A pipeline liquid-accumulation thickness prediction method, device, equipment and storage medium

Abstract

The application discloses a pipeline liquid accumulation thickness prediction method, device, equipment and storage medium. The method comprises the following steps: constructing a pipeline physical model and carrying out grid division, acquiring a gas-liquid two-phase flow numerical calculation model and a boundary condition and completing parameter configuration, carrying out numerical simulation under multiple working conditions to obtain liquid accumulation thickness simulation data sets under different working conditions; significant factors affecting liquid accumulation thickness are screened out through orthogonal test design and range analysis, response surface fitting is carried out based on the significant factors and the simulation data sets, and a pipeline liquid accumulation thickness rapid prediction model is established; finally, the gas phase flow rate and the water phase volume fraction of the pipeline to be predicted are input, and the pipeline liquid accumulation thickness result can be obtained. The application considers the accuracy of numerical simulation and the rapidity of the response surface model, can efficiently and accurately predict the pipeline liquid accumulation thickness, and provides reliable technical support for corrosion risk assessment, liquid discharge pigging and operation and maintenance strategy optimization of the gathering pipeline.

Description

Technical Field

[0001] This application relates to the field of natural gas gathering and transmission pipeline safety technology, and in particular to a method, device, equipment and storage medium for predicting the thickness of liquid accumulation in pipelines. Background Technology

[0002] With the rapid development of the natural gas industry, the mileage of natural gas gathering and transmission pipelines is constantly increasing. 90-degree elbows, as one of the most common local components in gathering and transmission pipelines, are widely used in various pipeline systems. However, when gas and liquid flow through elbows, the curvature-induced centrifugal effect causes significant secondary flow within the cross-section, leading to phase distribution reconstruction. The liquid phase easily accumulates at the elbow and in adjacent pipe sections, manifesting as thickened liquid films, localized stagnation, and even liquid accumulation, thus forming corrosion-sensitive areas and operational risk points, seriously threatening the safe and stable operation of the pipeline. To ensure pipeline operational safety, accurate prediction of the liquid accumulation thickness is necessary to provide a basis for corrosion risk assessment and operation and maintenance decisions.

[0003] Current methods for predicting pipeline liquid accumulation thickness typically employ on-site measurements, empirical formulas, or single numerical simulations. On-site measurements require the installation of numerous monitoring devices on the pipeline, resulting in high costs, long cycles, and difficulty in covering all operating conditions. Empirical formulas are derived from fitting limited experimental data, have a narrow range of applicability, and exhibit low prediction accuracy under complex operating conditions. While single numerical simulations offer higher accuracy, they involve large computational loads and are time-consuming, failing to meet the rapid response requirements of on-site operations.

[0004] Therefore, how to balance prediction accuracy and computational efficiency to quickly and accurately predict the thickness of liquid accumulation in pipelines is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the pipe liquid accumulation thickness prediction method, apparatus, device, and storage medium provided in this application can balance prediction accuracy and computational efficiency, and quickly and accurately predict the pipe liquid accumulation thickness. The pipe liquid accumulation thickness prediction method, apparatus, device, and storage medium provided in this application are implemented as follows: This application provides a method for predicting the thickness of liquid accumulation in a pipeline, including: The pipeline is physically modeled to obtain a pipeline model, and the pipeline model is then meshed to obtain a computational mesh model. A numerical calculation model and boundary conditions for gas-liquid two-phase flow are obtained, and the parameters of the numerical calculation model and boundary conditions are set to obtain the simulation calculation configuration. Multi-condition numerical simulation processing is performed on the computational grid model and the simulation computation configuration to obtain simulated datasets of liquid accumulation thickness under different conditions. Orthogonal experimental design and range analysis were performed on the simulated liquid thickness dataset to obtain significant influencing factors; Response surface methodology was applied to the significant influencing factors and the simulated liquid thickness dataset to obtain a fast prediction model for pipe liquid thickness. The gas phase velocity and water phase volume fraction of the pipeline are obtained, and the gas phase velocity and water phase volume fraction are input into the pipeline liquid thickness prediction model for prediction processing to obtain the liquid thickness result of the pipeline.

[0006] In some embodiments, the physical modeling process of the pipeline to obtain a pipeline model, and the meshing process of the pipeline model to obtain a computational mesh model, includes: The inlet straight pipe section length, outlet straight pipe section length, and elbow curvature radius of the pipeline are set with geometric parameters to obtain the basic geometric parameters of the pipeline. The pipe is classified according to its elbow configuration to obtain the elbow configuration. The pipeline basic geometric parameters and the elbow configuration are integrated to obtain the pipeline model; The bend region and pipeline system of the pipeline model are processed by structured mesh and unstructured mesh respectively to obtain the initial mesh model; The pipe cross-section of the initial mesh model is segmented and the boundary layer mesh is grown to obtain the optimized boundary layer mesh. The optimized boundary layer mesh is locally refined and its size matched to obtain a computational mesh model.

[0007] In some embodiments, obtaining the numerical calculation model and boundary conditions of the gas-liquid two-phase flow, and performing parameter setting processing on the numerical calculation model and the boundary conditions to obtain the simulation calculation configuration, includes: The continuous phase control model, turbulence calculation model, discrete phase calculation model, two-phase interface tracking model, and numerical solver of the gas-liquid two-phase flow are obtained to obtain the basic set of numerical calculation models. The set of basic numerical calculation models is processed to obtain the numerical calculation model configuration. Obtain the inlet boundary type, outlet boundary type, and pipe wall boundary type of the pipeline to obtain the basic boundary condition set; The basic boundary condition set is processed by parameter configuration to obtain the boundary condition configuration; The numerical calculation model configuration and the boundary condition configuration are integrated to obtain the simulation calculation configuration.

[0008] In some embodiments, performing multi-condition numerical simulation processing on the computational grid model and the simulation configuration to obtain a simulated dataset of liquid accumulation thickness under different conditions includes: The reflection conditions and roughness parameters of the pipe wall boundary in the simulation calculation configuration are set to obtain the processed boundary condition configuration; The processed boundary condition configuration and the computational grid model are subjected to pressure coupling solution algorithm settings to obtain the flow field solution configuration; The flow field solution configuration is processed by setting the discretization format of the pressure and convection terms to obtain the discretized calculation configuration; The residual threshold and maximum number of iterations are set for the discretized calculation configuration to obtain the simulation convergence condition; Multiple sets of steady-state numerical simulations were performed on the computational grid model, the discretized computational configuration, and the simulation convergence conditions to obtain simulation datasets of liquid accumulation thickness under different working conditions.

[0009] In some embodiments, the orthogonal experimental design and range analysis of the simulated fluid thickness dataset are used to obtain significant influencing factors, including: The influencing factors and corresponding level parameters of the simulated liquid thickness dataset are extracted and processed to obtain the basic parameters of the orthogonal experiment; The orthogonal experimental parameters are subjected to factor level combination design processing to obtain multiple sets of orthogonal experimental conditions; The orthogonal test conditions and the simulated liquid thickness dataset are matched and extracted to obtain orthogonal test result data. The orthogonal experiment results data were statistically processed for each factor level to obtain the average liquid accumulation thickness at different factor levels; The average thickness of the accumulated fluid at different levels of the aforementioned factors was calculated for range and significance was determined to identify the significant influencing factors.

[0010] In some embodiments, performing response surface fitting on the significant influencing factors and the simulated liquid accumulation thickness dataset to obtain a fast prediction model for pipe liquid accumulation thickness includes: Data extraction and processing are performed on the significant influencing factors and the simulated liquid thickness dataset to obtain the basic data for response surface fitting; The response surface fitting data is subjected to response surface equation fitting to obtain the initial liquid accumulation thickness prediction equation. The fitting effect of the initial liquid accumulation thickness prediction equation is evaluated to obtain the fitting effect evaluation result; The fitting effect evaluation results are processed to determine whether they meet the standards, and the liquid accumulation thickness prediction equation is obtained. The applicable scope of the liquid accumulation thickness prediction equation is clarified to obtain a rapid prediction model for the liquid accumulation thickness in the pipeline.

[0011] In some embodiments, obtaining the gas phase velocity and water phase volume fraction of the pipeline, and inputting the gas phase velocity and water phase volume fraction into the pipeline liquid accumulation thickness fast prediction model for prediction processing to obtain the liquid accumulation thickness result of the pipeline includes: The gas phase velocity and water phase volume fraction of the pipeline are processed to obtain the parameters of the operating condition to be predicted. The applicable range of the model is verified for the parameters of the operating condition to be predicted, and effective prediction parameters are obtained. The effective prediction parameters and the rapid prediction model for the thickness of liquid accumulation in the pipeline are substituted into the calculation process to obtain the initial calculated value of the liquid accumulation thickness. The initial calculated liquid accumulation thickness is subjected to a rationality verification process to obtain the verified liquid accumulation thickness value. The verified liquid accumulation thickness value is processed to obtain the liquid accumulation thickness result of the pipeline.

[0012] This application provides a device for predicting the thickness of liquid accumulation in a pipeline, comprising: The construction module is used to perform physical model construction on the pipeline to obtain a pipeline model, and to perform mesh generation on the pipeline model to obtain a computational mesh model; The acquisition module is used to acquire the numerical calculation model and boundary conditions of the gas-liquid two-phase flow, and to perform parameter setting processing on the numerical calculation model and the boundary conditions to obtain the simulation calculation configuration. The processing module is used to perform multi-condition numerical simulation processing on the computational grid model and the simulation computation configuration to obtain a simulated dataset of liquid accumulation thickness under different conditions. The processing module is also used to perform orthogonal experimental design and range analysis on the simulated liquid thickness dataset to obtain significant influencing factors; The processing module is also used to perform response surface fitting on the significant influencing factors and the simulated liquid thickness dataset to obtain a fast prediction model for pipe liquid thickness. The acquisition module is further configured to acquire the gas phase velocity and water phase volume fraction of the pipeline, input the gas phase velocity and water phase volume fraction into the pipeline liquid thickness prediction model for prediction processing, and obtain the liquid thickness result of the pipeline.

[0013] The computer device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0014] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method described in this application embodiment.

[0015] This application provides a method, apparatus, device, and storage medium for predicting pipeline liquid accumulation thickness. It constructs a pipeline physical model and performs mesh generation, obtains a gas-liquid two-phase flow numerical calculation model and boundary conditions, configures parameters, and conducts multi-condition numerical simulations to obtain simulated datasets of liquid accumulation thickness under different conditions. Through orthogonal experimental design and range analysis, it identifies factors significantly affecting liquid accumulation thickness. Based on these significant influencing factors and the simulated dataset, it performs response surface fitting to establish a rapid prediction model for pipeline liquid accumulation thickness. Finally, by inputting the gas phase velocity and water phase volume fraction of the pipeline to be predicted, the pipeline liquid accumulation thickness result can be obtained. This approach balances the accuracy of numerical simulation with the speed of response surface modeling, enabling efficient and accurate prediction of pipeline liquid accumulation thickness. It provides reliable technical support for corrosion risk assessment, liquid drainage and cleaning, and operation and maintenance strategy optimization of gathering and transportation pipelines, solving the technical problems mentioned in the background art. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram illustrating the implementation process of a pipe liquid accumulation thickness prediction method provided in this application embodiment; Figure 2 A schematic diagram illustrating the implementation process of obtaining a computational grid model, provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a pipe liquid accumulation thickness prediction device provided in an embodiment of this application; Figure 4 A schematic diagram illustrating four typical installation configurations of a pipe elbow provided for embodiments of this application; Figure 5 A liquid accumulation cloud map of a pipeline simulation result provided in an embodiment of this application; Figure 6 A pressure cloud map of pipeline simulation results provided in an embodiment of this application; Figure 7 A velocity cloud map of a pipeline simulation result provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0020] Figure 1 This is a schematic diagram illustrating the implementation process of a pipe liquid accumulation thickness prediction method provided in an embodiment of this application, including steps 101 to 106. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order for a method of predicting the thickness of liquid accumulation in pipes. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0021] Step 101: Perform physical model construction on the pipeline to obtain the pipeline model, and perform mesh generation on the pipeline model to obtain the computational mesh model.

[0022] In this embodiment, a complete computational domain is constructed, comprising an inlet straight pipe section, a 90-degree elbow, and an outlet straight pipe section. To ensure sufficient development of the two-phase flow at the inlet and to avoid the influence of the boundary on the phase distribution within the elbow, the length of the inlet horizontal straight pipe section is set to 3 meters, the length of the outlet vertical pipe section to 5 meters, and the radius of curvature of the elbow to be 1.5 times the pipe diameter. Based on the combination of gravity direction and fluid flow direction, the 90-degree elbow is divided into four typical installation configurations: Type A elbow (horizontal inflow - vertical upward), Type B elbow (horizontal inflow - vertical downward), Type C elbow (vertical upward - horizontal outflow), and Type D elbow (vertical downward - horizontal outflow). Figure 4 As shown.

[0023] When meshing the pipeline model, structured meshes are drawn in the bend region and unstructured meshes are drawn in the complex pipeline system. The pipeline cross-section is subjected to O-shaped cutting, so that the mesh length inward of the boundary layer increases by a factor of 1.1. Local refinement is performed in the region with large flow gradient in the bend, and the mesh size at the junction of the straight pipe and the bend is matched. Finally, a computational mesh model with a total number of meshes of 908,189 is obtained.

[0024] Step 102: Obtain the numerical calculation model and boundary conditions for the gas-liquid two-phase flow, and perform parameter setting processing on the numerical calculation model and boundary conditions to obtain the simulation calculation configuration.

[0025] In this embodiment, a set of basic numerical calculation models is formed by acquiring a continuous phase control model, a turbulence calculation model, a discrete phase calculation model, a two-phase interface tracking model, and a numerical solver for gas-liquid two-phase flow. The continuous phase control model is established based on the laws of conservation of mass and momentum, and its equations are as follows:

[0026]

[0027] in, Indicates the density of the liquid. Represents the instantaneous velocity vector. Indicates pressure, Represents the stress tensor. Represents gravitational acceleration. It is a momentum source term.

[0028] The standard k-ε model is used for turbulence calculation, and the corresponding empirical constants are set. Its governing equations are as follows:

[0029]

[0030] in, The turbulent kinetic energy generated by the average velocity gradient. The turbulent kinetic energy generated by buoyancy, The effect of compressible turbulent pulsating expansion on total dissipation. For turbulent viscosity, , Let Prandtl number and turbulent energy dissipation rate represent the turbulent kinetic energy, respectively. =1.3, =1.0, These are empirical constants; =1.44, =1.92, =0.09; , It is the source item.

[0031] The discrete phase computational model assumes that the particles are spherical, neglects the mutual collisions between particles, as well as the virtual mass force, pressure gradient force, and Suffman lift. The force governing equations for the discrete phase particles are established as follows:

[0032]

[0033]

[0034]

[0035] in, For continuous phase medium velocity, ; For discrete phase medium velocity, ; For discrete phase medium density, ; For the density of the continuous phase medium, ; The particle diameter is ; The dynamic viscosity of the fluid. ; This is the relative Reynolds number; For unit mass drag force, ; This is the drag coefficient. ; for Gravitational acceleration in the direction of gravity, , downwards; for Other forces acting in the direction of gravity include virtual mass force, pressure gradient force, and Saffman lift. For spherical particles, within a certain Reynolds number range, , It is a constant.

[0036] The volume fraction method was used for the two-phase interface tracking model; the pressure-based transient solver was selected as the numerical solver. After configuring the parameters of the above models, the inlet boundary, outlet boundary, and pipe wall boundary types were obtained. The inlet boundary was set as a velocity inlet with a turbulence intensity of 1% and a hydraulic diameter of 0.07 meters; the outlet boundary was set as a pressure outlet; the standard wall function method was used at the pipe wall location, assuming that the fluid was stagnant at the pipe wall. The configured numerical calculation model and boundary conditions were integrated to obtain the simulation calculation configuration.

[0037] Step 103: Perform multi-condition numerical simulation processing on the computational grid model and simulation configuration to obtain simulation datasets of liquid accumulation thickness under different conditions.

[0038] In this embodiment, reflection conditions are added to the pipe wall boundary in the simulation calculation configuration, the wall roughness height is set to 0.5 mm, and the roughness constant is 0.5; the pressure field and velocity field are solved by a semi-implicit method (SIMPLE algorithm) to solve the pressure coupling equation, and the convergence is improved through an iterative process of prediction and correction; a second-order discretization scheme is adopted for the pressure term, and a second-order upwind discretization scheme is adopted for the convection term; the convergence criterion for steady-state simulation is set to the residual of each equation in the control volume not exceeding 0.1%, and the maximum number of iterations is set to 2000.

[0039] Based on the above configuration, multiple steady-state numerical simulations were conducted for different combinations of gas phase flow velocity and inlet water content. The maximum liquid accumulation thickness data for the elbow and adjacent pipe sections under each working condition were extracted, and the simulation datasets for liquid accumulation thickness under different working conditions were compiled. The flow field distribution, pressure distribution, and liquid accumulation distribution for each working condition are presented in the form of velocity contour maps, pressure contour maps, and liquid accumulation contour maps, respectively. Typical simulation results are shown below. Figures 5 to 7 As shown.

[0040] Step 104: Perform orthogonal experimental design and range analysis on the simulated liquid thickness dataset to obtain significant influencing factors.

[0041] In this embodiment, the pipe inclination angle, gas phase velocity, and inlet water content are extracted as initial influencing factors from the simulated liquid accumulation thickness dataset, and five levels are set for each factor. Eighteen orthogonal experimental conditions are designed based on the level combinations of each factor. Liquid accumulation thickness data for the corresponding conditions are extracted from the simulated liquid accumulation thickness dataset to obtain the orthogonal experimental results. The sum and average values ​​of the liquid accumulation thickness for each factor at different levels are calculated, and the influence of different levels on the liquid accumulation thickness is observed through changes in the average value. The range value corresponding to each factor is calculated; the range is the difference between the maximum and minimum values ​​of the average value of that factor at different levels. The influence of each factor is ranked according to the size of the range value; the larger the range value, the more significant the influence of that factor on the liquid accumulation thickness. Finally, factors with significant influence on the liquid accumulation thickness are selected.

[0042] Step 105: Perform response surface fitting on the simulation dataset of significant influencing factors and liquid accumulation thickness to obtain a fast prediction model for pipe liquid accumulation thickness.

[0043] In this embodiment, all data corresponding to pipes with a nominal diameter of 300 mm are extracted from the simulated liquid accumulation thickness dataset. Combined with the screened significant influencing factors, this forms the basic data for response surface fitting. A program is written using numerical computation software to perform response surface equation fitting on the basic data, obtaining the initial liquid accumulation thickness prediction equation. The coefficient of determination of the initial prediction equation is calculated. When the coefficient of determination reaches 0.9, the fitting effect is confirmed to be good. The final liquid accumulation thickness prediction equation is as follows:

[0044] Where y is the thickness of the liquid accumulation in the pipe, mm; X1 is the gas phase velocity, m / s; and X2 is the volume fraction of the water phase, dimensionless. (X1 ranges from 0.5 to 3 m / s, and X2 ranges from 0.02 to 0.2).

[0045] The applicable range of the prediction equation was clarified, with the applicable range of gas phase flow velocity being 0.5 m / s to 3 m / s and the applicable range of water phase volume fraction being 0.02 to 0.2, and finally a rapid prediction model for the thickness of liquid accumulation in the pipeline was obtained.

[0046] Step 106: Obtain the gas phase velocity and water phase volume fraction of the pipeline, input the gas phase velocity and water phase volume fraction into the pipeline liquid thickness prediction model for prediction processing, and obtain the liquid thickness result of the pipeline.

[0047] In this embodiment, the real-time gas phase velocity and water phase volume fraction parameters of the pipeline to be predicted are obtained. First, it is verified whether the parameters are within the applicable range of the prediction model. If the parameters are valid, they are input into the pipeline liquid thickness rapid prediction model for numerical calculation to obtain the initial liquid thickness calculation value. The initial calculation value is verified for rationality. After eliminating abnormal results, the final pipeline liquid thickness result is output.

[0048] To support the prediction of liquid accumulation and corrosion in natural gas gathering and transmission pipelines, this application establishes a database of liquid accumulation in gas transmission pipelines. This database systematically archives and structurally manages a large number of CFD (Computational Fluid Dynamics) numerical simulation results and typical operating condition information. The database uses a single numerical calculation condition as the basic recording unit. Based on a thorough investigation of field operating conditions, it designs combinations of operating conditions around key factors such as pipe diameter, pipe inclination angle, gas phase velocity, inlet water content, operating pressure and temperature, and gas composition. Batch numerical calculations are then conducted to form a sample set of operating conditions covering typical operating ranges. In terms of physical organization, this application adopts a hierarchical structure of operating condition folders. Each group of operating conditions corresponds to an independent folder. The folder name is coded according to parameters such as "pipe diameter – pipe inclination angle – gas phase velocity – inlet water content". For example, "300MM9020.06" indicates a pipe diameter of 300mm, an inclination angle of 90°, a gas phase velocity of 2m / s, and an inlet water content of 0.06%. The physical structure of the database and the pressure cloud map, velocity cloud map, and liquid accumulation cloud map of typical operating conditions are as follows: Figure 5-7 As shown.

[0049] This application's embodiments obtain a large amount of liquid accumulation thickness data under various operating conditions through numerical simulation, combine orthogonal experimental design to screen significant influencing factors, and then establish a rapid prediction model based on the response surface methodology. This approach retains the accuracy of numerical simulation methods while also possessing the advantage of fast calculation speed of response surface models, enabling rapid and accurate prediction of pipeline liquid accumulation thickness. This provides reliable technical support for corrosion risk assessment, liquid drainage and cleaning cycle formulation, and operation and maintenance strategy optimization of natural gas gathering and transmission pipelines.

[0050] In the above Figure 1 Based on the above, this application embodiment also provides a schematic diagram of the implementation process for obtaining a computational grid model. For example... Figure 2 As shown, steps 201 to 206 are included: Step 201: Set the geometric parameters for the inlet straight pipe section length, outlet straight pipe section length, and elbow curvature radius of the pipeline to obtain the basic geometric parameters of the pipeline.

[0051] In this embodiment, a complete computational domain is constructed, including an inlet straight pipe section, a 90-degree bend, and an outlet straight pipe section. To ensure the full development of the gas-liquid two-phase flow at the inlet and to avoid interference from boundary conditions on the phase distribution inside the bend, the length of the inlet horizontal straight pipe section is set to 3 meters, and the length of the outlet vertical pipe section is set to 5 meters. Combining the bend design standards commonly used in natural gas gathering and transmission pipelines, the bend curvature radius is set to 1.5 times the pipe diameter, thereby determining the basic geometric parameters of the pipeline.

[0052] Step 202: Classify the pipes by elbow configuration to obtain the elbow configuration.

[0053] In this embodiment, based on the actual installation method of the pipeline on site and according to the combination relationship between the direction of gravity and the direction of fluid flow, the 90-degree elbow is divided into four typical configurations: horizontal inflow vertical upward elbow, horizontal inflow vertical downward elbow, vertical upward horizontal outflow elbow, and vertical downward horizontal outflow elbow, covering all common elbow installation forms in natural gas gathering and transmission pipelines.

[0054] Step 203: Integrate the pipeline foundation geometric parameters and elbow configuration to obtain the pipeline model.

[0055] In this embodiment, the geometric parameters of the inlet straight pipe section, the outlet straight pipe section, and the elbow are combined with any elbow configuration to construct a complete pipeline physical model under the corresponding installation method, which is used for subsequent numerical simulation of gas-liquid two-phase flow and liquid accumulation process.

[0056] Step 204: Perform structured and unstructured meshing on the bend area and pipeline system of the pipeline model to obtain the initial mesh model.

[0057] In this embodiment, for the bend area with regular structure, ICEM (Integrated Computer Engineering and Manufacturing) software is used to draw a structured mesh to ensure fast calculation speed, good convergence and high calculation accuracy; for the actual pipeline system with complex structure, an unstructured mesh is drawn to realize the rapid mesh generation of complex structure. After integrating the two meshes, the initial mesh model of the pipeline is obtained.

[0058] Step 205: The pipe cross-section of the initial mesh model is segmented and the boundary layer mesh is grown to obtain the optimized boundary layer mesh.

[0059] In this embodiment, all cross-sections of the pipe are meshed using an O-shaped cutting method. From the pipe wall boundary layer inward, the mesh length gradually increases at a multiplier of 1.1, thereby accurately capturing the flow boundary layer characteristics near the pipe wall and improving the flow field calculation accuracy in the near-wall region.

[0060] Step 206: Perform local refinement and mesh size matching processing on the optimized boundary layer mesh to obtain the computational mesh model.

[0061] In this embodiment, regions with large flow gradients inside the elbow are identified, and the mesh in these regions is locally refined. At the same time, the mesh size at the junction of the straight pipe and the elbow is adjusted to make the mesh sizes of the two similar, so as to avoid calculation errors caused by excessive differences in mesh volume. Finally, a computational mesh model with a total of 908,189 meshes is obtained.

[0062] This application's embodiments ensure the full development of the inlet gas-liquid two-phase flow by rationally setting pipeline geometric parameters, avoiding interference from boundary conditions on the phase distribution inside the elbow; classify elbow configurations to cover all common installation forms in natural gas gathering and transmission pipelines; adopt a regional meshing strategy, using structured meshes in regularly structured elbow regions to ensure computational accuracy and convergence, and using unstructured meshes in complex pipeline systems to improve modeling efficiency; through O-shaped partitioning, boundary layer mesh growth, local refinement, and mesh size matching, the computational accuracy in near-wall regions and regions with large flow gradients is effectively improved, numerical errors are reduced, and a foundation is laid for subsequent accurate numerical simulations.

[0063] In some embodiments, a numerical calculation model and boundary conditions for gas-liquid two-phase flow are obtained, and parameter settings are applied to the numerical calculation model and boundary conditions to obtain a simulation calculation configuration, including: obtaining a continuous phase control model, a turbulence calculation model, a discrete phase calculation model, a two-phase interface tracking model, and a numerical solver for gas-liquid two-phase flow, to obtain a set of basic numerical calculation models.

[0064] Specifically, a continuous phase control model based on the laws of conservation of mass and momentum is obtained to describe the flow law of continuous phase fluid in the pipeline; a standard turbulence calculation model suitable for turbulent flow in the pipeline is obtained; a discrete phase calculation model is obtained to describe the motion law of liquid phase particles; a volume fraction method two-phase interface tracking model is obtained to track the gas-liquid two-phase interface; and a pressure-based transient numerical solver suitable for gas-liquid two-phase flow calculation is obtained. The above models and solvers are integrated to obtain a set of basic numerical calculation models.

[0065] Furthermore, parameter configuration processing is performed on the set of basic numerical calculation models to obtain the numerical calculation model configuration.

[0066] Specifically, the standard turbulence calculation model is configured with corresponding empirical constants, including the constants for turbulent kinetic energy generation, turbulent dissipation rate generation, and turbulent viscosity. The Prandtl number for turbulent kinetic energy and the Prandtl number for turbulent energy dissipation rate are also set. Assumptions are made for the discrete phase calculation model, assuming the discrete phase particles are spherical, ignoring inter-particle collisions, virtual mass forces, pressure gradient forces, and Suffman lift, and establishing the force governing equations for the discrete phase particles. Basic parameter configurations are performed for the two-phase interface tracking model and the numerical solver. After completing the parameter settings for all models, the numerical calculation model configuration is obtained.

[0067] Furthermore, the inlet boundary type, outlet boundary type, and pipe wall boundary type of the pipeline are obtained to obtain the basic boundary condition set.

[0068] Specifically, based on the actual operating conditions of the natural gas gathering and transmission pipeline, the pipeline inlet adopts a velocity boundary type, the outlet adopts a pressure boundary type, and the pipe wall adopts a non-slip wall boundary type. The above three boundary types are integrated to obtain the basic boundary condition set.

[0069] Furthermore, the basic boundary condition set is processed by parameter configuration to obtain the boundary condition configuration.

[0070] Specifically, parameters are set for the inlet velocity boundary, with the apparent flow velocity of the oil phase input, the turbulence intensity set to 1%, and the hydraulic diameter set to 0.07 meters; parameters are set for the outlet pressure boundary, using the default operating pressure; parameters are set for the pipe wall non-slip boundary, using the standard wall function method, assuming the fluid is stagnant on the pipe wall. After completing the parameter settings for all boundary conditions, the boundary condition configuration is obtained.

[0071] Furthermore, the numerical calculation model configuration and boundary condition configuration are integrated to obtain the simulation calculation configuration.

[0072] Specifically, the configured continuous phase control model, turbulence calculation model, discrete phase calculation model, two-phase interface tracking model, and numerical solver are integrated with the configured inlet boundary, outlet boundary, and pipe wall boundary to form a complete numerical simulation calculation configuration for gas-liquid two-phase flow, which is used for subsequent multi-condition numerical simulation calculations.

[0073] This application embodiment acquires and configures a continuous phase control model, turbulence calculation model, discrete phase calculation model, two-phase interface tracking model, and numerical solver suitable for gas-liquid two-phase flow, which can accurately describe the flow law, phase distribution characteristics, and interface evolution process of gas-liquid two-phase flow in a pipeline. It sets inlet, outlet, and pipe wall boundary conditions in combination with actual on-site operating conditions, making the numerical simulation closer to engineering reality. The configured numerical calculation model and boundary conditions are integrated to form a complete and standardized simulation calculation configuration, ensuring the stability of the numerical simulation process and the reliability of the calculation results.

[0074] In some embodiments, multi-condition numerical simulation processing is performed on the computational grid model and the simulation computation configuration to obtain a simulated dataset of liquid accumulation thickness under different conditions, including: setting reflection conditions and roughness parameters for the pipe wall boundary in the simulation computation configuration to obtain the processed boundary condition configuration.

[0075] Specifically, based on the existing pipe wall boundary configuration, a reflective boundary is added to simulate the motion characteristics of the fluid after colliding with the pipe wall. At the same time, according to the actual wall roughness of the natural gas gathering and transmission pipeline, the wall roughness height is set to 0.5 mm and the roughness constant is 0.5 to complete the pipe wall boundary conditions and obtain the processed boundary condition configuration.

[0076] Furthermore, the processed boundary condition configuration and computational grid model are subjected to pressure coupling solution algorithm settings to obtain the flow field solution configuration.

[0077] Specifically, a semi-implicit method for solving the pressure coupling equations is selected as the algorithm for solving the pressure and velocity fields. This algorithm gradually improves the convergence of the solution through an iterative process of prediction and correction, ensuring the integral conservation of physical quantities within any control volume, while efficiently handling complex flow field structures. The algorithm is matched with the processed boundary condition configuration and computational grid model to obtain the flow field solution configuration.

[0078] Furthermore, the flow field solution configuration is processed by setting the discretization format for the pressure and convection terms to obtain the discretized calculation configuration.

[0079] Specifically, a second-order discretization scheme is adopted for the pressure term to improve the calculation accuracy of the pressure field; a second-order upwind discretization scheme is adopted for the convection term. This scheme is an improvement on the first-order upwind scheme. By considering more information from neighboring nodes and assigning appropriate weights, it can effectively reduce numerical dissipation while maintaining the stability of numerical calculation; after completing the settings of all discretization schemes, the discretization calculation configuration is obtained.

[0080] Furthermore, the residual threshold and maximum number of iterations are set for the discretization calculation configuration to obtain the simulation convergence conditions.

[0081] Specifically, considering the characteristics of steady-state numerical simulation, the convergence criterion is set to ensure that the residual of each equation in the control volume is no greater than 0.1%, thus guaranteeing the accuracy of the calculation results. At the same time, the maximum number of iterations is set to 2000 to avoid infinite loops caused by non-convergence of calculations, balancing calculation accuracy and calculation efficiency, and finally obtaining the simulation convergence condition.

[0082] Furthermore, multiple sets of steady-state numerical simulations were performed on the computational grid model, discretized computational configuration, and simulation convergence conditions to obtain simulation datasets of liquid accumulation thickness under different working conditions.

[0083] Specifically, based on multiple sets of operating parameters designed by orthogonal experiment, different combinations of gas phase flow velocity and inlet water content are input sequentially to carry out steady-state numerical simulation calculations. After each set of simulation calculations is completed, the maximum liquid accumulation thickness data of the elbow and its adjacent pipe sections are extracted, and the liquid accumulation thickness data under all operating conditions are summarized and organized to obtain the liquid accumulation thickness simulation dataset under different operating conditions.

[0084] This application's embodiments, by supplementing the pipe wall reflection conditions and roughness parameters, more realistically simulate the interaction between the fluid and the actual pipe wall; employing a semi-implicit solution algorithm for the pressure coupling equation ensures the conservation and convergence of the flow field solution; using a second-order discretization scheme for the pressure and convection terms effectively reduces numerical dissipation and improves the accuracy of flow field calculations; reasonably setting the residual threshold and maximum number of iterations balances computational accuracy and efficiency; and based on a simulated dataset of liquid accumulation thickness obtained from multiple steady-state numerical simulations, covering typical operating conditions of natural gas gathering and transmission pipelines, providing a sufficient and reliable data foundation for the establishment of subsequent prediction models.

[0085] In some embodiments, orthogonal experimental design and range analysis are performed on the liquid accumulation thickness simulation dataset to obtain significant influencing factors, including: extracting influencing factors and corresponding level parameters from the liquid accumulation thickness simulation dataset to obtain the basic parameters of the orthogonal experiment.

[0086] Specifically, from the operating parameters corresponding to the liquid accumulation thickness simulation dataset, the main factors affecting liquid accumulation in natural gas pipelines were selected, including pipeline inclination angle, gas phase velocity, and inlet water content. Five levels were set for each factor, with the pipeline inclination angle at 90 degrees for all five levels, the gas phase velocity at 0.5 m / s, 1 m / s, 1.5 m / s, 2 m / s, and 3 m / s for the five levels, and the inlet water content at 0.02, 0.06, 0.1, 0.14, and 0.2 for the five levels. The above influencing factors and their corresponding level parameters were integrated to obtain the basic parameters for the orthogonal experiment.

[0087] Furthermore, the factor level combination design was performed on the basic parameters of the orthogonal experiment to obtain multiple sets of orthogonal experimental conditions.

[0088] Specifically, based on the principle of orthogonal experimental design, different levels of pipeline inclination angle, gas phase velocity and inlet moisture content were scientifically combined to design 18 sets of orthogonal experimental conditions covering typical operating ranges. Each set of conditions corresponds to a unique combination of gas phase velocity and inlet moisture content, which is used for subsequent result analysis and factor significance judgment.

[0089] Furthermore, the orthogonal test conditions and liquid accumulation thickness simulation datasets were matched and extracted to obtain the orthogonal test results data.

[0090] Specifically, the 18 sets of orthogonal test conditions were matched one by one with the simulated liquid thickness dataset obtained from the previous multi-condition numerical simulation. The maximum liquid thickness data of the elbow and adjacent pipe sections corresponding to each set of orthogonal test conditions were extracted. All the matched data were sorted and summarized to obtain the orthogonal test results data.

[0091] Furthermore, statistical calculations were performed on the orthogonal experiment results data for each factor level to obtain the average liquid accumulation thickness at different factor levels.

[0092] Specifically, calculate the sum of liquid accumulation thickness for each influencing factor at each level across all corresponding test conditions; then divide the sum of liquid accumulation thickness at each level by the number of test conditions included at that level to obtain the average liquid accumulation thickness at different levels for each factor; by comparing the average values ​​of the same factor at different levels, the influence trend of different values ​​of the factor on liquid accumulation thickness can be observed intuitively.

[0093] Furthermore, the range of the average liquid accumulation thickness at different levels of each factor was calculated and significance was determined to identify the significant influencing factors.

[0094] Specifically, the range value corresponding to each influencing factor is calculated. The range value is the difference between the maximum and minimum values ​​of the average liquid thickness at all levels of the factor. The influence of each factor is ranked according to the size of the range value. The larger the range value, the more significant the influence of the factor on the liquid thickness, and vice versa. Finally, based on the ranking results of the range values, the factors that have a significant impact on the liquid thickness in the pipeline are selected.

[0095] This application's embodiments employ orthogonal experimental design, covering multi-factor, multi-level parameter combinations with fewer trials, significantly reducing the computational load of numerical simulation. Range analysis enables rapid and accurate quantification of the influence of each factor on the thickness of the effluent, scientifically screening out significant influencing factors and eliminating interference from secondary factors. This provides a reasonable basis for selecting independent variables for subsequent response surface fitting, simplifies the structure of the prediction model, and improves the model's prediction accuracy and generalization ability.

[0096] In some embodiments, response surface fitting is performed on the simulated dataset of significant influencing factors and liquid accumulation thickness to obtain a fast prediction model for pipe liquid accumulation thickness, including: data extraction processing of the simulated dataset of significant influencing factors and liquid accumulation thickness to obtain basic data for response surface fitting.

[0097] Specifically, significant influencing factors were used as independent variables for response surface fitting, and liquid accumulation thickness was used as the dependent variable. From the simulated liquid accumulation thickness dataset, all operating condition data corresponding to pipes with a nominal diameter of 300 mm were extracted, including the parameter values ​​of significant influencing factors for each operating condition and the corresponding maximum liquid accumulation thickness data for elbows and adjacent pipe sections. The extracted independent variable data and dependent variable data were matched one-to-one, and outliers were removed to obtain the basic data for response surface fitting.

[0098] Furthermore, the response surface equation is fitted to the basic data to obtain the initial liquid accumulation thickness prediction equation.

[0099] Specifically, a response surface fitting program is written using numerical calculation software, and the basic data for response surface fitting is imported into the program; a quadratic polynomial response surface model is selected for fitting calculation, which can accurately describe the nonlinear relationship between independent and dependent variables and the interaction between factors; the coefficients of each term in the model are solved by the least squares method to obtain the initial liquid accumulation thickness prediction equation containing constant terms, linear terms, quadratic terms and interaction terms.

[0100] Furthermore, the fitting effect of the initial liquid accumulation thickness prediction equation is evaluated to obtain the fitting effect evaluation results.

[0101] Specifically, the coefficient of determination of the initial liquid accumulation thickness prediction equation is calculated. The coefficient of determination is used to measure the degree of fit of the regression equation to the observed data. Its value ranges from 0 to 1. The closer the value is to 1, the better the fit. At the same time, the adjusted coefficient of determination and F-test value of the equation are calculated. The overall significance of the model and the significance of each coefficient are comprehensively evaluated to obtain a comprehensive evaluation result of the fit effect.

[0102] Furthermore, the fitting effect evaluation results are processed to determine compliance, and the liquid accumulation thickness prediction equation is obtained.

[0103] Specifically, the preset standard for a good fit is a coefficient of determination of not less than 0.9. The calculated coefficient of determination is compared with the preset standard. When the coefficient of determination reaches 0.9, the initial liquid accumulation thickness prediction equation is considered to have a good fit. If the coefficient of determination does not reach the preset standard, the order of the response surface model is readjusted or more simulation data is added, and the fitting calculation is performed again until the fit meets the standard, and finally a liquid accumulation thickness prediction equation that meets the requirements is obtained.

[0104] Furthermore, the applicable scope of the liquid accumulation thickness prediction equation is clarified to obtain a rapid prediction model for the liquid accumulation thickness in pipelines.

[0105] Specifically, based on the parameter range of the response surface data used for fitting, the applicable conditions of the liquid accumulation thickness prediction equation are determined; the applicable range of gas phase flow velocity is 0.5 m / s to 3 m / s, and the applicable range of water phase volume fraction is 0.02 to 0.2; by integrating the liquid accumulation thickness prediction equation with its applicable range, the final rapid prediction model for pipeline liquid accumulation thickness is obtained.

[0106] This application's embodiments avoid the influence of redundant variables on the model by fitting response surfaces based on the screened significant influencing factors; employing a quadratic polynomial response surface model, it can accurately describe the nonlinear relationship between independent variables and liquid accumulation thickness, as well as the interaction between factors; evaluating the fitting effect through indicators such as the coefficient of determination ensures the reliability of the prediction model; clearly defining the applicable scope of the prediction model avoids model misuse; the final rapid prediction model for pipeline liquid accumulation thickness has low computational load and fast response speed, capable of completing calculations in milliseconds, meeting the needs of rapid on-site pipeline liquid accumulation risk assessment.

[0107] In some embodiments, the gas phase velocity and water phase volume fraction of the pipeline are obtained, and the gas phase velocity and water phase volume fraction are input into the pipeline liquid thickness prediction model for prediction processing to obtain the liquid thickness result of the pipeline. This includes: performing parameter acquisition processing on the gas phase velocity and water phase volume fraction of the pipeline to obtain the operating condition parameters to be predicted.

[0108] Specifically, by installing a gas flow meter and an online moisture content detector at the inlet of the pipeline to be predicted, the gas flow velocity and water volume fraction data in the pipeline are collected in real time. The collected raw data are filtered and denoised to remove interference signals caused by pipeline vibration, fluid pulsation and other factors, so as to obtain accurate operating parameters to be predicted.

[0109] Furthermore, the applicable range of the model is verified for the parameters of the operating conditions to be predicted, and effective prediction parameters are obtained.

[0110] Specifically, the parameters to be predicted are compared with the applicable range of the rapid prediction model for pipeline liquid thickness. The applicable range for gas phase flow velocity is 0.5 m / s to 3 m / s, and the applicable range for water phase volume fraction is 0.02 to 0.2. If the parameters to be predicted are within the above applicable range, they are determined to be valid prediction parameters. If the parameters exceed the applicable range, the prompt message "Parameters exceed the applicable range of the model, and effective prediction cannot be performed" is output, and the prediction process is terminated.

[0111] Furthermore, the effective prediction parameters and the rapid prediction model for pipeline liquid thickness are substituted into the calculation to obtain the initial liquid thickness calculation value.

[0112] Specifically, the verified gas phase velocity and water phase volume fraction are used as independent variables and substituted into the quadratic polynomial response surface equation corresponding to the rapid prediction model of pipeline liquid thickness. Numerical calculation is completed through simple algebraic operations to obtain the initial liquid thickness calculation value of the elbow and adjacent pipe section under the predicted working condition. The entire calculation process can be completed in milliseconds, realizing rapid prediction of liquid thickness.

[0113] Furthermore, the initial calculated liquid accumulation thickness is verified to obtain the verified liquid accumulation thickness value.

[0114] Specifically, the initial liquid accumulation thickness calculation value is judged for reasonableness by combining basic parameters such as the nominal diameter and operating pressure of the pipeline to be predicted. If the initial calculated value is greater than half of the inner diameter of the pipeline, it is judged as an abnormal result, and the input parameters and model calling process are re-checked. If the initial calculated value is within a reasonable physical range, it is judged as a valid result, and the verified liquid accumulation thickness value is obtained.

[0115] Furthermore, the verified liquid accumulation thickness value is processed to obtain the liquid accumulation thickness result of the pipeline.

[0116] Specifically, the verified liquid accumulation thickness value is output in numerical form, along with the corresponding operating parameters and model applicability range for this prediction. The output results can be directly used for pipeline corrosion risk assessment, liquid drainage and cleaning cycle formulation, and operation and maintenance strategy optimization, providing data support for the safe and stable operation of natural gas gathering and transmission pipelines.

[0117] This application embodiment ensures the accuracy of the predicted operating condition parameters by filtering and denoising the input parameters; it adds a model applicability range verification step to avoid erroneous prediction results when the model exceeds its applicable range; it verifies the rationality of the initial calculated values ​​to further eliminate abnormal results and improve the reliability of the prediction results; the entire prediction process is highly automated and simple to operate, and the output results can be directly used for pipeline operation and maintenance decisions, effectively improving the efficiency and scientific nature of natural gas gathering and transmission pipeline safety management.

[0118] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0119] like Figure 3 As shown in the illustration, this application also provides a pipe liquid accumulation thickness prediction device 300. The device includes: Module 301 is used to construct a physical model of the pipeline to obtain a pipeline model, and then to perform mesh generation on the pipeline model to obtain a computational mesh model.

[0120] The acquisition module 302 is used to acquire the numerical calculation model and boundary conditions of the gas-liquid two-phase flow, and to perform parameter setting processing on the numerical calculation model and boundary conditions to obtain the simulation calculation configuration.

[0121] The processing module 303 is used to perform multi-condition numerical simulation processing on the computational grid model and simulation configuration to obtain simulation datasets of liquid accumulation thickness under different conditions.

[0122] The processing module 303 is also used to perform orthogonal experimental design and range analysis on the simulated liquid thickness dataset to obtain significant influencing factors.

[0123] The processing module 303 is also used to perform response surface fitting on the simulation dataset of significant influencing factors and liquid accumulation thickness to obtain a fast prediction model for the liquid accumulation thickness in the pipeline.

[0124] The acquisition module 302 is further configured to acquire the gas phase velocity and water phase volume fraction of the pipeline, input the gas phase velocity and water phase volume fraction into the pipeline liquid thickness prediction model for prediction processing, and obtain the liquid thickness result of the pipeline.

[0125] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0126] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0127] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, such as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module implementing the method or a structure within a hardware component.

[0128] This application also provides an apparatus, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein, when the processor executes the executable instructions, it implements the method described in this application.

[0129] This application also provides a non-volatile computer-readable storage medium storing a computer program or instructions thereon, which, when executed, enables the method described in this application embodiment to be implemented.

[0130] Furthermore, in the various embodiments of the present invention, each functional module can be integrated into a processing module, or each module can exist independently, or two or more modules can be integrated into a single module.

[0131] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0132] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0133] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0134] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for predicting the thickness of liquid accumulation in a pipeline, characterized in that, include: The pipeline is physically modeled to obtain a pipeline model, and the pipeline model is then meshed to obtain a computational mesh model. A numerical calculation model and boundary conditions for gas-liquid two-phase flow are obtained, and the parameters of the numerical calculation model and boundary conditions are set to obtain the simulation calculation configuration. Multi-condition numerical simulation processing is performed on the computational grid model and the simulation computation configuration to obtain simulated datasets of liquid accumulation thickness under different conditions. Orthogonal experimental design and range analysis were performed on the simulated liquid thickness dataset to obtain significant influencing factors; Response surface methodology was applied to the significant influencing factors and the simulated liquid thickness dataset to obtain a fast prediction model for pipe liquid thickness. The gas phase velocity and water phase volume fraction of the pipeline are obtained, and the gas phase velocity and water phase volume fraction are input into the pipeline liquid thickness prediction model for prediction processing to obtain the liquid thickness result of the pipeline.

2. The method according to claim 1, characterized in that, The process of constructing a physical model of the pipeline to obtain a pipeline model, and then performing mesh generation on the pipeline model to obtain a computational mesh model, includes: The inlet straight pipe section length, outlet straight pipe section length, and elbow curvature radius of the pipeline are set with geometric parameters to obtain the basic geometric parameters of the pipeline. The pipe is classified according to its elbow configuration to obtain the elbow configuration. The pipeline basic geometric parameters and the elbow configuration are integrated to obtain the pipeline model; The bend region and pipeline system of the pipeline model are processed by structured mesh and unstructured mesh respectively to obtain the initial mesh model; The pipe cross-section of the initial mesh model is segmented and the boundary layer mesh is grown to obtain the optimized boundary layer mesh. The optimized boundary layer mesh is locally refined and its size matched to obtain a computational mesh model.

3. The method according to claim 1, characterized in that, The process of obtaining the numerical calculation model and boundary conditions for the gas-liquid two-phase flow, and setting parameters for the numerical calculation model and boundary conditions to obtain the simulation calculation configuration includes: The continuous phase control model, turbulence calculation model, discrete phase calculation model, two-phase interface tracking model, and numerical solver of the gas-liquid two-phase flow are obtained to obtain the basic set of numerical calculation models. The set of basic numerical calculation models is processed to obtain the numerical calculation model configuration. Obtain the inlet boundary type, outlet boundary type, and pipe wall boundary type of the pipeline to obtain the basic boundary condition set; The basic boundary condition set is processed by parameter configuration to obtain the boundary condition configuration; The numerical calculation model configuration and the boundary condition configuration are integrated to obtain the simulation calculation configuration.

4. The method according to claim 1, characterized in that, The process of performing multi-condition numerical simulation on the computational grid model and the simulation configuration to obtain a simulated dataset of liquid accumulation thickness under different conditions includes: The reflection conditions and roughness parameters of the pipe wall boundary in the simulation calculation configuration are set to obtain the processed boundary condition configuration; The processed boundary condition configuration and the computational grid model are subjected to pressure coupling solution algorithm settings to obtain the flow field solution configuration; The flow field solution configuration is processed by setting the discretization format for the pressure and convection terms to obtain the discretized calculation configuration; The residual threshold and maximum number of iterations are set for the discretization calculation configuration to obtain the simulation convergence condition; Multiple sets of steady-state numerical simulations were performed on the computational grid model, the discretized computational configuration, and the simulation convergence conditions to obtain simulation datasets of liquid accumulation thickness under different working conditions.

5. The method according to claim 1, characterized in that, The orthogonal experimental design and range analysis of the simulated fluid thickness dataset yielded significant influencing factors, including: The influencing factors and corresponding level parameters of the simulated liquid thickness dataset are extracted and processed to obtain the basic parameters of the orthogonal experiment; The factor level combination design was performed on the basic parameters of the orthogonal experiment to obtain multiple sets of orthogonal experimental conditions. The orthogonal test conditions and the simulated liquid thickness dataset are matched and extracted to obtain orthogonal test result data. The orthogonal experiment results data were statistically processed for each factor level to obtain the average liquid accumulation thickness at different factor levels; The average thickness of the accumulated fluid at different levels of the aforementioned factors was calculated for range and significance was determined to identify the significant influencing factors.

6. The method according to claim 1, characterized in that, The process of performing response surface fitting on the significant influencing factors and the simulated liquid accumulation thickness dataset to obtain a fast prediction model for pipe liquid accumulation thickness includes: Data extraction and processing are performed on the significant influencing factors and the simulated liquid thickness dataset to obtain the basic data for response surface fitting; The response surface fitting data is subjected to response surface equation fitting to obtain the initial liquid accumulation thickness prediction equation. The fitting effect evaluation process is performed on the initial liquid accumulation thickness prediction equation to obtain the fitting effect evaluation result; The fitting effect evaluation results are processed to determine whether they meet the standards, and the liquid accumulation thickness prediction equation is obtained. The applicable scope of the liquid accumulation thickness prediction equation is clarified to obtain a rapid prediction model for the liquid accumulation thickness in the pipeline.

7. The method according to claim 1, characterized in that, The process of obtaining the gas phase velocity and water phase volume fraction in the pipeline, and inputting the gas phase velocity and water phase volume fraction into the pipeline liquid accumulation thickness fast prediction model for prediction processing to obtain the liquid accumulation thickness result in the pipeline includes: The gas phase velocity and water phase volume fraction of the pipeline are processed to obtain the parameters of the operating condition to be predicted. The applicable range of the model is verified for the parameters of the operating condition to be predicted, and effective prediction parameters are obtained. The effective prediction parameters and the rapid prediction model for the thickness of liquid accumulation in the pipeline are substituted into the calculation process to obtain the initial calculated value of the liquid accumulation thickness. The initial calculated liquid accumulation thickness is subjected to a rationality verification process to obtain the verified liquid accumulation thickness value. The verified liquid accumulation thickness value is processed to obtain the liquid accumulation thickness result of the pipeline.

8. A device for predicting the thickness of liquid accumulation in a pipeline, characterized in that, include: The construction module is used to perform physical model construction on the pipeline to obtain a pipeline model, and to perform mesh generation on the pipeline model to obtain a computational mesh model; The acquisition module is used to acquire the numerical calculation model and boundary conditions of the gas-liquid two-phase flow, and to perform parameter setting processing on the numerical calculation model and the boundary conditions to obtain the simulation calculation configuration. The processing module is used to perform multi-condition numerical simulation processing on the computational grid model and the simulation computation configuration to obtain a simulated dataset of liquid accumulation thickness under different conditions. The processing module is also used to perform orthogonal experimental design and range analysis on the simulated liquid thickness dataset to obtain significant influencing factors; The processing module is also used to perform response surface fitting on the significant influencing factors and the simulated liquid thickness dataset to obtain a fast prediction model for pipe liquid thickness. The acquisition module is further configured to acquire the gas phase velocity and water phase volume fraction of the pipeline, input the gas phase velocity and water phase volume fraction into the pipeline liquid thickness prediction model for prediction processing, and obtain the liquid thickness result of the pipeline.

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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