Method and device for predicting migration state of solid particles in coal mine underground reservoir

Abstract

The invention relates to a method and device for predicting the migration state of solid particles in a coal mine underground reservoir, and relates to the field of mine water treatment.The method comprises the steps that an experimental result is obtained by conducting a physical experiment simulating actual working conditions, and a numerical simulation result of fluid dynamics simulation is obtained and calculated; the experimental result is converted into experimental data used for being compared with a numerical simulation result, a comparison result between the experimental data and the numerical simulation result is obtained, and simulation parameters in the computational fluid dynamics simulation process are adjusted and optimized according to the comparison result. And determining a prediction model of the migration state of the solid particles in the coal mine underground reservoir, and predicting the migration state of the solid particles in the coal mine underground reservoir under different working conditions based on the prediction model. According to the technical scheme, the simulation process can be corrected based on the experimental result to determine the theoretical prediction model, the migration state and the deposition position of the solid particles are predicted, and the method can be used for regulating and controlling diffusion and distribution conditions of the solid particles in the coal mine underground reservoir.

Description

Technical Field

[0001] The present disclosure relates to the field of mine water treatment, and particularly, to a method and device for predicting the migration state of solid particles in a coal mine underground reservoir. Background Art

[0002] The mine water entering the coal mine underground reservoir usually has the problem of exceeding the standard of harmful substances. Common harmful substances include fluorides. For example, the fluoride content in the mine water of western mining areas in China is mostly between 3 - 7 mg / L, far exceeding the discharge standard of 1 mg / L for surface water of Class III. In the prior art, coal-based solid waste defluorinating agents such as fly ash and coal gangue are applied to the coal mine underground reservoir, and the self-purification effect of the underground reservoir is utilized to cooperate with the physical and chemical effects of the defluorinating agent to remove fluoride ions in the mine water. This defluorination method has the advantages of being directly treatable underground, simple process, and low operating cost. However, in this type of harmful substance removal process, the migration process and distribution law of solid particles such as defluorinating agents in the water flow field of the coal mine underground reservoir are not yet clear, which often leads to problems such as solid particle deposition in the coal mine underground reservoir and uneven mine water treatment effect. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method and device for predicting the migration state of solid particles in a coal mine underground reservoir to solve the above technical problems.

[0004] To achieve the above purpose, according to the first aspect of the embodiments of the present disclosure, a method for predicting the migration state of solid particles in a coal mine underground reservoir is provided. The method includes: Obtaining experimental results through a physical experiment simulating actual working conditions; wherein, the actual working conditions include the flow field situation of the coal mine underground reservoir; the experimental results include multi-angle fixed shooting and sampling for the experimental process and results; Obtaining the numerical simulation results of computational fluid dynamics simulation; Converting the experimental results into experimental data for comparison with the numerical simulation results; Obtaining the comparison result between the experimental data and the numerical simulation results; Adjusting and optimizing the simulation parameters in the computational fluid dynamics simulation process according to the comparison result to determine a prediction model for the migration state of solid particles in the coal mine underground reservoir; Predicting the migration state of solid particles in the coal mine underground reservoir under different working conditions based on the prediction model.

[0005] Optionally, the obtaining experimental results through a physical experiment simulating actual working conditions includes: Determine the specified shooting positions of the sampled pictures during the experiment; the specified shooting positions include the shooting positions from the front view, side view, and top view; Conduct the physical experiment through the experimental device according to the set experimental conditions; the experimental device is an experimental device for simulating actual working conditions, and the experimental device includes a reaction tank, a solid particle adding device, and a water flow control system. The experimental conditions include one or more of water body flow rate, temperature, and solid particle content; During the physical experiment, under the preset shooting conditions, take multiple pictures of the experimental situation at the specified shooting positions; wherein, the preset shooting conditions include one or more of environmental light requirements, position requirements, and angle requirements; the experimental situation includes one or more of water body flow conditions, solid particle distribution conditions, solid particle diffusion paths, and solid particle siltation conditions.

[0006] Optionally, the conversion of the experimental results into experimental data for comparison with the numerical simulation results includes: Perform grayscale processing on the multiple pictures in the experimental results using an image processing method to obtain the pixel grayscale values in the pictures; According to the boundary contour of the filler in the experimental device, divide the first area to be compared in the picture into multiple value areas by equal value division; Determine the points to be compared in the value areas by calculating the geometric mean of the pixel points in each value area; wherein, the positions of the points to be compared in the picture correspond to the physical coordinates of the experimental device; the pixel grayscale values of the points to be compared are used to compare with the data at the corresponding coordinate points in the numerical simulation results.

[0007] Optionally, the obtaining of the numerical simulation results of computational fluid dynamics simulation includes: Establish a theoretical simulation model based on the experimental device of the physical experiment through computational fluid dynamics preprocessing software; Set the simulation conditions of the theoretical simulation model according to the experimental conditions of the physical experiment; Based on the simulation conditions, perform a flow field analysis on the theoretical simulation model; Obtain the numerical simulation results obtained after the flow field analysis; the numerical simulation results include multiple simulation parameters and data visualization results; wherein, the simulation parameters include at least one of computational grid division parameters, multiphase flow model setting parameters, turbulence model setting parameters, water flow velocity, and fluid mechanics equation solving setting parameters; the data visualization results include at least one of the diffusion rate of solid particles in different regions and the solid particle concentration distribution.

[0008] Optionally, obtaining the comparison result between the experimental data and the numerical simulation result includes: According to the result of the equal value division, divide the second area to be compared in the numerical simulation result into corresponding multiple value areas; the second area to be compared corresponds to the first area to be compared in the experimental result; Obtain the concentration distribution of the solid particles in each value area in the second area to be compared; Compare the concentration distribution of the solid particles in the first area to be compared with the concentration distribution of the solid particles in the second area to be compared to obtain the comparison result.

[0009] Optionally, comparing the concentration distribution of the solid particles in the first area to be compared with the concentration distribution of the solid particles in the second area to be compared to obtain the comparison result includes: Respectively obtain the concentration distribution curve of the solid particles in the first area to be compared and the concentration distribution curve of the solid particles in the second area to be compared; Obtain the comparison result by calculating the cosine similarity between the two concentration distribution curves; the cosine similarity is used to evaluate the shape similarity degree of the two curves.

[0010] Optionally, adjusting and optimizing the simulation parameters in the computational fluid dynamics simulation process according to the comparison result to determine the prediction model of the solid particle migration state in the underground coal mine reservoir includes: When the comparison result does not meet the preset conditions, adjust and optimize the theoretical simulation model; wherein, the adjustment and optimization include: During the flow field analysis process, adjust the type of fluid model used to describe the sedimentation dynamics; Adjust the parameter settings in the simulation conditions, and the parameters include turbulence parameters and solid particle size distribution; Adjust the grid resolution in the theoretical simulation model; Correct the physical property parameters in the theoretical simulation model; the physical property parameters include one or more of solid particle density, solid particle viscosity, and solid particle surface tension.

[0011] According to the second aspect of the embodiments of the present disclosure, a prediction device for the solid particle migration state in an underground coal mine reservoir is provided, and the device includes: A first acquisition module, configured to obtain an experimental result by performing a physical experiment simulating an actual working condition; wherein, the actual working condition includes the flow field condition of the underground coal mine reservoir; the experimental result includes multi-angle fixed shooting sampling for the experimental process and results; A second acquisition module, configured to acquire the numerical simulation results of computational fluid dynamics simulation; A data conversion module, configured to convert the experimental results into experimental data for comparison with the numerical simulation results; A third acquisition module, configured to acquire the comparison results between the experimental data and the numerical simulation results; A model optimization module, configured to adjust and optimize the simulation parameters in the computational fluid dynamics simulation according to the comparison results, so as to determine a prediction model for the migration state of solid particles in the underground coal mine reservoir; A prediction module, configured to predict the migration state of solid particles in the underground coal mine reservoir under different working conditions based on the prediction model.

[0012] According to a third aspect of the embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method according to any one of the first aspects of the embodiments of the present disclosure are implemented.

[0013] According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic device, including: A memory, on which a computer program is stored; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of the first aspects of the embodiments of the present disclosure.

[0014] In the above technical solution, experimental results are obtained by conducting a physical experiment simulating the actual working conditions, where the actual working conditions include the flow field situation of the underground coal mine reservoir, and the experimental results include multi-angle fixed shooting and sampling for the experimental process and results. The numerical simulation results of computational fluid dynamics simulation are acquired, the experimental results are converted into experimental data for comparison with the numerical simulation results, the comparison results between the experimental data and the numerical simulation results are obtained, and the simulation parameters in the computational fluid dynamics simulation are adjusted and optimized according to the comparison results, so as to determine a prediction model for the migration state of solid particles in the underground coal mine reservoir. Based on the prediction model, the migration state of solid particles in the underground coal mine reservoir under different working conditions is predicted. Through the above technical solution, the experiment and the simulation are combined, and the simulation process is corrected based on the experimental results to determine the theoretical prediction model, which can predict the migration state and deposition position of solid particles in the underground coal mine reservoir through the prediction model, and can be used to control the diffusion and distribution of solid particles in the underground coal mine reservoir, thereby solving the problems of solid particle deposition and uneven mine water treatment effect to a certain extent.

[0015] Other features and advantages of the present disclosure will be described in detail in the subsequent specific implementation part. Description of the Drawings

[0016] The accompanying drawings are used to provide a further understanding of the present disclosure, and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the accompanying drawings: Figure 1 is a flowchart of a method for predicting the migration state of solid particles in an underground coal mine reservoir according to an exemplary embodiment; Figure 2 is a flowchart of a method for obtaining experimental results by conducting a physical experiment simulating actual working conditions according to an exemplary embodiment; Figure 3a is a side view of an experimental device according to an exemplary embodiment; Figure 3b is a top view of an experimental device according to an exemplary embodiment; Figure 4 is a flowchart of a method for converting experimental results into experimental data for comparison with numerical simulation results according to an exemplary embodiment; Figure 5 is a schematic diagram of the device at a certain water inlet time according to an exemplary embodiment; Figure 6 is a flowchart of a method for obtaining numerical simulation results based on computational fluid dynamics simulation according to an exemplary embodiment; Figure 7 is a schematic diagram of the structure model of the experimental device built by SpaceClaim according to an exemplary embodiment; Figure 8 is a schematic diagram of mesh division by Meshing according to an exemplary embodiment; Figure 9 is a flowchart of a method for obtaining the comparison result between experimental data and numerical simulation results according to an exemplary embodiment; Figure 10 is a schematic diagram of the concentration sampling point path consistent with the experimental picture in the CFD model; Figure 11 is a block diagram of a device for predicting the migration state of solid particles in an underground coal mine reservoir according to an exemplary embodiment; Figure 12 is a block diagram of an electronic device for predicting the migration state of solid particles in an underground coal mine reservoir according to an exemplary embodiment. Specific Embodiments

[0017] The following further describes in detail the specific embodiments of the present disclosure with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0018] It should be noted that all actions of obtaining signals, information, or data in this disclosure are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where it is located and with the authorization given by the owner of the corresponding device.

[0019] Figure 1 It is a flowchart of a method for predicting the migration state of solid particles in an underground coal mine reservoir shown according to an exemplary embodiment, as Figure 1 shown, and the method includes the following steps.

[0020] In step S11, experimental results are obtained by conducting a physical experiment simulating the actual working conditions.

[0021] Among them, the actual working conditions include the flow field situation of the underground coal mine reservoir; the experimental results include multi-angle fixed shooting and sampling for the experimental process and results. Exemplarily, by building a simulation experimental device similar to the actual working conditions, the complex flow field of the underground coal mine reservoir is restored to a certain extent. The flow field situation of the underground coal mine reservoir may include water flow velocity (the velocity distribution of water flow in the underground reservoir), water flow direction (the flow direction of water flow in the underground reservoir), pressure distribution (the water pressure distribution in the underground reservoir), flow field stability (the stable state of water flow in the underground reservoir, including whether there are vortices, dead water areas, etc.), and water quality changes (the water quality change situation of water flow in the underground reservoir, including the distribution of dissolved substances, suspended substances, etc.).

[0022] In step S12, numerical simulation results of computational fluid dynamics simulation are obtained.

[0023] Among them, computational fluid dynamics (CFD) simulation is a method of using a computer to simulate the flow of fluids (such as gases or liquids). This simulation method is based on the basic principles of fluid mechanics and solves the control equations of fluid flow (such as the Navier - Stokes equations) through numerical methods to predict the behavior of fluids under different conditions.

[0024] In step S13, the experimental results are converted into experimental data for comparison with the numerical simulation results.

[0025] It can be understood that before data comparison, the pictures obtained during the experiment need to be converted into experimental data that can be used for comparison with the numerical simulation results.

[0026] In step S14, the comparison result between the experimental data and the numerical simulation results is obtained.

[0027] Exemplarily, the comparison method may include comparing curves generated from two types of data respectively using cosine similarity. Among them, cosine similarity is a method for measuring the similarity between two vectors, which determines their similarity degree by calculating the cosine value of the included angle between the two vectors. The value range of cosine similarity is between -1 and 1, where 1 represents exactly the same, 0 represents irrelevant, and -1 represents exactly the opposite.

[0028] In step S15, the simulation parameters in the computational fluid dynamics simulation process are adjusted and optimized according to the comparison result to determine a prediction model for the migration state of solid particles in the underground coal mine reservoir.

[0029] In step S16, based on the prediction model, the migration state of solid particles in the underground coal mine reservoir under different working conditions is predicted.

[0030] It can be understood that by comparing and analyzing the data obtained from experiments with the numerical simulation results, the accuracy of the CFD simulation can be evaluated. When the accuracy does not meet the preset conditions, the simulation parameters in the CFD simulation process are adjusted and optimized to determine a prediction model for the migration state of solid particles in the underground coal mine reservoir, so that the migration state of solid particles in the underground coal mine reservoir under different working conditions can be predicted using this prediction model.

[0031] In the above technical solution, experimental results are obtained by conducting a physical experiment simulating the actual working conditions. The actual working conditions include the flow field situation of the underground coal mine reservoir. The experimental results include multi-angle fixed shooting and sampling for the experimental process and results. The numerical simulation results of the computational fluid dynamics simulation are obtained. The experimental results are converted into experimental data for comparison with the numerical simulation results. The comparison result between the experimental data and the numerical simulation results is obtained. The simulation parameters in the computational fluid dynamics simulation process are adjusted and optimized according to the comparison result to determine a prediction model for the migration state of solid particles in the underground coal mine reservoir. Based on the prediction model, the migration state of solid particles in the underground coal mine reservoir under different working conditions is predicted. Through the above technical solution, experiments and simulations are combined, and the simulation process is corrected based on the experimental results to determine a theoretical prediction model, which can be used to predict the migration state and deposition position of solid particles in the underground coal mine reservoir, and can be used to control the diffusion and distribution of solid particles in the underground coal mine reservoir, thereby solving the problems of solid particle deposition in the underground coal mine reservoir and uneven treatment effect of mine water to a certain extent.

[0032] Figure 2 It is a flowchart of a method for obtaining experimental results by conducting a physical experiment simulating the actual working conditions shown according to an exemplary embodiment, as Figure 2As shown, the step of obtaining experimental results through physical experiments simulating actual working conditions in the above step S11 may include the following steps: Step S111, determining the specified shooting positions of the sampling pictures during the experiment.

[0033] Among them, the specified shooting positions include the shooting positions from the front view, the side view, and the top view.

[0034] Step S112, performing the physical experiment through the experimental device according to the set experimental conditions.

[0035] Among them, the experimental device is an experimental device for simulating actual working conditions. The experimental device includes a reaction tank, a solid particle adding device, and a water flow control system. The experimental conditions include one or more of water body flow rate, temperature, and solid particle content.

[0036] Exemplarily, in a possible implementation manner, the solid particle is a modified coal gangue defluorinating agent. The construction method of the experimental device for simulating actual working conditions is as Figure 3a and Figure 3b shown. Figure 3a is a side view of an experimental device shown according to an exemplary embodiment. Figure 3b is a top view of an experimental device shown according to an exemplary embodiment. This experimental device is used to simulate the migration behavior of the modified coal gangue defluorinating agent in water, and obtain the kinetic parameters of the defluorination reaction process and the hydrodynamic characteristics of the modified coal gangue defluorinating agent. As Figure 3a and Figure 3b shown, this experimental device uses a water tank to simulate the underground coal mine reservoir. The water tank is built with transparent acrylic plates for easy observation and picture collection. The size of the water tank is 148 cm in length, 38 cm in width, and 38.5 cm in height. 48 cylinders with a diameter of 9.9 cm and a height of 10 cm are placed at the bottom of the water tank to simulate the rock accumulation structure at the bottom of the underground reservoir. Three octahedron protrusions with a pore diameter of DN15 (nominal diameter of 15 mm) and an outer edge contour side length of 21 mm (thickness of 17 mm) are placed on each side to represent the water inlet and outlet, but only the bottom water inlet is open. A circular sewage outlet (with the same size specification as the water inlet) is set at 65 mm from the bottom of the water outlet end of the water tank. The water inlet end at the bottom of the water tank is 3.5 cm higher than the water outlet end. Using this experimental device for the experiment, the experimental conditions are set as follows: the fluid is pure water containing 5 g / L defluorinating agent at room temperature, and the inlet water flow rate is 16 L / h. Before injecting the defluorinating agent, the water tank is filled with a certain amount of pure water. By changing conditions such as water body flow rate, temperature, and defluorinating agent content, the influence on the diffusion and deposition of the defluorinating agent in the water flow is studied, and the concentration change of the defluorinating agent in the water body is monitored.

[0037] Step S113, during the physical experiment, take multiple pictures of the experimental situation at the specified shooting position under preset shooting conditions.

[0038] Among them, the preset shooting conditions include one or more of environmental light requirements, position requirements, and angle requirements; the experimental situation includes one or more of water body flow conditions, solid particle distribution conditions, solid particle diffusion paths, and solid particle deposition conditions.

[0039] Exemplarily, when the solid particles are defluorinating agents, during the experiment operation, under the condition of keeping the environmental light consistent, at the same shooting point, take pictures of the water body flow and the distribution of defluorinating agents from multiple angles (front view, side view, top view), and collect pictures at a shooting frequency of about once every 20 minutes starting from the inlet water. Focus on recording the diffusion path and deposition situation of the defluorinating agents. The obtained pictures are used for subsequent data processing and model verification.

[0040] Figure 4 It is a flowchart of a method for converting experimental results into experimental data for comparison with numerical simulation results according to an exemplary embodiment, as Figure 4 shown. The conversion of the experimental results into experimental data for comparison with the numerical simulation results described in step S13 may include the following steps: Step S131, perform grayscale processing on the multiple pictures in the experimental results using an image processing method to obtain the pixel grayscale values in the pictures.

[0041] Step S132, according to the boundary contour of the filler in the experimental device, divide the first area to be compared in the picture into multiple value areas by equal value partitioning.

[0042] It can be understood that equal value partitioning of the pixel grayscale values in the picture (also known as grayscale threshold segmentation or grayscale quantization) can separate the target area from the background area in the image, thereby enhancing the contrast of the target and making it more obvious. During the equal value partitioning process, appropriate threshold selection can help remove noise in the image and make the target area clearer. And equal value partitioning can divide the image into different areas, and each area represents a set of pixels with similar grayscale values, which is helpful for subsequent image analysis and processing.

[0043] Step S133, determine the points to be compared in the value area by calculating the geometric mean of the pixel points in each value area.

[0044] Among them, the position of the point to be compared in the picture corresponds to the physical coordinates of the experimental device; the pixel grayscale value of the point to be compared is used to compare with the data at the corresponding coordinate point in the numerical simulation result.

[0045] Exemplarily, the process of converting the experimental results may include: performing image recognition on the collected pictures, using a self-designed Python program to call OpenCV to perform grayscale processing on the images, selecting a key area (the water tank) for perspective transformation, selecting points with a cylinder as a reference, and obtaining the grayscale values of the selected points. Exemplarily, Figure 5 is a schematic diagram of the device at a certain water inlet moment shown according to an exemplary embodiment, such as Figure 5 shown. The black dots on the cylinder in the figure are the grayscale sampling points in image processing. There is a certain corresponding relationship between the grayscale value of this water body part and the defluorinating agent concentration, which can be used for subsequent result comparison.

[0046] It can be understood that during the equal value partitioning process, by performing noise reduction and normalization processing on the captured pictures, on the basis of removing the environmental background, the pictures can be quantified through an image analysis program self-designed based on the Python programming language, and the data characteristics of the distribution and flow path of the defluorinating agent can be obtained, so as to convert the experimental pictures into data for subsequent comparison with the results of theoretical simulation.

[0047] Figure 6 is a flowchart of a method for obtaining a numerical simulation result based on computational fluid dynamics simulation shown according to an exemplary embodiment, such as Figure 6 shown. The obtaining of the numerical simulation result based on computational fluid dynamics simulation described in the above step S12 may include the following steps: Step S121, through a computational fluid dynamics preprocessing software, establish a theoretical simulation model based on the experimental device of the physical experiment; Step S122, set the simulation conditions of the theoretical simulation model according to the experimental conditions of the physical experiment; Step S123, based on the simulation conditions, perform a flow field analysis on the theoretical simulation model; Step S124, obtain the numerical simulation result obtained after the flow field analysis; the numerical simulation result includes multiple simulation parameters and data visualization results.

[0048] Among them, the simulation parameters include at least one of computational grid division parameters, multiphase flow model setting parameters, turbulence model setting parameters, water flow velocity, and fluid mechanics equation solving setting parameters; the data visualization results include at least one of the diffusion rate of solid particles and the concentration distribution of solid particles in different regions.

[0049] Exemplarily, based on the above physical experiment, the experimental device is modeled by a computational fluid dynamics (CFD) preprocessing software, including the overall structure and local details of the experimental device, so as to accurately reproduce the flow characteristics in the water body through CFD simulation, especially the influence on the diffusion and deposition of the defluorinating agent. According to the experimental conditions, the boundary conditions of the CFD model are set, and a suitable multiphase flow model and turbulence model are selected. The flow field analysis of the CFD model is carried out, including the velocity distribution and turbulence structure, so as to obtain the flow and diffusion of the defluorinating agent. During the numerical simulation process, the key parameters of the CFD simulation are extracted, such as the parameters set in the multiphase flow model, the water flow velocity, the diffusion rate of the defluorinating agent and its interaction with minerals. After obtaining the simulation results, through the CFD postprocessing software, the numerical simulation results are visually analyzed to obtain the concentration distribution of the defluorinating agent in any selected area, so as to obtain the migration path and distribution pattern of the defluorinating agent under different flow field conditions.

[0050] Exemplarily, in one possible implementation, the above numerical simulation process may include: 1) Computational fluid dynamics (CFD) modeling Using a three-dimensional modeling software (such as SpaceClaim), set parameters according to the size of the experimental device to build a structural model, and then use Meshing to divide the grid. Exemplarily, Figure 7 is a schematic diagram of the structural model of the experimental device built by SpaceClaim shown in an exemplary embodiment. Figure 8 is a schematic diagram of Meshing dividing the grid shown in an exemplary embodiment. For example, in this division setting, the number of grid nodes can be 611479, the number of grid cells is 2947195, and the maximum grid skewness is less than 0.7.

[0051] 2) Flow field analysis Use Ansys Fluent to perform CFD simulation calculations. The materials include water, silicon and air, where silicon (sand) is used to simulate the defluorinating agent, with a density of 2000 kg / m 3 , and a viscosity of 1.72×10 -5 kg / (m·s). Set the Mixture model of the multiphase flow, and select the Realizable k-ε turbulence model to establish the transport equation. Set the inlet and outlet boundary conditions according to the experimental parameters. The water flow inlet is a mass flow inlet, the outlet is a pressure outlet, and the inner wall of the water tank and the substrate are both fixed wall surfaces. Select the semi-implicit algorithm (SIMPLE) of the pressure correlation equation to handle the coupling of pressure and velocity, and run the steady-state simulation.

[0052] 3) Obtain key parameters Record the parameter settings in the CFD simulation for subsequent comparative analysis with the data obtained from the experiment.

[0053] 4) Obtain the simulation results For the steady-state simulation results, use the post-processing software CFD-Post to visualize the results and obtain the volume fraction of water in the flow field. The volume fraction of sand (defluorinating agent) can be directly derived from the volume fraction of water.

[0054] Figure 9 It is a flowchart of a method for obtaining the comparison result between experimental data and numerical simulation results shown according to an exemplary embodiment, as Figure 9 shown. The obtaining of the comparison result between the experimental data and the numerical simulation result described in the above step S14 may include the following steps: Step S141: According to the result of the equal-value division, divide the second region to be compared in the numerical simulation result into corresponding multiple value regions.

[0055] Among them, the second region to be compared corresponds to the first region to be compared in the experimental result.

[0056] Step S142: Obtain the concentration distribution of the solid particles in each value region in the second region to be compared.

[0057] Step S143: Compare the concentration distribution of the solid particles in the first region to be compared with the concentration distribution of the solid particles in the second region to be compared to obtain the comparison result.

[0058] Optionally, step S143 may include the following steps: (1) Respectively obtain the concentration distribution curve of the solid particles in the first region to be compared and the concentration distribution curve of the solid particles in the second region to be compared; (2) Obtain the comparison result by calculating the cosine similarity between the two concentration distribution curves; the cosine similarity is used to evaluate the shape similarity degree of the two curves.

[0059] Exemplarily, in the CFD simulation results, divide the area path consistent with the sampling points of the experimental pictures, and perform averaging processing on the values included in the area, so that the distribution of the defluorinating agent in the analysis experiment and the simulation results are comparable, thereby verifying whether the model can accurately reflect the actual situation. In the comparison process, for the experimental data, according to the pixel gray value of the pictures in the collected photos, perform equal-value division, and according to the object boundary in the device, determine the reasonable size of the value region, count the pixel points in the selected value region, calculate the geometric mean value, and obtain a representative point. Correlate the position of the point in the picture with the physical coordinates of the experimental device. According to the corresponding coordinates, find the concentration value in the CFD calculation model and compare it with the pixel gray value result of the experimental picture.

[0060] It should be noted that Figure 10It is a schematic diagram of the concentration sampling point path in the CFD model that is consistent with the experimental pictures. As Figure 10 shown, the white straight path in the figure is consistent with the path of the black dots in Figure 5 .

[0061] It can be understood that first, the concentration distribution curves corresponding to the experimental data and the concentration distribution curves in the numerical simulation results are obtained by plotting distribution curves respectively, and then they are compared by calculating the cosine similarity method.

[0062] Among them, the calculation method of the cosine similarity is as follows:

[0063] Among them, and respectively represent the vectors corresponding to the two concentration distribution curves, including n points to be compared. It can be understood that the closer the cosine similarity is to 1, the higher the similarity degree of the shapes of the two curves. When the value of the cosine similarity meets the preset conditions, the theoretical simulation model is determined as the prediction model, and the CFD model can be used to predict the migration law of the defluorinating agent in the underground coal mine reservoir.

[0064] It can be understood that the adjustment and optimization of the simulation parameters in the computational fluid dynamics simulation process according to the comparison result described in step S15 above to determine the prediction model of the solid particle migration state in the underground coal mine reservoir may include: when the comparison result does not meet the preset conditions, adjusting and optimizing the theoretical simulation model.

[0065] Exemplarily, the adjustment and optimization may include: (1) During the flow field analysis process, adjusting the type of fluid model used to describe sedimentation dynamics; (2) Adjusting the parameter settings in the simulation conditions, and the parameters include turbulence parameters and solid particle size distribution; (3) Adjusting the grid resolution in the theoretical simulation model; (4) Calibrating the physical property parameters in the theoretical simulation model; the physical property parameters include one or more of solid particle density, solid particle viscosity, and solid particle surface tension.

[0066] In a possible implementation manner, the adjustment and optimization of the simulation process include: 1) Modify the structural model according to the experimental parameters to: the inlet diameter is 1.5 cm, the outlet diameter is 1.5 cm, and it is 10 cm higher than the bottom of the water tank; 2) Modify the surface tension coefficient according to the experimental parameters to: 72.8 mN / m; 3) Modify the viscosity according to the experimental parameters to: 2.2655×10 -3 kg / m.s; 4) Modify the computational fluid domain and intercept the volume space composed of the inlet, cylinder, outlet, and bottom in the entire water tank; (Since the space above is all air and has little impact on fluid calculation, this part of the space is discarded to avoid unnecessary calculations.) 5) Select the multiphase flow Mixture model; Realize k- turbulence model, enhance wall treatment, curvature correction; Set the boundary condition of the velocity inlet to 0.1 m / s, the turbulence diameter to 0.015 m, the turbulence intensity to 5%, and the sediment volume fraction to 5%; Pressure outlet, sediment density 2500 kg / m 3 . Conduct 100 iterations of steady-state simulation.

[0067] Based on the CFD simulation results, the volume fraction of sand can be calculated according to the volume fraction of water. According to the above comparison method, through calculating the cosine similarity, the similarity between the simulation results and the experimental data in this simulation is 0.977, indicating that the theoretical prediction model obtained by optimizing the simulation settings is consistent with the concentration change trend of the experimental data, and this prediction model can be applied to predict the migration state of defluorinating agents in coal mine reservoirs.

[0068] In the above technical solution, experimental results are obtained by conducting physical experiments simulating actual working conditions, which include the flow field conditions of coal mine underground reservoirs. The experimental results include multi-angle fixed shooting and sampling of the experimental process and results to obtain numerical simulation results of computational fluid dynamics simulation. The experimental results are converted into experimental data for comparison with the numerical simulation results, and the comparison results between the experimental data and the numerical simulation results are obtained. According to the comparison results, the simulation parameters in the computational fluid dynamics simulation process are adjusted and optimized to determine a prediction model for the migration state of solid particles in coal mine underground reservoirs. Based on this prediction model, the migration state of solid particles in coal mine underground reservoirs under different working conditions is predicted. Through the above technical solution, experiments and simulations are combined, and the simulation process is corrected based on the experimental results to determine a theoretical prediction model, which can predict the migration state and deposition position of solid particles in coal mine underground reservoirs through this prediction model, and can be used to control the diffusion and distribution of solid particles in coal mine underground reservoirs, thus solving to a certain extent the problems of solid particle deposition and uneven mine water treatment efficiency.

[0069] Figure 11 is a block diagram of a prediction device for the migration state of solid particles in a coal mine underground reservoir shown according to an exemplary embodiment, as Figure 11As shown, the device 1100 includes a first acquisition module 1110, a second acquisition module 1120, a data conversion module 1130, a third acquisition module 1140, a model optimization module 1150, and a prediction module 1160.

[0070] The first acquisition module 1110 is used to obtain experimental results by conducting physical experiments simulating actual working conditions; wherein, the actual working conditions include the flow field situation of the underground coal mine reservoir; the experimental results include multi-angle fixed shooting samples for the experimental process and results; The second acquisition module 1120 is used to obtain numerical simulation results of computational fluid dynamics simulation; The data conversion module 1130 is used to convert the experimental results into experimental data for comparison with the numerical simulation results; The third acquisition module 1140 is used to obtain the comparison results between the experimental data and the numerical simulation results; The model optimization module 1150 is used to adjust and optimize the simulation parameters in the computational fluid dynamics simulation process according to the comparison results to determine a prediction model for the migration state of solid particles in the underground coal mine reservoir; The prediction module 1160 is used to predict the migration state of solid particles in the underground coal mine reservoir under different working conditions based on the prediction model.

[0071] Optionally, the first acquisition module 1110 includes a determination sub-module, an experiment sub-module, and a shooting sub-module; The determination sub-module is used to determine the specified shooting positions of the sampling pictures during the experiment; the specified shooting positions include the shooting positions from the front view, side view, and top view; The experiment sub-module is used to conduct the physical experiment through the experimental device according to the set experimental conditions; the experimental device is an experimental device for simulating actual working conditions, and the experimental device includes a reaction tank, a solid particle adding device, and a water flow control system, and the experimental conditions include one or more of water body flow rate, temperature, and solid particle content; The shooting sub-module is used to take multiple pictures of the experimental situation at the specified shooting positions under preset shooting conditions during the physical experiment; wherein, the preset shooting conditions include one or more of environmental light requirements, position requirements, and angle requirements; the experimental situation includes one or more of water body flow situation, solid particle distribution situation, solid particle diffusion path, and solid particle deposition situation.

[0072] Optionally, the data conversion module 1130 includes a picture processing sub-module, a division sub-module, and a comparison point determination sub-module; The image processing sub-module is used to grayscale multiple images in the experimental results by using an image processing method to obtain the pixel grayscale values in the images; The division sub-module is used to divide the first region to be compared in the image into multiple value regions by equal value division according to the boundary contour of the filler in the experimental device; The point to be compared determination sub-module is used to determine the point to be compared in each value region by calculating the geometric mean of the pixel points in the value region; wherein, the position of the point to be compared in the image corresponds to the physical coordinates of the experimental device; the pixel grayscale value of the point to be compared is used to compare with the data of the corresponding coordinate point in the numerical simulation result.

[0073] Optionally, the second acquisition module 1120 is further used for: Establish a theoretical simulation model based on the experimental device of the physical experiment through computational fluid dynamics preprocessing software; Set the simulation conditions of the theoretical simulation model according to the experimental conditions of the physical experiment; Perform a flow field analysis on the theoretical simulation model based on the simulation conditions; Obtain the numerical simulation result obtained after the flow field analysis; the numerical simulation result includes multiple simulation parameters and data visualization results; wherein, the simulation parameters include at least one of computational grid division parameters, multiphase flow model setting parameters, turbulence model setting parameters, boundary inlet water flow velocity, and fluid mechanics equation solving setting parameters; the data visualization results include at least one of the diffusion rate of solid particles in different regions and the solid particle concentration distribution.

[0074] Optionally, the third acquisition module 1140 includes a region division sub-module and an acquisition sub-module; The region division sub-module is used to divide the second region to be compared in the numerical simulation result into corresponding multiple value regions according to the result of the equal value division; the second region to be compared corresponds to the first region to be compared in the experimental result; The acquisition sub-module is used to obtain the concentration distribution of the solid particles in each value region in the second region to be compared; The acquisition sub-module is further used to compare the concentration distribution of the solid particles in the first region to be compared with the concentration distribution of the solid particles in the second region to be compared to obtain the comparison result.

[0075] Optionally, the acquisition sub-module is further used for: Respectively obtain the concentration distribution curve of the solid particles in the first region to be compared and the concentration distribution curve of the solid particles in the second region to be compared; The comparison result is obtained by calculating the cosine similarity between two concentration distribution curves; the cosine similarity is used to evaluate the shape similarity degree of the two curves.

[0076] Optionally, the model optimization module 1150 is further configured to: In the case that the comparison result does not meet the preset condition, adjust and optimize the theoretical simulation model; wherein, the adjustment and optimization include: During the flow field analysis process, adjust the type of fluid model used to describe sedimentation kinetics; Adjust the parameter settings in the simulation conditions, where the parameters include turbulence parameters and solid particle size distribution; Adjust the grid resolution in the theoretical simulation model; Correct the physical property parameters in the theoretical simulation model; the physical property parameters include one or more of solid particle density, solid particle viscosity, and solid particle surface tension.

[0077] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0078] Figure 12 It is a block diagram of an electronic device 1200 for predicting the migration state of solid particles in an underground coal mine reservoir shown according to an exemplary embodiment. As Figure 12 shown, the electronic device 1200 may include: a processor 1201, a memory 1202. The electronic device 1200 may further include one or more of a multimedia component 1203, an input / output (I / O) interface 1204, and a communication component 1205.

[0079] Among them, the processor 1201 is used to control the overall operation of the electronic device 1200 to complete all or part of the steps in the above-mentioned method for predicting the migration state of solid particles in the underground coal mine reservoir. The memory 1202 is used to store various types of data to support the operation of the electronic device 1200. These data may include, for example, instructions for any application or method operating on the electronic device 1200, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 1202 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc. The multimedia component 1203 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 1202 or sent through the communication component 1205. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 1204 provides an interface between the processor 1201 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 1205 is used for wired or wireless communication between the electronic device 1200 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited herein. Therefore, the corresponding communication component 1205 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0080] In an exemplary embodiment, the electronic device 1200 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above-mentioned method for predicting the migration state of solid particles in an underground coal mine reservoir.

[0081] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned method for predicting the migration state of solid particles in an underground coal mine reservoir are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 1202 including program instructions, and the above program instructions may be executed by the processor 1201 of the electronic device 1200 to complete the above-mentioned method for predicting the migration state of solid particles in an underground coal mine reservoir.

[0082] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned method for predicting the migration state of solid particles in an underground coal mine reservoir when executed by the programmable device.

[0083] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept scope of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0084] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not separately describe various possible combination methods.

[0085] Furthermore, any combination can be made between various different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

Claims

1. A method for predicting the migration state of solid particles in underground reservoirs of coal mines, characterized in that: The method comprises: The experimental results are obtained by conducting physical experiments simulating actual working conditions; wherein the actual working conditions include the flow field of underground water reservoirs in coal mines; the experimental results include multi-angle fixed shooting sampling of the experimental process and results; Obtain numerical simulation results of computational fluid dynamics simulations; Converting the experimental results into experimental data for comparison with the numerical simulation results; Obtaining a comparison result between the experimental data and the numerical simulation result; According to the comparison results, the simulation parameters in the computational fluid dynamics simulation process are adjusted and optimized to determine a prediction model for the solid particle migration state in the coal mine underground reservoir; Based on the prediction model, the migration state of solid particles in the coal mine underground reservoir under different working conditions is predicted.

2. The method according to claim 1, characterized in that The step of obtaining the experimental results by conducting a physical experiment simulating the actual working conditions includes: Determine the designated shooting positions of the sampled pictures during the experiment; the designated shooting positions include the shooting positions of the front view angle, the shooting positions of the side view angle, and the shooting positions of the top view angle; The physical experiment is carried out according to set experimental conditions by an experimental device; the experimental device is an experimental device for simulating actual working conditions, the experimental device includes a reaction tank, a solid particle adding device and a water flow control system, and the experimental conditions include one or more of water flow rate, temperature and solid particle content; During the physical experiment, multiple pictures of the experimental conditions are taken at the designated shooting position under preset shooting conditions; wherein the preset shooting conditions include one or more of ambient light requirements, position requirements, and angle requirements; and the experimental conditions include one or more of water flow conditions, solid particle distribution conditions, solid particle diffusion paths, and solid particle sedimentation conditions.

3. The method according to claim 1, characterized in that The converting the experimental results into experimental data for comparison with the numerical simulation results comprises: Grayscale the multiple images in the experimental results using an image processing method to obtain pixel grayscale values ​​in the images; According to the boundary contour of the filler in the experimental device, the first area to be compared in the image is divided into a plurality of value areas by equal value division; By calculating the geometric mean of the pixel points in each of the value-taking areas, the points to be compared in the value-taking area are determined; wherein the positions of the points to be compared in the image correspond to the physical coordinates of the experimental device; and the pixel grayscale values ​​of the points to be compared are used to compare with the data of the corresponding coordinate points in the numerical simulation results.

4. The method according to claim 1, characterized in that: The obtaining of numerical simulation results of computational fluid dynamics simulation includes: By using computational fluid dynamics pre-processing software, a theoretical simulation model is established according to the experimental device of the physical experiment; Setting simulation conditions of the theoretical simulation model according to the experimental conditions of the physical experiment; Based on the simulation conditions, performing flow field analysis on the theoretical simulation model; The numerical simulation results obtained after flow field analysis are obtained; the numerical simulation results include multiple simulation parameters and data visualization results; wherein the simulation parameters include at least one of calculation grid division parameters, multiphase flow model setting parameters, turbulence model setting parameters, water flow velocity, and fluid mechanics equation solution setting parameters; the data visualization results include at least one of the diffusion rate of solid particles in different areas and the solid particle concentration distribution.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of the comparison result between the experimental data and the numerical simulation result includes: According to the result of the equal value division, the second area to be compared in the numerical simulation result is divided into a plurality of corresponding value areas; the second area to be compared corresponds to the first area to be compared in the experimental result; Obtaining the concentration distribution of the solid particles in each value region in the second area to be compared; The concentration distribution of solid particles in the first area to be compared is compared with the concentration distribution of solid particles in the second area to be compared to obtain the comparison result.

6. The method according to claim 5, characterized in that The step of comparing the concentration distribution of solid particles in the first area to be compared with the concentration distribution of solid particles in the second area to be compared to obtain the comparison result includes: Respectively acquiring a concentration distribution curve of solid particles in the first area to be compared and a concentration distribution curve of solid particles in the second area to be compared; The comparison result is obtained by calculating the cosine similarity between the two concentration distribution curves; the cosine similarity is used to evaluate the shape similarity of the two curves.

7. The method according to claim 1, characterized in that The method of adjusting and optimizing the simulation parameters in the computational fluid dynamics simulation process according to the comparison results to determine the prediction model of the solid particle migration state in the coal mine underground reservoir includes: When the comparison result does not meet the preset conditions, the theoretical simulation model is adjusted and optimized; wherein the adjustment and optimization includes: During flow analysis, adjust the type of fluid model used to describe sedimentation dynamics; adjusting parameter settings in simulation conditions, the parameters including turbulence parameters and solid particle size distribution; Adjusting the grid resolution in the theoretical simulation model; Correcting the physical property parameters in the theoretical simulation model; the physical property parameters include one or more of solid particle density, solid particle viscosity and solid particle surface tension.

8. A device for predicting the migration state of solid particles in underground reservoirs of coal mines, characterized in that: The device comprises: The first acquisition module is used to obtain experimental results by performing physical experiments simulating actual working conditions; wherein the actual working conditions include the flow field of an underground water reservoir in a coal mine; and the experimental results include multi-angle fixed shooting sampling of the experimental process and results; A second acquisition module is used to obtain numerical simulation results of computational fluid dynamics simulation; A data conversion module, used to convert the experimental results into experimental data for comparison with the numerical simulation results; A third acquisition module is used to obtain a comparison result between the experimental data and the numerical simulation result; A model optimization module, used to adjust and optimize simulation parameters in the computational fluid dynamics simulation process according to the comparison results, so as to determine a prediction model for the migration state of solid particles in the underground reservoir of the coal mine; The prediction module is used to predict the migration state of solid particles in the coal mine underground reservoir under different working conditions based on the prediction model.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

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