A method and system for simulating and predicting sudden water inrush in different geological media

Abstract

The present disclosure provides a method and system for simulating and predicting sudden water inrush applicable to different geological media, which relates to the technical field of simulating and predicting sudden water inrush, including constructing a medium model based on actual geological conditions and a particle discrete element method; analyzing the force changes of particle contact in the medium model to obtain geometric information of the medium characteristic structure and the connectivity of the medium structure; constructing the topological structure of the medium model through a graph theory pipe network method, and performing fluid mechanics calculations to determine the fluid pressure and flow distribution; introducing a mapping algorithm based on nearest point search and a pressure interpolation method to feed back the fluid pressure and flow distribution information to the medium model, updating the hydraulic conditions in the medium model, and realizing the dynamic coupling of water flow and particle motion in the medium; establishing a coupling feedback mechanism between the medium model and the fluid pressure and flow distribution, updating the interaction between particle motion and fluid migration in real time, and realizing the simulation prediction of sudden water inrush.

Description

Technical Field

[0001] The present disclosure relates to the technical field of sudden water inrush simulation prediction, and in particular to a sudden water inrush simulation prediction method and system applicable to different geological media. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Water inrush is a common geological hazard in underground projects, particularly in complex pore-fracture dual media. This phenomenon is particularly frequent, posing significant challenges to projects such as tunnel construction, mining, and water conservancy projects. Water inrush not only poses a safety hazard to projects but can also cause severe economic losses and casualties. Therefore, studying the mechanisms of water inrush in pore-fracture dual media is of great theoretical and practical significance for predicting water inrush risks, implementing effective prevention and control measures, and ensuring project safety.

[0004] Current research has yielded some progress in analyzing water inrush. Traditional analytical methods typically rely on seepage theories for a single medium and fail to fully account for the complex coupling effects of pores and fractures. Simulation methods based on the discrete element method (DEM) can effectively describe the forces and motions in granular media, but they have limitations when addressing fracture structures and their effects on fluid seepage. Furthermore, while fluid dynamics calculations based on graph-based pipe network methods are highly efficient in addressing network topology, they struggle to accurately reflect particle motion in multiple media and its impact on fluid flow.

[0005] Existing technologies lack a simulation method that can simultaneously integrate the mechanical behavior of granular media with the dynamic seepage characteristics of fluids in pores, fractures, and pore-fracture dual media. Traditional coupling methods struggle to capture the interaction between fluids and solids in real time, and thus cannot provide high-precision water inrush predictions. Summary of the Invention

[0006] In order to solve the above problems, the present disclosure proposes a sudden water inrush simulation prediction method and system applicable to different geological media. By constructing a medium topology model, the particle contact and force conditions are simulated under different medium conditions, the characteristic structural geometric information of the geological medium is generated, the dynamic changes of the geological structure network are captured, and a comprehensive analysis is performed on the fluid pressure and flow distribution, particle movement and geological structure network changes in the geological medium, completing the coupled calculation of fluid mechanics and solid mechanics in different media.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] A water inrush simulation prediction method applicable to different geological media includes: obtaining actual geological conditions, constructing a medium model based on a particle discrete element method;

[0009] By analyzing the displacement, deformation and force changes of particle contacts in the medium model, the geometric information of the characteristic structure of the geological medium and the connectivity of the characteristic structure of the medium are obtained;

[0010] Based on the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure, the topological structure of the medium model is constructed through the graph theory pipe network method, and fluid dynamics calculations are performed to determine the fluid pressure and flow distribution;

[0011] A mapping algorithm based on nearest point search and pressure interpolation method are introduced to feed back fluid pressure and flow distribution information to the medium model, update the hydraulic conditions in the medium model, and realize the dynamic coupling of water flow and particle motion in the medium;

[0012] A coupling feedback mechanism is established between the medium model and the fluid pressure and flow distribution, and the interaction between particle motion and fluid migration is updated in real time to achieve simulation prediction of sudden water inrush.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] A water inrush simulation and prediction system applicable to different geological media, including:

[0015] Model building module, used to obtain actual geological conditions and build medium models based on particle discrete element method;

[0016] The structural analysis module is used to obtain the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure by analyzing the displacement, deformation and force changes of the particle contact in the medium model;

[0017] The fluid mechanics calculation module is used to construct the topological structure of the medium model through the graph theory pipe network method based on the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure, and to perform fluid mechanics calculations to determine the fluid pressure and flow distribution;

[0018] The coupling feedback module is used to introduce a mapping algorithm based on the nearest point search and the pressure interpolation method to feed back the fluid pressure and flow distribution information to the medium model, update the hydraulic conditions in the medium model, and realize the dynamic coupling of water flow and particle motion in the medium; establish a coupling feedback mechanism between the medium model and the fluid pressure and flow distribution, update the interaction between particle motion and fluid migration in real time, and realize the simulation prediction of sudden water inrush.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] A computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements the sudden water inrush simulation prediction method applicable to different geological media.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, a sudden water inrush simulation prediction method applicable to different geological media is implemented.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] An electronic device includes: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the sudden water inrush simulation prediction method applicable to different geological media.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] This paper presents a water inrush simulation and prediction method applicable to diverse geological media. By combining the particle discrete element method with a graph-theoretic pipe network approach, it accurately simulates particle motion and mechanical behavior in a variety of media, including porous media, fractured media, and dual pore-fracture media. It can efficiently handle complex water flow diffusion and pressure fluctuations. A bidirectional feedback mechanism enables real-time updates of fluid-solid interactions, ensuring the accuracy and stability of fluid-solid coupling simulations.

[0027] The present invention discloses a method for simulating and predicting sudden water inrush applicable to different geological media. By adopting a multi-scale collaborative network division and dynamic adjustment method, it can perform adaptive grid division according to the different geometric structures and flow characteristics of geological media, effectively reducing unnecessary computational overhead.

[0028] The present invention discloses a method for simulating and predicting sudden water inrush applicable to different geological media. By dynamically capturing the geometric changes and fluid distribution of the geological structure network in the calculation, the present invention can accurately reflect the migration process of the fluid in the medium, predict sudden water inrush phenomena and their impacts, and provide a scientific basis for the risk assessment and prevention of sudden water inrush in underground engineering.

[0029] The disclosed method for simulating and predicting sudden water inrush, which is applicable to different geological media, can be widely used in various complex geological environments such as underground engineering, tunnel construction, and mining. In particular, it can provide a more sophisticated water flow-mechanical coupling simulation scheme for the characteristics of various different media such as pores, cracks, and pore-crack dual media, and has strong practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0031] Figure 1 This is a flow chart of a water inrush simulation prediction method applicable to different geological media according to an embodiment of the present disclosure;

[0032] Figure 2 A schematic diagram of forming a water flow channel of a particle discrete element medium according to an embodiment of the present disclosure;

[0033] Figure 3 A data interaction diagram between the particle discrete element method and the graph theory pipe network method according to an embodiment of the present disclosure;

[0034] Figure 4 Schematic diagram of a multi-scale time step control method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0038] Example 1

[0039] In one embodiment of the present disclosure, a method for simulating and predicting sudden water inrush applicable to different geological media is provided, comprising the following steps:

[0040] Step 1: Obtain the actual geological conditions and construct a medium model based on the particle discrete element method;

[0041] Step 2: Obtain the geometric information and connectivity of the geological medium’s characteristic structure by analyzing the displacement, deformation, and force changes of the particle contacts in the medium topology model;

[0042] Step 3: Based on the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure, the topological structure of the medium model is constructed through the graph theory pipe network method, and fluid dynamics calculations are performed to determine the fluid pressure and flow distribution;

[0043] Step 4: Introduce a mapping algorithm based on the nearest point search and a pressure interpolation method to feed back the fluid pressure and flow distribution information to the medium model, update the hydraulic conditions in the medium model, and realize the dynamic coupling of water flow and particle motion in the medium;

[0044] Step 5: Establish a coupled feedback mechanism between the medium model and the fluid pressure and flow distribution to update the interaction between particle motion and fluid migration in real time, thereby achieving simulation prediction of sudden water inrush.

[0045] As an embodiment, the different geological media disclosed herein may be porous media, fractured media, or porous-fractured dual media.

[0046] Example 2

[0047] In one embodiment of the present disclosure, a water inrush simulation and prediction method applicable to different geological media is provided. When the medium is a pore-fracture dual medium, the method accurately simulates the mechanical behavior in the pore-fracture dual medium, efficiently handles water diffusion, pressure changes, and fluid-solid coupling processes, and solves the problems of existing methods such as low computational efficiency, inaccurate description of fracture deformation, and inability to effectively predict water inrush phenomena. The specific implementation process is as follows:

[0048] Step 1: Obtain the actual geological conditions and construct a pore-fracture dual medium model based on the particle discrete element method;

[0049] Specifically, based on actual geological conditions, a pore-fracture dual medium model was constructed using the particle discrete element method (DEM). This model simulates the contact and stress conditions of particles in the pore-fracture dual medium, generates information about the pore space, initial porosity, and initial fracture geometry, and calibrates the DEM based on field survey results. The specific process involves obtaining actual geological data based on field geological survey results. Based on the characteristics of the pore-fracture dual medium, the model's boundary conditions, material parameters, particle contact conditions, hydrological conditions, and particle size and distribution are set. The DEM model is then constructed and numerically calculated based on the actual initial stress field to simulate the mechanical behavior between particles and the evolution of the pore-fracture network. After the calculations are completed, the numerical results are compared with field data (surrounding rock pressure and displacement), and the DEM (pore-fracture dual medium model) is calibrated to ensure that the model accurately reflects the fluid seepage and mechanical properties in the pore-fracture dual medium. Improve the accuracy and reliability of the model in porous media simulation through comparison and calibration;

[0050] Step 2: By analyzing the displacement, deformation, and force changes of particle contacts in the pore-fracture dual medium model, the porosity, fracture geometry information, and pore-fracture connectivity are obtained;

[0051] Specifically, by calculating particle displacement and deformation, parameters such as porosity, pore connectivity, fracture distribution, and geometry are obtained, capturing the dynamic changes of the pore-fracture network and providing basic data for the graph-theoretic pipe network calculation model. The specific process is as follows: The pore network in the pore-fracture dual-medium model is composed of the spaces between particles. The movement of particles and changes in the forces acting on them affect the pore geometry and connectivity. When particle displacement or contact force changes significantly, the porosity and connectivity change accordingly. By tracking particle displacement and deformation in real time, the three-dimensional geometry of the pore network can be constructed. At the same time, by analyzing the contact relationships between particles, fracture regions can be identified. Fractures are typically composed of particles with reduced contact forces. When the contact force drops below a certain threshold, fractures begin to form or expand. Recording the positions of these low-contact-force particles can determine the location and morphology of the fractures, thereby forming the three-dimensional geometry of the fractures. The distinction between pores and fractures is primarily based on interparticle contact forces and geometry. Pores are formed by large spaces between particles and exhibit minimal contact force variation, while fractures are characterized by a significant decrease in contact force or a break, manifesting as separation and breakage between particles. The porosity, connectivity, and fracture distribution information captured from the three-dimensional geometry of pore networks and fractures serve as crucial foundational data, providing accurate support for fluid seepage calculations using graph-based pipe network methods.

[0052] When the tangential stress between particles is greater than their shear strength and the normal stress is greater than their tensile strength, the particles separate and break, forming new cracks.

[0053] (1)

[0054] (2)

[0055] in, F shear and F normal are the tangential stress and normal stress between particles, respectively; and are the maximum strength of particle bonding in the shear direction and the tensile direction, respectively; A is the cross-sectional area of ​​bonding.

[0056] Step 3: Based on the porosity, fracture geometry information, and pore-fracture connectivity, the topological structure of the pore-fracture dual medium model is constructed using the graph theory pipeline network method, and fluid dynamics calculations are performed to determine the fluid pressure and flow distribution;

[0057] Specifically, the extracted porosity, fracture geometry, and pore-fracture connectivity are input into the graph-theoretic pipe network method to construct the topological structure of the pore-fracture dual-medium model. Fluid dynamics calculations are then performed to determine the fluid pressure and flow distribution. The specific process is as follows: Under the pore-fracture dual-medium conditions, the geometric change data of the fractures and pores (such as node coordinates, pipe length, width, and permeability) must be packaged into a suitable data format, such as a matrix or table format, for rapid transmission to the graph-theoretic pipe network module for hydraulic calculations. The three-dimensional geometric structures of the fractures and pores are converted into the node and pipe systems of the graph-theoretic pipe network method through a multi-scale collaborative dynamic network partitioning and adjustment method. Different node-pipeline network partitioning densities are applied to different media regions, and the node-pipeline networks of the pores and fractures are coupled to ensure the coherence of the overall flow.

[0058] Furthermore, a multi-scale collaborative dynamic network partitioning and adjustment method is introduced to construct the node and pipeline system. The pores and fractures in the dual medium have significant scale differences. Therefore, it is particularly important to apply different densities of node-pipeline network partitioning in different medium regions. In addition, the node-pipeline networks of pores and fractures need to be coupled to ensure the coherence of the overall flow. The multi-scale collaborative dynamic network partitioning and adjustment method includes the following steps:

[0059] Step (1): Fracture scale refinement, specifically: In the fracture area, since the fracture provides the main fast seepage channel, a high-density, fine node-pipeline network is used to dynamically capture the geometric changes of the fracture (such as the opening and closing state) to ensure that the impact of the fracture on fluid migration can be accurately reflected.

[0060] Step (2): Pore scale optimization: In the porous medium area, based on the dynamic changes of fluid pressure gradient and pore structure, an adaptive network node density is applied to avoid the network being too dense, saving computing resources while maintaining sensitivity to pore structure changes.

[0061] Step (3): Dynamic coordinated adjustment: As the fluid migrates in the pores and fractures, the flow and pressure distribution at the pore-fracture interface are monitored in real time, and the connection relationship between regions of different scales is dynamically adjusted through the coupled node-pipe network to ensure the global coupling effect of the pore and fracture regions and the coherence of the overall flow.

[0062] Specifically, to improve the computational efficiency of the graph-theoretic pipe network method in pore-fracture dual media, computational resources are dynamically allocated based on the local behavior of the fluid in the dual medium, including:

[0063] 1) Adaptive Node Weight Adjustment: Dynamically adjusts the computational weight of each node based on the magnitude of the fluid pressure gradient and the importance of the flow path. For regions with more active flow (such as fracture intersections or pore-fracture interfaces), node weights are increased to obtain a more detailed flow velocity and pressure distribution; for regions with slower flow (such as deep within pores), node weights are decreased to reduce unnecessary computational overhead.

[0064] 2) Local computational acceleration: In localized areas of fracture expansion and pore closure, high-weight nodes are used to dynamically monitor fluid behavior, ensuring that computational focus is on the most critical fluid migration paths, thereby improving computational efficiency and accuracy.

[0065] Step 4: Introduce a mapping algorithm based on the nearest point search and a pressure interpolation method to feed back the fluid pressure and flow distribution information to the pore-fracture dual medium model, update the hydraulic conditions in the pore-fracture dual medium model, and realize the dynamic coupling of water flow and particle motion in the medium;

[0066] Specifically, information such as fluid pressure and flow calculated using the graph-theoretic network method is fed back into the particle discrete element model to update the hydraulic conditions in the pore-fracture dual medium and simulate the effects of fluid on particles. The specific process is as follows: the pressures calculated by the graph-theoretic network method act on the particles on the pore-fracture walls, and these pressures are converted into external loading conditions in the particle discrete element model. In the particle discrete element model, external pressures combine with the contact forces between particles to affect their displacement, rotation, and deformation, and may even cause them to rupture. Fluid pressure in local areas, especially at the tips of the pore-fracture network, can lead to stress concentrations, further causing the expansion and connectivity of pores / fractures. By processing these local pressures, the particle discrete element model calculates the forces acting on the particles and predicts the direction and speed of expansion of the pore-fracture network.

[0067] Furthermore, a mapping algorithm based on nearest point search and a pressure interpolation method are introduced to input the calculation results of the graph theory network method into the pore-fracture dual medium particle discrete element model. In order to accurately transfer the node pressure or flow information in the porous medium to the particle discrete element model, the KD-Tree nearest neighbor search algorithm can be used. First, a KD-Tree is constructed to efficiently find several nearest neighbor particles of each graph theory network node in the particle discrete element model. Then, based on the distance or other geometric relationship between each particle and the node (see formula (3)), the pressure or flow of the node is distributed to these particles according to a certain weight rule. This method can ensure the reasonable transmission of pressure between multiple particles and realize the dynamic coupling of water flow and particle motion in the porous medium.

[0068] (3)

[0069] in, p 、 q Represent the coordinate positions of the two particles respectively; d(p,q) Indicates the distance between them; k It is the spatial dimension.

[0070] Furthermore, in fractured media, when there are multiple possible contact points between the graph network nodes and the discrete element particles, interpolation can be used to distribute the pressure to multiple particles. The calculation formula is shown in (4). For example, weighted interpolation can be performed based on the distance between each particle and the graph network node. Particles that are closer are assigned more pressure, while particles that are farther away are assigned less pressure. Interpolation methods are helpful in dealing with pressure distribution problems on irregular fracture surfaces.

[0071] (4)

[0072] in, p k is the interpolated pressure on the kth particle; p i is the graph network node P i Pressure on d k is the particle k and the graph network node P i The distance; n is the graph network node P i Total number of particles exposed.

[0073] Step 5: Establish a coupled feedback mechanism between the pore-fracture dual medium model and the fluid pressure and flow distribution to update the interaction between particle motion and fluid migration in real time, thereby achieving simulation prediction of sudden water inrush.

[0074] Specifically, a feedback mechanism is established between the particle discrete element model and the calculation results of the graph theory network method to update the interaction between particle motion and fluid migration in real time. When the equilibrium condition is reached, the calculation is stopped to ensure the accuracy of fluid-solid coupling. The specific process is: through Python programming, the particle physical information (such as displacement, velocity, stress, etc.) in the particle discrete element model is obtained in real time. This information will be used in the graph theory network method model to dynamically update fluid dynamics related variables (such as water pressure, flow rate, etc.). At the same time, the flow and pressure change results in the graph theory network method model are transmitted back to the particle discrete element model in real time to update the water pressure conditions in the porous medium, thereby affecting the movement state of the particles. For example, if the water pressure calculated by the graph theory network method increases in the pores, the particle discrete element model will recalculate the forces between the particles based on this pressure and adjust their positions and velocities.

[0075] Furthermore, when the solid pressure and fluid pressure match and reach a dynamic equilibrium state, the coupled calculation is considered to have reached equilibrium, the deformation has reached a stable state, and the calculation results can be output. Formula (5) and Formula (6) are the judgment formulas for the matching of solid pressure and fluid pressure and the dynamic equilibrium judgment formula, respectively:

[0076] (5)

[0077] in, P soild Indicates the contact pressure between solid particles; P fluid Indicates fluid pressure; is the pressure difference threshold.

[0078] (6)

[0079] When the total energy change of the system is less than a certain threshold of the global energy change, the total energy change of the system tends to be stable, and it is considered that the interaction between the solid and the fluid has reached a state of equilibrium.

[0080] As an embodiment, a comprehensive analysis is conducted on the fluid pressure and flow distribution, particle movement, and pore-fracture network changes in the pore-fracture dual medium to complete the coupled calculation of fluid mechanics and solid mechanics in the porous medium. The specific process is: by analyzing the particle movement in the model, the dynamic evolution of the pore-fracture network during the fluid-solid coupling process is obtained, including phenomena such as the expansion, compression, and redistribution of pores and the opening and closing of fractures. The water flow pressure, flow, and velocity distribution calculated by the graph theory network method can be used to evaluate the water flow path in the pore-fracture dual medium and predict the occurrence of phenomena such as sudden water inrush. Combining the results of the particle discrete element model and the graph theory network method, the mutual coupling of water flow and pore network evolution is further analyzed.

[0081] Example 3

[0082] In one embodiment of the present disclosure, a water inrush simulation prediction method applicable to different geological media is provided. When the geological medium is a porous medium, the method accurately simulates the mechanical behavior in the porous medium and efficiently handles the diffusion, pressure change, and fluid-solid coupling process of the water flow. The method steps are as follows:

[0083] Obtain actual geological conditions and construct a porous medium model based on the particle discrete element method;

[0084] By analyzing the displacement, deformation and force changes of particle contacts in the porous medium model, the pore geometry information and pore connectivity are obtained;

[0085] Based on pore geometry information and pore connectivity, the topological structure of the porous medium model is constructed using the graph theory pipe network method, and fluid dynamics calculations are performed to determine the fluid pressure and flow distribution;

[0086] A mapping algorithm based on nearest point search and pressure interpolation method are introduced to feed back fluid pressure and flow distribution information to the porous medium model, update the hydraulic conditions in the porous medium model, and realize the dynamic coupling of water flow and particle motion in the porous medium.

[0087] A coupled feedback mechanism is established between the porous medium model and the fluid pressure and flow distribution, and the interaction between particle motion and fluid migration is updated in real time to achieve simulation prediction of sudden water inrush.

[0088] The specific implementation process is consistent with that of Example 2, with only the medium being different.

[0089] Example 4

[0090] In one embodiment of the present disclosure, a water inrush simulation prediction method applicable to different geological media is provided. When the geological medium is a fractured medium, the method accurately simulates the mechanical behavior in the fractured medium and efficiently handles the diffusion, pressure change, and fluid-solid coupling of water flow. The method steps are as follows:

[0091] Obtain actual geological conditions and construct a fracture medium model based on the particle discrete element method;

[0092] By analyzing the displacement, deformation and force changes of particle contact in the fracture medium model, the fracture geometry information and fracture connectivity are obtained;

[0093] Based on the fracture geometry information and fracture connectivity, the topological structure of the fracture medium model is constructed through the graph theory pipe network method, and fluid dynamics calculations are performed to determine the fluid pressure and flow distribution;

[0094] A mapping algorithm based on nearest point search and pressure interpolation method are introduced to feed back fluid pressure and flow distribution information to the fractured medium model, update the hydraulic conditions in the fractured medium model, and realize the dynamic coupling of water flow and particle motion in the fractured medium.

[0095] A coupling feedback mechanism is established between the fracture medium model and the fluid pressure and flow distribution, and the interaction between particle motion and fluid migration is updated in real time to achieve simulation prediction of sudden water inrush.

[0096] The specific implementation process is consistent with that of Example 2, with only the medium being different.

[0097] Example 5

[0098] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the computer program implements the method for simulating and predicting sudden water inrush applicable to different geological media.

[0099] Example 6

[0100] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the method for simulating and predicting sudden water inrush applicable to different geological media is implemented.

[0101] Example 7

[0102] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the method for simulating and predicting sudden water inrush applicable to different geological media.

[0103] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for simulating and predicting sudden water inrush applicable to different geological media, characterized in that: include: Obtain actual geological conditions and construct a medium model based on the particle discrete element method; By analyzing the displacement, deformation and force changes of particle contacts in the medium model, the geometric information of the characteristic structure of the geological medium and the connectivity of the characteristic structure of the medium are obtained; Based on the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure, the topological structure of the medium model is constructed through the graph theory pipe network method, and fluid dynamics calculations are performed to determine the fluid pressure and flow distribution; A mapping algorithm based on nearest point search and pressure interpolation method are introduced to feed back fluid pressure and flow distribution information to the medium model, update the hydraulic conditions in the medium model, and realize the dynamic coupling of water flow and particle motion in the medium; A coupling feedback mechanism is established between the medium model and the fluid pressure and flow distribution, and the interaction between particle motion and fluid migration is updated in real time to achieve simulation prediction of sudden water inrush.

2. A method for simulating and predicting sudden water inrush applicable to different geological media according to claim 1, characterized in that: Based on the results of on-site geological surveys, actual geological condition data is obtained. Based on the particle discrete element method, the boundary conditions, material parameters, particle contact conditions, hydrological conditions, and particle size and distribution of the medium model are set according to the characteristics of the geological medium. Combined with the actual initial stress field, a medium model is constructed and numerical calculations are performed on the medium model to simulate the mechanical behavior between particles and the evolution of the geological structure network. After the calculations are completed, the numerical results are compared with the field data, and the constructed medium model is calibrated to ensure the fluid seepage and mechanical properties in the model medium.

3. A method for simulating and predicting sudden water inrush applicable to different geological media according to claim 1, characterized in that: The geological structure network in the medium model is composed of characteristic gaps between particles. The movement and force changes of the particles affect the geometric structure and connectivity of the geological medium structure. The three-dimensional geometric structure of the geological structure network is constructed by real-time tracking of the displacement and deformation of the particles. The pore or fracture areas are identified by analyzing the contact relationship between the particles. The fractures are composed of particles with reduced contact force. When the contact force drops below the set threshold, the fractures begin to generate or expand. The position of the low contact force particles is recorded to determine the position and morphology of the fractures, and then the three-dimensional geometric structure of the geological structure network is constructed. The characteristic geometric information of the geological medium structure and the characteristic connectivity of the medium are captured from the three-dimensional geometric structure of the geological structure network.

4. A method for simulating and predicting sudden water inrush applicable to different geological media according to claim 1, characterized in that: The acquired geometric information of geological medium structural characteristics and medium characteristic connectivity information are input into the graph theory pipe network method. Under the corresponding medium conditions, the geometric change data of the geological medium structural characteristics are uniformly packaged and transmitted to the graph theory pipe network method for hydraulic calculations. The three-dimensional geometric structure of the geological structure network is converted into nodes and pipeline systems of the graph theory pipe network method through a multi-scale collaborative dynamic network division and adjustment method. Different densities of node-pipeline network division are applied in different medium areas, and the node-pipeline network is coupled to ensure the continuity of the overall flow.

5. The method for simulating and predicting sudden water inrush applicable to different geological media according to claim 1, characterized in that: A mapping algorithm based on nearest point search and a pressure interpolation method are introduced to feed back the fluid pressure and flow distribution information to the medium model. First, a KD-Tree is constructed to find several nearest neighbor particles of each graph theory network method node in the medium model. Then, based on the distance or other geometric relationship between each particle and the node, the pressure or flow of the node is distributed to these particles according to the set weight rule, ensuring that the pressure is reasonably transmitted between multiple particles and realizing the dynamic coupling of water flow and particle motion in the medium.

6. The method for simulating and predicting sudden water inrush applicable to different geological media according to claim 1, characterized in that: When there are multiple possible contact points between the graph theory network method node and the particle discrete element particles, the interpolation method is used to distribute the pressure to multiple particles. Weighted interpolation is performed based on the distance between each particle and the graph theory network method node. Particles that are closer are assigned more pressure, while particles that are farther away are assigned less pressure.

7. A water inrush simulation and prediction system applicable to different geological media, characterized in that: include: Model building module, used to obtain actual geological conditions and build medium models based on particle discrete element method; The structural analysis module is used to obtain the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure by analyzing the displacement, deformation and force changes of the particle contact in the medium model; The fluid mechanics calculation module is used to construct the topological structure of the medium model through the graph theory pipe network method based on the geometric information of the geological medium characteristic structure and the connectivity of the medium characteristic structure, and to perform fluid mechanics calculations to determine the fluid pressure and flow distribution; The coupling feedback module is used to introduce a mapping algorithm based on the nearest point search and the pressure interpolation method to feed back the fluid pressure and flow distribution information to the medium model, update the hydraulic conditions in the medium model, and realize the dynamic coupling of water flow and particle motion in the medium; establish a coupling feedback mechanism between the medium model and the fluid pressure and flow distribution, update the interaction between particle motion and fluid migration in real time, and realize the simulation prediction of sudden water inrush.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for simulating and predicting sudden water inrush applicable to different geological media as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the method for simulating and predicting sudden water inrush applicable to different geological media as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement a sudden water inrush simulation prediction method applicable to different geological media as described in any one of claims 1 to 6.

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