other parameters in the prediction function by using a fit tool in ORIGIN or a fit function in Python, to improve the fitting accuracy and further verify the accuracy of the prediction function, and to adjust other parameters of the prediction function as needed.

[0085] In addition, the process of performing parameter optimization on the prediction model by using the verification data set can also be: integrating data in the historical data set into a mathematical formula by using a neural network method. First, pre-process the data in the historical data set, including normalization, denoising, removing outliers, etc. Select a feedforward neural network, determine the number of neurons in the input layer, hidden layer and output layer, and use the training data set to train the neural network. Use the verification data set to evaluate the performance of the neural network, adjust hyperparameters (such as learning rate, number of hidden layers, etc.) to avoid overfitting or underfitting. Use the verification data set to finally evaluate the generalization ability of the neural network. Finally, use polynomial regression, support vector machine (SVM), etc. to obtain the prediction model.

[0086] As a possible implementation manner, the step 103 of obtaining the historical data set can be: establishing simulation models of a plurality of different pipeline leakage scenarios;

[0087] performing simulation calculation on the simulation models of the plurality of different pipeline leakage scenarios based on a CFD calculation method, to obtain pressure change values of each pipeline under different influence factor variable values;

[0088] mapping and storing the pressure change values of each pipeline under different influence factor variable values, to obtain the historical data set.

[0089] Computational Fluid Dynamics (CFD) is equivalent to "virtually" doing experiments on a computer to simulate actual fluid flow conditions. Its basic principle is to numerically solve the differential equations governing fluid flow to obtain the discrete distribution of the flow field of the fluid flow in the continuous region, thereby approximating the fluid flow conditions. It can be considered as a kind of modern simulation technology.

[0090] In actual execution, a pipeline leakage position model can be established, a test working condition is designed according to a Latin hypercube sampling design method, and a pressure change value of the pipeline under different influence factor variable values in different pipeline leakage scenarios is obtained through simulation calculation using the fluent software, so as to obtain the historical data set. In this way, the efficiency of obtaining the historical data set can be improved.

[0091] As another possible implementation, the step 103 of obtaining the historical data set can further be:

[0092] obtaining pressure change values of different influence factor variable values obtained by using a plurality of sensors to test a plurality of different pipelines in different leakage scenarios at a historical time; and mapping and storing the pressure change values of different influence factor variable values to obtain the historical data set.

[0093] The method for determining a pipeline leakage point provided by the embodiments of the present application applies the dimensional analysis method to the deduction process of the leakage point, which can help researchers quickly identify key parameters in the leakage process, such as the leakage rate, the leakage position and the fluid properties, in a plurality of physical quantities, greatly simplifying the real-time detection of the leakage position to optimize the layout of the pipeline sensors. The embodiments of the present application convert specific physical parameters (such as the pressure change, the leakage rate and the leakage position) into dimensionless forms, thereby developing a mathematical model between the pressure change and the leakage position and the leakage rate, and associating the dimensionless variables with the leakage position. The dimensionless parameters converted by the dimensional analysis can effectively simplify the mathematical model, can eliminate the influence of the units of physical quantities by the method of dimensionless, so that the model result is more general and is not affected by the specific measurement units, and can be more easily applied in different pipeline systems and conditions. Several unknown parameters contained in the model need to be optimized by changing the pressure change under the dimensionless leakage position and the dimensionless leakage rate through CFD simulation, building a database, and fitting the unknown parameters in the mathematical model through the fit tool in ORIGIN. By measuring the pressure change and the leakage rate of the pipeline, the leakage position can be detected in real time, thereby improving the efficiency and accuracy of the pipeline safety management.

[0094] The embodiments of the present application also provide a device for determining a pipeline leakage point based on dimensional analysis and CFD simulation, as shown in Figure 2 The device comprises:

[0095] A first obtaining module 11 is configured to obtain a plurality of physical variables affecting the pipeline leakage behavior, wherein the physical variables include influence factor variables and result variables, the influence factor variables include a pipeline leakage rate, a pipeline fluid density, a pipeline diameter, a pipeline fluid flow rate, and a distance between a leakage point and a pipeline outlet, and the result variables include a pressure change between a pipeline inlet and an outlet.

[0096] The first processing module 12 is configured to perform dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group.

[0097] The second obtaining module 13 is configured to obtain a historical data set, wherein the historical data set stores a mapping relationship between a parameter value corresponding to the influence factor variable and a parameter value corresponding to the result variable, and the historical data set is divided into a training data set and a verification data set.

[0098] The generating module 14 is configured to generate a prediction model by using the training data set and the dimensionless relationship group.

[0099] The third obtaining module 15 is configured to obtain collected data of a current pipeline, wherein the collected data includes a pipeline leakage rate value, a fluid density value in the pipeline, a fluid flow rate value in the pipeline, a pipeline diameter value, and a pressure change value between a pipeline inlet and an outlet.

[0100] The prediction module 16 is configured to input the collected data into the prediction model to predict a leakage position of the pipeline.

[0101] In an embodiment, the first processing module 12 is specifically configured to:

[0102] select a target variable for dimensionless processing from the plurality of physical variables;

[0103] determine a basic variable from the plurality of physical variables;

[0104] construct the dimensionless relationship group according to the target variable and the basic variable.

[0105] In an embodiment, the target variable includes the pipeline leakage rate, the distance, and the pressure change; the basic variable includes the fluid density, the fluid flow rate, and the pipeline diameter; and the first processing module 12 is specifically configured to:

[0106] perform dimensionless processing on the pipeline leakage rate to form a first dimensionless relationship based on the pipeline leakage rate, the fluid density, the fluid flow rate, and the pipeline diameter;

[0107] perform dimensionless processing on the distance to form a second dimensionless relationship based on the distance and the pipeline diameter;

[0108] perform dimensionless processing on the pressure change to form a third dimensionless relationship based on the pressure change, the fluid density, and the fluid flow rate.

[0109] In an embodiment, the generating module 14 is specifically configured to:

[0110] inputting the pressure change values under different influence factor variable values in the training data set into corresponding dimensionless relationships to obtain a plurality of corresponding first conversion values, second conversion values and third conversion values;

[0111] determining the prediction model by using the plurality of corresponding first conversion values, second conversion values and third conversion values.

[0112] In an embodiment, the generating module 14 is further configured to:

[0113] performing parameter optimization on the prediction model by using the verification data set until the prediction accuracy of the prediction model reaches a preset accuracy, to obtain a target prediction model;

[0114] The prediction module 16 is further configured to input the collected data into the target prediction model.

[0115] In an embodiment, the third obtaining module 15 is specifically configured to:

[0116] establishing simulation models of a plurality of different pipeline leakage scenarios;

[0117] performing simulation calculation on the simulation models of the plurality of different pipeline leakage scenarios based on a CFD calculation method to obtain pressure change values of each pipeline under different influence factor variable values;

[0118] mapping and storing the pressure change values of each pipeline under different influence factor variable values to obtain the historical data set.

[0119] In an embodiment, the third obtaining module 15 is specifically configured to:

[0120] obtaining pressure change values under different influence factor variable values obtained by using a plurality of sensors to test a plurality of different pipelines under different leakage scenarios at a historical time;

[0121] mapping and storing the pressure change values under different influence factor variable values to obtain the historical data set.

[0122] The device for determining a pipeline leakage point based on dimension analysis and CFD simulation provided in the embodiments of the present application can execute the method embodiments for determining a pipeline leakage point based on dimension analysis and CFD simulation described above, and has similar implementation principles and technical effects, which will not be described in more detail here.

[0123] Regarding the specific limitations of the device for determining pipeline leakage points based on dimensional analysis and CFD simulation, please refer to the limitations of the method for determining pipeline leakage points based on dimensional analysis and CFD simulation above, and will not be repeated here. The various modules in the above-mentioned device for determining pipeline leakage points based on dimensional analysis and CFD simulation can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor of the electronic device in hardware form, or can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0124] The executor of the method for determining pipeline leakage points based on dimensional analysis and CFD simulation provided in the embodiment of the present application can be an electronic device, which may include a server, a server cluster, a computer device, a terminal device or a processing chip, etc. The embodiment of the present application does not make specific limitations on this.

[0125] Figure 3 This is a schematic diagram of the internal structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device includes a processor and memory connected via a system bus. The processor is used to provide computing and control capabilities. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by the processor to implement the steps of the method for determining pipeline leak points based on dimensional analysis and CFD simulation provided in each of the above embodiments. The internal memory provides a cached operating environment for the operating system and computer program stored in the non-volatile storage medium.

[0126] Those skilled in the art will understand that Figure 3 The internal structure diagram of the electronic device shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In another embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of determining the pipeline leakage point based on dimensional analysis and CFD simulation as in the embodiment of the present application are implemented.

[0128] In another embodiment of the present application, a computer program product is also provided, which includes computer instructions, when the computer instructions are executed on the device for determining the pipeline leakage point based on the dimensional analysis and the CFD simulation, cause the device for determining the pipeline leakage point based on the dimensional analysis and the CFD simulation to perform each step of the method for determining the pipeline leakage point based on the dimensional analysis and the CFD simulation in the method flow shown in the method embodiment.

[0129] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or data storage device including one or more servers, data centers, etc. integrated with the medium. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0130] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0131] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for determining pipeline leakage points based on dimensional analysis and CFD simulation, characterized in that: The method comprises: Acquire multiple physical variables that affect pipeline leakage behavior, the physical variables including influencing factor variables and result variables, the influencing factor variables including: pipeline leakage rate, density of fluid in the pipeline, pipeline diameter, fluid flow rate in the pipeline, and distance between the leakage point and the pipeline outlet, the result variables including: pressure change between the pipeline inlet and outlet; The plurality of physical variables are dimensionally non-dimensionalized to obtain a dimensionless relationship group, including: Selecting a target variable to be dimensionless from the plurality of physical variables; determining a basic variable from the plurality of physical variables; Constructing the dimensionless relationship group according to the target variable and the basic variables; The target variables include: the pipeline leakage rate, the distance, and the pressure change; the basic variables include: the fluid density, the fluid flow rate, and the pipeline diameter; the dimensionless relationship group constructed based on the target variables and the basic variables includes: Performing dimensionless processing on the pipeline leakage rate to form a first dimensionless relationship based on the pipeline leakage rate, the fluid density, the fluid flow rate, and the pipeline diameter; performing dimensionless processing on the distance to form a second dimensionless relationship based on the distance and the pipe diameter; performing dimensionless processing on the pressure change to form a third dimensionless relationship based on the pressure change, the fluid density, and the fluid flow rate; Acquire a historical data set, wherein the historical data set stores a mapping relationship between parameter values ​​corresponding to the influencing factor variables and parameter values ​​corresponding to the result variables, and the historical data set is divided into a training data set and a validation data set; generating a prediction model using the training data set and the dimensionless relationship group; Acquire the collected data of the current pipeline, wherein the collected data includes: pipeline leakage rate value, pipeline fluid density value, pipeline fluid flow rate value, pipeline diameter value, and pressure change value between pipeline inlet and outlet; The collected data is input into the prediction model to predict the leakage location of the pipeline.

2. The method according to claim 1, characterized in that The generating of a prediction model by utilizing the training data set and the dimensionless relationship combination includes: Inputting the pressure change values ​​under different influencing factor variable values ​​in the training data set into the corresponding dimensionless relationship to obtain a plurality of corresponding first conversion values, second conversion values, and third conversion values; The prediction model is determined using a plurality of corresponding first conversion values, second conversion values, and third conversion values.

3. The method according to claim 1, characterized in that After generating a prediction model by combining the training data set and the dimensionless relationship, the method further includes: Optimizing the parameters of the prediction model using the validation data set until the prediction accuracy of the prediction model reaches a preset accuracy, thereby obtaining a target prediction model; Inputting the collected data into the prediction model comprises: The collected data is input into the target prediction model.

4. The method according to claim 1, wherein The obtaining of the historical data set includes: Establish simulation models for multiple different pipeline leakage scenarios; Performing simulation calculations on the simulation models of the multiple different pipeline leakage scenarios based on a CFD calculation method to obtain pressure change values ​​of the pipeline under different influencing factor variable values; The pressure change values ​​of the pipeline under different influencing factor variable values ​​are mapped and stored to obtain the historical data set.

5. The method according to claim 1, wherein The obtaining of the historical data set includes: Obtain pressure change values ​​under different influencing factor variable values ​​obtained by using multiple sensors to test multiple pipelines in different leakage scenarios at historical times; The pressure change values ​​under different influencing factor variable values ​​are mapped and stored to obtain the historical data set.

6. A device for determining pipeline leakage points based on dimensional analysis and CFD simulation, characterized in that: The device comprises: A first acquisition module is configured to acquire a plurality of physical variables that affect pipeline leakage behavior, wherein the physical variables include influencing factor variables and result variables, wherein the influencing factor variables include: pipeline leakage rate, density of fluid in the pipeline, pipeline diameter, flow velocity of fluid in the pipeline, and distance between the leakage point and the pipeline outlet; and the result variables include: pressure change value between the pipeline inlet and outlet; A first processing module is configured to perform dimensionless processing on the plurality of physical variables to obtain a dimensionless relationship group, the steps comprising: selecting a target variable to be dimensionless from the plurality of physical variables; determining a basic variable from the plurality of physical variables; and constructing the dimensionless relationship group based on the target variable and the basic variables; The target variables include: the pipeline leakage rate, the distance, and the pressure change; the basic variables include: the fluid density, the fluid flow rate, and the pipeline diameter; constructing the dimensionless relationship group based on the target variables and the basic variables includes: dimensionally converting the pipeline leakage rate to form a first dimensionless relationship based on the pipeline leakage rate, the fluid density, the fluid flow rate, and the pipeline diameter; dimensionally converting the distance to form a second dimensionless relationship based on the distance and the pipeline diameter; and dimensionally converting the pressure change to form a third dimensionless relationship based on the pressure change, the fluid density, and the fluid flow rate. A second acquisition module is used to acquire a historical data set, wherein the historical data set stores a mapping relationship between parameter values ​​corresponding to the influencing factor variables and parameter values ​​corresponding to the result variables, and the historical data set is divided into a training data set and a validation data set; A generation module, configured to generate a prediction model using the training data set and the dimensionless relationship group; The third acquisition module is used to acquire the collected data of the current pipeline, wherein the collected data includes: pipeline leakage rate value, pipeline fluid density value, pipeline fluid flow rate value, pipeline diameter value, and pressure change value between pipeline inlet and outlet; The prediction module is used to input the collected data into the prediction model to predict the leakage position of the pipeline.

7. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for determining the pipeline leakage point based on dimensional analysis and CFD simulation according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the method for determining pipeline leakage points based on dimensional analysis and CFD simulation as described in any one of claims 1 to 5.

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