Simulation method, device and equipment for solute transport under thermal convection and storage medium

Abstract

The invention provides a simulation method, device and equipment for solute transport under thermal convection and a storage medium. The method comprises the following steps: based on a kernel function, according to a first concentration and a second concentration, obtaining an initial concentration flow of basic particles under an athermal convection condition; obtaining a target concentration flow of the basic particles under the thermal condition according to the concentration diffusion coefficient, and obtaining a concentration change function under the thermal convection condition according to the target concentration flow; on the basis of the kernel function, the initial heat flow of the basic particles under the non-convection condition is obtained according to the first temperature and the second temperature; obtaining a temperature change function under the convection condition according to the initial heat flow; the target concentration flow, the concentration change function, the initial heat flow and the temperature change function serve as simulation functions of solute transport; and simulating solute transport in a time period from the initial moment to the target moment by using the simulation function to obtain a concentration field and a temperature field at the target moment.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a simulation method, device, equipment and storage medium for solute transport under thermal convection. Background Art

[0002] Underground thermal convection is a key factor in some geothermal resource development, waste disposal and environmental pollution control. However, current simulation studies on solute transport under thermal convection conditions have the problem of inaccurate simulation results.

[0003] In view of this, how to simulate solute transport under thermal convection conditions has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the purpose of the present disclosure is to propose a simulation method, device, equipment and storage medium for solute migration under thermal convection to solve or partially solve the above technical problems.

[0005] Based on the above objectives, the first aspect of the present disclosure proposes a simulation method for solute transport under thermal convection, the method comprising:

[0006] Determine neighboring particles within the influence domain of a base particle, obtain a first concentration and a first temperature of the base particle, and obtain a second concentration and a second temperature of the neighboring particles;

[0007] determining a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particles;

[0008] Based on the kernel function, obtaining the initial concentration flow rate of the elementary particles under the condition of no thermal convection according to the first concentration and the second concentration;

[0009] Obtaining a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtaining a concentration variation function under thermal convection conditions according to the target concentration flow rate;

[0010] Based on the kernel function, obtaining an initial heat flow of the elementary particle under a non-convection condition according to the first temperature and the second temperature;

[0011] Obtaining a temperature change function under convection conditions according to the initial heat flow;

[0012] Using the target concentration flow, the concentration variation function, the initial heat flow and the temperature variation function as simulation functions of solute transport;

[0013] The simulation function is used to simulate the solute migration in the time period from the initial time to the target time, and the concentration field and temperature field at the target time are obtained.

[0014] Based on the same inventive concept, the second aspect of the present disclosure proposes a simulation device for solute transport under thermal convection, comprising:

[0015] an acquisition module, configured to determine neighboring particles within an influence domain of a base particle, acquire a first concentration and a first temperature of the base particle, and acquire a second concentration and a second temperature of the neighboring particles;

[0016] a kernel function determination module configured to determine a kernel function of smooth particle fluid dynamics of the base particle and the neighboring particles;

[0017] an initial concentration flow determination module, configured to obtain an initial concentration flow of elementary particles under a condition of no thermal convection according to the first concentration and the second concentration based on the kernel function;

[0018] A concentration change function determination module is configured to obtain a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtain a concentration change function under thermal convection conditions according to the target concentration flow rate;

[0019] an initial heat flux determination module, configured to obtain an initial heat flux of the elementary particles under a non-convection condition according to the first temperature and the second temperature based on the kernel function;

[0020] a temperature change function determination module, configured to obtain a temperature change function under convection conditions according to the initial heat flow;

[0021] a simulation function determination module configured to use the target concentration flow, the concentration change function, the initial heat flow and the temperature change function as simulation functions of solute transport;

[0022] The solute transport simulation module is configured to use the simulation function to simulate the solute transport in the time period from the initial time to the target time to obtain the concentration field and temperature field at the target time.

[0023] Based on the same inventive concept, the third aspect of the present disclosure proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0024] Based on the same inventive concept, a fourth aspect of the present disclosure proposes a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method as described above.

[0025] From the above, it can be seen that the simulation method, device, equipment and storage medium for solute transport under thermal convection provided by the present disclosure. Determine the neighboring particles within the influence domain of the basic particle, obtain the first concentration and first temperature of the basic particle, and obtain the second concentration and second temperature of the neighboring particles; determine the kernel function of the smooth particle fluid dynamics of the basic particle and the neighboring particles; based on the kernel function, obtain the initial concentration flow of the basic particle under the condition of no thermal convection according to the first concentration and the second concentration; obtain the target concentration flow of the basic particle under the thermal condition according to the concentration diffusion coefficient, and obtain the concentration change function under the condition of thermal convection according to the target concentration flow; based on the kernel function, obtain the initial heat flow of the basic particle under the condition of no convection according to the first temperature and the second temperature; obtain the temperature change function under the convection condition according to the initial heat flow; use the target concentration flow, concentration change function, initial heat flow and temperature change function as the simulation function of solute transport; use the simulation function to simulate the solute transport in the time period from the initial moment to the target moment, and obtain the concentration field and temperature field at the target moment. In this way, the concentration change function is used to simulate the concentration change in the solute migration process under thermal convection conditions, and the temperature change function is used to simulate the temperature change in the solute migration process under convection conditions. This can realize the solute migration simulation under thermal convection conditions, improve the efficiency of geothermal development, and improve the waste isolation effect and the stability and safety of pollution remediation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flow chart of a method for simulating solute transport under thermal convection according to an embodiment of the present disclosure;

[0028] Figure 2 It is a schematic diagram of an experimental device for solute transport under thermal convection conditions in porous media and a TC-SPH model according to an embodiment of the present disclosure;

[0029] Figure 3 A schematic diagram of solute distribution and temperature transfer distribution calculated by the TC-SPH model of an embodiment of the present disclosure;

[0030] Figure 4 A schematic diagram showing the comparison of normalized concentrations monitored at the output end under different background flow rates between the test of the embodiment of the present disclosure and the TC-SPH model;

[0031] Figure 5It is a schematic structural diagram of a simulation device for solute transport under thermal convection according to an embodiment of the present disclosure;

[0032] Figure 6 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0034] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0035] Based on the description of background technology, underground thermal convection is a key factor in some geothermal resource development, waste disposal and environmental pollution control. Thermal convection not only affects the flow path of groundwater, but also changes the migration speed and direction of solutes, and even triggers chemical reactions. These effects have a direct impact on geothermal development efficiency, waste isolation effect and pollution remediation results. At present, simulation studies on solute migration under thermal convection conditions are mostly based on COMSOL Multiphysics based on finite element methods, TOUGH2 based on finite difference methods, and HYDRUS based on random models. For the finite element method, grid generation of complex geological structures is a challenge, especially when there are irregular boundaries and highly heterogeneous media, generating high-quality grids becomes more difficult and time-consuming. For the finite difference method, when simulating solute migration, numerical dispersion may occur, resulting in excessive smoothing of the solute diffusion process, thereby affecting the accuracy of the simulation results. HYDRUS is mainly aimed at water flow and solute migration in unsaturated and saturated zones, and may not be suitable for some special underground environments, such as geothermal systems with high temperature and pressure or systems with strong nonlinear reactions. Therefore, there is an urgent need for a simulation method that can target solute transport under thermal flow conditions.

[0036] As mentioned above, how to simulate solute transport under thermal convection conditions has become an important research issue.

[0037] Based on the above description, if Figure 1 As shown, the simulation method of solute transport under thermal convection proposed in this embodiment includes:

[0038] Step 101, determining neighboring particles within the influence domain of a base particle, obtaining a first concentration and a first temperature of the base particle, and obtaining a second concentration and a second temperature of the neighboring particles.

[0039] In the specific implementation, the initial particle parameters of the heat and mass transfer model are obtained, wherein the initial particle parameters include: particle position information, material parameters (thermodynamic parameters, solute transport characteristic parameters) and boundary conditions (thermal boundary, solute transport boundary).

[0040] Based on the position information of the particles, the neighboring particles within the influence domain of the basic particles can be determined. The thermodynamic parameters include the first temperature of the basic particles and the second temperature of the neighboring particles. The solute transport characteristic parameters include the first concentration of the basic particles and the second concentration of the neighboring particles.

[0041] Among them, the mechanical parameters of particle position information mainly include: density, elastic modulus and Poisson's ratio. Thermodynamic parameters include: specific heat capacity (rock and water), thermal diffusion coefficient and permeability coefficient. Chemical parameters include: solute concentration and diffusion coefficient. Boundary conditions include: thermal boundary and solute migration boundary.

[0042] Step 102: determining a kernel function of smooth particle fluid dynamics of the base particle and the neighboring particles.

[0043] In specific implementation, Smoothed Particle Hydrodynamics (SPH) is a computational method for simulating fluid dynamics, which is gradually applied to the fields of fluid dynamics, such as engineering, oceanography, and biomedicine. The core feature of the SPH algorithm is to treat the fluid as a set of discrete particles, which represent tiny volume elements of the fluid and carry the physical properties of the fluid (such as density, pressure, and velocity, etc.).

[0044] The SPH algorithm does not rely on the traditional grid (grid point) method to solve the motion equations of continuous media, but expresses the spatial and temporal distribution of physical quantities through the interaction between particles, which makes the SPH algorithm particularly suitable for dealing with large deformation and free surface flow problems.

[0045] The SPH algorithm uses a weight function called a smooth kernel to estimate the physical quantities of particles and the spatial derivatives of the physical quantities of particles. The kernel function (B-spline function) determines the interaction strength between particles and usually has a limited support domain. Only neighboring particles will affect the current particle, thereby reducing the impact of instability when the particles are compressed.

[0046] Step 103: Based on the kernel function, the initial concentration flow of the elementary particles under the condition of no thermal convection is obtained according to the first concentration and the second concentration.

[0047] In a specific implementation, the initial concentration flow of the basic particles under the condition of no thermal convection is determined based on the field function of the basic particles. When the property parameter of the basic particles is the concentration flow, the initial concentration flow of the basic particles under the condition of no thermal convection is obtained according to the first concentration and the second concentration.

[0048] Step 104, obtaining a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtaining a concentration variation function under thermal convection conditions according to the target concentration flow rate.

[0049] In specific implementation, the convection term and the thermodynamic condition, namely, temperature, will affect the movement of the solute, and the concentration change function under the condition of thermal convection is obtained based on the concentration change function under the condition of no thermal convection.

[0050] Step 105: Based on the kernel function, the initial heat flux of the elementary particles under the non-convection condition is obtained according to the first temperature and the second temperature.

[0051] In a specific implementation, the initial heat flux of the elementary particle under the condition of no convection is determined based on the field function of the elementary particle. When the property parameter of the elementary particle is heat flux, the initial heat flux of the elementary particle under the condition of no convection is obtained according to the first temperature and the second temperature.

[0052] Step 106: Obtain a temperature change function under convection conditions according to the initial heat flow.

[0053] In specific implementation, since the fluid will take away some heat when it flows, it is called convection term. Therefore, based on the temperature change function under the condition of no convection, the temperature change function under the condition of convection is obtained by considering the influence of the convection term.

[0054] Step 107: using the target concentration flow, the concentration variation function, the initial heat flow and the temperature variation function as simulation functions of solute transport.

[0055] In the specific implementation, the SPH equation under solute transport and thermal conditions is considered as the simulation function of solute transport. The concentration change function is used to simulate the concentration change during solute transport under thermal convection conditions, and the temperature change function is used to simulate the temperature change during solute transport under convection conditions.

[0056] Step 108, using the simulation function, simulate the solute migration in the time period from the initial time to the target time to obtain the concentration field and temperature field at the target time.

[0057] In specific implementation, the particle material concentration field and temperature field are calculated to form a single loop. If the loop is not completed, it returns to step 102 and continues to loop until a predetermined total time step is reached, and the leapfrog algorithm is used for time integration to obtain the final solute and temperature distribution field of each particle through recursion.

[0058] The disclosed embodiments provide a simulation method and system for solute migration under thermal convection conditions, aiming to improve the efficiency of geothermal development, improve the waste isolation effect, and improve the stability and safety of pollution remediation. The method comprises the following steps: First, the geothermal parameters, the physical and chemical properties of the pollution, and the migration characteristic parameters of the study area under thermal convection conditions are obtained. Then, a two-dimensional model is established using the SPH method, and parameter settings are performed in combination with the reservoir seepage characteristics, temperature distribution characteristics, and solute concentration information. Next, a thermal-chemical coupling model is used to simulate and calculate the temperature field and solute concentration of the surrounding rock. At present, SPH has not yet achieved solute migration simulation under geothermal conditions.

[0059] The present disclosure relates to the field of civil engineering technology, especially a method for simulating heat transfer in nuclear storage facilities. The present invention is based on the theoretical framework of smooth particle fluid dynamics and is divided into seven modules: solute conduction and heat transfer module, thermal convection affecting solute transfer module, and convection affecting heat conduction module. Heat conduction and the mutual influence of solute conduction and convection are based on partial differential equations. The influence of heat on solute transfer is through the Arrhenius formula. This method accurately captures the entire process of solute migration and heat conduction in porous media under high temperature and strong osmotic pressure conditions; multi-field simulation is implemented under a single SPH framework, and its calculation is more efficient, with fewer input parameters and a relatively simple parameter acquisition process. It can continue to couple stress fields and damage algorithms with random crack networks to solve the rock loss process of nuclear waste storage and enhanced geothermal systems.

[0060] Through the above embodiments, the entire process of solute migration and heat conduction in porous media under high temperature and strong osmotic pressure conditions is accurately captured; multi-field simulation is implemented under a single SPH framework, the calculation is more efficient, and the input parameters are few and the parameter acquisition process is relatively simple. The stress field and damage algorithm can continue to be coupled with the random crack network to solve the rock loss process of nuclear waste storage and enhanced geothermal systems.

[0061] In some embodiments, step 102 includes:

[0062] Step 1021, obtaining the relative distance between the basic particle and the neighboring particle, and obtaining the smooth core radius of the basic particle's influence domain.

[0063] Step 1022: taking the ratio of the relative distance to the smooth core radius as the normalized distance between the base particle and the neighboring particle.

[0064]

[0065] Wherein, p is the normalized distance between the base particle and the neighboring particle, i is the serial number of the base particle, j is the serial number of the neighboring particle, and r ij is the relative distance between the base particle and the neighboring particle, and h is the smooth core radius of the base particle's influence domain.

[0066] Step 1023, obtaining a kernel function of smooth particle hydrodynamics of the base particle and the neighboring particle according to the normalized distance,

[0067]

[0068] Among them, W ij (p, h) is the kernel function of the smooth particle hydrodynamics of the base particle and the neighboring particles, and λ is the radius coefficient.

[0069] In specific implementation, the radius coefficient λ is a constant, and the radius coefficient λ has different values ​​for different dimensional spaces (such as one-dimensional, two-dimensional and three-dimensional). For example, for the kernel function, the radius coefficient λ in one-dimensional space is 1 / h, and the radius coefficient λ in two-dimensional space is 15 / 7πh 2 , in three-dimensional space, the radius coefficient λ is 3 / 2πh 2 .

[0070] Through the above scheme, the kernel function determines the interaction strength between particles, usually with a limited support domain, and only neighboring particles will affect the current particle, thereby reducing the impact of instability when particles are compressed.

[0071] The basic particles act in pairs with the neighboring particles in the influence domain, based on the linklist search method, where the basic particles act on the neighboring particles through the following formula:

[0072]

[0073] Among them, f(x i) is the field function of the elementary particle i, which is used to represent the property parameters of the elementary particle i (e.g., concentration flux and heat flux), N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, f(x j ) is the field function of the neighboring particle j, which is used to represent the property parameters of the basic particle j. i -x j , h) is the smooth kernel function of the smooth particle hydrodynamics of the base particle and the neighboring particles.

[0074]

[0075] in, is the field function derivative of the fundamental particle i, ρi is the density of the fundamental particle i, is the gradient of the smooth kernel function.

[0076] In some embodiments, step 103 includes:

[0077] Step 1031, based on the kernel function, the initial concentration flow of the elementary particles under the condition of no thermal convection is obtained according to the first concentration and the second concentration.

[0078]

[0079] in, is the initial concentration flow rate of the basic particles under the condition of no thermal convection, k ci is the concentration diffusion coefficient, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, c i is the concentration of fundamental particle i, c j is the concentration of neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

[0080] In specific implementation, the initial concentration flow of the elementary particles under the condition of no thermal convection is determined based on the field function of the elementary particles. i ) is the concentration flow, the initial concentration flow of the basic particles under the condition of no thermal convection is obtained according to the first concentration and the second concentration.

[0081] After step 103, the method further includes:

[0082] According to the initial concentration flow rate, the concentration change function under the condition of no thermal convection is obtained,

[0083]

[0084] in, is the concentration change function under the condition of no thermal convection, is the initial concentration flow The derivative of is the initial concentration flow of neighboring particles under the condition of no thermal convection.

[0085] By using the above scheme, the property parameters of the basic particles are determined based on the kernel function, which can reduce the influence of instability when the particles are compressed. When the property parameter of the basic particles is the concentration flow rate, the initial concentration flow rate of the basic particles under the condition of no thermal convection can be obtained according to the first concentration of the basic particles and the second concentration of the neighboring particles, and it is ensured that the obtained initial concentration flow rate can reduce the influence of instability when the particles are compressed.

[0086] In some embodiments, step 104 includes:

[0087] Step 1041, based on the Arrhenius formula, the relationship between the concentration diffusion coefficient and the temperature is obtained.

[0088]

[0089] Among them, k ci is the concentration diffusion coefficient, D0 is the frequency factor of the concentration diffusion coefficient, E a is the activation energy, R is the gas constant, T i is the absolute temperature.

[0090] In specific implementation, the solute transport under convection conditions and the influence of thermal distribution is determined. Since the solute transport is affected by the temperature gradient, it is mainly manifested in the concentration diffusion coefficient k ci The relationship between temperature and humidity satisfies the Arrhenius equation.

[0091] Among them, the frequency factor D0 of the concentration diffusion coefficient is a constant, the gas constant R is about 8.314 J / (mol·K), and the absolute temperature T i Measured in Kelvin.

[0092] Step 1042, obtaining the target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient,

[0093]

[0094] in, is the target concentration flow rate of the basic particle under thermal conditions, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, ci is the concentration of fundamental particle i, c j is the concentration of neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

[0095] In specific implementation, the concentration diffusion coefficient k ci Substitute the formula of initial concentration flow into the target concentration flow of basic particles under thermal convection conditions.

[0096] Step 1043, obtaining a concentration variation function under thermal convection conditions according to the target concentration flow rate,

[0097]

[0098] in, is the concentration change function under thermal convection conditions, is the target concentration flow rate of neighboring particles under thermal conditions, q vc is the background flow rate.

[0099] In a specific implementation, the convection term and temperature will affect the movement of the solute, and the concentration change function under the condition of thermal convection is obtained based on the concentration change function under the condition of no thermal convection.

[0100] Through the above scheme, based on the relationship between the concentration diffusion coefficient and temperature, the target concentration flow rate of the basic particles under thermal convection conditions can be accurately obtained, and the concentration change function under thermal convection conditions can be determined, so that the solute migration under thermal convection conditions can be simulated according to the target concentration flow rate and concentration change function of the basic particles under thermal convection conditions.

[0101] In some embodiments, step 105 includes:

[0102] Step 1051, based on the kernel function, obtaining the initial heat flow of the elementary particles under the condition of no thermal convection according to the first temperature and the second temperature,

[0103]

[0104] in, is the initial heat flux of the base particle under no convection conditions, is the heat transfer conductivity, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, T i is the temperature of elementary particle i, T j is the temperature of the neighboring particle j, W ijis the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

[0105] In specific implementation, the initial heat flux of the elementary particle under the condition of no convection is determined based on the field function of the elementary particle. i ) is the heat flow, the initial heat flow of the basic particle under the condition of no convection is obtained according to the first temperature and the second temperature. Specifically, the change of thermal distribution is calculated according to the heat convection diffusion equation, and the initial heat flow is obtained by discrete solution based on the SPH algorithm.

[0106] After step 105, the method further includes:

[0107] According to the initial heat flow, the temperature change function under the condition of no convection is obtained,

[0108]

[0109] in, is the temperature change function under no convection conditions, is the initial heat flow The derivative of is the initial heat flux of neighboring particle j under no convection conditions.

[0110] By using the above scheme, the property parameters of the basic particles are determined based on the kernel function, which can reduce the influence of instability when the particles are compressed. When the property parameter of the basic particles is heat flow, the initial heat flow of the basic particles under non-convection conditions can be obtained according to the first temperature of the basic particles and the second temperature of the adjacent particles, and it is ensured that the obtained initial heat flow can reduce the influence of instability when the particles are compressed.

[0111] In some embodiments, step 106 includes:

[0112] Step 1061, obtaining a temperature change function under convection conditions according to the initial heat flow,

[0113]

[0114] in, is the temperature change function under convection conditions, ρ i is the density of the elementary particle i, c i is the concentration of the base particle i, N is the total number of neighboring particles, and m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, is the initial heat flux of the elementary particle i under the condition of no convection, is the initial heat flux of neighboring particle j under no convection conditions, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i, q vc is the background flow rate, T i is the temperature of elementary particle i, T j is the temperature of the neighboring particle j.

[0115] In specific implementation, since the fluid will take away some heat when it flows, it is called the convection term. Therefore, based on the temperature change function under the condition of no convection, the temperature change function under the condition of thermal convection is obtained by considering the influence of the solute flow term.

[0116] Through the above scheme, the temperature change function under thermal convection conditions is obtained by considering the influence of the solute flow term, so that the solute migration under thermal convection conditions can be simulated according to the temperature change function under convection conditions.

[0117] In some embodiments, step 107 includes:

[0118] Step 1071, using the target concentration flow, the concentration change function, the initial heat flow and the temperature change function as simulation functions of solute transport,

[0119]

[0120] in, is the target concentration flow rate of the basic particles under thermal conditions, is the concentration change function under thermal convection conditions, is the initial heat flux of the base particle under no convection conditions, is a function of temperature change under convection conditions.

[0121] In the specific implementation, the SPH equation under solute transport and thermal conditions is considered as the simulation function of solute transport. In addition, the density change function under thermal convection conditions is added, and the simulation function of solute transport can be expressed as:

[0122]

[0123] in, is the density variation function under thermal convection conditions, ρi is the density of the basic particle i, is the velocity of the fundamental particle i relative to the neighboring particle j, is the property parameter of the basic particle i.

[0124] Through the above scheme, the simulation functions of solute transport include density change function, initial concentration flow, concentration change function, initial heat flow and temperature change function. The density change function is used to simulate the density change in the solute transport process under thermal convection conditions, the concentration change function is used to simulate the concentration change in the solute transport process under thermal convection conditions, and the temperature change function is used to simulate the temperature change in the solute transport process under convection conditions.

[0125] In some embodiments, step 108 includes:

[0126] Step 1081, using the simulation function, simulate the solute migration in the time period from the initial time to the target time to obtain the concentration field and temperature field at the target time.

[0127]

[0128] Among them, c i (t0+Δt / 2) is the concentration field at the target time, t0 is the initial time, Δt is the calculation time step, t is the target time, T i (t0) is the temperature of the elementary particle i at the initial moment, Δc i (t0) is the concentration change rate of the fundamental particle i, T i (t0+Δt / 2) is the temperature field at the target time, ΔT i (t0) is the temperature change rate of the base particle i.

[0129] In specific implementation, the simulation function is used to simulate the solute migration in the time period from the initial time to the target time, and the density field, concentration field and temperature field at the target time are obtained.

[0130]

[0131] Among them, ρ i (t0+Δt / 2) is the density field at the target time, ρ i (t0) is the density of elementary particle i at the initial moment, Δρ i (t0) is the density change rate of the base particle i.

[0132] Calculating the particle material concentration field and temperature field forms a single loop. If the loop is not completed, return to step 102 and continue looping until a predetermined total time step is reached, and the leapfrog algorithm is used for time integration to obtain the final solute and temperature distribution field of each particle through recursion.

[0133] Through the above scheme, using the simulation function of solute transport, the density change, concentration change and temperature change of the solute from the initial moment to the target moment can be accurately simulated.

[0134] Through the above embodiments, the entire process of solute migration and heat conduction in porous media under high temperature and strong osmotic pressure conditions is accurately captured; multi-field simulation is implemented under a single SPH framework, the calculation is more efficient, and the input parameters are few and the parameter acquisition process is relatively simple. The stress field and damage algorithm can continue to be coupled with the random crack network to solve the rock loss process of nuclear waste storage and enhanced geothermal systems.

[0135] It should be noted that the embodiments of the present disclosure may be further described in the following manner:

[0136] The feasibility and accuracy of the present disclosure are verified by comparative tests. The experimental device is introduced as follows: Figure 2 As shown in the figure, the experimental device consists of a water injection device with a stable flow rate, a porous medium model and a stable pressure water outlet device. The water injection device with a stable flow rate includes a constant water temperature water tank and a peristaltic pump, the porous medium model includes an outer insulation layer, a porous medium column, a flow distributor, a sampling hole and a temperature sensor, and the stable pressure water outlet device consists of a fixed position water tank and an overflow tank. The porous medium model is filled with well-sorted quartz sand with similar particle sizes to simulate a homogeneous aquifer. The sampling hole is located at the end of the experimental device to detect the solute concentration at that point.

[0137] According to the indoor test model, the room temperature of the TC-SPH model is set to 23.8℃. The porous medium model is about 600mm long and 100mm wide. The porosity of the porous medium is 40.8%. The fluid is introduced at the bottom. After the background flow rate of the model is 40prm, the solute and high temperature are introduced at the bottom. The total number of SPH solid particles is about 2400, the initial particle spacing of the particles is set to 5mm, and the smooth core radius is set to 1 times the initial particle spacing. The time step is set to 0.01s. The SPH particle parameters, mass transfer parameters, and thermodynamic parameters are shown in Table 1. Regarding the boundary setting, it can be seen that there are a total of 3 boundaries in this TC-SPH: the first boundary is the temperature boundary, which is set as follows: the top output section and both the left and right sides are adiabatic boundaries. The bottom is the high temperature (50℃) input end. The second is the solute transfer boundary, which is set as follows: the top output section and both the left and right sides are non-solute transfer boundaries, and the bottom is the solute (0.00856mol / L) input end. The third type is a seepage boundary, with the bottom as the inflow boundary, the overall flow rate of the background field being 40prm, the left and right sides being non-seepage boundaries, and the top being the outflow boundary. TC-SPH performed a total of 3500s of mass and heat transfer simulation.

[0138] Table 1 Mechanical and physical parameters of heat and mass transfer

[0139]

[0140]

[0141] (1) The initial parameters assigned to the porous medium include: thermodynamic parameters, such as the initial temperature of the porous medium, the specific heat capacity of the medium; injection temperature and thermal conductivity; solute transport parameters such as activation energy, molecular collision coefficient, and left injection concentration. Hydraulic parameters such as background water velocity, porosity, and boundary conditions (thermal boundary, seepage boundary, solute transport boundary) and SPH modeling parameters such as kernel function radius and particle density, i.e., porous medium density of about 2700 kg / m 3 、Water density is about 1000kg / m 3 , initial particle spacing, and time step.

[0142] (2) Calculation of key parameters of model particles in a single time step: including pairing between particles based on linklist search method (1)-(2), updating smooth kernel function (3), density rate change (4), calculation of heat flux and real-time temperature change (6), (11), change of solute transport coefficient (8), solute distribution change (9)-(10), and finally obtaining characteristic parameter values ​​in a single time step according to (12);

[0143] (3) Perform time integration (13) on the key parameters within a single time step to obtain the characteristic parameter value of the particle at each time step;

[0144] (4) Determine whether the calculation step has reached the set calculation step. If it has not reached the maximum calculation step, repeat steps (2) to (3). If it has reached the maximum calculation step, that is, the tunnel, the calculation is terminated.

[0145] Through the above steps, the distribution diagram of solute migration and temperature transfer under thermal convection in porous media with a background flow rate of 40prm is obtained, as shown in Figure 3 As shown; the temperature conduction diagram of the porous medium is obtained, as shown in Figure 3 As shown; the solute distribution diagram of the porous medium is obtained, as shown in Figure 3 As shown, the experiment adopted three different background flow rate schemes. By comparing the real-time concentration distribution diagram obtained by the experiment and TC-SPH calculation, we found that the invention can effectively realize the solute migration and temperature transfer under thermal convection in porous media.

[0146] like Figure 4As shown in the figure, when the background flow rate is 20prm, the 20prm EXP curve obtained by the experiment is consistent with the 20prm SPH curve obtained by the SPH method, indicating that the SPH algorithm can accurately simulate solute transport when the background flow rate is 20prm. When the background flow rate is 30prm, the 30prm EXP curve obtained by the experiment is consistent with the 30prm SPH curve obtained by the SPH method, indicating that the SPH algorithm can accurately simulate solute transport when the background flow rate is 30prm. When the background flow rate is 40prm, the 40prm EXP curve obtained by the experiment is consistent with the 40prm SPH curve obtained by the SPH method, indicating that the SPH algorithm can accurately simulate solute transport when the background flow rate is 40prm.

[0147] The present disclosure relates to the field of civil engineering technology, and in particular to a multi-field coupled fracture simulation method for deep tunnels. The present disclosure is based on the SPH theoretical framework of smooth particle hydrodynamics. A crack simulation method based on the stress drop method with constant center sphere is introduced for the first time in the SPH method, which enhances the stress adjustment and robustness of damaged SPH particles. Hydraulic conduction in rock fractures is controlled by the cubic law and Darcy's law. And it is mutually coupled with heat conduction in convection terms and dynamic viscosity, thereby realizing water heat exchange. The invention can effectively construct a fully coupled SPH multi-field fracture model, which can be realized without mesh division and mesh distortion, and the loss stress can be solved by mapping stress redistribution on stress bonds. Since SPH is a continuous method and its parameters are easy to calibrate, it has high precision due to the absence of mesh constraints, and it is based on the linklist search method, and the stress characteristics of all particles can be calculated by searching once in a single time step, which is efficient. The method of the present invention not only has significant advantages in multi-field simulation technology, but also provides a powerful tool and broad prospects for research and application in the field of deep tunnels.

[0148] Since the SPH method does not require traditional meshing, it has obvious advantages over the finite element method in dealing with complex boundaries and large deformation problems. The SPH method can naturally adapt to the heterogeneity and irregular boundaries in underground media. The grid generation of complex geological structures for the finite element method is a challenge, especially when there are irregular boundaries and highly heterogeneous media, it becomes more difficult and time-consuming to generate high-quality grids. The SPH method can easily introduce complex physical and chemical reaction mechanisms, and it is more feasible to simulate the adsorption, desorption, precipitation, dissolution and other reactions of particles in the solute transport process. For the finite difference method, numerical dispersion may occur when simulating solute transport, resulting in the need to use finer grids or smaller time steps. The SPH model of thermal solute transfer has a gridless and adaptive time step. The SPH method has significant advantages such as gridless, adaptive, strong parallel computing capabilities, multi-scale simulation and processing of complex reactions when simulating thermal convection and solute transport in underground media. The invention can further couple stress fields, damage algorithms and random crack networks to solve the rock loss process of nuclear waste storage and enhanced geothermal systems.

[0149] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0150] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a simulation device for solute migration under thermal convection.

[0152] refer to Figure 5 , the simulation device for solute transport under thermal convection comprises:

[0153] An acquisition module 301 is configured to determine neighboring particles within an influence domain of a base particle, acquire a first concentration and a first temperature of the base particle, and acquire a second concentration and a second temperature of the neighboring particles;

[0154] A kernel function determination module 302 is configured to determine a kernel function of smooth particle fluid dynamics of the base particle and the neighboring particles;

[0155] An initial concentration flow determination module 303 is configured to obtain an initial concentration flow of elementary particles under a condition of no thermal convection according to the first concentration and the second concentration based on the kernel function;

[0156] The concentration variation function determination module 304 is configured to obtain a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtain a concentration variation function under thermal convection conditions according to the target concentration flow rate;

[0157] An initial heat flux determination module 305 is configured to obtain an initial heat flux of the elementary particles under a non-convection condition according to the first temperature and the second temperature based on the kernel function;

[0158] A temperature change function determination module 306 is configured to obtain a temperature change function under convection conditions according to the initial heat flow;

[0159] The simulation function determination module 307 is configured to use the target concentration flow, the concentration change function, the initial heat flow and the temperature change function as simulation functions of solute transport;

[0160] The solute transport simulation module 308 is configured to use the simulation function to simulate the solute transport in the time period from the initial time to the target time, and obtain the concentration field and temperature field at the target time.

[0161] In some embodiments, the kernel function determination module 302 includes:

[0162] an acquisition unit, configured to acquire the relative distance between the basic particle and the neighboring particle, and acquire the smooth core radius of the influence domain of the basic particle;

[0163] a normalized distance determination unit configured to use the ratio of the relative distance to the smooth core radius as the normalized distance between the base particle and the neighboring particle,

[0164]

[0165] Wherein, p is the normalized distance between the base particle and the neighboring particle, i is the serial number of the base particle, j is the serial number of the neighboring particle, and r ij is the relative distance between the basic particle and the neighboring particle, and h is the smooth core radius of the influence domain of the basic particle;

[0166] A kernel function determination unit is configured to obtain a kernel function of smooth particle fluid dynamics of the base particle and the neighboring particle according to the normalized distance,

[0167]

[0168] Among them, W ij (p, h) is the kernel function of the smooth particle hydrodynamics of the base particle and the neighboring particles, and λ is the radius coefficient.

[0169] In some embodiments, the initial concentration flow determination module 303 includes:

[0170] an initial concentration flow determination unit, configured to obtain an initial concentration flow of the elementary particles under a condition of no thermal convection according to the first concentration and the second concentration based on the kernel function,

[0171]

[0172] in, is the initial concentration flow rate of the basic particles under the condition of no thermal convection, k ci is the concentration diffusion coefficient, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, c i is the concentration of fundamental particle i, c j is the concentration of neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

[0173] In some embodiments, the concentration change function determination module 304 includes:

[0174] The concentration diffusion coefficient determination unit is configured to obtain a relationship between the concentration diffusion coefficient and the temperature based on the Arrhenius formula,

[0175]

[0176] Among them, k ci is the concentration diffusion coefficient, D0 is the frequency factor of the concentration diffusion coefficient, E a is the activation energy, R is the gas constant, T i is the absolute temperature;

[0177] a target concentration flow rate determination unit, configured to obtain a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient,

[0178]

[0179] in, is the target concentration flow rate of the basic particle under thermal conditions, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, c i is the concentration of fundamental particle i, c j is the concentration of neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i;

[0180] a concentration variation function determination unit, configured to obtain a concentration variation function under thermal convection conditions according to the target concentration flow rate,

[0181]

[0182] in, is the concentration change function under thermal convection conditions, is the target concentration flow rate of neighboring particles under thermal conditions, q vc is the background flow rate.

[0183] In some embodiments, the initial heat flow determination module 305 includes:

[0184] an initial heat flow determination unit, configured to obtain an initial heat flow of the elementary particles under a non-convection condition according to the first temperature and the second temperature based on the kernel function,

[0185]

[0186] in, is the initial heat flux of the base particle under no convection conditions, is the heat transfer conductivity, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, T i is the temperature of elementary particle i, T j is the temperature of the neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

[0187] In some embodiments, the temperature change function determination module 306 includes:

[0188] a temperature change function determining unit, configured to obtain a temperature change function under convection conditions according to the initial heat flow,

[0189]

[0190] in, is the temperature change function under convection conditions, ρ i is the density of the elementary particle i, c i is the concentration of the base particle i, N is the total number of neighboring particles, and m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, is the initial heat flux of the elementary particle i under the condition of no convection, is the initial heat flux of neighboring particle j under no convection conditions, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i, q vc is the background flow rate, T i is the temperature of elementary particle i, T j is the temperature of the neighboring particle j.

[0191] In some embodiments, the solute transport simulation module 308 includes:

[0192] The solute transport simulation unit is configured to use the simulation function to simulate the solute transport in the time period from the initial time to the target time to obtain the concentration field and temperature field at the target time.

[0193]

[0194] Among them, c i (t0+Δt / 2) is the concentration field at the target time, t0 is the initial time, Δt is the calculation time step, t is the target time, T i (t0) is the temperature of elementary particle i at the initial moment, Δc i (t0) is the concentration change rate of the fundamental particle i, T i (t0+Δt / 2) is the temperature field at the target time, ΔT i (t0) is the temperature change rate of the base particle i.

[0195] For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0196] The device of the above embodiment is used to implement the simulation method of solute migration under thermal convection corresponding to any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0197] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the simulation method for solute migration under thermal convection described in any of the above embodiments is implemented.

[0198] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0199] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0200] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0201] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0202] The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB (Universal Serial Bus), network cable, etc.), or through a wireless mode (such as mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0203] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0204] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0205] The electronic device of the above embodiment is used to implement the simulation method of solute migration under thermal convection corresponding to any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0206] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the simulation method of solute transport under thermal convection as described in any of the above embodiments.

[0207] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0208] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the simulation method of solute migration under thermal convection as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0209] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0210] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0211] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0212] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present disclosure. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for simulating solute transport under thermal convection, characterized in that: The method comprises: Determine neighboring particles within the influence domain of a base particle, obtain a first concentration and a first temperature of the base particle, and obtain a second concentration and a second temperature of the neighboring particles; determining a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particles; Based on the kernel function, obtaining the initial concentration flow rate of the elementary particles under the condition of no thermal convection according to the first concentration and the second concentration; Obtaining a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtaining a concentration variation function under thermal convection conditions according to the target concentration flow rate; Based on the kernel function, obtaining an initial heat flow of the elementary particle under a non-convection condition according to the first temperature and the second temperature; Obtaining a temperature change function under convection conditions according to the initial heat flow; Using the target concentration flow, the concentration variation function, the initial heat flow and the temperature variation function as simulation functions of solute transport; The simulation function is used to simulate the solute migration in the time period from the initial time to the target time, and the concentration field and temperature field at the target time are obtained.

2. The method according to claim 1, characterized in that The kernel function for determining the smooth particle fluid dynamics of the base particle and the neighboring particles comprises: Obtaining the relative distance between the basic particle and the neighboring particle, and obtaining the smooth core radius of the basic particle's influence domain; The ratio of the relative distance to the smooth core radius is used as the normalized distance between the base particle and the neighboring particle. Wherein, p is the normalized distance between the base particle and the neighboring particle, i is the serial number of the base particle, j is the serial number of the neighboring particle, and r ij is the relative distance between the basic particle and the neighboring particle, and h is the smooth core radius of the influence domain of the basic particle; According to the normalized distance, a kernel function of smooth particle hydrodynamics of the base particle and the neighboring particle is obtained, Among them, W ij (p,h) is the kernel function of the smooth particle hydrodynamics of the base particle and the neighboring particles, and λ is the radius coefficient.

3. The method according to claim 1, characterized in that The step of obtaining the initial concentration flow of the elementary particles under the condition of no thermal convection based on the kernel function and according to the first concentration and the second concentration includes: Based on the kernel function, the initial concentration flow of the basic particles under the condition of no thermal convection is obtained according to the first concentration and the second concentration. in, is the initial concentration flow rate of the basic particles under the condition of no thermal convection, k ci is the concentration diffusion coefficient, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, c i is the concentration of fundamental particle i, c j is the concentration of neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

4. The method according to claim 1, characterized in that: The method of obtaining a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtaining a concentration change function under thermal convection conditions according to the target concentration flow rate, includes: Based on the Arrhenius formula, the relationship between the concentration diffusion coefficient and temperature is obtained: Among them, k ci is the concentration diffusion coefficient, D0 is the frequency factor of the concentration diffusion coefficient, E a is the activation energy, R is the gas constant, T i is the absolute temperature; According to the concentration diffusion coefficient, the target concentration flow rate of the basic particles under thermal conditions is obtained. in, is the target concentration flow rate of the basic particle under thermal conditions, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, c i is the concentration of fundamental particle i, c j is the concentration of neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i; According to the target concentration flow rate, a concentration variation function under thermal convection conditions is obtained, in, is the concentration change function under thermal convection conditions, is the target concentration flow rate of neighboring particles under thermal conditions, q vc is the background flow rate.

5. The method according to claim 1, characterized in that The obtaining, based on the kernel function and according to the first temperature and the second temperature, an initial heat flow of the elementary particles under a non-convection condition comprises: Based on the kernel function, the initial heat flow of the elementary particles under the condition of no convection is obtained according to the first temperature and the second temperature, in, is the initial heat flux of the base particle under no convection conditions, is the heat transfer conductivity, N is the total number of neighboring particles, m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, T i is the temperature of elementary particle i, T j is the temperature of the neighboring particle j, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i.

6. The method according to claim 1, characterized in that The step of obtaining a temperature change function under convection conditions according to the initial heat flow comprises: According to the initial heat flow, the temperature change function under convection conditions is obtained, in, is the temperature change function under convection conditions, ρ i is the density of the elementary particle i, c i is the concentration of the base particle i, N is the total number of neighboring particles, and m j is the mass of neighboring particle j, ρ j is the density of neighboring particle j, is the initial heat flux of the elementary particle i under the condition of no convection, is the initial heat flux of neighboring particle j under no convection conditions, W ij is the kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is the property parameter of the basic particle i, q vc is the background flow rate, T i is the temperature of elementary particle i, T j is the temperature of the neighboring particle j.

7. The method according to claim 1, characterized in that The simulation function is used to simulate the solute migration in the time period from the initial time to the target time to obtain the concentration field and temperature field at the target time, including: By using the simulation function, the solute transport in the time period from the initial time to the target time is simulated to obtain the concentration field and temperature field at the target time. Among them, c i (t0+Δt / 2) is the concentration field at the target time, t0 is the initial time, Δt is the calculation time step, t is the target time, T i (t0) is the temperature of elementary particle i at the initial moment, Δc i (t0) is the concentration change rate of the fundamental particle i, T i (t0+Δt / 2) is the temperature field at the target time, ΔT i (t0) is the temperature change rate of the base particle i.

8. A device for simulating solute transport under thermal convection, characterized in that: include: an acquisition module, configured to determine neighboring particles within an influence domain of a base particle, acquire a first concentration and a first temperature of the base particle, and acquire a second concentration and a second temperature of the neighboring particles; a kernel function determination module configured to determine a kernel function of smooth particle fluid dynamics of the base particle and the neighboring particles; an initial concentration flow determination module, configured to obtain an initial concentration flow of elementary particles under a condition of no thermal convection according to the first concentration and the second concentration based on the kernel function; A concentration change function determination module is configured to obtain a target concentration flow rate of the basic particles under thermal conditions according to the concentration diffusion coefficient, and obtain a concentration change function under thermal convection conditions according to the target concentration flow rate; an initial heat flux determination module, configured to obtain an initial heat flux of the elementary particles under a non-convection condition according to the first temperature and the second temperature based on the kernel function; a temperature change function determination module, configured to obtain a temperature change function under convection conditions according to the initial heat flow; A simulation function determination module is configured to use the target concentration flow, the concentration change function, the initial heat flow and the temperature change function as simulation functions of solute transport; The solute transport simulation module is configured to use the simulation function to simulate the solute transport in the time period from the initial time to the target time to obtain the concentration field and temperature field at the target time.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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