Methods, equipment, and media for analyzing flow characteristics based on electrical resistivity tomography.

Abstract

This application provides a flow characteristic analysis method, device, and medium based on resistivity tomography. The flow characteristic analysis method based on resistivity tomography provided in this application utilizes test materials and test pipelines to conduct multiphase flow experiments. It combines resistivity tomography technology to obtain the test multiphase flow process parameters. Preset material parameters for the test materials and preset transport parameters for the test pipelines can be used as input to the initial simulation model. In this case, the simulated multiphase flow process parameters obtained from the simulation can be compared with the test multiphase flow process parameters. Based on the differences between the two, the preset model parameters of the simulation model are adjusted so that the simulation results of the final target simulation model are closer to the actual test results. This results in the target simulation model having superior multiphase flow analysis and prediction capabilities, achieving a more accurate analysis of multiphase flow characteristics.

Description

Technical Field

[0001] This application relates to the field of simulation technology, and in particular to a method, device and medium for analyzing flow characteristics based on resistivity tomography, especially a method, device and storage medium for analyzing flow characteristics that integrates multiphase flow particle tracing and resistivity tomography. Background Technology

[0002] Fluidized beds utilize the passage of gas or liquid through a layer of particulate solids, suspending the solid particles and enabling gas-solid or liquid-solid phase reactions. When the apparent velocity in a fluidized bed is high, and the content or replenishment rate of particulate solids is high or fast, multiphase flow will occur.

[0003] Currently, 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 multiphase fluidized beds, and have provided a theoretical basis for computational fluid dynamics (CFD) simulation of multiphase fluidized beds.

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

[0005] This application provides a method, device, and medium for analyzing flow characteristics based on resistivity tomography, in order to solve the problem that there is a large difference between the simulation results and the actual results of multiphase flow characteristics in related technologies.

[0006] To solve the above-mentioned technical problems, this application is implemented as follows:

[0007] In a first aspect, embodiments of this application provide a method for analyzing flow characteristics based on resistivity tomography, including:

[0008] During the process of test material with preset material parameters flowing in the test pipeline according to preset conveying parameters, the test multiphase flow process parameters of the test pipeline are obtained by combining resistivity tomography technology.

[0009] An initial simulation model is constructed based on preset model parameters. The initial simulation model is then simulated and calculated based on preset material parameters and preset material parameters and preset conveying parameters to obtain the simulation multiphase flow process parameters.

[0010] Based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters with preset material parameters, the preset material parameters and preset model parameters are adjusted to obtain the target simulation model.

[0011] Analysis of multiphase flow characteristics based on a target simulation model with preset material parameters.

[0012] Secondly, embodiments of this application also provide a flow characteristic analysis device based on electrical resistivity tomography, comprising:

[0013] The acquisition module is used to acquire the test multiphase flow process parameters of the test pipeline with preset material parameters by combining electrical resistance tomography technology during the process of test material with preset material parameters flowing in the test pipeline according to preset conveying parameters.

[0014] The trial calculation module is used to build an initial simulation model based on preset model parameters, and to perform simulation calculations on the initial simulation model based on preset material parameters and preset material parameters and preset conveying parameters to obtain the simulation multiphase flow process parameters.

[0015] The adjustment module is used to adjust the preset material parameters and preset model parameters based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters with preset material parameters, so as to obtain the target simulation model.

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

[0017] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0018] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0019] The flow characteristic analysis method based on resistivity tomography provided in this application uses test materials and test pipelines to conduct multiphase flow experiments. It combines resistivity tomography technology to obtain the test multiphase flow process parameters. Preset material parameters and preset transport parameters of the test pipeline can be used as input to the initial simulation model. In this case, the simulated multiphase flow process parameters obtained from the simulation can be compared with the test multiphase flow process parameters. Based on the difference between the two, the preset model parameters of the simulation model are adjusted so that the simulation results of the final target simulation model are closer to the actual test results. This results in the target simulation model having superior multiphase flow analysis and prediction capabilities, enabling a more accurate analysis of the multiphase flow characteristics. Attached Figure Description

[0020] Figure 1 A schematic flowchart of the flow property analysis method based on resistivity tomography provided in this application embodiment;

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

[0022] Figure 3 This is a schematic diagram of the electrical resistance tomography system.

[0023] Figure 4 This is a schematic diagram illustrating the working principle of an ERT-based resistivity tomography system.

[0024] Figure 5 This is a schematic diagram illustrating the working principle of combining experimental and simulation methods for a ring pipe.

[0025] Figure 6 Example figure showing the particle size distribution calculation curve fitted based on the Rosin-Rammler distribution function;

[0026] Figure 7 This is a schematic diagram of the flow property analysis device based on resistivity tomography provided in an embodiment of this application. Detailed Implementation

[0027] To make the technical problems, technical solutions, and advantages of this application clearer, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments. In the following description, specific details such as particular configurations and components are provided merely to aid in a comprehensive understanding of the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Furthermore, for clarity and brevity, descriptions of known functions and structures have been omitted.

[0028] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "an" or "a," and similar terms, do not indicate a quantity limitation, but rather indicate the presence of at least one.

[0029] like Figure 1 As shown in the embodiments of this application, the flow characteristic analysis method based on electrical resistivity tomography includes:

[0030] Step 101: During the process of the test material with preset material parameters flowing in the test pipeline according to preset conveying parameters, the test multiphase flow process parameters of the test pipeline with preset material parameters are obtained by combining resistance tomography technology.

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

[0032] Step 103: Adjust the preset material parameters and preset model parameters based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters to obtain the target simulation model;

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

[0034] The flow characteristic analysis method based on resistivity tomography provided in this application uses test materials and test pipelines to conduct multiphase flow experiments. It combines resistivity tomography technology to obtain the test multiphase flow process parameters. Preset material parameters and preset transport parameters of the test pipeline can be used as input to the initial simulation model. In this case, the simulated multiphase flow process parameters obtained from the simulation can be compared with the test multiphase flow process parameters. Based on the difference between the two, the preset model parameters of the simulation model are adjusted so that the simulation results of the final target simulation model are closer to the actual test results. This results in the target simulation model having superior multiphase flow analysis and prediction capabilities, enabling a more accurate analysis of the multiphase flow characteristics.

[0035] To facilitate a better understanding of the implementation process of the method provided in the embodiments of this application, the following provides examples of some feasible multiphase flow test platforms, including test materials and test pipelines.

[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 test material, and the mixer 202, pump 203, and ERT sensor 204 can be sequentially arranged on the test pipe. The processor 205 is electrically connected to the ERT sensor 204.

[0037] The test material can be a mixture of solid particles and liquid. In some feasible implementations, the test material can also be a mixture of solid particles and gas. In practice, test materials of different concentrations can be designed and prepared for testing. For example, when the analyte is dense-phase flow particles, the test material can be a slurry composed of tailings, cement, and water mixed in a certain proportion, with the slurry concentration controlled above 60%. For simplicity, the following examples mainly use dense-phase flow particles as the test material.

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

[0039] The mixer 202 may include a mixing tank and mixing blades, etc., for thoroughly mixing the test materials. The pump 203 can be used to drive the test materials to flow in the test pipeline 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 can be placed in the particle enrichment area of ​​the test pipe 201, such as on the bend of the test pipe 201, or the ERT sensor 204 can be placed in other locations where analysis is required.

[0041] As examples, a 16-electrode ERT sensor 204 with rectangular electrodes is installed in the particle-rich region, with measurement lines arranged along both sides of the parallel test pipe 201. Adjacent mode is used for data acquisition, adjacent current excitation is used for excitation, and a bipolar pulsed current source is used as the excitation source, with a constant current of 10mA. A total of 208 voltage signals are sampled over 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, based on the friction loss test of the test pipe 201 and real-time flow pattern imaging, 3-5 adjustable monitoring areas are set up with ERT sensors 204 to adapt to different monitoring requirements.

[0042] like Figure 3 As shown, a typical electrical resistance tomography (ERT) system based on the ERT sensor 204 includes a sensor unit, a data acquisition and processing unit, and an image reconstruction unit. The sensor unit includes an electrode sensing array to acquire current or voltage signals. 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 ERT technology will be further illustrated below.

[0043] The processor 205 can be used to execute the above-mentioned flow characteristic analysis method based on resistive 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 to control the pumping flow rate of the pump 203.

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

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

[0046] In some embodiments, the test material containing the preset material parameters contains tracer particles. Based on this, step 101 above, which combines the preset material parameters with resistivity tomography to obtain the test multiphase flow process parameters of the test pipeline with the preset material parameters, may include:

[0047] The resistivity tomography (RTG) technique was used to obtain the RTG results of the test pipeline with preset material parameters, and the particle tracing technique was used to obtain the tracing observation data of the tracer particles with preset material parameters.

[0048] By integrating the resistivity tomography results and the tracer observation data of the preset material parameters, the multiphase flow process parameters of the preset material parameters experiment are obtained.

[0049] In this embodiment, particle tracing and electrical resistance tomography techniques can be combined 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 provides an illustrative explanation of the methods for obtaining experimental multiphase flow process parameters and the specific composition of these parameters.

[0051] like Figure 4 As shown, with the ERT sensors and other components arranged according to the above-described multiphase flow test platform, a multi-channel high-frequency data acquisition system can be used to acquire the voltage and current signals of each electrode. During data acquisition, time-domain filtering and spatial filtering techniques can be applied to suppress noise and eliminate environmental interference. Data standardization processing is then performed to improve the reconstruction accuracy of the resistance tomography images.

[0052] The acquired voltage signal was imaged and reconstructed using an Iterative Back Projection (IBP) algorithm to obtain resistivity tomography results. These results reflect the conductivity variations of particles distributed in the fluid. Through a high-precision conductivity distribution map, the location, size, and dynamic evolution of particle enrichment areas can be clearly depicted.

[0053] By combining dense phase flow particle tracer technology, tracer particles with electrical conductivity different from the matrix are added to the test material. The position and trajectory of these particles are tracked in real time using an imaging system, and parameters such as particle concentration, flow pattern, sensitivity, and phase content are monitored in real time and accurately. The flow velocity, diffusion range, and local aggregation characteristics of the particles are obtained. These tracer observation data can help to gain a deeper understanding of the macroscopic characteristics and microscopic behavior of particle flow.

[0054] By performing time-series analysis on continuously acquired imaging data during the filling process, the evolutionary behavior of particle-enriched regions in the flow is monitored, including their morphological expansion and contraction, location migration, and changes in enrichment intensity. Quantitative parameters, such as the area of ​​the particle-enriched region and the rate of change of its center position, are used to analyze the dynamic evolution of the manifold and reveal potential instability factors.

[0055] When resistive tomography (RTG) results are obtained using resistive tomography (RTG) technology and particle tracing (PT) data are obtained using particle tracing (PT) technology, multi-source data can be fused, for example, using Kalman filtering, to perform comprehensive analysis. Through data fusion, the uniformity of flow, the degree of particle enrichment, and the overall stability of the flow can be accurately assessed, identifying key factors affecting flow characteristics.

[0056] In some examples, the experimental multiphase flow process parameters may include phase content and particle size distribution, or, more specifically, the phase content and particle size distribution of the particle enrichment zone in the aforementioned experimental pipeline. In conjunction with the embodiments described above, the experimental multiphase flow process parameters may be obtained by fusing resistivity tomography results with preset material parameter tracer observation data, or they may be obtained solely based on resistivity tomography results.

[0057] In other examples, the experimental multiphase flow process parameters may also include parameters such as pressure, flow rate, and velocity, which can be acquired by installing corresponding sensors on the experimental pipeline. Accordingly, in some embodiments, the processor can fuse the results of resistivity tomography of preset material parameters, pressure data acquired by the preset material parameter pressure sensor, flow rate data acquired by the preset material parameter flow sensor, and velocity data acquired by the preset material parameter velocity sensor to obtain the experimental multiphase flow process parameters with preset material parameters. In other embodiments, the processor can further combine the preset material parameter tracer observation data to obtain the experimental 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 loop 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 example illustrates the specific implementation process of a single loop test.

[0060] In this loop pipe test, three loop pipe arrangements were selected: horizontal conveying pipeline, inclined downward conveying pipeline, and inclined upward conveying pipeline. The loop pipe test system used seamless steel pipes, and the pipelines employed quick-connection. The data acquisition and recording of pipe resistance at the test platform's measuring points were transmitted via a remote pressure transmitter. The apparatus for this loop pipe test required the installation of pressure sensors, flow sensors, velocity sensors, and ERT sensors, all connected to a processor to maintain a good signal-to-noise ratio and ensure data accuracy and reliability.

[0061] Tailings, cement, and water are mixed in a predetermined ratio using a mixer for 10-20 minutes to create a homogeneous slurry. The slurry concentration is adjusted from thick to thin to ensure uniformity and stability. After preparation, the slurry is poured into the receiving hopper of a slurry pump, where it circulates within pipelines under pressure. Each time the concentration is changed, continuous pumping and mixing for 10-20 minutes is required to ensure slurry homogeneity and test accuracy.

[0062] The slurry flow rate is altered by changing the pump discharge rate. Data such as flow rate, concentration, and pipeline pressure are collected by flow sensors, pressure sensors, and ERT sensors, and transmitted to the processor via a data collector for recording. This data will be used for subsequent data analysis and evaluation of experimental results. During the experiment, the slurry flow rate, pressure, and concentration are monitored in real time, and the pump discharge rate and stirring time are adjusted as needed to ensure the stability of experimental conditions and the reliability of experimental data.

[0063] By installing flow sensors and velocity sensors inside the pipeline, the instantaneous flow rate, cumulative flow rate, average velocity, and velocity distribution are monitored. The measured flow rate and velocity parameters are transmitted to the processor via a data collector for observation and analysis to ensure stable fluid transport within the pipeline. Simultaneously, the velocity is monitored to optimize the operating parameters during pipeline transport, resulting in a flow rate-velocity curve.

[0064] Pressure sensors installed on the pipe wall are used to monitor pressure values ​​near the pipe opening, as well as pressure values ​​at each location under different pipeline transport parameters, including static pressure, dynamic pressure, and total pressure. This data is transmitted to a processor via a data collector. By monitoring pressure, safety and fluid flow conditions are predicted, and a pipeline pressure curve is derived. Combining pressure, flow rate, and velocity parameters, the fluid flow data is studied, and the aforementioned 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) incorporating the Kinetic Theory of Granular Flow (KTGF).

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

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

[0068] The DDPM model is an Eulerian-Lagrange method that can model the collision process of particles, track particle packs, and is computationally efficient and accurate.

[0069] A detailed geometric model of the pipeline transportation system was created using CFD simulation software, including pipes, inlets and outlets, slurry pumps, elbows, valves, pipe supports, and fixed structures. The pipeline model mainly consists of three parts: a horizontal transport section, an inclined downward transport section, and an inclined upward transport section.

[0070] The geometric model was meshed, with hexahedral meshes used for the pipes to ensure computational accuracy and efficiency. A boundary layer was generated near the wall to capture flow details. The average unit mass of the mesh was above 0.8 to ensure the mesh quality met simulation requirements.

[0071] Initial conditions are set: The standard k-ε turbulence model is used to describe the turbulent characteristics of the flow, and the standard wall function is used near the wall to simulate the interaction between the fluid and the wall. The DDPM model is enabled, with both the Eulerian phase and the discrete phase having a quantity of 1.

[0072] The inlet is set to a velocity inlet boundary condition, the continuous phase and the discrete phase are set to the same inlet velocity, and the outlet is set to a free outflow boundary to simulate the flow condition without pressure.

[0073] The particle injection method was selected to inject from the inlet surface normal, and all particles were considered spherical. A random walk model was enabled to simulate the interaction between particles and discrete eddies in the fluid. The wall was set as a stationary wall, and a reflection mode was selected after the particles collided with the wall to simulate the rebound of the particles against the wall.

[0074] A 16-electrode ERT two-dimensional finite element simulation model was established using modeling software to simulate the experimental pipe loop. Sixteen equally spaced electrodes were arranged on the model, and the sensitive field was divided into 625 nodes and 1052 elements, thereby enabling the monitoring of the dense phase flow particle flow characteristics inside the pipe.

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

[0076]

[0077] In the formula, i is the phase number; ρ represents the fluid density, in kg / m³. 3 ; For Laplace operator; α is the fluid volume fraction, in %; u represents the fluid velocity, in m / s; m ls For interphase mass transfer.

[0078] Simulations were conducted using the DDPM model, taking into account the impact of collisions between particles, and the KTGF theory was introduced.

[0079] Based on KTGF theory, assuming interparticle collisions are spherical straight-plane collisions, and introducing the particle pseudo-temperature parameter M, the momentum equation of the model is determined as follows:

[0080]

[0081] In the formula, p represents the static pressure in Pa; τ is the particle force tensor in Pa; g is the gravitational acceleration; and M is the particle pseudo-temperature parameter, representing the interphase momentum interaction in kg / m³. 2 s 2 .

[0082] Determine the solid force tensor:

[0083]

[0084]

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

[0086] As seen in the examples above, the initial simulation model may contain many parameters that need to be set, such as shear viscosity and viscosity coefficient. These parameters can have corresponding preset values, which correspond to the preset model parameters mentioned above. Of course, other parameters in the simulation model not shown in the examples may also be used as preset model parameters. During the simulation, by adjusting these preset model parameters, the simulated multiphase flow process parameters obtained from the simulation model can be made closer to the experimental multiphase flow process parameters, thus better reflecting the multiphase flow process parameters in actual fluidized bed applications.

[0087] Based on the above embodiments, the experimental multiphase flow process parameters can be parameters collected from the particle-rich region of the experimental pipeline and simulated in the simulation model. Similarly, the multiphase flow process parameters of particle-rich regions such as bends in the simulation results can be extracted to obtain simulated multiphase flow process parameters that match the preset material parameters for particle-rich regions. In this way, on the one hand, the simulated multiphase flow process parameters can better correspond to the experimental 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 evolution of the transport manifold in particle-rich regions such as bends.

[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 experimental multiphase flow process parameters and the preset material parameter simulated multiphase flow process parameters to obtain the target simulation model, including:

[0089] Obtain multiple sets of preset material parameters corresponding to multiple sets of preset material parameters and preset model parameters for simulating multiphase flow process parameters;

[0090] The process parameter deviations corresponding to the preset material parameters and preset model parameters for each group are determined separately. The process parameter deviations of the preset material parameters are used to indicate the differences between the simulated multiphase flow process parameters corresponding to the preset material parameters and preset model parameters and the experimental multiphase flow process parameters corresponding to the preset material parameters.

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

[0092] In some specific implementations, an inversion objective function is established based on the characteristics of the filling and conveying process to quantify the differences between the loop test measurement data and the simulation model, i.e., the differences between the simulated multiphase flow process parameters and the preset material parameters of the experimental multiphase flow process. 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 particle enrichment zones obtained by ERT technology were compared and analyzed with the simulation model. Multiple sets of data were monitored to verify the accuracy of the simulation model and provide a reference for its improvement.

[0094] Iterative optimization algorithms, such as least squares and genetic algorithms, are used to solve the inversion objective function. Through multiple iterations, the optimal parameters are gradually obtained, and key parameters in the material transport and flow process are corrected to obtain the target simulation model. Based on the inversion analysis results, the evolution characteristics of the particle enrichment zone are analyzed in depth, including particle aggregation intensity, location migration, and morphological changes.

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

[0096] Optionally, the preset material parameters are adjusted based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters to obtain the target simulation model, including:

[0097] Based on the preset material parameters, the experimental multiphase flow process parameters are fitted with a preset distribution function to obtain the first fitting parameters; based on the preset material parameters, the simulated multiphase flow process parameters are fitted with a preset distribution function to obtain the second fitting parameters.

[0098] The preset material parameters and preset model parameters are adjusted based on the difference between the first and second fitting parameters to obtain the target simulation model.

[0099] In some examples, the experimental multiphase flow process parameters and the simulated multiphase flow process parameters include parameters such as phase content, phase distribution 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 the cumulative mass fraction as a function of particle diameter can be obtained. Generally, this curve satisfies the Rosin-Rammler distribution function.

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

[0101] In this embodiment, the processor can modify the preset model parameters of the initial simulation model based on the Rosin-Rammler distribution function, achieving synchronization with the loop pipe experiment. Specifically, this embodiment uses the morphology of the Rosin-Rammler particle size distribution curve as the main indicator, combined with the simulation model for parameter correction and inversion analysis, to analyze the multiphase flow characteristics such as the flow pattern evolution during the transport process. The derivation process of the particle size distribution function includes:

[0102] Rosin-Rammler distribution function expression:

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

[0104] In the formula, 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] Differentiating the Rosin-Rammler distribution function yields its density function:

[0106]

[0107] make get

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

[0109]

[0110] In the formula, d represents the particle size in mm; d0 represents the morphological diameter, indicating the location of the peak value of the function in mm; and n represents the Rosin-Rammler distribution index, n>1.

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

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

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

[0114] Of course, the differences between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters with preset material parameters can include differences in other index parameters besides the aforementioned differences in particle size. Combining the examples of multiphase flow experimental platforms and methods for obtaining experimental multiphase flow process parameters described above, in some specific applications, by solving for parameters such as pressure, velocity, and flow rate in the simulation model, the processor, based on the results of loop pipe experiments and simulations with preset material parameters, integrates indices such as local phase content, particle size distribution, concentration gradient, and Reynolds stress at different transport durations, in addition to friction loss, velocity, and volume fraction. By comparing each index with the results, a more accurate analysis and prediction result of pipeline transport manifold evolution, integrating dense phase flow particle tracing and resistivity tomography, is obtained.

[0115] like Figure 7 As shown in the figure, this application embodiment also provides a flow characteristic analysis device based on electrical resistivity tomography, including:

[0116] The acquisition module 701 is used to acquire the test multiphase flow process parameters of the test pipeline with preset material parameters by combining electrical resistance tomography technology during the process of test material with preset material parameters flowing 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 to perform simulation calculations on the initial simulation model based on preset material parameters and preset material parameters and preset conveying parameters to obtain the simulation multiphase flow process parameters.

[0118] The adjustment module 703 is used to adjust the preset material parameters and the preset model parameters based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters with preset material parameters, so as to obtain the target simulation model.

[0119] Analysis module 704 is used to analyze the multiphase flow characteristics based on a target simulation model with preset material parameters.

[0120] The flow characteristic analysis device based on electrical resistance tomography provided in this 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] This 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, it implements the above-described flow characteristic analysis method based on resistivity tomography.

[0122] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described flow characteristic analysis method based on resistive tomography.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to 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 embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0124] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] In the embodiments provided in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

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

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If an integrated module / unit is implemented as 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0130] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for analyzing flow characteristics based on resistivity tomography, characterized in that, include: During the process of test material with preset material parameters flowing in the test pipeline according to preset conveying parameters, the test multiphase flow process parameters of the test pipeline are obtained by combining resistivity tomography technology. An initial simulation model is constructed based on preset model parameters. The initial simulation model is then simulated and calculated based on the preset material parameters and the preset conveying parameters to obtain the simulation multiphase flow process parameters. The preset model parameters are adjusted based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters to obtain the target simulation model; Analyze the multiphase flow characteristics based on the target simulation model; The step of adjusting the preset model parameters based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters to obtain the target simulation model includes: Obtain multiple sets of simulation multiphase flow process parameters corresponding to multiple sets of preset model parameters; The process parameter deviations corresponding to the preset model parameters of each group are determined respectively. The process parameter deviations are used to indicate the differences between the simulated multiphase flow process parameters and the experimental multiphase flow process parameters corresponding to the preset model parameters. An inversion objective function is constructed, and the inversion objective function is iteratively solved based on multiple sets of preset model parameters and the process parameter deviations corresponding to each set of preset model parameters to obtain the target model parameters and the target simulation model with the target model parameters. or The step of adjusting the preset model parameters based on the difference between the experimental multiphase flow process parameters and the simulated multiphase flow process parameters to obtain the target simulation model includes: Based on the experimental multiphase flow process parameters, a preset distribution function is fitted to obtain the first fitting parameters; based on the simulated multiphase flow process parameters, a preset distribution function is fitted to obtain the second fitting parameters. The preset model parameters are adjusted based on the difference between the first fitting parameter and the second fitting parameter to obtain the target simulation model.

2. The method according to claim 1, characterized in that, The test material contains tracer particles; the acquisition of test multiphase flow process parameters of the test pipeline using resistivity tomography includes: The resistivity tomography results of the test pipeline were obtained based on resistivity tomography technology, and the tracking observation data of the tracking particles were obtained based on particle tracking technology. By fusing the resistivity tomography results and the tracer observation data, the parameters of the experimental multiphase flow process are obtained.

3. The method according to claim 1, characterized in that, The acquisition of test multiphase flow process parameters of the test pipeline using resistivity tomography includes: The experimental multiphase flow process parameters of the particle enrichment zone in the test pipeline were obtained by combining resistivity tomography. The simulation calculation of the initial simulation model based on the preset material parameters and the preset conveying parameters yields the simulation parameters of the multiphase flow process, including: Based on the preset material parameters and the preset conveying parameters, the initial simulation model is simulated and calculated to obtain the simulated multiphase flow process parameters that match the particle enrichment zone.

4. The method according to claim 1, characterized in that, The construction of the initial simulation model based on preset model parameters includes: An initial simulation model was constructed based on a dense discrete phase model incorporating particle dynamics theory.

5. The method according to claim 2, characterized in that, The test pipeline is also equipped with a pressure sensor, a flow sensor, and a flow velocity sensor; The parameters of the experimental multiphase flow process are obtained by fusing the resistivity tomography results and the tracer observation data, including: By integrating the resistivity 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 velocity sensor, the parameters of the experimental multiphase flow process are obtained.

6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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