Flow characteristic analysis method and equipment based on electrical resistance tomography and medium

Abstract

The embodiment of the invention provides a flow characteristic analysis method and device based on electrical resistance tomography and a medium. According to the flow characteristic analysis method based on electrical resistance tomography provided by the embodiment of the invention, the multiphase flow test is carried out by using the test material and the test pipeline, and the test multiphase flow process parameters are obtained by combining the electrical resistance tomography technology; the preset material parameters of the test material and the preset conveying parameters of the test pipeline can be used for inputting the initial simulation model, the simulation multiphase flow process parameters obtained through simulation trial calculation can be compared with the test multiphase flow process parameters under the condition, and the preset model parameters of the simulation model are adjusted based on the comparison difference between the simulation multiphase flow process parameters and the test multiphase flow process parameters. The simulation result of the finally obtained target simulation model can be closer to the actual test result, so that the target simulation model has better multiphase flow analysis and prediction capability, and more accurate analysis of multiphase flow flow characteristics is realized.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and in particular to a flow characteristic analysis method, device and medium based on electrical resistance tomography, and in particular to a flow characteristic analysis method, device and storage medium integrating multiphase flow particle tracing and electrical resistance tomography. Background Art

[0002] Fluidized bed can use gas or liquid to pass through the granular solid layer to make the solid particles in suspension and carry out gas-solid phase reaction process or liquid-solid phase reaction process. When the superficial velocity in the fluidized bed is high, and the content of granular solid is high or the replenishment rate is fast, multiphase flow will be formed.

[0003] At present, mathematical models such as the Euler-Euler model and the Euler-Lagrange model have been applied to describe the complex gas-solid or liquid-solid flow characteristics in a multiphase fluidized bed, and provide a theoretical basis for the computational fluid dynamics (CFD) simulation of a multiphase fluidized bed.

[0004] However, due to the complex interactions between particles and between particles and liquid / gas in different multiphase flows, the simulation results such as flow characteristics obtained based on CFD simulation may be significantly different from the actual results. Summary of the invention

[0005] The embodiments of the present application provide a flow characteristics analysis method, device and medium based on electrical resistance tomography to solve the problem in the related art that there is a large difference between the simulation results and the actual results of the multiphase flow characteristics.

[0006] In order to solve the above technical problems, this application is implemented as follows:

[0007] In a first aspect, an embodiment of the present application provides a flow characteristic analysis method based on electrical resistance tomography, comprising:

[0008] In the process of a test material with preset material parameters flowing in a test pipeline according to preset conveying parameters, the test multiphase flow process parameters of the test pipeline are obtained by combining electrical resistance tomography technology;

[0009] An initial simulation model is constructed based on preset model parameters, and a simulation trial calculation is performed on the initial simulation model based on preset material parameters, preset material parameters, and preset material parameters and preset conveying parameters to obtain simulated multiphase flow process parameters;

[0010] Based on the difference between the preset material parameter test multiphase flow process parameters and the preset material parameter simulation multiphase flow process parameters, the preset material parameter preset model parameters are adjusted to obtain the target simulation model;

[0011] Analyze multiphase flow characteristics based on preset material parameter target simulation model.

[0012] In a second aspect, the present application also provides a flow characteristic analysis device based on electrical resistance tomography, comprising:

[0013] An acquisition module, used for acquiring test multiphase flow process parameters of the test pipeline with preset material parameters in combination with electrical resistance tomography technology when a test material with preset material parameters flows in the test pipeline according to preset conveying parameters;

[0014] A trial calculation module is used to construct an initial simulation model based on preset model parameters, and to simulate and calculate the initial simulation model based on preset material parameters, preset material parameters and preset material parameters and preset conveying parameters to obtain simulated multiphase flow process parameters;

[0015] An adjustment module, used for adjusting preset material parameters and preset model parameters based on the difference between preset material parameter test multiphase flow process parameters and preset material parameter simulation multiphase flow process parameters to obtain a target simulation model;

[0016] The analysis module is used to analyze the flow characteristics of multiphase flow based on the preset material parameter target simulation model.

[0017] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the above method is implemented when the processor executes the computer program.

[0018] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program implements the above method when executed by a processor.

[0019] The flow characteristic analysis method based on electrical resistance tomography provided in the embodiment of the present application uses test materials and test pipelines to carry out multiphase flow tests, and combines electrical resistance tomography technology to obtain the test multiphase flow process parameters. The preset material parameters of the test materials and the preset conveying parameters of the test pipeline can be used as the input of the initial simulation model. In this case, the simulated multiphase flow process parameters obtained by simulation trial calculation can be compared with the test multiphase flow process parameters, and the preset model parameters of the simulation model are adjusted based on the difference between the two, so that the simulation results of the target simulation model finally obtained can be closer to the actual test results, thereby enabling the target simulation model to have better multiphase flow analysis and prediction capabilities, thereby achieving a more accurate analysis of the multiphase flow characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a flow chart of a flow characteristics analysis method based on electrical resistance tomography provided in an embodiment of the present application;

[0021] Figure 2 This is a structural example diagram of a multiphase flow test platform;

[0022] Figure 3 It is a structural schematic diagram of the electrical resistance tomography system;

[0023] Figure 4 Schematic diagram of the working principle of the electrical resistance tomography system based on the ERT sensor;

[0024] Figure 5 It is a schematic diagram of the working principle of the combination of ring tube experiment and simulation;

[0025] Figure 6 This is an example diagram of the particle size distribution calculation curve based on the Rosin-Rammler distribution function;

[0026] Figure 7 A schematic diagram of the structure of a flow characteristic analysis device based on electrical resistance tomography provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the technical problems, technical solutions and advantages to be solved by the application clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to help fully understand the embodiments of the application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the application. In addition, for clarity and brevity, the description of known functions and structures has been omitted.

[0028] Unless otherwise defined, the technical terms or scientific terms used in this application shall have the usual meanings understood by persons with ordinary skills in the field to which this application belongs. The words "first", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, the words "one" or "an" and similar words do not indicate a quantitative limitation, but indicate the presence of at least one.

[0029] like Figure 1 As shown, the flow characteristic analysis method based on electrical resistance tomography provided in the embodiment of the present application includes:

[0030] Step 101, in the process of a test material with preset material parameters flowing in a test pipeline according to preset conveying parameters, a test multiphase flow process parameter of the test pipeline with preset material parameters is obtained by combining electrical resistance tomography technology;

[0031] Step 102, constructing an initial simulation model based on preset model parameters, and performing simulation calculations on the initial simulation model based on preset material parameters, preset material parameters and preset material parameters and preset conveying parameters to obtain simulated multiphase flow process parameters;

[0032] Step 103, adjusting the preset material parameter preset model parameters based on the difference between the preset material parameter test multiphase flow process parameters and the preset material parameter simulation multiphase flow process parameters to obtain a target simulation model;

[0033] Step 104: Analyze the multiphase flow characteristics based on the preset material parameter target simulation model.

[0034] The flow characteristic analysis method based on electrical resistance tomography provided in the embodiment of the present application uses test materials and test pipelines to carry out multiphase flow tests, and combines electrical resistance tomography technology to obtain the test multiphase flow process parameters. The preset material parameters of the test materials and the preset conveying parameters of the test pipeline can be used as the input of the initial simulation model. In this case, the simulated multiphase flow process parameters obtained by simulation trial calculation can be compared with the test multiphase flow process parameters, and the preset model parameters of the simulation model are adjusted based on the difference between the two, so that the simulation results of the target simulation model finally obtained can be closer to the actual test results, thereby enabling the target simulation model to have better multiphase flow analysis and prediction capabilities, thereby achieving a more accurate analysis of the multiphase flow characteristics.

[0035] In order to better understand the implementation process of the method provided in the embodiment of the present application, some feasible multiphase flow test platforms including test materials and test pipelines are exemplified below.

[0036] like Figure 2 As shown, in some examples, the multiphase flow test platform includes a test pipe 201, a mixer 202, a pump 203, an electrical resistance tomography (ERT) sensor 204, and a processor 205. The test pipe 201 is filled with a test material, the mixer 202, the pump 203, and the ERT sensor 204 can be sequentially arranged on the test pipe, and the processor 205 is electrically connected to the ERT sensor 204.

[0037] The test material can be a mixture of solid particles and liquid. Of course, in some feasible implementations, the test material can also be a mixture of solid particles and gas, etc. In actual operation, test materials of different concentrations can be designed and prepared for testing. For example, when the analysis object is dense phase flow particles, the test material can be a slurry composed of tailings, cement, and water mixed in a certain ratio, and the slurry concentration parameter is controlled to be above 60%. To simplify the description, the following embodiments are mainly introduced by taking the test material as dense phase flow particles as an example.

[0038] The test pipe 201 can be connected end to end to form a ring pipe, or can also be designed in a non-end to end form. In some embodiments, the test pipe 201 can include pipe fittings such as flat pipes, curved pipes, horizontal pipes, inclined pipes, etc., and the number, bending radius, bending angle, inclination angle, etc. of the pipe fittings can be selected as needed.

[0039] The mixer 202 may include a mixing barrel and a mixing blade, etc., for fully mixing the test material. The pump 203 may be used to drive the test material to flow in the test pipe 201. In some embodiments, the pump 203 may have the ability to adjust the pumping flow rate to meet the test requirements of different test material flow rates.

[0040] The ERT sensor 204 may be disposed in a particle-enriched region of the test pipe 201, such as on a bend of the test pipe 201, or the ERT sensor 204 may be disposed at other locations where analysis is required.

[0041] As some examples, a 16-electrode ERT sensor 204 is set in the particle enrichment area, and rectangular electrodes are used, and measurement lines are arranged along both sides of the parallel test pipeline 201. The adjacent mode is used as the data acquisition mode, the adjacent current excitation is used as the excitation mode, the bipolar pulse current source is used as the excitation source, the excitation current is a constant current source of 10mA, and 208 voltage signals are sampled in 16 measurement cycles. Of course, the number of electrodes, electrode shape and other parameters of the ERT sensor 204 can be selected as needed. In some examples, according to the resistance test along the test pipeline 201 and the real-time imaging detection of the flow pattern, 3-5 adjustable monitoring areas are set to install the ERT sensor 204 to meet different monitoring needs.

[0042] like Figure 3 As shown, the electrical resistance tomography system based on the ERT sensor 204 generally includes a sensor unit, a data acquisition and processing unit, and an image reconstruction unit. The sensor unit includes an electrode sensor array to collect current or voltage signals, and the data acquisition and processing unit includes a data acquisition and excitation module, an A / D conversion and control module, and a communication module. The specific working process of the electrical resistance tomography technology will be further illustrated below.

[0043] The processor 205 can be used to execute the above-mentioned flow characteristic analysis method based on electrical resistance tomography. In addition to being electrically connected to the ERT sensor 204, the processor 205 can also be electrically connected to the mixer 202 and the pump 203 to control the start and stop of the mixer 202, or control the pumping flow of the pump 203.

[0044] In other examples, the multiphase flow test platform may further include other sensors, such as a pressure sensor 206, a flow sensor 207, and a flow velocity sensor 208, etc., for collecting pressure data, flow data, and flow velocity data in the test pipeline, etc. The number, type, and location of the sensors may be set as required.

[0045] Taking the test pipe 201 as an annular tube as an example, before conducting an annular tube test on the test material, a clean water test can be conducted first to check the air tightness of the annular tube test and whether the pumping flow of the pump 203 has the ability to be adjusted, and to ensure that instruments such as the ERT sensor 204 are working properly.

[0046] In some embodiments, the preset material parameter test material contains tracer particles. On this basis, the above step 101, the preset material parameter combined with the electrical resistance tomography technology to obtain the test multiphase flow process parameters of the preset material parameter test pipeline, may include:

[0047] Obtain the resistance tomography results of the preset material parameter test pipeline based on the resistance tomography technology, and obtain the tracing observation data of the preset material parameter tracing particles based on the particle tracing technology;

[0048] The preset material parameter test multiphase flow process parameters are obtained by fusing the preset material parameter resistance tomography results and the preset material parameter tracer observation data.

[0049] In this embodiment, the particle tracing and electrical resistance tomography techniques may be integrated to obtain the test multiphase flow process parameters of the test pipeline, thereby effectively improving the accuracy and rationality of the test multiphase flow process parameters.

[0050] The following is an exemplary description of the method for obtaining the experimental multiphase flow process parameters and the specific composition of the experimental multiphase flow process parameters.

[0051] like Figure 4 As shown in the figure, when the ERT sensors and other components are arranged according to the multiphase flow test platform, a multi-channel high-frequency data acquisition system can be used to collect the voltage and current signals of each electrode. During the data acquisition process, time domain filtering and spatial filtering techniques can be applied to suppress noise and eliminate environmental interference. The data is standardized to improve the reconstruction accuracy of the electrical resistance tomography image.

[0052] The collected voltage signal is imaged and reconstructed using an iterative back projection (IBP) algorithm to obtain the resistance tomography results, which reflect the conductivity changes of particles distributed in the fluid. The high-precision conductivity distribution map can clearly depict the location, size and dynamic evolution of the particle-enriched area.

[0053] Combined with the dense phase flow particle tracing technology, tracer particles with conductivity different from that of the matrix are added to the test material. The position and movement trajectory of these particles are tracked in real time by the imaging system, and the parameters such as the concentration flow pattern, sensitivity, and phase content of the dense phase flow particles are monitored in real time and accurately to obtain the flow velocity, diffusion range, and local aggregation characteristics of the particles. These tracer observation data can help to deeply understand the macroscopic characteristics and microscopic behavior of particle flow.

[0054] By analyzing the time series of imaging data collected continuously during the filling process, the evolution of the particle-enriched area in the flow is monitored, including the expansion and contraction of its shape, the migration of its position, and the change of the enrichment intensity. Through quantitative parameters such as the area of ​​the particle-enriched area and the speed of change of the center position, the dynamic evolution law of the manifold is analyzed and possible unstable factors are revealed.

[0055] When the resistance tomography results are obtained based on the resistance tomography technology and the tracer observation data are obtained based on the particle tracer technology, the multi-source data can be fused, for example, using Kalman filtering to conduct a comprehensive analysis of the multi-source data. Through data fusion, the uniformity of the flow, the degree of particle enrichment, and the stability of the overall flow can be accurately evaluated to find out the key factors affecting the flow characteristics.

[0056] In some examples, the test multiphase flow process parameters may include phase content and particle size distribution, etc., or, further, may be the phase content and particle size distribution of the particle-enriched area of ​​the test pipeline, etc. In combination with the above embodiments, the test multiphase flow process parameters may be obtained by fusing the electrical resistance tomography results and the preset material parameter tracer observation data, or may be obtained based solely on the electrical resistance tomography results.

[0057] In other examples, the test multiphase flow process parameters may also include parameters such as pressure, flow rate and flow velocity, which can be obtained by setting corresponding sensors on the test pipeline. Accordingly, in some embodiments, the processor may fuse the preset material parameter resistance tomography results, the pressure data collected by the preset material parameter pressure sensor, the flow data collected by the preset material parameter flow sensor, and the flow velocity data collected by the preset material parameter flow velocity sensor to obtain the preset material parameter test multiphase flow process parameters. In other embodiments, the processor may further combine the preset material parameter tracer observation data to obtain the test multiphase flow process parameters.

[0058] As shown above, in actual operation, test materials of different concentrations can be designed and prepared for testing. At the same time, the pumping flow rate of the pump can also be adjusted, so that different annular pipe tests can be carried out to simulate the actual pipeline transportation process and obtain test parameters such as slurry flow rate, mass concentration and particle size. These test parameters can be used directly or after data processing as test multiphase flow process parameters.

[0059] The following is an example of the specific implementation process of an EPC test.

[0060] In this ring pipe test, the test pipelines selected three ring pipe layout forms: horizontal transmission pipelines, inclined downward transmission pipelines, and inclined upward transmission pipelines. The ring pipe test system uses seamless steel pipes, and the pipelines use quick connections. The collection and recording of the pipe transmission resistance data at the test platform pipeline measuring points are transmitted by remote pressure transmitters. The device for this ring pipe test needs to be installed with pressure sensors, flow sensors, flow velocity sensors, ERT sensors and other instruments, and connect the signals with the processor to maintain a good signal-to-noise ratio to ensure the accuracy and reliability of the data;

[0061] The tailings, cement and water are stirred in a mixer for 10-20 minutes according to a predetermined ratio to form a uniform slurry. The slurry is adjusted from thick to thin to ensure the uniformity and stability of the slurry. After preparation, it is placed in the hopper of the slurry pump, and the slurry circulates in the pipeline with the pressure provided by the slurry pump. Each time the concentration is changed, it is necessary to continuously pump and circulate and stir for 10-20 minutes to ensure the uniformity of the slurry and the accuracy of the test.

[0062] The slurry flow rate is changed by changing the pumping displacement. The flow rate, concentration and pipeline pressure data are collected by flow sensors, pressure sensors and ERT sensors and transmitted to the processor through the data collector and recorded. These data will be used for subsequent data analysis and evaluation of test results. During the test, the slurry flow rate, pressure and concentration are monitored in real time, and the pumping displacement and stirring time are adjusted as needed to ensure the stability of the test conditions and the reliability of the test data.

[0063] By installing flow sensors and flow velocity sensors in the pipeline to monitor the instantaneous flow, cumulative flow, average flow velocity, and flow velocity distribution in the pipeline, the measured flow rate, flow velocity and other parameters are transmitted to the processor through the data collector for observation and analysis to ensure stable transportation of the fluid in the pipeline. At the same time, the flow velocity is monitored to optimize the operating parameters during pipeline transportation and obtain the flow velocity curve.

[0064] By installing a pressure sensor on the pipeline wall to monitor the pressure value near the pipeline port, as well as the pressure value under different pipeline transmission parameters at each position, including static pressure, dynamic pressure and total pressure, the pressure is transmitted to the processor through the data collector, so as to monitor the pressure to estimate the safety and fluid flow state, and obtain the pipeline pressure curve. Combined with parameters such as pressure, flow rate, flow velocity, etc., the flow data of the fluid is studied, and the above-mentioned experimental multiphase flow process parameters are obtained.

[0065] Optionally, in step 102, the processor may construct an initial simulation model based on a dense discrete phase model (DDPM) that introduces Kinetic Theory of Granular Flow (KTGF).

[0066] In some examples, the preset material parameters may include the composition of the material, or equivalent parameters such as particle size and content of the particles, and the preset conveying parameters may include pipeline diameter, flow rate, etc. In general, these preset material parameters and preset material parameters and preset conveying parameters can be used to input into the initial simulation model for simulation calculations.

[0067] like Figure 5 As shown, the following is an example of how to establish the initial simulation model.

[0068] The DDPM model in the Euler-Lagrange method, which can simulate the collision process of particles and track particle packets, is selected with high computational efficiency and accuracy.

[0069] CFD simulation software is used to create a detailed geometric model of the pipeline transportation system, including pipelines, inlets and outlets, slurry pumps, elbows, valves, pipeline supports and fixed structures, etc. The pipeline model mainly consists of three parts, namely the horizontal transmission pipeline section, the inclined downward transmission pipeline section, and the inclined upward transmission pipeline section.

[0070] The geometric model is meshed, and the pipeline is meshed using hexahedral meshing to ensure the accuracy and efficiency of the calculation. A boundary layer is generated near the wall to capture the flow details. The average unit mass of the mesh is above 0.8 to ensure that the mesh quality meets the simulation requirements.

[0071] Set initial conditions: Use the standard k-ε turbulence model to describe the turbulent characteristics of the flow, and use the standard wall function near the wall to simulate the interaction between the fluid and the wall. Enable the DDPM model, in which the number of Euler phases and discrete phases are both 1.

[0072] The inlet is set as a velocity inlet boundary condition, the continuous phase and the discrete phase are set to the same inlet velocity, and the outlet is set as a free outflow boundary to simulate the flow condition of a pressure-free boundary.

[0073] The particle injection method is selected to be injected from the normal line of the inlet surface. The particles are all regarded as spherical particles. The random walk model is enabled to simulate the interaction between the particles and the discrete vortices of the fluid. The wall is set to a stationary wall. After the particles collide with the wall, the reflect method is selected to simulate the rebound of the particles and the wall.

[0074] A 16-electrode ERT two-dimensional finite element simulation model was established using modeling software to simulate the loop test pipeline. 16 equally spaced electrodes were arranged on the model, and the sensitive field was divided into 625 nodes and 1052 units, thereby realizing the monitoring of the flow characteristics of dense phase flow particles in the pipeline.

[0075] Based on the DDPM model, the Euler-Granular flow field model is used in this simulation to further analyze the pipeline transportation characteristics. The continuity equation based on the Euler-Granular flow field model is expressed as:

[0076]

[0077] Where i is the phase number; ρ represents the fluid density in kg / m 3 ; is the Laplace operator; α is the fluid volume fraction, in %; u is the fluid velocity, in m / s; m ls It is interphase mass transfer.

[0078] The DDPM model is used for simulation, the impact of collisions between particles is considered, and the KTGF theory is introduced.

[0079] According to the KTGF theory, the collision between particles is assumed to be a spherical face collision, the particle pseudo-temperature parameter M is introduced, and the model momentum equation is determined:

[0080]

[0081] Where p is the static pressure, in Pa; τ is the particle force tensor, in Pa; g is the gravitational acceleration; M is the particle pseudo-temperature parameter, which represents the phase momentum interaction, in kg / m 2 s 2 .

[0082] Determine the solid phase stress tensor:

[0083]

[0084]

[0085] In the formula, p s Represents the internal normal stress of the discrete phase, in Pa; α s represents the particle volume fraction, τ s Represents shear stress, in Pa; μ s Indicates shear viscosity, unit is kg / m·s; λ s represents the viscosity coefficient, and its unit is kg / m·s; I represents the unit tensor.

[0086] Based on the above examples, it can be seen that in the initial simulation model, there may be many parameters that need to be set, such as shear viscosity, viscosity coefficient, etc. These parameters that need to be set can have corresponding preset values, corresponding to the above preset model parameters. Of course, other parameters in the simulation model that are not reflected in the examples may also be used as preset model parameters. During the simulation process, by adjusting these preset model parameters, the simulated multiphase flow process parameters obtained by the simulation model can be made closer to the experimental multiphase flow process parameters, and thus more in line with the multiphase flow process parameters in actual fluidized bed applications.

[0087] In combination with the above embodiments, the test multiphase flow process parameters can be the parameters collected from the particle-enriched area of ​​the test pipeline. By simulating and calculating in the simulation model, the multiphase flow process parameters of the particle-enriched areas such as the elbow in the simulation results can also be extracted to obtain the simulated multiphase flow process parameters that match the particle-enriched areas of the preset material parameters. In this way, on the one hand, the simulated multiphase flow process parameters can be better matched with the test multiphase flow process parameters, and the preset model parameters of the initial simulation model can be adjusted based on the deviation between the two; on the other hand, the obtained target simulation model can also be used to better analyze the transport flow evolution of particle-enriched areas such as elbows.

[0088] As shown in the above embodiment, the preset model parameters in the simulation model can be adjusted. In this embodiment, the preset material parameters are adjusted based on the difference between the preset material parameter test multiphase flow process parameters and the preset material parameter simulation multiphase flow process parameters to obtain the target simulation model, including:

[0089] Acquire multiple sets of preset material parameter simulation multiphase flow process parameters corresponding to multiple sets of preset material parameter preset model parameters;

[0090] Determine the process parameter deviations corresponding to each set of preset material parameter preset model parameters respectively, the preset material parameter process parameter deviations are used to indicate the difference between the simulated multiphase flow process parameters corresponding to the preset material parameter preset model parameters and the preset material parameter test multiphase flow process parameters;

[0091] An inversion objective function is constructed, and the preset material parameter inversion objective function is iteratively solved based on multiple groups of preset material parameters, preset model parameters and process parameter deviations corresponding to each group of preset material parameters and preset model parameters to obtain target model parameters and a target simulation model with preset material parameter target model parameters.

[0092] In some specific embodiments, according to the characteristics of the filling and conveying process, an inversion objective function is established to quantify the difference between the loop test measurement data and the simulation model, that is, the difference between the simulated multiphase flow process parameters and the multiphase flow process parameters of the preset material parameter test. The establishment of the inversion objective function can comprehensively consider the spatial distribution of particles, conductivity characteristics and other experimental measurement data.

[0093] The distribution characteristics of the particle enrichment area obtained by ERT technology are compared and analyzed with the simulation model. The accuracy of the simulation model is verified through multiple sets of monitoring data, which provides a reference for the improvement of the simulation model.

[0094] The inversion objective function is solved using iterative optimization algorithms, such as the least squares method and genetic algorithm. Through multiple iterations, the optimal parameters are gradually obtained, and the key parameters in the material conveying flow process are corrected to obtain the target simulation model. Combined with the inversion analysis results, the evolution characteristics of the particle enrichment area are deeply analyzed, including the aggregation intensity, position migration, and morphological changes of the particles.

[0095] Based on the inversion analysis results, the manifold evolution characteristics during the filling and conveying process are studied. By comparing the manifold change laws under different working conditions, the key factors affecting the stability and efficiency of the filling process are revealed to ensure the applicability and accuracy of the target simulation model under different working conditions.

[0096] Optionally, the preset material parameters adjust the preset material parameters and preset model parameters based on the difference between the preset material parameter test multiphase flow process parameters and the preset material parameter simulation multiphase flow process parameters to obtain the target simulation model, including:

[0097] Based on the preset material parameters, test the multiphase flow process parameters, fit the preset distribution function, and obtain the first fitting parameters; based on the preset material parameters, simulate the multiphase flow process parameters, fit the preset distribution function, and obtain the second fitting parameters;

[0098] Based on the difference between the first fitting parameter and the second fitting parameter of the preset material parameter, the preset material parameter and the preset model parameter are adjusted to obtain the target simulation model.

[0099] In some examples, both the experimental multiphase flow process parameters and the simulated multiphase flow process parameters include parameters of phase content, distribution of each phase in the pipeline, and particle size distribution. Based on these parameters, data such as particle diameter and particle mass fraction can be obtained, and then a curve of the cumulative mass fraction changing with the particle diameter can be obtained. Generally speaking, the curve satisfies the Rosin-Rammler distribution function.

[0100] Based on this, in some preferred embodiments, the preset material parameter test multiphase flow process parameters and the simulation multiphase flow process parameters both include phase content and particle size distribution, the preset distribution function of the preset material parameters is the Rosin-Rammler distribution function, and the preset material parameter first fitting parameters and the second fitting parameters both include morphological diameter and distribution index.

[0101] In this embodiment, the processor can modify the preset model parameters of the initial simulation model according to the Rosin-Rammler distribution function to achieve synchronization with the ring tube experiment. Specifically, in this embodiment, the morphological diameter of the Rosin-Rammler particle size distribution curve is used as the main indicator, and the simulation model is combined to perform parameter correction and inversion analysis, and then analyze the multiphase flow characteristics such as the flow pattern evolution characteristics during the transportation process. The derivation process of obtaining the particle size distribution function includes:

[0102] Rosin-Rammler distribution function expression:

[0103] G = 1-exp[-ad n ]

[0104] Where G represents the cumulative percentage of particles, d represents the particle size, n represents the Rosin-Rammler distribution index, n>1, and a represents the particle size coefficient.

[0105] Derivative the Rosin-Rammler distribution function expression to obtain its density function:

[0106]

[0107] make get

[0108] Substituting the above two equations into the Rosin-Rammler distribution function expression, using d0 and n to represent the particle size distribution function, we get:

[0109]

[0110] In the formula, d represents the particle size, the unit is mm; d0 represents the morphological diameter, which represents the position of the function peak, the unit is mm; n represents the Rosin-Rammler distribution index, n>1.

[0111] By fitting the Rosin-Rammler distribution function to the experimental multiphase flow process parameters, the corresponding morphological diameter d0 and distribution index n can be obtained, corresponding to the first fitting parameters mentioned above. Similarly, by fitting the Rosin-Rammler distribution function to the simulation multiphase flow process parameters, the corresponding morphological diameter d0 and distribution index n can be obtained, corresponding to the second fitting parameters mentioned above.

[0112] This embodiment is based on the application of the Rosin-Rammler distribution function, which can better quantify the difference between the experimental multiphase flow process parameters and the multiphase flow process parameters simulated by preset material parameters, and better provide direction for the optimization of model parameters of the simulation model.

[0113] In a specific application example, the particle morphology diameter d0 and the Rosin-Rammler distribution index n are used as particle size parameters, and the particle size distribution data is compared using the function formula to obtain a calculation curve that conforms to the Rosin-Rammler distribution function. Substituting the obtained d0 = 0.004 mm, n = 1.04 into the model initial particle size parameters for simulation, the following is obtained: Figure 6 The calculated curve is shown.

[0114] Of course, the difference between the experimental multiphase flow process parameters and the preset material parameter simulation multiphase flow process parameters, in addition to the above-mentioned particle size parameters, can also include differences in other index parameters. Combined with the above examples of the multiphase flow test platform and the method of obtaining the experimental multiphase flow process parameters, in some specific application examples, by solving the pressure, flow rate and other parameters in the simulation model, the processor integrates the local phase content, particle size distribution, concentration gradient and flow field Reynolds stress of different conveying times based on the results of the preset material parameter loop test and simulation, on the basis of the resistance, flow rate and volume fraction along the way, and compares each indicator with the results to obtain a more accurate pipeline transport manifold evolution analysis and prediction result that integrates dense phase flow particle tracing and electrical resistance tomography.

[0115] like Figure 7 As shown, the embodiment of the present application also provides a flow characteristic analysis device based on electrical resistance tomography, comprising:

[0116] An acquisition module 701 is used to acquire test multiphase flow process parameters of the test pipeline with preset material parameters by combining electrical resistance tomography technology when a test material with preset material parameters flows in the test pipeline according to preset conveying parameters;

[0117] The trial calculation module 702 is used to construct an initial simulation model based on preset model parameters, and simulate the initial simulation model based on preset material parameters, preset material parameters and preset material parameters and preset conveying parameters to obtain simulated multiphase flow process parameters;

[0118] An adjustment module 703 is used to adjust preset material parameters and preset model parameters based on the difference between preset material parameter test multiphase flow process parameters and preset material parameter simulation multiphase flow process parameters to obtain a target simulation model;

[0119] The analysis module 704 is used to analyze the flow characteristics of the multiphase flow based on the preset material parameter target simulation model.

[0120] The flow characteristic analysis device based on electrical resistance tomography provided in the embodiment of the present application is a device corresponding to the flow characteristic analysis method based on electrical resistance tomography in the above embodiment. The method embodiment can be applied to the device embodiment and achieve the same technical effect, which will not be repeated here.

[0121] An embodiment of the present application 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, the above-mentioned flow characteristic analysis method based on electrical resistance tomography is implemented.

[0122] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned flow characteristic analysis method based on electrical resistance tomography is implemented.

[0123] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0124] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0125] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0126] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0130] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A flow characteristics analysis method based on electrical resistance tomography, characterized in that: include: In the process of a test material with preset material parameters flowing in a test pipeline according to preset conveying parameters, the test multiphase flow process parameters of the test pipeline are obtained by combining electrical resistance tomography technology; Building an initial simulation model based on preset model parameters, and performing simulation calculations on the initial simulation model based on the preset material parameters and the preset conveying parameters to obtain simulated multiphase flow process parameters; Adjust the preset model parameters based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters to obtain a target simulation model; The multiphase flow characteristics are analyzed based on the target simulation model.

2. The method according to claim 1, characterized in that The test material contains tracer particles; the test multiphase flow process parameters of the test pipeline are obtained by combining the electrical resistance tomography technology, including: Obtaining an electrical resistance tomography result of the test pipeline based on electrical resistance tomography technology, and obtaining tracing observation data of the tracing particles based on particle tracing technology; The resistance tomography results and the tracer observation data are integrated to obtain the experimental multiphase flow process parameters.

3. The method according to claim 1, characterized in that The method of obtaining the test multiphase flow process parameters of the test pipeline by combining the electrical resistance tomography technology includes: Acquiring test multiphase flow process parameters of the particle-enriched area of ​​the test pipeline in combination with electrical resistance tomography technology; The initial simulation model is simulated and calculated based on the preset material parameters and the preset conveying parameters to obtain simulated multiphase flow process parameters, including: The initial simulation model is simulated and calculated based on the preset material parameters and the preset conveying parameters to obtain simulated multiphase flow process parameters that match the particle-enriched area.

4. The method according to claim 1, characterized in that: The step of adjusting the preset model parameters based on the difference between the test multiphase flow process parameters and the simulation multiphase flow process parameters to obtain a target simulation model includes: Acquire a plurality of sets of simulated multiphase flow process parameters corresponding to a plurality of sets of preset model parameters; Determine the process parameter deviation corresponding to each group of the preset model parameters respectively, wherein the process parameter deviation is used to indicate the difference between the simulated multiphase flow process parameters corresponding to the preset model parameters and the experimental multiphase flow process parameters; An inversion objective function is constructed, and the inversion objective function is iteratively solved based on multiple groups of preset model parameters and process parameter deviations corresponding to each group of preset model parameters to obtain target model parameters and a target simulation model having the target model parameters.

5. The method according to claim 1, characterized in that The step of adjusting the preset model parameters based on the difference between the test multiphase flow process parameters and the simulation multiphase flow process parameters to obtain a target simulation model includes: Based on the experimental multiphase flow process parameters, a preset distribution function is fitted to obtain a first fitting parameter; based on the simulated multiphase flow process parameters, a preset distribution function is fitted to obtain a second fitting parameter; The preset model parameters are adjusted based on the difference between the first fitting parameter and the second fitting parameter to obtain a target simulation model.

6. The method according to claim 5, characterized in that The experimental multiphase flow process parameters and the simulated multiphase flow process parameters both include phase content and particle size distribution, the preset distribution function is a Rosing-Rammler distribution function, and the first fitting parameter and the second fitting parameter both include morphological diameter and distribution index.

7. The method according to claim 1, characterized in that The initial simulation model is constructed based on preset model parameters, including: The initial simulation model was constructed based on a dense discrete phase model that introduced particle dynamics theory.

8. The method according to claim 2, characterized in that: The test pipeline is also provided with a pressure sensor, a flow sensor and a flow velocity sensor; The step of fusing the electrical resistance tomography result and the tracer observation data to obtain the experimental multiphase flow process parameters includes: The test multiphase flow process parameters are obtained by fusing the resistance tomography results, the tracer observation data, the pressure data collected by the pressure sensor, the flow data collected by the flow sensor, and the flow velocity data collected by the flow velocity sensor.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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