Method and apparatus for constructing an equivalent conduit flow model for solute transport in groundwater through intersecting fractures

Abstract

This invention discloses a method and apparatus for constructing an equivalent conduit flow model for solute transport in intersecting fractured groundwater, belonging to the field of groundwater pollution prevention and risk management technology. The method for constructing the equivalent conduit flow model includes: selecting relevant characteristic variables related to the spatiotemporal evolution of groundwater solutes; determining a conceptual model for groundwater solute migration in intersecting fractures under typical conditions; selecting relevant characteristic variables for the spatiotemporal evolution of groundwater solute transport; obtaining the mathematical governing equations for groundwater solute transport; obtaining a numerical simulation system for the spatiotemporal evolution of groundwater solute transport; obtaining a quantitative model of equivalent dispersion of groundwater solute transport in fractured fractures, as well as corresponding quantitative models of equivalent permeability and equivalent circular pipe seepage cross-sections, thereby establishing an equivalent conduit flow model for groundwater solute transport. This invention can provide strong support for rapid prediction, early warning, and effective risk management of groundwater pollution in large-scale complex media.

Description

Technical Field

[0001] This invention belongs to the field of groundwater pollution prediction, early warning and risk management technology in complex media, specifically involving a method and device for constructing an equivalent conduit flow model for solute transport in groundwater through intersecting fractures. Background Technology

[0002] Currently, in the prediction, early warning, and risk assessment of solute pollution in groundwater in fractured complex media, the use of discrete fracture network numerical simulation systems to simulate and assess the state of groundwater pollution and environmental risks is a commonly used quantitative method to obtain accurate and reliable results. However, accurate and reliable numerical simulation requires comprehensive consideration of numerous influencing factors and complex evolution processes, especially considering and refining the solute transport process of each fracture on a large scale. This is time-consuming, computationally resource-intensive, and even difficult to complete. Furthermore, the modeling and prediction of groundwater systems in complex media inherently involves significant uncertainties. Uncertainty assessment using methods such as Montto Carlo requires hundreds or thousands of numerical simulation experiments, resulting in huge or even unacceptable computational costs and low efficiency. Therefore, there is an urgent need for a practical, efficient, fast, and reliable method for constructing a quantitative model of solute transport in intersecting fractured groundwater. Summary of the Invention

[0003] The purpose of this invention is to provide a method and apparatus for constructing an equivalent conduit flow model for solute transport in groundwater in intersecting fractured media, so as to solve the problems of high uncertainty, high cost and low efficiency in the quantitative results of groundwater pollution prediction, early warning and risk management in complex fractured media in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a method for constructing an equivalent conduit flow model for solute transport in groundwater through intersecting fractures, comprising:

[0006] Step A: Determine the conceptual model of the spatiotemporal evolution of groundwater solute transport in intersecting fractures under typical conditions and obtain the corresponding set of typical features, i.e., the typical condition framework. Select the characteristic variables (including solute concentration) associated with the spatiotemporal evolution of groundwater solute transport. Obtain the mathematical governing equations for groundwater solute transport in fractured media and obtain the numerical simulation system for the spatiotemporal evolution of groundwater solute transport in fractured media.

[0007] Step B: Select the number of inlets and outlets of the intersecting fracture lines for groundwater seepage in the intersecting fractures;

[0008] Step C: Determine the set of controlling parameters (hydraulic conductivity, diffusion and dispersal parameters, etc.) for the spatiotemporal evolution of groundwater solute transport V=[v1,v2,...,v k Select the range of variation for each control parameter to form the control parameter range set U=[u1,u2, …u… ... k ];

[0009] Step D: Select the change levels of each control parameter, where the two endpoints of the change interval of each control parameter are mandatory level values, and the change level of each control parameter is at least 2; obtain basic typical scenario sampling of the spatiotemporal evolution of groundwater solute transport under given conditions based on orthogonal experimental methods or full combination of control parameters, and generate corresponding different combinations of control parameter values ​​for each typical scenario, i.e., parameter combinations;

[0010] Step E: Obtain the calculated values ​​of the characteristic variables associated with the topology of the fracture network corresponding to each basic typical scenario;

[0011] Step F: Based on the numerical simulation system for the spatiotemporal evolution of groundwater solute transport, obtain the simulated values ​​of the associated characteristic variables corresponding to each basic typical scenario and the calculated values ​​of each related associated characteristic variable (including head difference, cross-line inflow and outflow, etc.).

[0012] Step G: Combine all parameter combinations with the corresponding simulated or calculated values ​​of associated feature variables to form the basic dataset for modeling;

[0013] Step H: Based on the preset number of scenario samples and the control parameters, a preset number of scenario samples are obtained to obtain a combination of data augmentation scenario parameters. The data augmentation scenario simulation value or calculated value of the associated feature variable corresponding to each scenario is obtained through the numerical simulation system and related calculations. The combination of data augmentation scenario parameters and the set of data augmentation scenario simulation values ​​or calculated values ​​are used as the modeling augmentation dataset.

[0014] Step 1: Based on the modeling base dataset or the modeling base dataset and the modeling enhancement dataset respectively, use a numerical simulation system to obtain the time variation values ​​of the average concentration of groundwater solutes at each outlet. Based on these variation values, obtain the corresponding equivalent dispersion and equivalent average flow velocity. Based on the equivalent average flow velocity and the corresponding hydraulic gradient, obtain the equivalent permeability. Based on the digital modeling method, establish and optimize the quantitative models of the equivalent dispersion and equivalent permeability with the control parameters according to the modeling dataset and the equivalent dispersion and equivalent permeability values ​​respectively.

[0015] Step J: Validate the established quantization model using modeling reinforcement datasets or sets of modeling reinforcement data that have not been applied to the quantization model and / or typical scenario physical model test data. The model validation shall include at least an equivalent diffusion quantization model.

[0016] Step K: Establish an equivalent pipeline flow conceptual model. Based on the basic modeling dataset and the corresponding scenario inlet and outlet flow rates and the equivalent average flow velocity, determine the equivalent seepage cross-sectional area of ​​each pipeline in each scenario. Then, based on the basic modeling dataset and the corresponding equivalent seepage cross-sectional area of ​​each pipeline, establish a quantitative model of the equivalent seepage cross-sectional area of ​​each pipeline and control parameters based on the digital modeling method.

[0017] Step L: For specific application scenarios, obtain the geometric parameters of intersecting fractures, establish a discrete fracture network model, obtain the structural parameters of the intersecting fracture network, and determine whether the typical feature set meets the given typical condition framework. If so, based on the structural parameters of the fracture intersecting fracture network, the seepage field characteristics of the intersecting fractures, and the equivalent pipe flow conceptual model, determine the distribution and length of each pipe; further determine the values ​​of each relevant control parameter variable, and based on the equivalent dispersion quantification model, the equivalent permeability quantification model, and the equivalent pipe seepage cross-sectional area quantification model, determine the cross-sectional area of ​​each equivalent pipe, the permeability of the corresponding seepage medium, and the corresponding dispersion parameters, thereby establishing a corresponding specific equivalent pipe flow model for groundwater solute transport in intersecting fractures.

[0018] Further, in step A, the conceptual model of the spatiotemporal evolution of groundwater solute transport under typical conditions and the acquisition of the corresponding set of typical features, i.e., the typical condition framework, include: determining the morphological characteristics of fractures, determining the number of intersection lines and the number of inlets and outlets, determining the characteristics of fracture surfaces, determining the topological characteristics of fracture intersections, determining the characteristics of groundwater seepage fracture width variation, determining the characteristics of groundwater flow regime, and determining the characteristics of groundwater head variation in fractures.

[0019] Further, in step A, the spatiotemporal evolution correlation characteristic variables of groundwater solute transport include: groundwater solute pollutant concentration, groundwater seepage velocity, groundwater solute concentration change at fracture intersections, and average groundwater solute concentration at fracture intersections; in step E, the correlation characteristic variables of fracture network topology include the mean and value distribution of each fracture network topology parameter, and the structural parameters include the number of inlet intersections, the number of outlet intersections, the equivalent seepage path length, the intersection length, the intersection location, and the fracture disk diameter.

[0020] Further, in step H, obtaining a preset number of scenario samples based on the preset scenario sampling number and the control parameters includes: selecting the random sampling number, determining or selecting the applicable random distribution type and corresponding distribution parameters for each control parameter, completing the random number generation of the control parameters, and thus forming a scenario with a new parameter combination.

[0021] Further, in step J, if the modeling enhancement dataset is used in the quantitative model modeling, a preset number of scenario samples are generated again based on the control parameters to obtain a data enhancement scenario parameter combination. The data enhancement scenario simulation value or calculated value of the associated feature variable corresponding to each scenario is obtained through the numerical simulation system and related calculations. The data enhancement scenario parameter combination and the set of data enhancement scenario simulation values ​​or calculated values ​​are then used as the modeling enhancement dataset generated again.

[0022] Further, in step K, the digital modeling method includes: statistical analysis modeling method and machine learning modeling method; the statistical analysis modeling method includes establishing a multiple regression statistical model of the associated feature variable and all its control parameter factors and a multiple regression statistical model of the associated feature variable and its main control parameter factors.

[0023] Furthermore, the multivariate regression statistical analysis modeling method includes: establishing a multivariate regression statistical model and directly providing specific parameters for each pipeline, as well as establishing a multivariate regression statistical model and obtaining the average value and distribution of each parameter of the pipeline flow model based on the multivariate regression statistical model, and then assigning an average value or randomly assigning a dispersion parameter to each pipeline.

[0024] Further, in step K, establishing the equivalent pipeline flow conceptual model includes:

[0025] Maintain equivalent total flow rate at the fissure inlet, equivalent total flow rate at the fissure outlet, equivalent average solute concentration variation characteristics at each inlet, and equivalent average solute concentration variation characteristics at each outlet;

[0026] Establish the connection characteristics of the pipelines between the groundwater inlet and outlet of intersecting fractures, including whether they pass through the fracture center point or fracture center region, and whether the solutes in each pipeline passing through the fracture center point or fracture center region are mixed and the degree of mixing, etc.

[0027] Determine the number of equivalent pipes used for each inlet intersection and each outlet intersection of the intersecting fractured groundwater.

[0028] The generalization method of the equivalent seepage path applied on the equivalent pipeline between the inlet and outlet of fractured groundwater and the corresponding calculation method of the equivalent hydraulic gradient are determined.

[0029] Furthermore, in step L, the establishment of the discrete fracture network model includes: a deterministic discrete fracture network model and a discrete fracture network model with random distributed parameters.

[0030] Secondly, the present invention provides a device for constructing an equivalent conduit flow model for solute transport in groundwater through intersecting fractures, comprising:

[0031] The conceptual model construction module for groundwater solute transport in intersecting fractures is used to select relevant characteristic variables of the groundwater environment and construct a conceptual model of the spatiotemporal evolution of the corresponding groundwater environment system under typical condition frameworks.

[0032] The numerical simulation module for intersecting fracture generation and solute transport is used to obtain control parameters affecting the spatiotemporal evolution of associated characteristic variables based on the spatiotemporal evolution conceptual model, orthogonal numerical model and numerical simulation system, and to obtain simulated or calculated values ​​of associated characteristic variables in typical scenarios.

[0033] The module for constructing the equivalent parameter quantification model of fracture solute transport pipeline flow is used to obtain different typical scenarios composed of different combinations of control parameters of the groundwater environment system based on the given typical scenario preset numbers, and to establish the equivalent parameter quantification model of fracture solute transport pipeline flow, including statistical model and machine learning model;

[0034] The module for constructing and applying the equivalent conduit flow model for solute transport in intersecting fractures is used to construct the conceptual model of the equivalent conduit flow for solute transport in groundwater in intersecting fractures, and supports the application of the equivalent conduit flow model for solute transport in groundwater in intersecting fractures at different scales.

[0035] Based on the above technical solution, the embodiments of the present invention can produce at least the following technical effects:

[0036] This invention provides a method for constructing an equivalent conduit flow model for solute transport in intersecting fractured groundwater. Based on the topology of 3D stochastic groundwater network (DFNs), it systematically solves key problems such as the equivalent dispersion of basic structural units in fractured networks. Simultaneously, it provides and establishes an effective and reliable model for estimating key parameters of pollutant transport and dispersion processes at field scales of tens to hundreds of meters, and proposes a method for effective upscaling modeling of groundwater pollutant transport in fractured media. This method can efficiently and reliably predict the spatiotemporal distribution of solute pollutants in intersecting fractured groundwater under given conditions and frameworks, significantly improving the accuracy, efficiency, and practicality of assessing and predicting the spatiotemporal evolution of groundwater pollution and the spatiotemporal distribution of pollutants in complex media. It can provide strong support for rapid prediction, early warning, and effective risk management of groundwater pollution in large-scale complex media. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0038] Figure 1 These are the best-fit curves for each experimental data in the embodiments of the present invention;

[0039] Figure 2 This is a bar chart comparing the predicted equivalent diffusion value of the regression with the experimental fitted value in the embodiments of the present invention.

[0040] Figure 3 This is a conceptual model of equivalent pipe flow for solute transport in cross-cracks according to an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0042] This embodiment presents a practical and feasible method for dimensionality reduction and generalization modeling of groundwater pollutant transport in disk fractured networks, as well as a parameter assignment model, making it possible to efficiently and rapidly calculate solute transport in large-scale fractured bedrock groundwater.

[0043] 1. Establishment of a conceptual model for the spatiotemporal evolution of solute transport in groundwater through intersecting fractures

[0044] 1) Determine the morphological characteristics of the fracture: disk-shaped;

[0045] 2) Determine the number of intersection lines and the quantity of inlets and outlets: one inlet and one outlet;

[0046] 3) Determine the characteristics of the fracture surface: smooth;

[0047] 4) Determine the topological characteristics of the intersecting fractures: including random intersection positions, rotatable intersections, intersection dimensions within the diameter of the fracture disk, and arbitrary intersection angles;

[0048] 5) Determine the variation in the width of different fissures in groundwater seepage: 1-5 cm;

[0049] 6) Determine the characteristics of groundwater flow: Darcy flow;

[0050] 7) Determine the characteristics of groundwater head variation in the fractures: based on the regional groundwater hydraulic gradient of 0.1%-1%;

[0051] 2. Selection of Mathematical Control Equations and Numerical Simulation System for Solute Transport in Intersecting Fracture Groundwater

[0052] Governing equations:

[0053] (1)

[0054] Saturated hydraulic conductivity coefficient K f [LT -1 ]:

[0055] (2)

[0056] In the formula: It is a two-dimensional gradient operator defined on the fracture plane, where b is the equivalent fracture width [L] and μ is the hydrodynamic viscosity coefficient (ML). -1 T -1 p is the fluid density (ML). -3 g is the acceleration due to gravity (LT). -2 ); and Z f These are the pressure head and elevation head [L] within the fracture, respectively. f It is the volumetric fluid exchange rate (L -3 T).

[0057] When only convection and dispersion processes are considered, the two-dimensional transport governing equations are given as follows:

[0058] (3)

[0059] In the formula: C f It is the concentration in the fracture [ML] -3 ], u f It is fluid flux [LT] -1 ], D f It is the hydrodynamic dispersion coefficient of the fracture [LT] -2 ].

[0060] Choose the DfnWorks and PFLOTRAN integrated numerical simulation system.

[0061] 3. Construction of a multivariate regression statistical model for the equivalent diffusion of solute transport in groundwater through intersecting fractures

[0062] (1) Equivalent diffusion identification method

[0063] Whether reducing the disk model to a one-dimensional pipe flow model or performing numerical calculations with coarse meshing, obtaining the equivalent dispersion value at the outlet is crucial for solving the aforementioned problems. This equivalent parameter reflects the comprehensive dispersion effect of the solute due to the complex velocity distribution and seepage path differences within the two-dimensional plane of the fracture. Using the analytical solution of the one-dimensional ADE equation, the equivalent dispersion and equivalent average velocity are obtained by fitting the solute mixing average concentration process line at the fracture outlet.

[0064] (4)

[0065] The initial and boundary conditions are as follows:

[0066] C(x,0)=0x≥0; C(0,t)=C0t≥0; C(∞,t)=0t≥0

[0067] Where C is the solute concentration (ML) -3 x represents the distance (L) from the observation point to the pollution source; t

[0068] Time (T); Co is the initial concentration (ML). -3 ); υ represents the average velocity (L / T) along the seepage path; D L Represents the dispersion coefficient (LT) -2 The value of α(L) is equal to the product of the dispersion α(L) and the average flow velocity υ. In this embodiment, α is obtained by fitting the equivalent penetration curve. eq At the same time, maintain It is expressed in accordance with the estimated average V in their respective Lagrange flow regions. Furthermore, to ensure the subsequent application of α... eq The spatial coordinates of the key computation nodes remain unchanged, using d in With d ot The sum is represented by .

[0069] (2) Design of typical scenarios for solute transport in groundwater through intersecting fractures

[0070] To investigate the influence of the geometric parameters of the disk-shaped crack on the equivalent dispersion, a 7-factor mixed-level L36(2^5 3^2) orthogonal numerical model was constructed. The fitting results of the equivalent dispersion and equivalent average velocity for the 36 models are shown in the table below. Here, the intersection line length L- and the distance d- from the intersection line to the center of the circle were normalized and denoted as Lr- and dr- respectively after being divided by the diameter D.

[0071] Table 1 Orthogonal Experimental Scenario Design

[0072]

[0073] To more accurately estimate the impact of the six control parameters (excluding b) on the equivalent dispersion, a full combination of control parameters was used to expand the sample data. The control parameters D (100, 300, 500 cm) and θ (π / 2, 3π / 4, π) were set as principal control factors with three levels. (0.2, 0.5), (0.2, 0.5), (0.3, 0.43) and (0.3, 0.43) were set to 2 levels respectively, and a total of 144 numerical models were established after combining all control parameters. Regression statistics were performed on the above 36 orthogonal experimental models and the 144 models with 7 control parameters (control factors), resulting in the following equation: Equation (5) represents the statistical regression relationship between each control parameter (all factors) and the equivalent diffusion. Their R... 2 The value is 0.911, indicating that the equation fits the data well, and the full factor regression model shows a high degree of fit.

[0074] (5)

[0075] 4. Numerical simulation verification of the multiple regression statistical model for solute transport in groundwater through intersecting fractures

[0076] Sixteen new numerical models were added to validate the predictive power and accuracy of the above regression equation, and the mean relative error (MRE) of the 16 validation models was calculated. The control parameter D was expanded to five levels at equal intervals from 100cm to 500cm, and θ was also increased to five levels (π, 8π / 9, 3π / 4, 11π / 18, π / 2). The levels of the other four control parameters were uniformly set as follows: =0.43, =0.3, =0.5, =0.2.

[0077] (6)

[0078] The actual value; These are predicted values.

[0079] The mean reactivity ratio (MRE) was obtained by applying the equivalent diffusion simulation values ​​of 16 models and the predicted values ​​of the regression equation, and the MRE was found to be 12%. This shows that the full factor regression equation has good predictive ability for interpolated data.

[0080] 5. Physical simulation test verification of typical scenarios for the multivariate regression statistical model of solute transport in groundwater through intersecting fractures.

[0081] Furthermore, the reliability of the statistical prediction model was further empirically demonstrated through physical experiments under four different typical conditions (Exp.1 to Exp.4). Figure 1 The values ​​of the main parameters under each experimental condition are given. These four sets of experiments verified the effects of b, Q, θ, and L on the equivalent dispersion. The solute concentration breakthrough process distribution points obtained from the experiments were fitted using the ADE equation. Figure 1 The best-fit curves for each experimental data are shown. Furthermore, by substituting the aforementioned experimental parameters into equation (5), the equivalent diffusion prediction value of the regression is obtained, and it is compared with the experimental fitted value. The results are as follows: Figure 2As shown, compared with the results of the physical experiment, the equivalent dispersion calculated by the regression equation achieved a satisfactory degree of agreement. It should be noted that Experiment 1 (Exp.1) and Experiment 2 (Exp.2) were conducted under the condition of a small average Reynolds number (<100). The flow velocity in the fracture was increased or decreased by changing the gap width and hydraulic gradient, respectively. The spatial distribution characteristics of the flow velocity in the fracture plane did not change, so the equivalent dispersion result did not change much. It can be seen from the results of Experiment 3 (Exp.3) that the angle has a significant impact on the dispersion, which is highly consistent with the conclusion obtained by numerical simulation. Due to the limitations of the device structure, the values ​​of Lr1 (=0.12), dr and D in Experiment 4 (Exp.4) are parametric extrapolation for the regression model in this embodiment. The equivalent dispersion still matches the regression equation (5) very well, which confirms that the full factor (all control parameters) regression equation has a good extrapolation ability.

[0082] 6. Construction of a conceptual model for equivalent conduit flow of solute transport in groundwater through intersecting fractures

[0083] Establish an equivalent pipe flow model. Through the center of the circle, the dispersion of the corresponding inlet and outlet pipe models is the same, and k is the same. Based on the equivalent dispersion, k, and A statistical model, and using known control parameters, calculate the equivalent pipe model k, A inlet, A outlet, and dispersion of each channel.

[0084] One application of the regression model in this embodiment is to attempt to transform the convection dispersion process within the two-dimensional disk crack surface into a one-dimensional circular tube problem while ensuring high accuracy.

[0085] A complete description of the one-dimensional convection diffusion process also requires determining the equivalent permeability K. eq With equivalent seepage cross-sectional area A eq This ensures that mass is conserved between computing nodes, meaning that flow equivalence and diffusion effects are simultaneously equivalent.

[0086] 7. Construction of Statistical Model of Equivalent Hydrodynamic Parameters for Groundwater Solute Transport Pipeline Flow Model in Intersecting Fractures

[0087] For the disc fissure, an equivalent average velocity per unit hydraulic gradient, i.e., equivalent permeability, is established, and its calculation formula is as follows:

[0088] (7)

[0089] Simultaneously, based on the equivalent average flow velocity and total flow rate, the equivalent seepage cross-sectional area A is calculated. eq The calculation formula is as follows:

[0090] (8)

[0091] Analysis of variance was performed on the influencing factors of these two equivalent hydraulic parameters, where b, dr, and Lr affect K. eq The impact is significant, and it differs considerably from the traditional cubic law derived from parallel plates. K eq The size of the solute transport depends not only on the gap width but also on other gap geometry parameters, which suggests that if the a priori formula under ideal conditions is used directly during the dimensionality reduction generalization process, the results of solute transport will be incorrectly estimated.

[0092] Select all factors and apply them to K. eq and A eq Multiple regression statistics were performed, and regression equations (9) and (10) were obtained:

[0093] (9)

[0094] R 2 =0.998

[0095] (10)

[0096] R 2 =0.994

[0097] 8. Large-scale application of the equivalent conduit flow model for solute transport in groundwater through intersecting fractures

[0098] In this application study, a large-scale application case of the equivalent conduit flow model for groundwater solute transport in intersecting fractures was examined: the seepage area is 20m x 20m x 20m, and the typical feature set satisfies the typical condition framework of the conceptual model of spatiotemporal evolution of groundwater solute transport in intersecting fractures given in this embodiment.

[0099] Specifically, the groundwater seepage in the fractured network is a steady flow, with both inflow and outflow boundaries being constant head boundaries. Considering continuous and stable pollution sources, the pollutant concentration at the inflow boundary is set to a constant concentration. The dispersion of each pipe in the equivalent pipe flow model is assigned a value based on the specific dispersion value calculated from the statistical model obtained in this embodiment.

[0100] The results show that, in terms of the cross-sectional average concentration at the outflow boundary, the equivalent pipe flow model achieves high accuracy compared to the numerical simulation results of pollutant transport using the discrete fracture network model. However, without assigning equivalent dispersion values ​​to each pipe, the pollutant concentration simulation results will show significant errors.

[0101] It is noteworthy that on a computer equipped with an i9-12900k processor and 96GB of RAM, simulating solute transport in a disk-shaped fracture network with a domain length of 30 meters takes over 5000 seconds. In contrast, using a channelized fracture network model, the computation time on a typical laptop equipped with an i5-13500H processor and 32GB of RAM is less than 5 seconds, demonstrating a computational efficiency more than 1000 times higher than conventional DFN numerical simulations.

[0102] This embodiment, based on the topology of 3D random DFNs, systematically solves key problems such as the equivalent dispersion of basic structural units in fractured networks. It also provides and establishes an effective and reliable model for estimating key parameters of pollutant transport and dispersion processes at field scales of tens to hundreds of meters, and proposes a method for effective upscaling modeling of groundwater pollutant transport in fractured media. This improves the accuracy, efficiency, and practicality of quantifying the spatiotemporal evolution of pollutant transport in complex groundwater within fractured media, providing strong support for groundwater pollution assessment and risk management, and possesses significant practical value.

[0103] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing an equivalent conduit flow model for solute transport in groundwater through intersecting fractures, characterized in that, include: Step A: Determine the conceptual model of the spatiotemporal evolution of groundwater solute transport in intersecting fractures under typical conditions and obtain the corresponding set of typical features, i.e., the typical condition framework. Select the associated characteristic variables of groundwater solute transport spatiotemporal evolution, obtain the mathematical control equations for groundwater solute transport in fractured media, and obtain the numerical simulation system for the spatiotemporal evolution of groundwater solute transport in fractured media. Step B: Select the number of inlets and outlets of the intersecting fracture lines for groundwater seepage in the intersecting fractures; Step C: Determine the set of control parameters V=[v1,v2,...,v] for the spatiotemporal evolution of groundwater solute transport. k Select the range of variation for each control parameter to form the control parameter range set U=[u1,u2,…,u…]. k ]; Step D: Select the change levels of each control parameter, obtain basic typical scenario sampling of the spatiotemporal evolution of groundwater solute transport, and generate corresponding different combinations of control parameter values ​​for each typical scenario, i.e., parameter combinations; Step E: Obtain the calculated values ​​of the characteristic variables associated with the topology of the fracture network corresponding to each basic typical scenario; Step F: Based on the numerical simulation system for the spatiotemporal evolution of groundwater solute transport, obtain the simulated values ​​of the associated characteristic variables and the calculated values ​​of the associated characteristic variables corresponding to each basic typical scenario; Step G: Collect all parameter combinations and their corresponding simulated or calculated values ​​of associated feature variables to form the basic dataset for modeling; Step H: Based on the preset number of scenario samples and control parameters, a preset number of scenario samples are obtained to obtain the data augmentation scenario parameter combination. The data augmentation scenario simulation value or calculated value of the associated feature variable corresponding to each scenario is obtained through a numerical simulation system and related calculations. The data augmentation scenario parameter combination and the set of data augmentation scenario simulation values ​​or calculated values ​​are used as the modeling augmentation dataset. Step 1: Based on the modeling base dataset or the modeling base dataset and the modeling enhancement dataset respectively, use a numerical simulation system to obtain the time variation values ​​of the average concentration of groundwater solutes at each outlet. Based on these variation values, obtain the corresponding equivalent dispersion and equivalent average flow velocity. Based on the equivalent average flow velocity and the corresponding hydraulic gradient, obtain the equivalent permeability. Based on the digital modeling method, establish and optimize the quantitative models of the equivalent dispersion and equivalent permeability with the control parameters according to the modeling dataset, the equivalent dispersion and the equivalent permeability. Step J: Validate the established quantization model using modeling reinforcement datasets or sets of modeling reinforcement data that have not been applied to the quantization model and / or typical scenario physical model test data. The model validation shall include at least an equivalent diffusion quantization model. Step K: Establish an equivalent pipeline flow conceptual model. Based on the basic modeling dataset and the corresponding scenario inlet and outlet flow rates and the equivalent average flow velocity, determine the equivalent seepage cross-sectional area of ​​each pipeline in each scenario. Then, based on the basic modeling dataset and the corresponding equivalent seepage cross-sectional area of ​​each pipeline, establish a quantitative model of the equivalent seepage cross-sectional area of ​​each pipeline and control parameters based on the digital modeling method. Step L: For specific application scenarios, obtain the geometric parameters of intersecting fractures, establish a discrete fracture network model, obtain the structural parameters of the intersecting fracture network, and determine whether the typical feature set meets the given typical condition framework. If so, based on the structural parameters of the fracture intersecting fracture network, the seepage field characteristics of the intersecting fractures, and the equivalent pipe flow conceptual model, determine the distribution and length of each pipe; further determine the values ​​of each relevant control parameter variable, and based on the equivalent dispersion quantification model, the equivalent permeability quantification model, and the equivalent pipe seepage cross-sectional area quantification model, determine the cross-sectional area of ​​each equivalent pipe, the permeability of the corresponding seepage medium, and the corresponding dispersion parameters, thereby establishing a corresponding specific equivalent pipe flow model for groundwater solute transport in intersecting fractures.

2. The construction method according to claim 1, characterized in that, In step A, determining the conceptual model of the spatiotemporal evolution of groundwater solute transport under typical conditions and obtaining the corresponding set of typical features, i.e., the typical condition framework, includes: determining the morphological characteristics of fractures, determining the number of intersection lines and the number of inlets and outlets, determining the characteristics of fracture surfaces, determining the topological characteristics of fracture intersections, determining the characteristics of groundwater seepage fracture width variation, determining the characteristics of groundwater flow regime, and determining the characteristics of groundwater head variation in fractures.

3. The construction method according to claim 1, characterized in that, In step A, the spatiotemporal evolution correlation characteristic variables of groundwater solute transport include: groundwater solute pollutant concentration, groundwater seepage velocity, groundwater solute concentration change at fracture intersections, and average groundwater solute concentration at fracture intersections; in step E, the correlation characteristic variables of fracture network topology include the mean and value distribution of each fracture network topology parameter, and the structural parameters include the number of inlet intersections, the number of outlet intersections, the equivalent seepage path length, the intersection length, the intersection location, and the fracture disk diameter.

4. The construction method according to claim 1, characterized in that, In step H, obtaining a preset number of scenario samples based on the preset scenario sampling number and the control parameters includes: selecting the random sampling number, determining or selecting the applicable random distribution type and corresponding distribution parameters for each control parameter, completing the random number generation of the control parameters, and thus forming a new parameter combination scenario.

5. The construction method according to claim 1, characterized in that, In step J, if the modeling enhancement dataset is used in the quantization model modeling, then step H is repeated, and the combination of the data enhancement scenario parameters and the set of simulated or calculated data enhancement scenario values ​​are used as the modeling enhancement dataset generated again.

6. The construction method according to claim 1, characterized in that, In step K, the digital modeling method includes: statistical analysis modeling method and machine learning modeling method; the statistical analysis modeling method includes establishing a multiple regression statistical model of the associated feature variable and all its control parameter factors and a multiple regression statistical model of the associated feature variable and its main control parameter factors.

7. The construction method according to claim 6, characterized in that, The multivariate regression statistical analysis modeling method includes: establishing a multivariate regression statistical model and directly giving the specific parameters of each pipeline, and establishing a multivariate regression statistical model and obtaining the average value and distribution of each parameter of the pipeline flow model based on the multivariate regression statistical model, and then assigning an average value or randomly assigning a dispersion parameter to each pipeline.

8. The construction method according to claim 1, characterized in that, In step K, establishing the equivalent pipeline flow conceptual model includes: Maintain equivalent total flow rate at the fissure inlet, equivalent total flow rate at the fissure outlet, equivalent average solute concentration variation characteristics at each inlet, and equivalent average solute concentration variation characteristics at each outlet; Establish the connection characteristics of the pipelines between the groundwater inlet and outlet of intersecting fractures, including whether they pass through the fracture center point or fracture center region, and whether the solutes in each pipeline passing through the fracture center point or fracture center region are mixed and the degree of mixing. Determine the number of equivalent pipes used for each inlet intersection and each outlet intersection of the intersecting fractured groundwater. The generalization method of the equivalent seepage path applied on the equivalent pipeline between the inlet and outlet of fractured groundwater and the corresponding calculation method of the equivalent hydraulic gradient are determined.

9. The construction method according to claim 1, characterized in that, In step L, the establishment of the discrete fracture network model includes: a deterministic discrete fracture network model and a discrete fracture network model with random distributed parameters.

10. A device for constructing an equivalent conduit flow model of groundwater solute transport in intersecting fractures, used to implement the construction method described in any one of claims 1-8, characterized in that, include: The conceptual model construction module for groundwater solute transport in intersecting fractures is used to select relevant characteristic variables of the groundwater environment and construct a conceptual model of the spatiotemporal evolution of the corresponding groundwater environment system under typical condition frameworks. The numerical simulation module for intersecting fracture generation and solute transport is used to obtain control parameters affecting the spatiotemporal evolution of associated characteristic variables based on the spatiotemporal evolution conceptual model, orthogonal numerical model and numerical simulation system, and to obtain simulated or calculated values ​​of associated characteristic variables in typical scenarios. The module for constructing the equivalent parameter quantification model of fracture solute transport pipeline flow is used to obtain different typical scenarios composed of different combinations of control parameters of the groundwater environment system based on the given typical scenario preset numbers, and to establish the equivalent parameter quantification model of fracture solute transport pipeline flow, including statistical model and machine learning model; The module for constructing and applying the equivalent conduit flow model for solute transport in intersecting fractures is used to construct the conceptual model of the equivalent conduit flow for solute transport in groundwater in intersecting fractures, and supports the application of the equivalent conduit flow model for solute transport in groundwater in intersecting fractures at different scales.

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