Construction operation method, unit and platform of digital twinborn body of pipeline equipment

Abstract

The invention provides a construction and operation method, unit and platform for digital twins of pipeline equipment, and belongs to the technical field of intelligent operation of oil and gas pipe networks. Comprising the steps of obtaining static data of pipeline equipment, and constructing a pipeline equipment geometric model; meshing division is carried out on the pipeline equipment geometric model, physical attributes and loading boundary conditions are given, and a pipeline equipment simulation model is obtained; generating multiple groups of training data by utilizing a Latin hypercube sampling method based on the static data; according to the multiple groups of training data, the pipeline equipment simulation model is compressed based on a singular value decomposition method, and a pipeline equipment reduced-order model is obtained; dynamic data of the pipeline equipment are obtained, the pipeline equipment order reduction model is input, the pipeline equipment order reduction model is solved, and a solving result is obtained and serves as an operation result of the digital twin of the pipeline equipment. By means of twin mapping of the pipeline, the performance of the pipeline equipment is monitored, predicted and evaluated, safe operation of the equipment is guaranteed, and data is provided for structural optimization of the pipeline equipment.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation technology for oil and gas pipeline networks, specifically to a method for constructing and operating a digital twin of pipeline equipment, a unit for constructing and operating a digital twin of pipeline equipment, a platform for a digital twin of pipeline equipment, an electronic device, and a computer-readable storage medium. Background Technology

[0002] As an important component of oil and gas pipeline networks, oil and gas pipeline owners have put forward new requirements and expectations for intelligent optimization design, production process monitoring, real-time operation data monitoring, fault early warning and diagnosis, and predictive maintenance of pipeline equipment.

[0003] Existing digital twins for oil and gas pipeline networks only achieve basic mapping of pipeline equipment's appearance, structural assembly, and operational actions. They lack deeper mapping of mechanical and fluid properties such as stress, strain, velocity, and pressure. Therefore, they cannot monitor the internal state of pipeline equipment in real time, leading to two problems: First, it's impossible to determine the overall stress, deformation, and flow field distribution of the equipment based solely on single-point pressure and flow monitoring. This makes damage assessment and early warning impossible when short-term overpressure or overflow conditions occur, and it's difficult to analyze the root cause of structural damage or performance degradation. Second, it cannot provide extreme operating condition data for pipeline equipment structural optimization or provide reliable design guidance. Summary of the Invention

[0004] The purpose of this invention is to provide a method, unit, and platform for constructing and operating a digital twin of pipeline equipment, which aims to solve one or more of the above-mentioned problems and has functions such as multi-dimensional performance online simulation, monitoring, early warning, and evaluation.

[0005] To achieve the above objectives, in one aspect, the present invention provides a method for constructing and operating a digital twin of a pipeline equipment, the method comprising:

[0006] Obtain static data of pipeline equipment;

[0007] Constructing geometric models of pipeline equipment based on static data;

[0008] The geometric model of the pipeline equipment is meshed and assigned physical properties and loading boundary conditions to obtain the simulation model of the pipeline equipment.

[0009] Based on static data, multiple sets of training data are generated using the Latin hypercube sampling method.

[0010] Based on multiple sets of training data, the pipeline equipment simulation model is compressed using the singular value decomposition method to obtain a reduced-order model of the pipeline equipment.

[0011] Real-time acquisition of dynamic data from pipeline equipment;

[0012] Real-time dynamic data is used as input to the pipeline equipment reduction model. The pipeline equipment reduction model is solved to obtain the solution result, which is used as the running result of the pipeline equipment digital twin.

[0013] Preferably, the reduced-order model of the pipeline equipment includes:

[0014] Stress reduction model is used to solve for stress in piping equipment;

[0015] Deformation reduction model is used to solve the deformation of pipeline equipment;

[0016] The fluid reduced-order model is used to solve for the flow field velocity, flow field pressure and flow field temperature in pipeline equipment.

[0017] Preferably, the real-time dynamic data includes: real-time pipeline pressure and real-time pipeline strain;

[0018] Using real-time dynamic data as input to a reduced-order model of pipeline equipment, the model is solved to obtain the solution results, including:

[0019] By inputting real-time pipeline pressure and real-time pipeline strain into a pre-built load reduction model, the pipe inlet torque and pipe inlet force of the pipeline equipment are obtained.

[0020] Input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the stress reduction model to obtain the stress of the pipeline equipment, and use the stress of the pipeline equipment as the solution result; or, input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the deformation reduction model to obtain the deformation of the pipeline equipment, and use the deformation of the pipeline equipment as the solution result.

[0021] Preferably, the method further includes: constructing a load reduction model, including:

[0022] Acquire multiple real-time pipeline strains;

[0023] Based on load identification algorithms and multiple real-time pipe strains, the pipe inlet torque and pipe inlet force are determined.

[0024] Based on response surface methodology, a load reduction model is constructed using multiple real-time pipeline strains as inputs and pipeline inlet torque and pipeline inlet force as outputs.

[0025] Preferably, the method further includes:

[0026] The solution results are rendered to obtain a rendered digital twin of the pipeline equipment;

[0027] The rendered digital twin of the pipeline equipment is visualized.

[0028] On the other hand, the present invention also provides a construction and operation unit for a digital twin of pipeline equipment, used to implement the above-mentioned method for constructing and operating a digital twin of pipeline equipment, the operation unit comprising:

[0029] The first data acquisition module is used to acquire static data and real-time dynamic data of pipeline equipment;

[0030] The geometry model building module is used to build geometric models of pipeline equipment based on static data;

[0031] The simulation model building module is used to mesh the geometric model of the pipeline equipment and assign physical properties and loading boundary conditions to obtain the simulation model of the pipeline equipment.

[0032] The data training module is used to generate multiple sets of training data based on static data using the Latin hypercube sampling method.

[0033] The reduced-order model construction module is used to compress the pipeline equipment simulation model based on the singular value decomposition method according to multiple sets of training data to obtain a reduced-order model of the pipeline equipment.

[0034] The second data acquisition module is used to acquire dynamic data of pipeline equipment in real time.

[0035] The reduced-order model solving module is used to solve the reduced-order model of pipeline equipment by taking real-time dynamic data as input and obtaining the solution result, which serves as the running result of the digital twin of the pipeline equipment.

[0036] Preferably, the reduced-order model of the pipeline equipment includes:

[0037] Stress reduction model is used to solve for stress in piping equipment;

[0038] Deformation reduction model is used to solve the deformation of pipeline equipment;

[0039] The fluid reduced-order model is used to solve for the flow field velocity, flow field pressure and flow field temperature in pipeline equipment.

[0040] Preferably, the real-time dynamic data includes: real-time pipeline pressure and real-time pipeline strain;

[0041] The reduced-order model solving module is specifically used for:

[0042] By inputting real-time pipeline pressure and real-time pipeline strain into a pre-built load reduction model, the pipe inlet torque and pipe inlet force of the pipeline equipment are obtained.

[0043] Input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the stress reduction model to obtain the stress of the pipeline equipment, and use the stress of the pipeline equipment as the solution result; or, input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the deformation reduction model to obtain the deformation of the pipeline equipment, and use the deformation of the pipeline equipment as the solution result.

[0044] Preferably, the build and run unit further includes:

[0045] The load reduction model building module is used for:

[0046] Acquire multiple real-time pipeline strains;

[0047] Based on load identification algorithms and multiple real-time pipe strains, the pipe inlet torque and pipe inlet force are determined.

[0048] Based on response surface methodology, a load reduction model is constructed using multiple real-time pipeline strains as inputs and pipeline inlet torque and pipeline inlet force as outputs.

[0049] Preferably, the build and run unit further includes:

[0050] The rendering and display module is used to render the solution results to obtain the rendered digital twin of the pipeline equipment; and to visualize the rendered digital twin of the pipeline equipment.

[0051] On the other hand, the present invention also provides a pipeline equipment digital twin platform, including the above-mentioned pipeline equipment digital twin construction and operation unit, the platform further including:

[0052] A physical entity unit, including multiple piping devices;

[0053] The data acquisition unit is used to collect real-time dynamic data of multiple pipeline devices in the physical entity unit, and to upload the collected real-time dynamic data to the construction and operation unit.

[0054] The visualization unit is used to visualize the operational results of the pipeline equipment digital twin obtained by the construction and operation unit.

[0055] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for constructing and operating a digital twin of a pipeline device.

[0056] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for constructing and operating a digital twin of a pipeline device.

[0057] Through the above technical solution, the present invention has at least the following technical effects:

[0058] 1. Online performance simulation extends the single-point detection of pipeline equipment sensors to three-dimensional field monitoring. It can also monitor parameters that are difficult to measure by conventional methods, such as stress and strain, thereby enabling performance monitoring, prediction, and evaluation of pipeline equipment and ensuring its safe operation.

[0059] 2. Multi-dimensional monitoring of pipeline equipment operation status, including mechanical and fluid dimensions, changes the previous approach where stress, deformation, and flow field of pipeline equipment were only calculated values ​​based on design conditions. It provides early warning of potential damage during pipeline equipment operation, solving the problem of difficulty in analyzing the cause when pipeline equipment fails. At the same time, it provides extreme working condition data for pipeline equipment structural optimization and provides guidance for safe and reliable design.

[0060] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0061] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0062] Figure 1 This is a flowchart of a method for constructing and operating a digital twin of pipeline equipment according to one embodiment of the present invention;

[0063] Figure 2 This is a technical implementation roadmap of a method for constructing and operating a digital twin of pipeline equipment according to one embodiment of the present invention;

[0064] Figure 3 This is a block diagram of a construction and operation unit for a digital twin of pipeline equipment provided in one embodiment of the present invention;

[0065] Figure 4 This is a block diagram of the architecture of a digital twin platform for pipeline equipment provided in one embodiment of the present invention;

[0066] Figure 5 This is a block diagram of the architecture of another pipeline equipment digital twin platform provided by an embodiment of the present invention;

[0067] Figure 6 This is a schematic diagram of the sensor arrangement on a cyclone separator and a filter separator according to one embodiment of the present invention;

[0068] Figure 7This is a schematic diagram of the stress solution process for a cyclone separator and a filter separator provided in one embodiment of the present invention;

[0069] Figure 8 This is a schematic diagram of the deformation solution process of a cyclone separator and a filter separator provided in one embodiment of the present invention;

[0070] Figure 9 This is a schematic diagram of the fluid velocity calculation process in a cyclone separator and a filter separator according to one embodiment of the present invention;

[0071] Figure 10 This is a schematic diagram of the fluid pressure calculation process in a cyclone separator and a filter separator provided in one embodiment of the present invention.

[0072] Explanation of reference numerals in the attached figures

[0073] 1-Strain gauge; 2-Flow meter; 4-Pressure transmitter; 5-Temperature transmitter. Detailed Implementation

[0074] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0075] Example 1

[0076] Figure 1 This is a flowchart of a method for constructing and operating a digital twin of pipeline equipment according to one embodiment of the present invention, as shown below. Figure 1 As shown, this embodiment provides a method for constructing and operating a digital twin of pipeline equipment, the method including:

[0077] Step S101: Obtain static data of the pipeline equipment.

[0078] In this embodiment, the static data of the pipeline equipment includes: geometric dimensions, material data, etc. The geometric dimensions can be measured using a dimensional measuring device, and the material data can be found in a technical manual. Then, the above static data is stored locally.

[0079] When static data is needed, it can be retrieved directly from the local storage device.

[0080] Step S102: Construct a geometric model of the pipeline equipment based on static data.

[0081] In this embodiment, the geometric model of the pipeline equipment is designed using modeling software based on its geometric dimensions. This includes a shell geometric model for solving stress and deformation, and an internal flow field geometric model for solving velocity and pressure. During the geometric model creation process, some subtle features (such as lifting lugs and inlet pipes) need to be simplified, and finally, the geometric model file format is converted to a common simulation analysis model format.

[0082] Step S103: Mesh the geometric model of the pipeline equipment and assign physical properties and loading boundary conditions to obtain the simulation model of the pipeline equipment.

[0083] Step S104: Based on static data, generate multiple sets of training data using the Latin hypercube sampling method.

[0084] Step S105: Based on multiple sets of training data, compress the pipeline equipment simulation model using the singular value decomposition method to obtain a reduced-order model of the pipeline equipment.

[0085] In this embodiment, the geometric model in a general format for simulation analysis is imported into the mechanical simulation analysis module and the fluid simulation analysis module, respectively. First, the material and fluid physical properties are set for the geometric model; the outer shell geometric model is meshed according to the requirements of mechanical simulation analysis, and the internal flow field geometric model is meshed according to the requirements of fluid simulation analysis; loads and boundary conditions are applied to the meshed geometric models according to the requirements of mechanical simulation analysis and fluid simulation analysis, respectively, and simulation calculations are performed to obtain the pipeline equipment simulation model.

[0086] In this embodiment, the pipeline equipment order reduction model includes: a stress order reduction model, a deformation order reduction model, and a fluid order reduction model;

[0087] Among them, the stress reduction model is used to solve the stress of the pipeline equipment, the deformation reduction model is used to solve the deformation of the pipeline equipment, and the fluid reduction model is used to solve the flow field velocity, flow field pressure and flow field temperature in the pipeline equipment.

[0088] In this embodiment, the mechanical simulation analysis of the pipeline equipment uses the Latin hypercube sampling method to generate multiple sets of design experimental data. All design experimental data are solved, and groups deviating from the required level are eliminated. For example, 15 sets of design experimental data are selected as training data. The pipeline physical model is compressed using singular value decomposition, generating stress reduction models and deformation reduction models with seven input quantities (force, torque, and internal pressure in the three directions of the inlet and outlet) and stress intensity as the output quantity. Load values ​​are randomly input into the stress reduction and deformation reduction models, and the output results are calculated. The calculation results are compared with the finite element analysis model to verify that the error should not exceed 3%. If this is not satisfied, new training data is selected to generate new stress reduction and deformation reduction models.

[0089] The same method was used to design and solve the experimental data for the fluid simulation analysis model. Fifteen sets of experimental data were selected as training data to generate a fluid reduced-order model with the inlet velocity and temperature of the cyclone separator as inputs and the velocity, pressure and temperature of the internal flow field as outputs.

[0090] In this embodiment, since the forces and torques acting on the pipeline equipment cannot be directly measured, the strain at multiple points on the surface of the equipment is measured. The load identification software is used to identify the measured multi-point strain as the inlet and outlet forces, torques, and internal pressures. The strains measured at each point under different operating conditions are used as input quantities, and the forces and torques identified by the load identification software are used as output quantities. The load reduction model is established using response surface analysis.

[0091] Step S106: Acquire dynamic data of pipeline equipment in real time.

[0092] In this embodiment, the real-time dynamic data includes: pressure, temperature, and medium flow rate in the pipeline inlet line of the pipeline equipment, as well as strain load data on the pipeline inlet line of the pipeline equipment;

[0093] The pressure, temperature, and flow data are acquired through pressure transmitters, temperature transmitters, and flow transmitters, respectively.

[0094] Among them, strain load data acquisition is achieved through a wireless distributed online monitoring platform, and the external load identification on the pipeline inlet is achieved by converting the strain collected on the pipeline inlet into external forces and torques that can be applied to the pipeline load calculation model.

[0095] When real-time dynamic data is needed, the aforementioned sensors are activated to collect real-time dynamic data and obtain real-time dynamic data from the aforementioned monitoring equipment.

[0096] Step S107: Using real-time dynamic data as input to the pipeline equipment reduction model, solve the pipeline equipment reduction model to obtain the solution result, which serves as the running result of the pipeline equipment digital twin.

[0097] By inputting the real-time dynamic data such as internal pressure, strain, gas velocity, and temperature of the pipeline into the reduced-order model of the pipeline equipment, the solution results such as stress, deformation, velocity, and pressure of the pipeline can be obtained.

[0098] In this embodiment, the solution process for the stress and deformation of the pipeline is not possible because the forces and torques acting on the pipeline equipment cannot be directly measured; the real-time dynamic data includes: real-time pipeline pressure and real-time pipeline strain.

[0099] As a further optimization of this embodiment, real-time dynamic data is used as input to the pipeline equipment reduction model, and the pipeline equipment reduction model is solved to obtain the solution results, including:

[0100] Step b01: Input the real-time pipeline pressure and real-time pipeline strain into the pre-built load reduction model to obtain the pipeline inlet torque and pipeline inlet force of the pipeline equipment;

[0101] Step b02: Input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force of the pipeline equipment into the stress reduction model to obtain the stress of the pipeline equipment, and use the stress of the pipeline equipment as the solution result; or, input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force of the pipeline equipment into the deformation reduction model to obtain the deformation of the pipeline equipment, and use the deformation of the pipeline equipment as the solution result.

[0102] In this embodiment, the method further includes: constructing a load reduction model, including:

[0103] Step c01: Obtain multiple real-time pipeline strains. Under different operating conditions, measure the strain at multiple points on the surface of the pipeline equipment to obtain the pipeline strain.

[0104] Step c02: Based on the load identification algorithm and multiple real-time pipe strains, determine the pipe inlet torque and pipe inlet force, that is, use load identification software to identify the measured multi-point strains as inlet and outlet forces and torques;

[0105] Step c03: Based on response surface methodology, a load reduction model is constructed using multiple real-time pipe strains as inputs and pipe inlet torque and pipe inlet force as outputs. That is, the load reduction model is established by using the strains measured at various points under different working conditions as inputs and the forces and torques identified by the load software as outputs.

[0106] As a further optimization of this embodiment, the method further includes:

[0107] Step d01: Render the solution results to obtain the rendered digital twin of the pipeline equipment;

[0108] Step d02: Visualize the rendered digital twin of the pipeline equipment.

[0109] The display methods include 3D cloud maps, charts, curves, etc. with different parameter types, realizing the mapping of physical entities to virtual digital twins with different performance characteristics. The visualization forms include fixed-end interface display and mobile-end augmented reality.

[0110] Figure 2 This is a technical implementation roadmap for a method of constructing and operating a digital twin of pipeline equipment according to one embodiment of the present invention, such as... Figure 2As shown, the implementation route of this method includes two parallel and interconnected main lines: physical entities and virtual digital space. The implementation steps of the physical entity main line are as follows: select and determine the pipeline equipment to be monitored, determine the unknown quantity to be monitored for the pipeline equipment, back-calculate the parameters required to solve for the unknown quantity that can be collected by existing sensor technology as known quantities, select appropriate locations on the pipeline equipment or nearby pipelines to deploy sensors to collect different data information, select a suitable communication protocol to transmit the collected information using a communication system, and after simplifying, organizing and unifying the format of the data, send it to the reduced-order model in the virtual digital space. The main implementation steps of the virtual digital space are as follows: A virtual digital geometric model is created based on the geometric dimensions of the pipeline equipment to be monitored and the unknown quantities to be monitored. A mesh is then created according to different computational domains. Physical quantities are added to the geometric model to establish a physical model. Loads and boundary conditions are applied to the physical model, and the simulation model is solved. The unknown quantities to be monitored and the collected known quantities are parameterized. Training data is generated within the range of collected parameters. Appropriate training data is selected to generate a reduced-order model. The accuracy of the reduced-order model is verified. Once the accuracy meets the requirements, a data interface is set up for the reduced-order model, and the real-time parameters collected from the physical entities are assigned to the reduced-order model. The reduced-order model is then used for calculation and solution. Finally, computer graphics technology is used to render and display the calculation results. The rendered display includes 3D cloud maps, charts, and curves.

[0111] The operating method of this embodiment monitors the operating status of pipeline equipment in real time and from multiple dimensions, including mechanical and fluid dimensions. This changes the previous practice where the stress, deformation, and flow field of pipeline equipment were only calculated based on design conditions. It provides early warning of potential damage to pipeline equipment during operation, solving the problem of difficulty in analyzing the cause when pipeline equipment cracks or when precision instruments such as compressors and flow meters protected by pipeline filtration and separation equipment malfunction. At the same time, it provides extreme operating condition data for the structural optimization of pipeline equipment, ensuring design safety and reliability.

[0112] Example 2

[0113] Figure 3 This is a block diagram of a construction and operation unit for a digital twin of pipeline equipment provided in one embodiment of the present invention, as shown below. Figure 3 As shown, based on the same inventive concept as Embodiment 1, this embodiment also provides a construction and operation unit for a digital twin of pipeline equipment. The operation unit is used to implement the construction and operation method for a digital twin of pipeline equipment in Embodiment 1. The operation unit includes:

[0114] The first data acquisition module is used to acquire static data and real-time dynamic data of pipeline equipment;

[0115] The geometry model building module is used to build geometric models of pipeline equipment based on static data.

[0116] The simulation model building module is used to mesh the geometric model of the pipeline equipment and assign physical properties and loading boundary conditions to obtain the simulation model of the pipeline equipment.

[0117] The data training module is used to generate multiple sets of training data based on static data using the Latin hypercube sampling method.

[0118] The reduced-order model construction module is used to compress the pipeline equipment simulation model based on the singular value decomposition method according to multiple sets of training data to obtain a reduced-order model of the pipeline equipment.

[0119] The second data acquisition module is used to acquire dynamic data of pipeline equipment in real time.

[0120] The reduced-order model solving module is used to solve the reduced-order model of pipeline equipment by taking real-time dynamic data as input and obtaining the solution result, which serves as the running result of the digital twin of the pipeline equipment.

[0121] Furthermore, the reduced-order model of the pipeline equipment includes:

[0122] Stress reduction model is used to solve for stress in piping equipment;

[0123] Deformation reduction model is used to solve the deformation of pipeline equipment;

[0124] The fluid reduced-order model is used to solve for the flow field velocity, flow field pressure and flow field temperature in pipeline equipment.

[0125] Furthermore, the real-time dynamic data includes: real-time pipeline pressure and real-time pipeline strain;

[0126] The reduced-order model solving module is specifically used for:

[0127] By inputting real-time pipeline pressure and real-time pipeline strain into a pre-built load reduction model, the pipe inlet torque and pipe inlet force of the pipeline equipment are obtained.

[0128] Input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the stress reduction model to obtain the stress of the pipeline equipment, and use the stress of the pipeline equipment as the solution result; or, input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the deformation reduction model to obtain the deformation of the pipeline equipment, and use the deformation of the pipeline equipment as the solution result.

[0129] Furthermore, the build and run unit also includes:

[0130] The load reduction model building module is used for:

[0131] Acquire multiple real-time pipeline strains;

[0132] Based on load identification algorithms and multiple real-time pipe strains, the pipe inlet torque and pipe inlet force are determined.

[0133] Based on response surface methodology, a load reduction model is constructed using multiple real-time pipeline strains as inputs and pipeline inlet torque and force as outputs.

[0134] Furthermore, the build and run unit also includes:

[0135] The rendering and display module is used to render the solution results to obtain the rendered digital twin of the pipeline equipment; and to visualize the rendered digital twin of the pipeline equipment.

[0136] Based on the same inventive concept as Embodiment 1, this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for constructing and running a digital twin of a pipeline device.

[0137] Based on the same inventive concept as Embodiment 1, this embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for constructing and operating a digital twin of a pipeline device.

[0138] The operating unit in this embodiment monitors the operating status of pipeline equipment in real time from multiple dimensions, including mechanical and fluid dimensions. This changes the previous practice where the stress, deformation, flow field, and separation performance of pipeline equipment were only calculated based on design conditions. It provides early warning of potential damage to pipeline equipment during operation, solving the problem of difficulty in analyzing the cause when pipeline equipment cracks or when precision instruments such as compressors and flow meters protected by pipeline filtration and separation equipment malfunction. At the same time, it provides extreme operating condition data for the structural optimization of pipeline equipment, ensuring design safety and reliability.

[0139] Example 3

[0140] Figure 4 This is a block diagram of the architecture of a digital twin platform for pipeline equipment provided in one embodiment of the present invention. Figure 5 This is a block diagram of the architecture of another pipeline equipment digital twin platform provided by an embodiment of the present invention; as shown below. Figure 4-5 As shown, based on the same inventive concept as Embodiment 2, this embodiment also provides a pipeline equipment digital twin platform. The platform includes the pipeline equipment digital twin construction and operation unit of Embodiment 2, and also includes: a physical entity unit, a data acquisition unit and a visualization display unit.

[0141] The physical entity unit includes multiple piping devices, such as cyclone separators, filter separators, launch and receiver tubes, filters, and coalescing filters.

[0142] The data acquisition unit is used to collect real-time dynamic data of multiple pipeline devices in the physical entity unit, and to upload the collected real-time dynamic data to the construction and operation unit. The data acquisition unit consists of sensors such as pressure transmitters, temperature transmitters, flow meters and strain transmitters.

[0143] The visualization unit is used to visualize the operational results of the pipeline equipment digital twin obtained by the construction and operation unit. The pipeline equipment digital twin construction and operation unit in this embodiment also has a model library, an algorithm library, a database, and a support platform. The data collected by the four are interconnected and coordinated to ensure the functionality of the twin. The model library includes: geometric models, mechanical models, and fluid models; the algorithm library includes: performance algorithms, order reduction algorithms, and diagnostic algorithms; the database includes: material data, physical property data, boundary data, historical data, and real-time data; the support platform includes: data interface, interactive operation, and data security.

[0144] In this embodiment, the pipeline equipment digital twin platform is built in the following way: The implementation route of the pipeline equipment digital twin platform in this embodiment includes two main lines: the physical entity unit main line and the virtual digital space.

[0145] The following detailed description of the pipeline equipment digital twin platform of this embodiment, using a cyclone separator and a filter separator as examples, is carried out according to the main line of physical equipment entity units and the main line of virtual digital space.

[0146] I. Main Line of Physical Equipment Entity Units

[0147] (1) Determine the piping equipment

[0148] Based on the pipe inlet load calculated by the pipeline stress analysis software, the flow field distribution analyzed by the fluid calculation software simulation, and the historical data from the site, one of the four cyclone separators and filter separators set up in the pipeline station process area was selected to be monitored. Considering the above data, this embodiment selected one of the gas collection pipes at the end as the physical entity device for implementing the digital twin.

[0149] (2) Determine the unknown demand and the known collectable quantity.

[0150] To ensure the safety and reliable performance of the pipeline equipment, it is necessary to monitor the specific distribution of stress and deformation in the equipment for safety purposes, and to monitor the flow field distribution of velocity and pressure, as well as the filtration and separation performance within the equipment for performance purposes. Therefore, stress, deformation, velocity, and pressure are unknowns in this embodiment. Solving for stress and deformation requires collecting pressure and pipe strain data, while solving for velocity and pressure flow field distribution requires collecting inlet flow rate and temperature data.

[0151] (3) Sensor placement

[0152] Figure 6 This is a schematic diagram of the sensor arrangement on a cyclone separator and a filter separator according to one embodiment of the present invention; as shown. Figure 6 As shown, a pressure transmitter is installed on the cyclone separator cylinder, eight strain gauges 1 (i.e. strain transmitters) are evenly distributed at the air inlet of the cyclone separator and the air outlet of the filter separator, a flow meter 2 is installed on the air inlet pipe of the cyclone separator, and a pressure transmitter 4 and a temperature transmitter 5 are installed on the cyclone separator.

[0153] (4) Data transmission and processing

[0154] The collected pressure, strain, temperature, and flow rate data signals are transmitted to the communication server via the communication module, and then downloaded to the work computer from the Internet using the wireless serial port software of the communication module. The program developed using the instrument development software converts the collected pressure, strain, temperature, and flow rate signals into corresponding physical quantities, identifies different data, processes them according to a unified sampling frequency and format, and outputs them in CSV format.

[0155] II. Main Theme of Virtual Digital Space

[0156] (1) Geometric model construction

[0157] Based on the actual dimensions of the aforementioned cyclone separator and filter separator, a geometric model of the equipment was designed using modeling software. This included a geometric model of the outer shell for solving stress and deformation, and a geometric model of the internal flow field for solving velocity and pressure. During the geometric model creation process, some subtle features (such as lifting lugs and inlet pipes) needed to be simplified. Finally, the file was converted into a general simulation analysis model format.

[0158] (2) Mesh generation, physical property and boundary condition loading, and simulation calculation

[0159] Import the shell geometry model into the finite element statics analysis module and first set the material of the geometry model to the cylinder Q345R flange 16Mn; set the contact of all components to bond; divide the shell geometry model into meshes according to the statics analysis requirements, and refine the mesh for key parts such as air inlets and outlets and welds of the cylinder.

[0160] Forces and moments in the X, Y, and Z directions (calculated from pipeline stress analysis software and used as boundary conditions) were applied to the inlet of the cyclone separator and the outlet of the filter separator, respectively. Design internal pressure was applied to the inner surface of the equipment. The cyclone separator legs were set as fixed constraints, and the filter separator saddle was set to be able to slide 20mm along the axial direction of the equipment. Stress intensity and deformation were set as output results for calculation to complete the simulation. Finally, the three calculation conditions (force, moment, and internal pressure) and the six calculation results (minimum, maximum, and average) of stress intensity and deformation were all set as parametric data.

[0161] The internal flow field geometric model was imported into the fluid dynamics and analysis modules. The geometric model was meshed according to fluid analysis requirements. An enhanced K-ε turbulence model was selected, and the energy equation was enabled. The fluid medium was set to natural gas. The cyclone classifier inlet was set as the velocity inlet, and turbulence and temperature parameters were configured. The filter separator outlet was set as the pressure outlet, and turbulence parameters were specified. The remaining surfaces were designated as walls, and wall parameters were set. Monitoring parameters and solution methods were initialized, and calculations were performed to complete the fluid simulation. Finally, the inlet velocity and temperature of the cyclone separator, along with the minimum, maximum, and average internal velocity, pressure, and temperature calculations (nine results) were parameterized.

[0162] (3) Training data and model reduction

[0163] In this example, since the forces and torques acting on the inlet of the cyclone separator and the outlet of the filter separator cannot be directly measured, a strain transmitter is used to measure the strain at multiple points on the surface of the equipment. Load identification software is used to identify the measured multi-point strains as inlet and outlet forces, torques, and internal pressures. The strains measured at each point under different operating conditions are used as input quantities, and the forces, torques, and internal pressures identified by the load identification software are used as output quantities. A load reduction model is established using response surface methodology, and a data interface is set for the input and output of the load reduction model. During subsequent monitoring, the strains collected in real time by the data acquisition unit can be transmitted to the operating unit through this data interface as input parameters for the load reduction model of the operating unit.

[0164] For the static analysis of the equipment, multiple sets of design experimental data were generated using the Latin hypercube sampling method. All design experimental data were solved, and groups deviating from the optimal range were removed. Fifteen sets of design experimental data were selected as training data. The pipe physical model of the equipment was compressed using the singular value decomposition method. Stress-reduced and deformation-reduced models were generated, with forces, moments, and internal pressures in the three directions of the inlet and outlet as inputs and stress intensity as the output. Random load values ​​were input to the stress-reduced and deformation-reduced models, and the output results were calculated. The calculation results were compared with those of the finite element analysis model to verify that the error should not exceed 3%. If this was not met, training data was reselected to generate a new reduced-order model. Finally, data interfaces were set for the input and output of the stress-reduced and deformation-reduced models respectively.

[0165] The same method was used to design and solve the experimental data for the fluid analysis model. Fifteen sets of experimental data were selected as training data to generate a fluid reduced-order model with the inlet velocity and temperature of the cyclone separator as inputs and the velocity, pressure and temperature of the internal flow field as outputs. After verification, the input and output data interfaces were set.

[0166] (4) Import collected data, solve, and encapsulate

[0167] Figure 7 This is a schematic diagram of the stress solution process for a cyclone separator and a filter separator provided in one embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the deformation solution process of a cyclone separator and a filter separator according to one embodiment of the present invention; as shown. Figure 7-8 As shown, the collected internal pressure and strain data are connected to the input interface of the load reduction model, and the force and moment data from the load reduction model's output interface are connected to the force and moment interfaces from the stress reduction model's input interface. Since the directly collected internal pressure is more accurate than that calculated by the load identification software, it is connected to the internal pressure interface from the stress reduction model's input interface. The same connection method is used to connect the load reduction model and the deformation reduction model. After the connection is completed, the solution is performed, and finally, the solution results are converted into a program package containing the algorithm and data such as stress and deformation results at each node under different conditions.

[0168] Figure 9 This is a schematic diagram of the fluid velocity calculation process in a cyclone separator and a filter separator according to one embodiment of the present invention; Figure 10 This is a schematic diagram illustrating the fluid pressure calculation process within a cyclone separator and a filter separator according to one embodiment of the present invention; as shown. Figure 9-10As shown, the collected cyclone separator inlet flow rate is converted into air velocity. The air velocity and temperature are then connected to the input interfaces of the fluid velocity reduction model and pressure reduction model, respectively, for solving. Finally, the solved flow field reduction model is converted into an algorithm package and data such as velocity results, pressure results, and expected lifetime results of each node under different conditions.

[0169] (5) Data rendering and platform construction

[0170] Data rendering and platform construction are completed through a software development platform. First, the geometric model completed in step (1) is lightweighted and converted into an fbx format that can be directly used by the software development platform through a format conversion software. In the software development platform, the rendering tool is used to render a certain result at each location point of the result model, such as the velocity distribution at each location under a gas velocity of 3m / s and 30℃. Then, a C# program is written to drive different result data to present different colors, completing the rendering of cloud maps such as stress, deformation, velocity, and pressure. Decorative elements are added to the software development scene, such as other equipment, pipes connecting the equipment, ground, and green trees, to simulate the scene of a real gas transmission station. The UI system of the software development platform is used to complete the design of the user interface, realizing the area division, display, and content switching of information such as cloud maps, collected data, alarms, and predictions.

[0171] In this embodiment, the entry data of the pipeline equipment digital twin platform comes directly from the data collected on site, thereby ensuring the online authenticity of the data.

[0172] In this embodiment, the mechanical and fluid models of the pipeline equipment digital twin are both processed by order reduction (i.e., stress order reduction model, deformation order reduction model, fluid order reduction model, and training with a large amount of data, which ensures the accuracy of three-dimensional simulation calculation and achieves real-time performance).

[0173] This embodiment uses real-time online simulation to extend the sensor point detection of pipeline equipment to three-dimensional field monitoring, and can monitor parameters such as stress and strain that are difficult to measure using conventional methods.

[0174] This embodiment monitors the operating status of pipeline equipment in real time from multiple dimensions, including mechanical and fluid dimensions. This changes the previous approach where the stress, deformation, and flow field of pipeline equipment were only calculated based on design conditions. It provides early warning of potential damage to pipeline equipment during operation, solving the problem of difficulty in analyzing the causes when pipeline equipment cracks or when precision instruments such as compressors and flow meters protected by pipeline filtration and separation equipment malfunction. At the same time, it provides extreme operating condition data for the structural optimization of pipeline equipment, ensuring design safety and reliability.

[0175] Those skilled in the art will understand that embodiments of this application can be provided as methods, platforms, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (platforms), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0177] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0179] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0180] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0181] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0182] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0183] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for constructing and operating a digital twin of pipeline equipment, characterized in that, The method includes: Obtain static data of pipeline equipment; Constructing geometric models of pipeline equipment based on static data; The geometric model of the pipeline equipment is meshed and assigned physical properties and loading boundary conditions to obtain the simulation model of the pipeline equipment. Based on static data, multiple sets of training data are generated using the Latin hypercube sampling method. Based on multiple sets of training data, the pipeline equipment simulation model is compressed using the singular value decomposition method to obtain a reduced-order model of the pipeline equipment. Real-time acquisition of dynamic data from pipeline equipment; Real-time dynamic data is used as input to the pipeline equipment reduction model. The pipeline equipment reduction model is solved to obtain the solution result, which is used as the running result of the pipeline equipment digital twin.

2. The method according to claim 1, characterized in that, The reduced-order model for the pipeline equipment includes: Stress reduction model is used to solve for stress in piping equipment; Deformation reduction model is used to solve the deformation of pipeline equipment; The fluid reduced-order model is used to solve for the flow field velocity, flow field pressure and flow field temperature in pipeline equipment.

3. The method according to claim 2, characterized in that, Real-time dynamic data includes: real-time pipeline pressure and real-time pipeline strain; Using real-time dynamic data as input to a reduced-order model of pipeline equipment, the model is solved to obtain the solution results, including: By inputting real-time pipeline pressure and real-time pipeline strain into a pre-built load reduction model, the pipe inlet torque and pipe inlet force of the pipeline equipment are obtained. Input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the stress reduction model to obtain the stress of the pipeline equipment, and use the stress of the pipeline equipment as the solution result; or, input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the deformation reduction model to obtain the deformation of the pipeline equipment, and use the deformation of the pipeline equipment as the solution result.

4. The method according to claim 3, characterized in that, The method further includes: constructing a load reduction model, including: Acquire multiple real-time pipeline strains; Based on load identification algorithms and multiple real-time pipe strains, the pipe inlet torque and pipe inlet force are determined. Based on response surface methodology, a load reduction model is constructed using multiple real-time pipeline strains as inputs and pipeline inlet torque and pipeline inlet force as outputs.

5. The method according to claim 1, characterized in that, The method further includes: The solution results are rendered to obtain a rendered digital twin of the pipeline equipment; The rendered digital twin of the pipeline equipment is visualized.

6. A construction and operation unit for a digital twin of pipeline equipment, used to implement the construction and operation method for a digital twin of pipeline equipment as described in any one of claims 1-5, characterized in that, The build and run unit includes: The first data acquisition module is used to acquire static data and real-time dynamic data of pipeline equipment; The geometry model building module is used to build geometric models of pipeline equipment based on static data. The simulation model building module is used to mesh the geometric model of the pipeline equipment and assign physical properties and loading boundary conditions to obtain the simulation model of the pipeline equipment. The data training module is used to generate multiple sets of training data based on static data using the Latin hypercube sampling method. The reduced-order model construction module is used to compress the pipeline equipment simulation model based on the singular value decomposition method according to multiple sets of training data to obtain a reduced-order model of the pipeline equipment. The second data acquisition module is used to acquire dynamic data of pipeline equipment in real time. The reduced-order model solving module is used to solve the reduced-order model of pipeline equipment by taking real-time dynamic data as input and obtaining the solution result, which serves as the running result of the digital twin of the pipeline equipment.

7. The construction and operation unit for a digital twin of pipeline equipment according to claim 6, characterized in that, The reduced-order model for the pipeline equipment includes: Stress reduction model is used to solve for stress in piping equipment; Deformation reduction model is used to solve the deformation of pipeline equipment; The fluid reduced-order model is used to solve for the flow field velocity, flow field pressure and flow field temperature in pipeline equipment.

8. The construction and operation unit for a digital twin of pipeline equipment according to claim 7, characterized in that, Real-time dynamic data includes: real-time pipeline pressure and real-time pipeline strain; The reduced-order model solution module is specifically used for: By inputting real-time pipeline pressure and real-time pipeline strain into a pre-built load reduction model, the pipe inlet torque and pipe inlet force of the pipeline equipment are obtained. Input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the stress reduction model to obtain the stress of the pipeline equipment, and use the stress of the pipeline equipment as the solution result; or, input the real-time pipeline pressure, the pipeline inlet torque, and the pipeline inlet force into the deformation reduction model to obtain the deformation of the pipeline equipment, and use the deformation of the pipeline equipment as the solution result.

9. The construction and operation unit for a digital twin of pipeline equipment according to claim 8, characterized in that, The build and run unit also includes: The load reduction model building module is used for: Acquire multiple real-time pipeline strains; Based on load identification algorithms and multiple real-time pipe strains, the pipe inlet torque and pipe inlet force are determined. Based on response surface methodology, a load reduction model is constructed using multiple real-time pipeline strains as inputs and pipeline inlet torque and pipeline inlet force as outputs.

10. The construction and operation unit of the pipeline equipment digital twin according to claim 6, characterized in that, The build and run unit also includes: The rendering and display module is used to render the solution results to obtain the rendered digital twin of the pipeline equipment; and to visualize the rendered digital twin of the pipeline equipment.

11. A digital twin platform for pipeline equipment, the platform comprising a construction and operation unit for the digital twin of pipeline equipment as described in any one of claims 6-10, characterized in that, The platform also includes: A physical entity unit, including multiple piping devices; The data acquisition unit is used to collect real-time dynamic data of multiple pipeline devices in the physical entity unit, and to upload the collected real-time dynamic data to the construction and operation unit. The visualization unit is used to visualize the operational results of the pipeline equipment digital twin obtained by the construction and operation unit.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for constructing and operating a digital twin of a pipeline device as described in any one of claims 1-5.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method for constructing and running a digital twin of a pipeline device as described in any one of claims 1-5.

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