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

Abstract

The present disclosure provides a simulation method, device and equipment for solute transport under thermal convection and a storage medium. The method comprises: obtaining, based on a kernel function, an initial concentration flux of a basic particle under a non-thermal convection condition according to a first concentration and a second concentration; obtaining a target concentration flux of the basic particle under a thermal condition according to a concentration diffusion coefficient, and obtaining a concentration change function under the thermal convection condition according to the target concentration flux; obtaining, based on the kernel function, an initial heat flux of the basic particle under a non-convection condition according to a first temperature and a second temperature; obtaining a temperature change function under the convection condition according to the initial heat flux; taking the target concentration flux, the concentration change function, the initial heat flux and the temperature change function as a simulation function of solute transport; and simulating solute transport in a time period from an initial time to a target time by using the simulation function to obtain a concentration field and a temperature field at the target time.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and particularly relates to a simulation method, device and equipment of solute transport under thermal convection and a storage medium. BACKGROUND

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

[0003] Therefore, how to simulate the solute transport under thermal convection becomes a technical problem to be solved. SUMMARY

[0004] Therefore, the purpose of the present disclosure is to provide a simulation method, device and equipment of solute transport under thermal convection and a storage medium to solve or partially solve the above technical problems.

[0005] To achieve the above purpose, the first aspect of the present disclosure provides a simulation method of solute transport under thermal convection, which comprises:

[0006] determining adjacent particles in the influence domain of a basic particle, obtaining a first concentration and a first temperature of the basic particle, and obtaining a second concentration and a second temperature of the adjacent particles;

[0007] determining a kernel function of smooth particle hydrodynamics of the basic particle and the adjacent particles;

[0008] based on the kernel function, obtaining an initial concentration flux of the basic particle under non-thermal convection conditions according to the first concentration and the second concentration;

[0009] obtaining a target concentration flux of the basic particle under thermal conditions according to a concentration diffusion coefficient, and obtaining a concentration change function under thermal convection conditions according to the target concentration flux;

[0010] based on the kernel function, obtaining an initial heat flux of the basic particle under non-convection conditions according to the first temperature and the second temperature;

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

[0012] taking the target concentration flux, the concentration change function, the initial heat flux and the temperature change function as a simulation function of solute transport;

[0013] using the simulation function to simulate the solute transport in a time period from an initial time to a target time, and obtaining a concentration field and a temperature field at the target time.

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

[0015] An acquisition module configured to determine a neighboring particle 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 particle;

[0016] A kernel function determination module configured to determine a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle;

[0017] An initial concentration flux determination module configured to obtain, based on the kernel function, an initial concentration flux of the base particle under a non-thermal convection condition according to the first concentration and the second concentration;

[0018] A concentration variation function determination module configured to obtain a target concentration flux of the base particle under a thermal condition according to a concentration diffusion coefficient, and obtain a concentration variation function under a thermal convection condition according to the target concentration flux;

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

[0020] A temperature variation function determination module configured to obtain a temperature variation function under a convection condition according to the initial heat flux;

[0021] A simulation function determination module configured to take the target concentration flux, the concentration variation function, the initial heat flux, and the temperature variation function as a simulation function for solute transport;

[0022] A solute transport simulation module configured to simulate solute transport in a time period from an initial time to a target time by using the simulation function, and obtain a concentration field and a temperature field at the target time.

[0023] Based on the same inventive concept, a third aspect of the present disclosure provides an electronic device, comprising 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 provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the method as described above.

[0025] It can be seen from the above that the simulation method, device, equipment and storage medium for solute transport under thermal convection provided by the present disclosure are provided. The adjacent particles in the influence domain of the basic particle are determined, the first concentration and the first temperature of the basic particle are obtained, and the second concentration and the second temperature of the adjacent particles are obtained; the kernel function of the smoothed particle hydrodynamics of the basic particle and the adjacent particles is determined; based on the kernel function, the initial concentration flux of the basic particle under the condition of no thermal convection is obtained according to the first concentration and the second concentration; the target concentration flux of the basic particle under the condition of thermal force is obtained according to the concentration diffusion coefficient, and the concentration change function under the condition of thermal convection is obtained according to the target concentration flux; based on the kernel function, the initial heat flux of the basic particle under the condition of no convection is obtained according to the first temperature and the second temperature; the temperature change function under the condition of convection is obtained according to the initial heat flux; the target concentration flux, the concentration change function, the initial heat flux and the temperature change function are used as the simulation function of solute transport; the solute transport in the time period from the initial time to the target time is simulated by using the simulation function, and the concentration field and the temperature field at the target time are obtained. In this way, the concentration change function is used to simulate the concentration change in the solute transport process under the condition of thermal convection, the temperature change function is used to simulate the temperature change in the solute transport process under the condition of convection, the solute transport simulation under the condition of thermal convection can be realized, the efficiency of geothermal development is improved, and the stability and safety of waste isolation effect and pollution remediation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present disclosure or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creating labor.

[0027] Figure 1 The flowchart of the simulation method for solute transport under thermal convection of the embodiments of the present disclosure;

[0028] Figure 2 The test device for solute transport under thermal convection condition of porous medium and the TC-SPH model schematic diagram of the embodiments of the present disclosure;

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

[0030] Figure 4 The schematic diagram of the normalized concentration comparison of the output end monitoring under different background flow rates of the test and the TC-SPH model of the embodiments of the present disclosure;

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

[0032] Figure 6 A structural schematic diagram 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 clearer, the present disclosure is further described in detail below with reference to specific embodiments and drawings.

[0034] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the embodiments of the present disclosure should be understood as their common meanings to those skilled in the art to which the present disclosure belongs. The terms "first", "second", and similar terms used in the embodiments of the present disclosure do not represent any order, number, or importance, but are only used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships may also change accordingly.

[0035] Based on the description of the background, in some geothermal resource development, waste disposal and environmental pollution control, underground thermal convection is a key factor. 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 the efficiency of geothermal development, the effect of waste isolation, and the result of pollution remediation. Currently, the simulation of solute transport under thermal convection is mainly based on COMSOL Multiphysics based on the finite element method, TOUGH2 based on the finite difference method, and HYDRUS based on the stochastic model. For the finite element method, the generation of grid for complex geological structure is a challenge, especially when there are irregular boundaries and strong heterogeneity of the medium, it becomes more difficult and time-consuming to generate high-quality grids. For the finite difference method, numerical dispersion may occur when simulating solute transport, which leads to over-smoothing of the solute diffusion process, thereby affecting the accuracy of the simulation results. HYDRUS mainly focuses on water flow and solute transport in unsaturated and saturated zones, and may not be suitable for some special underground environments, such as high-temperature and high-pressure geothermal systems or systems with strong nonlinear reactions. Therefore, there is an urgent need for a simulation method for solute transport under thermal flow conditions.

[0036] As described above, how to simulate the solute transport under the condition of thermal convection becomes an important research problem.

[0037] Based on the above description, as Figure 1 indicated, the simulation method for solute transport under thermal convection provided by the embodiment includes:

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

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

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

[0041] The position information of the particle mainly includes: density, elastic modulus and Poisson's ratio. The thermodynamic parameters include: specific heat capacity (of rock and water body), thermal diffusivity coefficient and permeability coefficient. The chemical parameters include: concentration and diffusion coefficient of solute. The boundary conditions include: thermal boundary and solute transport boundary.

[0042] Step 102, determining the kernel function of the smoothed particle hydrodynamics of the base particle and the adjacent particles.

[0043] In specific implementation, the smoothed particle hydrodynamics (SPH) is a calculation method for simulating fluid dynamics, which is gradually applied in the field of fluid dynamics, such as engineering, oceanography and biomedical engineering. The core feature of the SPH algorithm is to regard the fluid as a group of discrete particles, which represent the micro volume elements of the fluid and carry the physical properties (such as density, pressure and velocity) of the fluid.

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

[0045] The SPH algorithm uses a weight function called a smoothing kernel to estimate the physical quantities of the particles and the spatial derivatives of the physical quantities of the particles. The kernel function (a B-spline function) determines the strength of the interaction between the particles, and generally has a finite support domain, so that only neighboring particles will have an impact on the current particle, thereby reducing the impact of instability when the particles are compressed.

[0046] In step 103, based on the kernel function, the initial concentration flux of the base particle under the condition of non-thermal convection is obtained according to the first concentration and the second concentration.

[0047] In specific implementation, the initial concentration flux of the base particle under the condition of non-thermal convection is determined based on the field function of the base particle. When the property parameter of the base particle is the concentration flux, the initial concentration flux of the base particle under the condition of non-thermal convection is obtained according to the first concentration and the second concentration.

[0048] In step 104, the target concentration flux of the base particle under the condition of thermal force is obtained according to the concentration diffusion coefficient, and the concentration change function under the condition of thermal convection is obtained according to the target concentration flux.

[0049] In specific implementation, the convection term and the thermal condition, i.e., the temperature, affect the movement of the solute. Based on the concentration change function under the condition of non-thermal convection, the concentration change function under the condition of thermal convection is obtained.

[0050] In step 105, based on the kernel function, the initial heat flux of the base particle under the condition of non-convection is obtained according to the first temperature and the second temperature.

[0051] In specific implementation, the initial heat flux of the base particle under the condition of non-convection is determined based on the field function of the base particle. When the property parameter of the base particle is the heat flux, the initial heat flux of the base particle under the condition of non-convection is obtained according to the first temperature and the second temperature.

[0052] In step 106, the temperature change function under the condition of convection is obtained according to the initial heat flux.

[0053] In specific implementation, when the fluid flows, part of the heat is taken away, which is called the convection term. Therefore, based on the temperature change function under the condition of non-convection, the temperature change function under the condition of convection is obtained by considering the impact of the convection term.

[0054] In step 107, the target concentration flux, the concentration change function, the initial heat flux, and the temperature change function are taken as the simulation function of the solute transport.

[0055] In specific implementation, the SPH equation under the conditions of solute transport and heat is taken as the simulation function of solute transport. The concentration change function is used to simulate the concentration change in the process of solute transport under the condition of heat convection, and the temperature change function is used to simulate the temperature change in the process of solute transport under the condition of convection.

[0056] In step 108, the simulation function is used to simulate the solute transport in the time period from the initial time to the target time, to obtain the concentration field and the temperature field at the target time.

[0057] In specific implementation, the calculation of the particle material concentration field and the temperature field forms a single cycle. If the cycle is not completed, return to step 102 to continue the cycle until the total time step is reached, and the frog leap algorithm is used for time integration to obtain the final solute and temperature distribution field of each particle through recursion.

[0058] The method provided by the embodiments of the present disclosure is a simulation method and system based on solute transport under the condition of heat convection, aiming to improve the efficiency of geothermal development, improve the stability and safety of waste isolation effect and pollution remediation. The method comprises the following steps: first, obtaining the geothermal parameters, physical and chemical properties and migration characteristic parameters of the research area under the condition of heat convection. Then, a two-dimensional model is established by using the SPH method, and parameterization is set in combination with the reservoir seepage characteristics and temperature distribution characteristics and solute concentration information. Next, the temperature field and solute concentration of the surrounding rock are simulated and calculated by using a heat-chemical coupling model. At present, SPH has not been realized in the simulation of solute transport under the condition of geothermal.

[0059] The present disclosure relates to the technical field of civil engineering, in particular to a simulation method of heat transfer in a nuclear storage facility. The present disclosure is based on the theoretical framework of smoothed particle hydrodynamics, and is divided into seven modules: solute conduction and heat transfer module, heat convection affecting solute transfer module, and convection affecting heat conduction module. The mutual influence of heat conduction, solute conduction and convection is based on partial differential equations. The influence of heat on solute transfer is realized through the Arrhenius formula. The method accurately captures the entire process of solute transport and heat conduction of porous media under high temperature and strong osmotic pressure conditions; multi-field simulation is realized in a single SPH framework, which is more efficient in calculation, has fewer input parameters, and the parameter acquisition process is relatively simple. The stress field and damage algorithm and random crack network can be further coupled to solve the rock loss process of nuclear waste storage and enhanced geothermal systems.

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

[0061] In some embodiments, step 102 comprises:

[0062] Step 1021, obtaining the relative distance between the base particle and the neighboring particle, and obtaining the smooth kernel radius of the base particle influence domain.

[0063] Step 1022, taking the ratio of the relative distance and the smooth kernel 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, r ij is the relative distance between the base particle and the neighboring particle, and h is the smooth kernel radius of the base particle influence domain.

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

[0067]

[0068] wherein W ij (p, h) is the kernel function of the smooth particle hydrodynamics of the base particle and the neighboring particle, 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 has a value of 1 / h in one-dimensional space, the radius coefficient λ has a value of 15 / 7πh 2 in two-dimensional space, and the radius coefficient λ has a value of 3 / 2πh 2 in three-dimensional space.

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

[0071] The base particle and the neighboring particles in the influence domain act in pairs, and the base particle acts on the neighboring particles by a linklist search method, wherein the base particle acts on the neighboring particles by the following formula:

[0072]

[0073] wherein f(x i) is a field function of the base particle i, used to represent a property parameter (e.g., concentration flux and heat flux) of the base particle i, N is the total number of neighboring particles, m j is the mass of the neighboring particle j, p j is the density of the neighboring particle j, c j ) is a field function of the neighboring particle j, used to represent a property parameter of the neighboring particle j, W(x i -x j , h) is a smoothing kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles.

[0074]

[0075] wherein, is a field function derivative of the base particle i, p is a gradient of the smoothing kernel function.

[0076] In some embodiments, step 103 comprises:

[0077] Step 1031, obtaining an initial concentration flux of the base particle under the non-thermal convection condition according to the first concentration and the second concentration based on the kernel function,

[0078]

[0079] wherein, is an initial concentration flux of the base particle under the non-thermal convection condition, k ci is a concentration diffusion coefficient, N is the total number of neighboring particles, m j is the mass of the neighboring particle j, p j is the density of the neighboring particle j, c i is the concentration of the base particle i, c j is the concentration of the neighboring particle j, W ij is a kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is a property parameter of the base particle i.

[0080] In particular implementation, the initial concentration flux of the base particle under the non-thermal convection condition is determined based on a field function of the base particle. When the property parameter f(x i ) of the base particle i is the concentration flux, the initial concentration flux of the base particle under the non-thermal convection condition is obtained according to the first concentration and the second concentration.

[0081] After step 103, further comprising:

[0082] obtaining a concentration variation function under the non-thermal convection condition according to the initial concentration flux,

[0083]

[0084] in, Let be the concentration change function under conditions without heat convection. It is the initial concentration flow rate. The derivative, The initial concentration flow rate of neighboring particles under non-thermal convection conditions.

[0085] The above scheme, which determines the property parameters of the basic particles based on kernel functions, can reduce the impact of instability during particle compression. When the property parameter of the basic particles is concentration-flow rate, the initial concentration-flow rate of the basic particles under heatless convection conditions can be obtained based on the first concentration of the basic particles and the second concentration of neighboring particles, ensuring that the obtained initial concentration-flow rate can reduce the impact of instability during particle compression.

[0086] In some embodiments, step 104 includes:

[0087] Step 1041: Based on the Arrhenius equation, the relationship between the concentration diffusion coefficient and temperature is obtained.

[0088]

[0089] Where, k ci Let D0 be the concentration diffusion coefficient, and E be the frequency factor of the concentration diffusion coefficient. a The activation energy is T, where R is the gas constant and T is the activation energy. i This refers to absolute temperature.

[0090] In practice, the transport of solute under convection conditions and the influence of heat distribution is determined. Since solute transport is affected by the temperature gradient, this is mainly reflected in the concentration diffusion coefficient k. ci The relationship between temperature and temperature satisfies the Arrhenius equation.

[0091] Wherein, the frequency factor D0 of the concentration diffusion coefficient is a constant, the gas constant R is approximately 8.314 J / (mol·K), and the absolute temperature T i The unit is Kelvin.

[0092] Step 1042: Obtain the target concentration flow rate of the basic particles under thermodynamic conditions based on the concentration diffusion coefficient.

[0093]

[0094] in, The target concentration flux of the basic particles under thermodynamic conditions, where N is the total number of neighboring particles, and m j Let ρ be the mass of the neighboring particle j. j Let c be the density of neighboring particle j.i The concentration of basic particle i, c j W represents the concentration of neighboring particle j. ij Let be the kernel function for the smooth particle hydrodynamics of the fundamental particle and its neighboring particles. These are the property parameters of the basic particle i.

[0095] In practical implementation, the concentration diffusion coefficient k ci Substituting the initial concentration flow rate into the formula, we obtain the target concentration flow rate of the basic particles under thermal convection conditions.

[0096] Step 1043: Obtain the concentration change function under thermal convection conditions based on the target concentration flow rate.

[0097]

[0098] in, Let be the concentration change function under thermal convection conditions. q represents the target concentration flow rate of neighboring particles under thermodynamic conditions. vc Background flow rate.

[0099] In practice, the convection term and temperature affect the movement of the solute. Based on the concentration change function under no-heat convection conditions, the concentration change function under heat convection conditions is obtained.

[0100] Using the above scheme, based on the relationship between concentration diffusion coefficient and temperature, the target concentration flow rate of basic particles under thermal convection conditions can be accurately obtained, and the concentration change function under thermal convection conditions can be determined. Thus, solute transport simulation under thermal convection conditions can be performed based on the target concentration flow rate and concentration change function of basic particles under thermal convection conditions.

[0101] In some embodiments, step 105 includes:

[0102] Step 1051: Based on the kernel function, obtain the initial heat flux of the basic particles under non-thermal convection conditions according to the first temperature and the second temperature.

[0103]

[0104] in, The initial heat flux of the basic particle under non-convection conditions, Where m is the thermal conductivity coefficient, N is the total number of neighboring particles, and m j Let ρ be the mass of the neighboring particle j. j Let T be the density of neighboring particle j. i The temperature of the basic particle i, T j W is the temperature of neighboring particle j. ija kernel function of smooth particle hydrodynamics for the base particle and the neighboring particle, a property parameter of the base particle i.

[0105] In practice, the initial heat flux of the base particle under the non-convective condition is determined based on a field function of the base particle. When the property parameter f(x i ) of the base particle i is the heat flux, the initial heat flux of the base particle under the non-convective condition is obtained according to the first temperature and the second temperature. Specifically, the heat flux distribution change is calculated according to the thermal convection diffusion equation, and the initial heat flux is obtained by discrete solving based on the SPH algorithm.

[0106] After step 105, further comprising:

[0107] obtaining a temperature change function under the non-convective condition according to the initial heat flux,

[0108]

[0109] wherein, is the temperature change function under the non-convective condition, is a derivative of the initial heat flux , and is the initial heat flux of the neighboring particle j under the non-convective condition.

[0110] Through the above scheme, the property parameter of the base particle is determined based on the kernel function, which can reduce the influence of instability when the particle is compressed. When the property parameter of the base particle is the heat flux, the initial heat flux of the base particle under the non-convective condition is obtained according to the first temperature of the base particle and the second temperature of the neighboring particle, and the obtained initial heat flux can reduce the influence of instability when the particle is compressed.

[0111] In some embodiments, step 106 comprises:

[0112] obtaining a temperature change function under the convective condition according to the initial heat flux,

[0113]

[0114] wherein, is the temperature change function under the convective condition, ρ i is the density of the base particle i, c i is the concentration of the base particle i, N is the total number of the neighboring particles, m j is the mass of the neighboring particle j, ρ j is the density of the neighboring particle j, is the initial heat flux of the base particle i under the non-convective condition, W0 is an initial heat flux of the base particle i under the non-convective condition, ij W0 is an initial heat flux of the base particle i under the non-convective condition, q is a property parameter of the base particle i, vc T is a background flow velocity, i T is a temperature of the base particle i, j T is a temperature of the base particle i,

[0115] In practice, the fluid flow will take away part of the heat, which is called the convection term. Therefore, based on the temperature change function under the non-convective condition, the temperature change function under the thermal convection condition is obtained by considering the effect of the solute flow term.

[0116] Through the above scheme, the temperature change function under the thermal convection condition is obtained by considering the effect of the solute flow term, so that the solute transport simulation under the thermal convection condition can be performed according to the temperature change function under the convection condition.

[0117] In some embodiments, step 107 includes:

[0118] Step 1071, the target concentration flux, the concentration change function, the initial heat flux and the temperature change function are used as the simulation function of the solute transport,

[0119]

[0120] wherein, W0 is an initial heat flux of the base particle i under the non-convective condition, C is a concentration change function under the thermal convection condition, W0 is an initial heat flux of the base particle i under the non-convective condition, T is a temperature change function under the convection condition.

[0121] In practice, the SPH equation under the thermal condition considering the solute transport is used as the simulation function of the solute transport. In addition, the density change function under the thermal convection condition is added, and the simulation function of the solute transport can be expressed as:

[0122]

[0123] wherein, C is a density change function under the thermal convection condition, and ρi is a density of the base particle i, vi is a velocity of the base particle i relative to the adjacent particle j, q is a property parameter of the base particle i.

[0124] By the above scheme, the simulation function of solute transport includes a density variation function, an initial concentration flow, a concentration variation function, an initial heat flow and a temperature variation function, the density variation function is used to simulate the density variation in the solute transport process under the thermal convection condition, the concentration variation function is used to simulate the concentration variation in the solute transport process under the thermal convection condition, and the temperature variation function is used to simulate the temperature variation in the solute transport process under the convection condition.

[0125] In some embodiments, step 108 comprises:

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

[0127]

[0128] Wherein, 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 basic particle i at the initial time, Δc i (t0) is the concentration variation rate of the basic particle i, T i (t0+Δt / 2) is the temperature field at the target time, ΔT i (t0) is the temperature variation rate of the basic particle i.

[0129] In specific implementation, using the simulation function, simulating the solute transport in the time period from the initial time to the target time to obtain the density field, the concentration field and the temperature field at the target time,

[0130]

[0131] Wherein, ρ i (t0+Δt / 2) is the density field at the target time, ρ i (t0) is the density of the basic particle i at the initial time, Δρ i (t0) is the density variation rate of the basic particle i.

[0132] The calculation of the particle material concentration field and the temperature field forms a single cycle. If the cycle is not completed, return to step 102 to continue the cycle until the predetermined total time step is reached, the time integration is performed by the frog leap algorithm, and the final solute and temperature distribution field of each particle is obtained by recursion.

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

[0134] Through the above examples, the whole process of solute transport and heat conduction of the porous medium under high temperature and strong osmotic pressure conditions is accurately captured; the multi-field simulation is implemented in a single SPH framework, the calculation is more efficient, the input parameters are less, the parameter acquisition process is relatively simple, and the stress field and damage algorithm and the random crack network can be further coupled 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 can also be further described in the following manner:

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

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

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

[0139]

[0140]

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

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

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

[0144] (4) Determine whether the calculation step has reached the set calculation step. If the maximum calculation step has not been reached, repeat steps (2)-(3); if the maximum calculation step has been reached, i.e. the tunnel, then terminate the calculation.

[0145] Through the above steps, the distribution of solute transport and temperature transfer under the background flow velocity of 40prm in the porous medium thermal convection is obtained, as shown in Figure 3 ; the temperature conduction diagram of the porous medium is obtained, as shown in Figure 3 ; the solute distribution diagram of the porous medium is obtained, as shown in Figure 3 , and three different background flow velocity schemes are used in the experiment. By comparing the real-time concentration distribution diagram obtained by the experiment and TC-SPH calculation, we find that the present application can effectively realize the solute transport and temperature transfer under the porous medium thermal convection.

[0146] As Figure 4As shown, at the background flow rate of 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 at the background flow rate of 20prm. At the background flow rate of 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 at the background flow rate of 30prm. At the background flow rate of 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 at the background flow rate of 40prm.

[0147] The present disclosure relates to the technical field of civil engineering, and particularly relates to a multi-field coupling fracture simulation method for deep tunnels. The present disclosure is based on a smoothed particle hydrodynamics (SPH) theoretical framework. A crack simulation method based on a central sphere invariant stress drop method is introduced for the first time in the SPH method, which enhances the stress adjustment and robustness of damaged SPH particles. The hydraulic conductivity in rock cracks is controlled by the cubic law and Darcy's law. And the heat conduction is coupled with the convection term and dynamic viscosity, so as to realize water-heat exchange. The present invention can effectively construct a fully coupled SPH multi-field cracking model, which can be realized without grid division and grid distortion, and the stress loss can be solved by mapping on the stress key through stress redistribution. Since SPH is a continuous method and therefore its parameters are easy to calibrate, it has high precision due to the absence of grid constraints, and it is efficient in calculating the stress characteristics of all particles through a single search at a single time step based on the linklist search method. The method of the present invention has significant advantages in multi-field simulation technology, and provides a powerful tool and broad prospects for research and application in the field of deep tunneling.

[0148] The SPH method has obvious advantages over the finite element method in dealing with complex boundaries and large deformation problems because it does not require traditional mesh partitioning. For the non-homogeneity and irregular boundary in the underground medium, the SPH method can adapt naturally. For the finite element method, the mesh generation of complex geological structure is a challenge, especially when there are irregular boundaries and strong heterogeneous media, it becomes more difficult and time-consuming to generate high-quality meshes. The SPH method can conveniently introduce complex physical and chemical reaction mechanisms, and it is more feasible to simulate the adsorption, desorption, precipitation and dissolution of particles in the solute transport process. For the finite difference method in simulating solute transport, numerical dispersion may occur, which requires the use of finer meshes or smaller time steps. The SPH model of thermal solute transport has the advantages of meshless, adaptive time step, and strong parallel computing capability. The SPH method has obvious advantages in simulating thermal convection and solute transport in underground media, such as meshless, adaptive, strong parallel computing capability, multi-scale simulation and processing complex reactions. The present application can further couple the stress field and the damage algorithm and the random crack network to solve the rock mass loss process of nuclear waste storage and enhanced geothermal systems.

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

[0150] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order described above and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or necessary.

[0151] Based on the same inventive concept, the present disclosure also provides a simulation device for solute transport under thermal convection corresponding to the method of any of the above embodiments.

[0152] Reference Figure 5 The simulation device for solute transport under thermal convection comprises:

[0153] The acquisition module 301 is configured to determine the adjacent particles in the influence domain of the basic particle, acquire the first concentration and the first temperature of the basic particle, and acquire the second concentration and the second temperature of the adjacent particles.

[0154] The kernel function determination module 302 is configured to determine a kernel function of smooth particle hydrodynamics of the base particle and the adjacent particle;

[0155] The initial concentration flux determination module 303 is configured to obtain an initial concentration flux of the base particle under the condition of non-thermal convection according to the first concentration and the second concentration based on the kernel function;

[0156] The concentration change function determination module 304 is configured to obtain a target concentration flux of the base particle under the condition of thermal force according to a concentration diffusion coefficient, and obtain a concentration change function under the condition of thermal convection according to the target concentration flux;

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

[0158] The temperature change function determination module 306 is configured to obtain a temperature change function under the condition of convection according to the initial heat flux;

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

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

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

[0162] The acquisition unit is configured to acquire a relative distance between the base particle and the adjacent particle, and acquire a smooth kernel radius of the base particle influence domain;

[0163] The normalized distance determination unit is configured to take a ratio of the relative distance and the smooth kernel radius as a normalized distance between the base particle and the adjacent particle,

[0164]

[0165] wherein p is the normalized distance between the base particle and the adjacent particle, i is a serial number of the base particle, j is a serial number of the adjacent particle, r ij is the relative distance between the base particle and the adjacent particle, and h is the smooth kernel radius of the base particle influence domain;

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

[0167]

[0168] wherein W ij is the kernel function of smooth particle hydrodynamics of the base particle and the neighboring particle, and λ is a radius coefficient.

[0169] In some embodiments, the initial concentration flux determination module 303 comprises:

[0170] an initial concentration flux determination unit configured to obtain, according to the first concentration and the second concentration, an initial concentration flux of the base particle under the condition of adiabatic convection, based on the kernel function,

[0171]

[0172] wherein, is the initial concentration flux of the base particle under the condition of adiabatic convection, k ci is a concentration diffusion coefficient, N is a total number of the neighboring particles, m j is a mass of the neighboring particle j, ρ j is a density of the neighboring particle j, c i is a concentration of the base particle i, c j is a concentration of the neighboring particle j, W ij is the kernel function of smooth particle hydrodynamics of the base particle and the neighboring particle, is a property parameter of the base particle i.

[0173] In some embodiments, the concentration variation function determination module 304 comprises:

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

[0175]

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

[0177] a target concentration flux determination unit configured to obtain a target concentration flux of the base particle under the condition of thermal convection, according to the concentration diffusion coefficient,

[0178]

[0179] wherein, is a target concentration flux of the base particle under thermal conditions, N is the total number of neighboring particles, m j is the mass of the neighboring particle j, p j is the density of the neighboring particle j, c i is the concentration of the base particle i, c j is the concentration of the neighboring particle j, W ij is a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particles, is a property parameter of the base particle i;

[0180] a concentration change function determination unit configured to obtain a concentration change function under thermal convection conditions according to the target concentration flux,

[0181]

[0182] wherein, is the concentration change function under thermal convection conditions, is a target concentration flux of the neighboring particle under thermal conditions, q vc is a background flow velocity.

[0183] In some embodiments, the initial heat flux determination module 305 comprises:

[0184] an initial heat flux determination unit configured to obtain an initial heat flux of the base particle under non-convection conditions according to the first temperature and the second temperature based on the kernel function,

[0185]

[0186] wherein, is the initial heat flux of the base particle under non-convection conditions, is a heat transfer conduction coefficient, N is the total number of neighboring particles, m j is the mass of the neighboring particle j, p j is the density of the neighboring particle j, T i is the temperature of the base particle i, T j is the temperature of the neighboring particle j, W ij is a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particles, is a property parameter of the base particle i.

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

[0188] a temperature change function determination unit configured to determine a temperature change function under a convection condition according to the initial heat flow,

[0189]

[0190] wherein, is a temperature change function under a convection condition, ρ i is a density of the base particle i, c i is a concentration of the base particle i, N is a total number of neighboring particles, m j is a mass of the neighboring particle j, ρ j is a density of the neighboring particle j, is an initial heat flow of the base particle i under a non-convection condition, is an initial heat flow of the neighboring particle j under a non-convection condition, W ij is a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle, is a property parameter of the base particle i, q vc is a background flow velocity, T i is a temperature of the base particle i, T j is a temperature of the neighboring particle j.

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

[0192] a solute transport simulation unit configured to simulate solute transport in a time period from an initial time to a target time using the simulation function to obtain a concentration field and a temperature field at the target time,

[0193]

[0194] wherein, c i is a concentration field at the target time, t0 is an initial time, Δt is a calculation time step, t is a target time, T i is a temperature of the base particle i at the initial time, Δc i is a concentration change rate of the base particle i, T i is a temperature field at the target time, ΔT i is a temperature change rate of the base particle i.

[0195] For the convenience of description, the above apparatus is described in various modules respectively according to functions. Of course, the functions of the modules can be implemented in one or more software and / or hardware when implementing the present disclosure.

[0196] The device of the above embodiment is used to implement the simulation method of solute transport under thermal convection in any of the preceding embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0197] Based on the same inventive concept, the disclosure also provides an electronic device corresponding to the method of any of the above embodiments, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the simulation method of solute transport under thermal convection according to any of the above embodiments when executing the program.

[0198] Figure 6 A more specific hardware structure of an electronic device according to the present embodiment is shown, which can 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 for communication within the device.

[0199] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the present embodiment.

[0200] The memory 1020 can be implemented by a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the present embodiment are implemented by software or firmware, the related program codes are stored in the memory 1020 and executed by the processor 1010.

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

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

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

[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 can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary for the implementation of the embodiments of the present specification, and does not have to contain all the components shown in the figure.

[0205] The electronic device of the above embodiment is used to realize the simulation method of solute transport under the corresponding thermal convection in any of the preceding embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here.

[0206] Based on the same inventive concept, the disclosure also provides a non-transitory computer readable storage medium storing computer instructions for causing 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 the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0208] The storage medium of the above embodiments stores computer instructions for causing the computer to execute the simulation method of solute transport under thermal convection as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here again.

[0209] It should be understood by those of ordinary skill in the art that the discussion of any of the above embodiments is merely exemplary and is not intended to suggest that the scope of the present disclosure is limited to these examples; under the idea of the present disclosure, the above embodiments or technical features among different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.

[0210] In addition, in order to simplify the description and discussion, and so as not to make the embodiments of the present disclosure difficult to understand, the well-known power / ground connections of integrated circuit (IC) chips and other components can or can not be shown in the provided drawings. In addition, the apparatus can be shown in the form of a block diagram in order 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 apparatuses are highly dependent on the platform to be implemented to implement the embodiments of the present disclosure (i.e., these details should be fully within the understanding of those skilled in the art). Where specific details (e.g., circuitry) are set forth in order to describe an illustrative embodiment of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure can be practiced without these specific details or with variations on these specific details. Therefore, these descriptions should be considered as illustrative rather than limiting.

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

[0212] The embodiments of the present disclosure are intended to cover all such alternatives, modifications and variations as falling within the broad scope of the present disclosure. Accordingly, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method of simulating solute transport under thermal convection, characterized by, The method comprises: determining a neighboring particle within an 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 particle; determining a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle; based on the kernel function, obtaining an initial concentration flux of the base particle under a non-thermal convection condition according to the first concentration and the second concentration; obtaining a target concentration flux of the base particle under a thermal condition according to a concentration diffusion coefficient, and obtaining a concentration variation function under a thermal convection condition according to the target concentration flux; based on the kernel function, obtaining an initial heat flux of the base particle under a non-convection condition according to the first temperature and the second temperature; obtaining a temperature variation function under a convection condition according to the initial heat flux; taking the target concentration flux, the concentration variation function, the initial heat flux, and the temperature variation function as a simulation function of solute transport; simulating solute transport in a time period from an initial time to a target time by using the simulation function to obtain a concentration field and a temperature field at the target time; the obtaining of the target concentration flux of the base particle under the thermal condition according to the concentration diffusion coefficient, and the obtaining of the concentration variation function under the thermal convection condition according to the target concentration flux, comprises: obtaining a relationship between the concentration diffusion coefficient and the temperature based on an Arrhenius formula, where k ci is the concentration diffusion coefficient, D0is 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; obtaining the target concentration flux of the base particle under the thermal condition according to the concentration diffusion coefficient, wherein, is the target concentration flux of the base particle under thermal conditions, N is the total number of neighboring particles, m j is the mass of neighboring particle j, p j is the density of neighboring particle j, c i is the concentration of base particle i, c j is the concentration of neighboring particle j, W ij is a kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is a property parameter of base particle i; obtaining the concentration variation function under the thermal convection condition according to the target concentration flux, wherein, is the concentration change function under thermal convection conditions, is the target concentration flux of neighboring particles under thermodynamic conditions, q vc is the background flow velocity; the obtaining of the temperature variation function under the convection condition according to the initial heat flux, comprises: obtaining the temperature variation function under the convection condition according to the initial heat flux, wherein, is a temperature change function under convection conditions, p i is a density of the base particle i, c i is a concentration of the base particle i, N is a total number of neighboring particles, m j is a mass of the neighboring particle j, p j is a density of the neighboring particle j, c is an initial heat flow of the base particle i without convection conditions, is an initial heat flow of the neighboring particle j without convection conditions, W ij is a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle, is a property parameter of the base particle i, q vc is a background flow velocity, T i is a temperature of the base particle i, T j is a temperature of the neighboring particle j.

2. The method of claim 1, wherein, the determining of the kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle, comprises: obtaining a relative distance between the base particle and the neighboring particle, and obtaining a smoothing kernel radius of the influence domain of the base particle; taking a ratio of the relative distance and the smoothing kernel radius as a normalized distance between the base particle and the neighboring particle, where p is a normalized distance between the base particle and the neighboring particle, i is a serial number of the base particle, j is a serial number of the neighboring particle, r ij is a relative distance between the base particle and the neighboring particle, and h is a smoothing kernel radius of the base particle influence domain. obtaining the kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle according to the normalized distance, where W ij (p, h) is the kernel function of the smooth particle hydrodynamics of the base particle and the neighboring particle, and λ is the radius factor.

3. The method of claim 1, wherein, the obtaining of the initial concentration flux of the base particle under the non-thermal convection condition according to the first concentration and the second concentration based on the kernel function, comprises: obtaining the initial concentration flux of the base particle under the non-thermal convection condition according to the first concentration and the second concentration based on the kernel function, wherein, is the initial concentration flux of the base particle 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 the neighboring particle j, p j is the density of the neighboring particle j, c i is the concentration of the base particle i, c j is the concentration of the neighboring particle j, W ij is a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle, is a property parameter of the base particle i.

4. The method of claim 1, wherein, the obtaining of the initial heat flux of the base particle under the non-convection condition according to the first temperature and the second temperature based on the kernel function, comprises: obtaining the initial heat flux of the base particle under the non-convection condition according to the first temperature and the second temperature based on the kernel function, wherein, Qi is the initial heat flow of the base particle under non-convective conditions, is the heat transfer conduction coefficient, N is the total number of neighboring particles, m j is the mass of neighboring particle j, p j is the density of neighboring particle j, T i is the temperature of base particle i, T j is the temperature 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 base particle i.

5. The method of claim 1, wherein, the simulating of solute transport in a time period from an initial time to a target time by using the simulation function to obtain a concentration field and a temperature field at the target time, comprises: simulate solute transport in a time period from an initial time to a target time by using the simulation function to obtain a concentration field and a temperature field at the target time, where c i (t0+ At / 2) is the concentration field at the target time, t0 is the initial time, At is the calculation time step, t is the target time, T i (t0) is the temperature of the base particle i at the initial time, Δc i (t0) is the concentration change rate of the base particle i, T i (t0+ At / 2) is the temperature field at the target time, ΔT i (t0) is the temperature change rate of the base particle i.

6. A device for simulating solute transport under thermal convection, characterized in that comprising: an obtaining module configured to determine a neighboring particle in an 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 particle; a kernel function determining module configured to determine a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particle; an initial concentration flux determining module configured to obtain an initial concentration flux of the base particle under a non-thermal convection condition according to the first concentration and the second concentration based on the kernel function; a concentration change function determining module configured to obtain a target concentration flux of the base particle under a thermal condition according to a concentration diffusion coefficient, and obtain a concentration change function under a thermal convection condition according to the target concentration flux; an initial heat flux determining module configured to obtain an initial heat flux of the base particle under a non-convection condition according to the first temperature and the second temperature based on the kernel function; a temperature change function determining module configured to obtain a temperature change function under a convection condition according to the initial heat flux; a simulation function determining module configured to take the target concentration flux, the concentration change function, the initial heat flux, and the temperature change function as a simulation function of solute transport; a solute transport simulation module configured to simulate solute transport in a time period from an initial time to a target time by using the simulation function to obtain a concentration field and a temperature field at the target time; the concentration change function determining module comprises: a concentration diffusion coefficient determining unit configured to obtain a relationship between a concentration diffusion coefficient and a temperature based on an Arrhenius formula, where k ci is the concentration diffusion coefficient, D0is the frequency factor for the concentration diffusion coefficient, E a is the activation energy, R is the gas constant, T i is the absolute temperature; a target concentration flux determining unit configured to obtain a target concentration flux of the base particle under a thermal condition according to the concentration diffusion coefficient, wherein, is the target concentration flux of the base particle under thermal conditions, N is the total number of neighboring particles, m j is the mass of the neighboring particle j, p j is the density of the neighboring particle j, c i is the concentration of the base particle i, c j is the concentration of the neighboring particle j, W ij is a kernel function of the smoothed particle hydrodynamics of the base particle and the neighboring particles, is a property parameter of the base particle i; a concentration change function determining unit configured to obtain a concentration change function under a thermal convection condition according to the target concentration flux, wherein, is the concentration change function under thermal convection conditions, is the target concentration flux of neighboring particles under thermodynamic conditions, q vc is the background flow velocity; the temperature change function determining module comprises: a temperature change function determining unit configured to obtain a temperature change function under a convection condition according to the initial heat flux, wherein, is a temperature change function under convection conditions, p i is a density of base particle i, c i is a concentration of base particle i, N is a total number of neighboring particles, m j is a mass of neighboring particle j, p j is a density of neighboring particle j, p is an initial heat flow of base particle i under no convection conditions, is an initial heat flow of neighboring particle j under no convection conditions, W ij is a kernel function of smoothed particle hydrodynamics of the base particle and the neighboring particles, is a property parameter of base particle i, q vc is a background flow velocity, T i is a temperature of base particle i, T j is a temperature of neighboring particle j.

7. An electronic device, comprising: a computer program stored on a memory and running on a processor, the processor implementing the method of any one of claims 1 to 5 when executing the program.

8. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium stores computer instructions for causing a computer to execute the method of any one of claims 1 to 5.

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