Method and system for predicting sudden gushing water path based on DEM-GPNM and field joint

Abstract

The disclosure provides a sudden gushing water path prediction method and system based on DEM-GPNM and field joint, relates to a sudden gushing water analysis method technical field, and comprises the following steps: constructing a particle discrete element model; calculating the displacement and deformation of particles by using the calibrated particle discrete element model; using the porosity, pore connectivity, fracture distribution and geometric information of the sudden gushing channel to perform fluid mechanics calculation by using the graph theory pipe network method, and determining the fluid pressure and flow distribution under the initial condition; constructing a two-way coupling feedback mechanism between the particle discrete element model and the graph theory pipe network method, feeding back the obtained fluid pressure and flow distribution information to the particle discrete element model, updating the hydraulic conditions in the particle discrete element model, mapping the calculation results of the graph theory pipe network method to the particle discrete element model by using the grid division method, and updating the field monitoring data parameters in real time; and through dynamic monitoring and simulation adjustment of the sudden gushing channel, the change of the water flow path is accurately predicted.

Description

Technical Field

[0001] This disclosure relates to the technical field of inrush water analysis methods, specifically to a method and system for predicting inrush water paths based on DEM-GPNM and field integration. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] In the field of tunnel engineering, sudden water inrush is a phenomenon caused by groundwater seeping outward through geological media under certain pressure, resulting in a sudden surge of water. This phenomenon is often accompanied by geological disasters, posing a serious threat to engineering construction and regional safety. Therefore, accurately predicting the occurrence and path of sudden water inrush is of great significance for engineering early warning and disaster prevention and mitigation.

[0004] Currently, various methods exist for monitoring and analyzing sudden water inrushes in fields such as tunnels and underground engineering. Common methods include statistical analysis based on field monitoring data, physical model simulation, and numerical simulation. While these methods each have their advantages and disadvantages, they still share the following common problems: 1) Accurate prediction of the inrush path is difficult, and traditional methods are unable to capture the path changes of the inrush water in real time; 2) On-site monitoring data often cannot be effectively integrated with the model, resulting in insufficient accuracy of prediction results; 3) The coupled simulation of water flow path and mechanical properties under complex geological conditions lacks accuracy and is difficult to reflect the dynamic changes of the surge channel. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure proposes a method and system for predicting inrush water paths based on DEM-GPNM and field joint analysis. It couples the Discrete Element Method (DEM) with the Graph Pipe Network Method (GPNM), and by incorporating joint field monitoring data, effectively improves the accuracy of the model's initial parameter settings. The coupled calculations reflect the dynamic changes in inrush paths under different geological conditions, thus providing a scientific basis for inrush channel decision-making.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions: A method for predicting the path of sudden water inrush based on DEM-GPNM and field joint methods includes: Obtain the physical properties of soil and rock at the engineering site, and construct a particle discrete element model based on the physical properties of soil and rock; Preliminary simulation calculations were performed on the particle discrete element model to simulate the changes in mechanical behavior between particles and obtain an initial numerical solution. The initial numerical solution and field monitoring data were then used to calibrate the particle discrete element model. The displacement and deformation of particles are calculated using the calibrated particle discrete element model to obtain information on the porosity, pore connectivity, fracture distribution and geometry of the surge channel; Using field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method was performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions. A two-way coupling feedback mechanism is constructed between the particle discrete element model and the graph theory pipe network method. The obtained fluid pressure and flow distribution information is fed back to the particle discrete element model to update the hydraulic conditions in the particle discrete element model. The calculation results of the graph theory pipe network method are mapped to the particle discrete element model by the mesh generation method, and the field monitoring data parameters are updated in real time. Through dynamic monitoring and simulation adjustment of the surge channel, the changes in water flow path can be accurately predicted.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: A surge water path prediction system based on DEM-GPNM and field collaboration includes: The model building module is used to obtain the physical properties of soil and rock at the engineering site and to build a particle discrete element model based on the physical properties of soil and rock. The model calibration module is used to perform preliminary simulation calculations on the particle discrete element model, simulate the changes in mechanical behavior between particles, obtain an initial numerical solution, and use the initial numerical solution and field monitoring data to calibrate the particle discrete element model. The coupled calculation module is used to calculate the displacement and deformation of particles using the calibrated particle discrete element model, and to obtain the porosity, pore connectivity, fracture distribution and geometric information of the surge channel; using the field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method is performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions; The dynamic mapping module is used to construct a two-way coupling feedback mechanism between the particle discrete element model and the graph theory pipe network method. It feeds back the obtained fluid pressure and flow distribution information to the particle discrete element model, updates the hydraulic conditions in the particle discrete element model, maps the calculation results of the graph theory pipe network method to the particle discrete element model using the mesh generation method, updates the field monitoring data parameters in real time, and accurately predicts the changes in the water flow path through dynamic monitoring and simulation adjustment of the surge channel.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for predicting the path of sudden water inrush based on DEM-GPNM and field integration.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned method for predicting the path of sudden water inrush based on DEM-GPNM and field conditions.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes 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 the inrush water path prediction method based on DEM-GPNM and field joint.

[0011] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosure presents a method for predicting inrush water paths based on a combination of DEM-GPNM and field simulation. The Discrete Element Method (DEM) can simulate the mechanical behavior between particles and the interaction between fluid and solid at the microscale, while the Graph Pipe Network Method (GPNM) can model and calculate the fluid transport path at the macroscale. The coupling of these two methods in the simulation of inrush water provides new possibilities for predicting the dominant inrush channels.

[0012] This disclosure presents a method for predicting inrush water paths based on a DEM-GPNM coupled model and field monitoring data. This method improves the accuracy of inrush channel prediction by combining the DEM-GPNM coupled model with actual field monitoring data. By incorporating field monitoring data, the accuracy of the model's initial parameter settings can be effectively improved. By combining the particle discrete element method with the graph theory network method, the advantages of both in mechanical behavior simulation and fluid dynamics calculation are fully utilized. Combined with actual field data, this method achieves accurate prediction and analysis of inrush channels under complex geological conditions. Through coupled calculations, the dynamic changes in inrush paths under different geological conditions can be accurately reflected, thus providing a scientific basis for inrush channel decision-making.

[0013] This disclosed method for predicting sudden water inrush paths based on DEM-GPNM and field joint computation improves the prediction accuracy and adaptability of inrush channels through coupled calculations. It obtains porosity, pore connectivity, and fracture geometry information through a particle discrete element model, and combines this with field monitoring data to set hydraulic boundary conditions. This makes the fluid calculations of the graph-based pipe network method more consistent with the real geological environment, thus avoiding misjudgments caused by initial condition setting deviations in traditional methods. Furthermore, this module ensures that the structural characteristics of the inrush channel can be accurately characterized, making the input data for fluid dynamics calculations more physically plausible, thereby improving the stability and reliability of inrush channel identification.

[0014] This disclosed method for predicting inrush water paths based on a combination of DEM-GPNM and field analysis employs a two-way feedback mechanism. The fluid calculation results are promptly applied to the particle discrete element model, allowing for dynamic adjustments to particle motion and hydraulic conditions based on changes in fluid pressure and flow rate. This ensures the computational model remains synchronized with actual field conditions. This mapping method effectively avoids the lag issues associated with unidirectional decoupled calculations, improves the simulation capability of the dynamic evolution of inrush channels, and makes the prediction results more accurate, providing more scientific data support for optimizing inrush paths. Attached Figure Description

[0015] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0016] Figure 1 This is a flowchart illustrating the method of an embodiment of this disclosure; Figure 2 This is a data interaction diagram of an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the generation of new water flow channels in a particle discrete element fractured medium according to an embodiment of this disclosure. Detailed Implementation

[0017] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0018] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0020] Example 1 One embodiment of this disclosure provides a method for predicting the path of sudden water inrush based on DEM-GPNM and field conditions. By combining the particle discrete element method with the graph theory pipe network method, it fully leverages the advantages of both in mechanical behavior simulation and fluid dynamics calculation, and combines them with actual field conditions to achieve accurate prediction and analysis of sudden water inrush channels under complex geological conditions. The steps include: Step 1: Obtain the physical properties of soil and rock at the engineering site, and construct a particle discrete element model based on the physical properties of soil and rock; Step 2: Perform preliminary simulation calculations on the particle discrete element model to simulate the changes in mechanical behavior between particles, obtain initial numerical solutions, and use the initial numerical solutions and field monitoring data to calibrate the particle discrete element model; Step 3: Calculate the displacement and deformation of the particles using the calibrated particle discrete element model to obtain the porosity, pore connectivity, fracture distribution and geometric information of the surge channel; Step 4: Using the field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method is performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions; Step 5: Construct a two-way coupling feedback mechanism between the particle discrete element model and the graph theory pipe network method. Feed back the obtained fluid pressure and flow distribution information to the particle discrete element model to update the hydraulic conditions in the particle discrete element model. Use the grid generation method to map the calculation results of the graph theory pipe network method to the particle discrete element model. Update the field monitoring data parameters in real time. Through dynamic monitoring and simulation adjustment of the surge channel, accurately predict the changes in the water flow path.

[0021] As one embodiment, this disclosure provides a method for predicting the path of sudden water inrush based on DEM-GPNM and on-site joint methods. The specific implementation process is as follows: Step 1: Obtain the physical properties of soil and rock at the engineering site, and construct a particle discrete element model based on the physical properties of soil and rock; Specifically, relevant soil and rock physical parameters are collected at the engineering site, and a particle discrete element model is established to simulate the contact and stress state between particles, generating the geometry of pore structure, initial porosity, and initial fractures. The specific process is as follows: rock physical and mechanical tests are conducted at the engineering site to obtain basic physical and mechanical parameters, including particle size, density, friction coefficient, and bond strength, etc., and the joint and fracture information at the work site is statistically analyzed as the basis for physical modeling and parameter setting of the particle discrete element model.

[0022] Step 2: Perform preliminary simulation calculations on the particle discrete element model to simulate the changes in mechanical behavior between particles, obtain initial numerical solutions, and use the initial numerical solutions and field monitoring data to calibrate the particle discrete element model; Preliminary calculations were performed on the particle discrete element model. The material parameters of the model were then calibrated multiple times using data from field drilling, testing, and monitoring to ensure the model accurately reflects the initial state of local geological characteristics and pore structure, thereby increasing the model's accuracy. This included: Under initial conditions, the DEM model calculation is initiated. The DEM calculation is implemented using the discrete element method (PFC) software to simulate the changes in mechanical behavior between particles and obtain an initial numerical solution, which includes important indicators such as surrounding rock displacement and surrounding rock pressure. The calculation results are compared with field monitoring data to check whether the magnitude and direction of important indicators such as surrounding rock displacement and surrounding rock pressure are consistent. Multiple checks are performed to ensure that the DEM model is highly consistent with the field conditions, accurately reflecting the initial state of local geological characteristics and pore structure, and increasing the accuracy of the model.

[0023] This disclosure is based on field monitoring and engineering test data, collecting relevant geotechnical physical parameters. These data are used to construct a particle discrete element model to simulate the contact and stress states between particles, generating the initial pore structure and fracture geometry. By repeatedly calibrating the model parameters, it is ensured that the model can reflect the initial state of the field geological characteristics and pore structure, thereby improving the model's accuracy.

[0024] Step 3: Calculate the displacement and deformation of the particles using the calibrated particle discrete element model to obtain the porosity, pore connectivity, fracture distribution and geometric information of the surge channel; In the discrete element model of particles, the formation of sudden flow channels is mainly manifested by two key parameters: the cohesive force between particles and the change in relative displacement. When fluid pressure or external force acts on the particle medium, the contact force between particles changes accordingly. If the normal stress or shear stress between particles exceeds their inherent cohesive strength, the cohesive force between particles will be destroyed, leading to cohesion failure. Cohesion failure will cause local voids to form between particles, thus creating a path for fluid seepage, which in turn evolves into a potential sudden flow channel. When particles undergo relative displacement under the action of fluid or other external forces, the relative displacement will cause the local particle structure to rearrange, especially in areas of stress concentration or unstable particle arrangement. This movement and rearrangement process between particles will further expand the pore space and increase the connectivity of the pores, thereby forming new fluid flow channels. This allows us to obtain information on the porosity, connectivity, and fracture distribution of potential sudden flow channels.

[0025] Specifically, the calibrated particle discrete element model will be used to acquire information on potential inrush channels. By calculating particle displacement and deformation, parameters such as porosity, pore connectivity, fracture distribution, and geometric information are obtained to capture the dynamic changes of the potential inrush channel network, providing basic data for the graph theory-based pipe network calculation model. The specific process is as follows: In the particle-based discrete element model, the formation of sudden flow channels is mainly represented by two key parameters: the interparticle bonding force and the change in relative displacement. Specifically, when fluid pressure or external forces act on the particle medium, the contact forces between particles change accordingly, particularly the normal stress and shear stress. If the normal stress or shear stress experienced by the particles exceeds their inherent bonding strength, the bonding force between the particles will be broken, leading to bonding failure. This bonding failure will create local voids between the particles, thus creating a path for fluid seepage, which in turn evolves into a potential sudden flow channel.

[0026] (1) (2) in, F shear and F normal These represent the tangential stress and normal stress experienced between particles, respectively. and , respectively, represent the maximum strength of the particle bond in the shear and tensile directions; A is the cross-sectional area of ​​the bond.

[0027] Furthermore, besides adhesion failure, relative displacement between particles is another important factor in the formation of surge channels. When particles undergo relative displacement under the influence of fluid or other external forces, these displacements can lead to rearrangement of local particle structures, especially in areas of stress concentration or unstable particle arrangement. This movement and rearrangement process between particles can further expand pore space and increase pore connectivity, thereby forming new fluid flow channels. The formation of these channels is highly correlated with the microscopic movement between particles and evolves continuously with changes in fluid pressure and interparticle interactions.

[0028] (3) in, d k It is the first k The position of each particle; , They are respectively t , t-1 At that moment, the k The position of each particle; This is the threshold for particle displacement change. When the displacement change satisfies this formula, the particle displacement is considered to have reached a steady state.

[0029] The porosity, connectivity, and fracture distribution information captured from granular systems serve as crucial foundational data, providing accurate support for fluid seepage calculations using graph-based pipe network methods.

[0030] Step 4: Using the field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method is performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions; Specifically, using actual on-site monitoring data as hydraulic boundary conditions, the graph theory network method was employed to perform the first fluid dynamics calculation on the potential surge channel, determining the fluid pressure and flow distribution under initial conditions. The process involved inputting porosity, pore connectivity, and fracture distribution and geometric information obtained from the DEM model into the graph theory network model, forming a network topology composed of nodes and pipes. Using on-site measured water pressure and flow data as boundary conditions, the graph theory network method was used to calculate the diffusion of water flow and pressure changes, obtaining information on fluid pressure and flow distribution within the surge channel.

[0031] Step 5: Construct a two-way coupling feedback mechanism between the particle discrete element model and the graph theory pipe network method. Feed back the obtained fluid pressure and flow distribution information to the particle discrete element model to update the hydraulic conditions in the particle discrete element model. Use the mesh generation method to map the calculation results of the graph theory pipe network method to the particle discrete element model. Update the field monitoring data parameters in real time. Through dynamic monitoring and simulation adjustment of the surge channel, accurately predict the changes in the water flow path.

[0032] Specifically, the fluid pressure and flow distribution calculated by the graph theory pipe network method are converted into external loading conditions in the particle discrete element model. The hydraulic conditions in the particle discrete element model are updated to simulate the influence of the fluid on the particles, thereby adjusting the stress state of the particles in the model and achieving coupling with the graph theory pipe network method. This includes: 1) Establish a feedback mechanism between the particle discrete element model and the calculation results of the graph theory pipe network method, update the interaction between particle motion and fluid transport at regular intervals, and introduce field monitoring data for model calculation when necessary. When the equilibrium condition is reached, stop the calculation to ensure the accuracy of fluid-structure interaction. Specifically, the fluid pressure and flow rate information calculated by the graph theory pipe network method are fed back to the particle discrete element model to update the hydraulic conditions in the particle discrete element model and simulate the influence of the fluid on the particles. The specific process is as follows: the pressure calculated by the graph theory pipe network method acts on the particles in the calculation model, and these pressures are converted into external loading conditions in the particle discrete element model to adjust the stress state of the particles in the model and realize the coupling effect with the graph theory pipe network method.

[0033] Furthermore, a mesh generation method is used to map the computational results of the graph theory pipe network method to the particle discrete element method. By dividing the space into a mesh, GPNM nodes and DEM particles can be directly classified into the same mesh, thus simplifying the mapping process and making it suitable for large-scale computations. The specific process is as follows: The entire discrete element simulation region is divided into a uniform cubic mesh, with each mesh cell containing a certain number of particles and GPNM nodes. The mesh size should be chosen to ensure that each mesh cell contains enough particles and nodes, avoiding being too dense or too sparse.

[0034] Based on the spatial coordinates of the GPNM nodes and DEM particles, they are assigned to their respective grid cells. Within each grid cell, the GPNM nodes are located relatively close to the particles, therefore the particles can be considered to bear the stress from the nodes.

[0035] For each grid cell, the pressure from all GPNM nodes within the grid is evenly distributed to the corresponding particles. Depending on the number of nodes and particles, a weighted average distribution or a direct average distribution can be chosen. If some grid cells lack GPNM nodes, pressure information can be obtained from adjacent grid cells for interpolation distribution to ensure pressure continuity.

[0036] After each mesh generation and calculation, the mesh is re-generated based on the particle motion state to ensure that the mapping between GPNM nodes and particles remains effective. Then, the fluid pressure distribution is updated based on the new particle positions.

[0037] As a further step, a feedback mechanism is established between the particle discrete element model and the graph-based pipe network method (BPBM) calculation results. This mechanism periodically updates the interaction between particle motion and fluid transport. Simultaneously, on-site monitoring data is incorporated into the model calculations when necessary. The calculation stops when equilibrium is reached to ensure the accuracy of fluid-structure interaction. Specifically, a Python script is used to alternately execute the information exchange mechanism between the particle discrete element model and the BPBM. At regular intervals, real-time data is first obtained from the particle discrete element model to update the BPBM model. Then, the updated fluid state information from the BPBM is transmitted back to the particle discrete element model. Both processes are synchronized in this manner to ensure that the interaction between fluid and particles is accurately reflected in each calculation step.

[0038] Furthermore, to facilitate joint decision-making regarding sudden surge channels on-site, when using the discrete element method (DEM) and graph theory-based pipeline network method for bidirectional information feedback, adjustments to the model should be made based on on-site monitoring data when necessary. For example, if a certain indicator undergoes a sudden change during on-site monitoring, becoming inconsistent with the parameters applicable to the DEM-GPNM model, the parameters in the calculation model need to be adjusted to reflect the actual on-site conditions.

[0039] Parameter adjustments in the DEM-GPNM model need to be made based on the installation location of the field monitoring equipment. The area corresponding to the installation location of the field monitoring equipment in the DEM should include all particles within a certain range, or multiple nodes / pipes in the GPNM. Therefore, when adjusting model parameters, the parameters of all particles / nodes / pipes within the influence range of the monitoring equipment should be adjusted. To achieve efficient parameter mapping, the Triangulated Irregular Network (TIN) method is used. This method connects known data points into an irregular triangular network and performs linear interpolation within each triangle, ensuring that the parameter values ​​of the intermediate region can be accurately calculated for irregularly distributed data points. This method is particularly suitable for spatial mapping of monitoring equipment data because the distribution of field monitoring points is often irregular. TIN interpolation can handle this uneven distribution well and ensure that data points are accurately mapped to the corresponding particles or nodes in the model. Simultaneously, TIN interpolation also ensures a smooth transition between regions during parameter adjustment, avoiding abrupt changes in local parameters, thereby improving the overall stability and accuracy of the model.

[0040] Furthermore, if there are no sudden changes in the on-site monitoring data, but the monitoring data shows an overall increasing or decreasing trend, adjustments should also be made in the DEM-GPNM model. In this case, it should be noted that the different meanings reflected by monitoring points at different locations are different. For example, if the data from all monitoring locations show the same trend of change within the same time period, it can be understood as an overall change in the engineering site environment, and the DEM-GPNM model should be adjusted accordingly to reflect the overall change. Conversely, if only a few monitoring devices show a trend change, adjustments can be made at the corresponding locations in the model to reflect local changes, thus achieving the integration of the DEM-GPNM model and the site.

[0041] Furthermore, when the water flow velocity and pressure distribution in the graph theory pipe network method reach a state where they no longer change significantly, and the relative displacement and velocity between particles in the particle discrete element numerical calculation model tend to converge and stabilize, it is considered that the coupled calculation has reached equilibrium, the water flow and particle states have reached stability, and the calculation results can be output. Formulas (4) and (5) are the water flow pressure state judgment formula and the water flow velocity state judgment formula, respectively; formulas (6) and (7) are the particle displacement convergence amount judgment formula and the velocity convergence amount judgment formula, respectively. (4) in, P k It is the pressure value of a node or edge; The threshold value for water flow pressure is defined as follows: when this formula is satisfied, the water flow pressure is considered to have reached a steady state.

[0042] (5) in, Q k This is the velocity of the water flow in the pipe; The threshold for water flow velocity variation is defined as follows: when this formula is satisfied, the water flow is considered to be in a stable state.

[0043] (6) in, d k It is the first k The position of each particle; This is the threshold for particle displacement change. When the displacement change satisfies this formula, the particle displacement is considered to have reached a steady state.

[0044] (7) in, v k That is the velocity of the k-th particle; The threshold value is set to the particle velocity change threshold. When the particle velocity change is less than the set threshold value, the particle motion is considered to be in a stable state.

[0045] Furthermore, by continuously combining the on-site monitoring results with the DEM-GPNM calculation model, the potential inrush channels obtained by the DEM-GPNM calculation model can be monitored on the engineering site. Specifically, by extracting the location information of potential inrush channels in the model, monitoring equipment such as piezometers and multi-point displacement gauges can be deployed at the corresponding locations on the engineering site to monitor the inrush element indicators in real time and improve the early warning capability.

[0046] 2) A comprehensive analysis is conducted on the dynamic changes of fluid pressure, flow distribution, particle motion, and potential surge channel network in the particle discrete element model. Based on the final results of fluid-structure interaction calculation, the dominant channel most likely to experience a surge is determined.

[0047] Specifically, a comprehensive analysis is conducted on the dynamic changes of fluid pressure, flow rate distribution, particle motion, and potential surge channel network in the particle discrete element model. Combined with the final results of fluid-structure interaction (FSI) calculations, the most likely surge channel is determined. The specific process is as follows: Based on the FSI calculation results, a detailed analysis of the fluid pressure, flow rate distribution, and particle motion characteristics in the particle discrete element model is performed. Potential surge channels are identified by comparing pressure gradients, velocity changes, and relative particle displacements in different regions. During the analysis, particular attention is paid to the interaction between velocity, flow rate, and particle bonding strength. If the particle bonding strength rapidly decreases and the velocity significantly increases in a certain region, that region is highly likely to form a surge channel. Finally, based on the overall analysis of the model, the most likely surge channel is determined. This dominant channel is identified using a multi-objective comprehensive evaluation method, considering factors such as fluid dynamic parameters (pressure, velocity) and the mechanical state between particles (bonding failure, relative displacement). This ensures that the selected channel possesses the maximum possible surge risk for further prevention and decision-making.

[0048] Furthermore, a method combining the Weighted Sum Method (WSM) with Pareto optimality is employed. This method maintains its simplicity and ease of operation while providing more accurate and comprehensive decision support through Pareto optimality analysis. The specific process is as follows: First, a weighted summation (WSM) method is used to initially identify potential dominant channels. Weights based on engineering experience are assigned to the flow velocity, flow rate, and water pressure of potential dominant channels. After calculating the comprehensive score for each channel, channels with lower scores can be immediately eliminated, narrowing down the candidate pool. This step eliminates low-scoring channels, leaving higher-scoring channels for further evaluation.

[0049] The remaining high-scoring channels were further filtered using the Pareto optimality method. Pareto optimality analysis does not require manually setting weights; instead, it uses multi-objective optimization to find the best performance of each channel under different criteria, generating multiple candidate solutions. Pareto solutions are all "non-dominated solutions," meaning that no other channel is better at one objective while performing unchanged at others. Therefore, the final result is multiple potential surge-dominant channels, all of which perform well under different criteria.

[0050] Finally, based on practical engineering applications, the final dominant surge channel was selected from multiple solutions obtained by the Pareto method, thus achieving accurate prediction and analysis of surge channels under complex geological conditions.

[0051] This disclosure effectively improves the accuracy and reliability of inrush channel prediction by combining field data with numerical simulation. This method is not only applicable to risk assessment and prevention in underground engineering projects such as tunnels and mines under complex geological conditions, but also provides a scientific basis and technical support for early warning and disaster prevention and mitigation of inrush water.

[0052] Example 2 One embodiment of this disclosure provides a sudden water inrush path prediction system based on DEM-GPNM and field joint methods, including: The model building module is used to obtain the physical properties of soil and rock at the engineering site and to build a particle discrete element model based on the physical properties of soil and rock. The model calibration module is used to perform preliminary simulation calculations on the particle discrete element model, simulate the changes in mechanical behavior between particles, obtain an initial numerical solution, and use the initial numerical solution and field monitoring data to calibrate the particle discrete element model. The coupled calculation module is used to calculate the displacement and deformation of particles using the calibrated particle discrete element model, and to obtain the porosity, pore connectivity, fracture distribution and geometric information of the surge channel; using the field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method is performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions; The dynamic mapping module is used to construct a two-way coupling feedback mechanism between the particle discrete element model and the graph theory pipe network method. It feeds back the obtained fluid pressure and flow distribution information to the particle discrete element model, updates the hydraulic conditions in the particle discrete element model, maps the calculation results of the graph theory pipe network method to the particle discrete element model using the mesh generation method, updates the field monitoring data parameters in real time, and accurately predicts the changes in the water flow path through dynamic monitoring and simulation adjustment of the surge channel.

[0053] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for predicting the path of sudden water inrush based on DEM-GPNM and on-site integration.

[0054] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement the inrush water path prediction method based on DEM-GPNM and field joint.

[0055] Example 5 One embodiment of this disclosure provides an electronic device, including 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 inrush water path prediction method based on DEM-GPNM and field joint.

[0056] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for predicting the path of sudden water inrush based on DEM-GPNM and field joint methods, characterized in that, include: Obtain the physical properties of soil and rock at the engineering site, and construct a particle discrete element model based on the physical properties of soil and rock; Preliminary simulation calculations were performed on the particle discrete element model to simulate the changes in mechanical behavior between particles and obtain an initial numerical solution. The initial numerical solution and field monitoring data were then used to calibrate the particle discrete element model. The displacement and deformation of particles are calculated using the calibrated particle discrete element model to obtain information on the porosity, pore connectivity, fracture distribution, and fracture geometry of the surge channel. Using field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method was performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions. The porosity, connectivity, and fracture distribution information of potential sudden flow channels obtained from the particle discrete element model are transformed into a topological network composed of nodes and pipes in the graph theory pipe network method. The water pressure and hydraulic parameters measured on-site are used as input parameters for the graph theory pipe network method to obtain the flow rate and velocity information in the sudden flow channel, determine the fluid pressure and flow distribution under the initial conditions, and convert the fluid pressure and flow distribution calculated by the graph theory pipe network method into external loading conditions in the particle discrete element model to adjust the stress state of the particles in the model and achieve coupling with the graph theory pipe network method. The fluid pressure and flow distribution calculated using the graph theory pipe network method are converted into external loading conditions in the particle discrete element model. This updates the hydraulic conditions in the particle discrete element model, simulates the influence of the fluid on the particles, and adjusts the stress state of the particles in the model. This achieves coupling with the graph theory pipe network method, including: A feedback mechanism is established between the particle discrete element model and the calculation results of the graph theory pipe network method. The interaction between particle motion and fluid transport is updated regularly. At the same time, field monitoring data is introduced for model calculation. When the equilibrium condition is reached, the calculation is stopped to ensure the accuracy of fluid-structure interaction. A two-way coupling feedback mechanism is constructed between the particle discrete element model and the graph theory pipe network method. The obtained fluid pressure and flow distribution information is fed back to the particle discrete element model to update the hydraulic conditions in the particle discrete element model. The calculation results of the graph theory pipe network method are mapped to the particle discrete element model by the mesh generation method, and the field monitoring data parameters are updated in real time. Through dynamic monitoring and simulation adjustment of the surge channel, the changes in water flow path can be accurately predicted.

2. The method for predicting the path of sudden water inrush based on DEM-GPNM and field joint as described in claim 1, characterized in that, Rock physical and mechanical tests are conducted at the engineering site to obtain soil and rock physical parameters, including particle size, density, friction coefficient and bond strength. The joint and fracture information at the work site is statistically analyzed to serve as the basis for physical modeling and parameter setting of the particle discrete element model. Based on the hydrological conditions at the site, corresponding initial hydraulic conditions are applied to the model to simulate the contact and stress state between particles, and to generate the pore structure, initial porosity and initial fracture geometry.

3. The method for predicting the path of sudden water inrush based on DEM-GPNM and field joint as described in claim 1, characterized in that, A preliminary simulation calculation of the particle discrete element model is performed to simulate the changes in mechanical behavior between particles and obtain an initial numerical solution. The initial numerical solution is then used to calibrate the particle discrete element model with field monitoring data. This includes: starting the particle discrete element model calculation under initial conditions, simulating the changes in mechanical behavior between particles, obtaining an initial numerical solution, including important indicators such as surrounding rock displacement and surrounding rock pressure, comparing the calculation results of the initial numerical solution with the field monitoring data, and performing multiple checks to ensure that the particle discrete element model matches the field data and reflects the initial state of local geological characteristics and pore structure.

4. The method for predicting the path of sudden water inrush based on DEM-GPNM and field joint as described in claim 1, characterized in that, In the discrete element model of particles, the formation of sudden flow channels is mainly manifested by two key parameters: the cohesive force between particles and the change in relative displacement. When fluid pressure or external force acts on the particle medium, the contact force between particles changes accordingly. If the normal stress or shear stress between particles exceeds their inherent cohesive strength, the cohesive force between particles will be destroyed, leading to cohesion failure. Cohesion failure will cause local voids to form between particles, thus creating a path for fluid seepage, which in turn evolves into a potential sudden flow channel. When particles undergo relative displacement under the action of fluid or other external forces, the relative displacement will cause local particle structure rearrangement. In areas of stress concentration or unstable particle arrangement, this movement and rearrangement process between particles will further expand the pore space and increase the connectivity of the pores, thereby forming new fluid flow channels. This allows us to obtain information on the porosity, connectivity, and fracture distribution of potential sudden flow channels.

5. The method for predicting the path of sudden water inrush based on DEM-GPNM and field joint as described in claim 1, characterized in that, The computational results of the graph theory pipe network method are mapped to the particle discrete element model using a mesh generation method. The entire discrete element simulation region is divided into uniform cubic meshes. Each mesh cell contains a set number of particles and nodes from the graph theory pipe network method. The mesh size is chosen to ensure that each mesh cell contains enough particles and nodes. Based on the spatial coordinates of the nodes from the graph theory pipe network method and the particles in the particle discrete element model, they are assigned to their respective mesh cells. The nodes from the graph theory pipe network method in each mesh cell are located relatively close to the particles. For each mesh cell, the pressure of all nodes from the graph theory pipe network method within the mesh is evenly distributed to the corresponding particles. After each mesh generation and calculation, the mesh is re-generated based on the state of particle motion to ensure that the mapping between the nodes from the graph theory pipe network method and the particles remains effective.

6. A surge water path prediction system based on DEM-GPNM and field integration, characterized in that, The method for predicting the path of sudden water inrush based on DEM-GPNM and field conditions as described in any one of claims 1-5 includes: The model building module is used to obtain the physical properties of soil and rock at the engineering site and to build a particle discrete element model based on the physical properties of soil and rock. The model calibration module is used to perform preliminary simulation calculations on the particle discrete element model, simulate the changes in mechanical behavior between particles, obtain an initial numerical solution, and use the initial numerical solution and field monitoring data to calibrate the particle discrete element model. The coupled calculation module is used to calculate the displacement and deformation of particles using the calibrated particle discrete element model, and to obtain the porosity, pore connectivity, fracture distribution and geometric information of the surge channel; using the field monitoring data as hydraulic boundary conditions, the first fluid dynamics calculation of the graph theory pipe network method is performed using the porosity, pore connectivity, fracture distribution and geometric information of the surge channel to determine the fluid pressure and flow distribution under the initial conditions; The dynamic mapping module is used to construct a two-way coupling feedback mechanism between the particle discrete element model and the graph theory pipe network method. It feeds back the obtained fluid pressure and flow distribution information to the particle discrete element model, updates the hydraulic conditions in the particle discrete element model, maps the calculation results of the graph theory pipe network method to the particle discrete element model using the mesh generation method, updates the field monitoring data parameters in real time, and accurately predicts the changes in the water flow path through dynamic monitoring and simulation adjustment of the surge channel.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the path of sudden water inrush based on DEM-GPNM and field combination as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the method for predicting the path of sudden water inrush based on DEM-GPNM and field combination as described in any one of claims 1-5.

9. An electronic device, characterized in that, include: The 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 perform the method for predicting the path of sudden inrush water based on DEM-GPNM and field combination as described in any one of claims 1-5.

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