Simulation method and system for fluid-solid junction temperature based on lattice Boltzmann method

Abstract

The invention provides a fluid-solid junction temperature simulation method and system based on a lattice Boltzmann method, and relates to the technical field of fluid mechanics. The method comprises the following steps: performing thermal fluid simulation by utilizing a lattice Boltzmann method to obtain grid point speed and grid point turbulence dynamic viscosity of each grid point; according to the turbulence dynamic viscosity of the multiple grid points, the turbulence dynamic viscosity at the position 1 / 2 grid away from the solid surface is obtained through calculation; calculating a dimensionless distance based on the turbulence dynamic viscosity, the thermal fluid physical parameters, the solid grid point speed and the first grid point speed; the solid surface temperature is obtained through correction according to the dimensionless distance and the turbulence thermal diffusion coefficient at the position 1 / 2 grid away from the solid surface; and solving a temperature lattice Boltzmann equation based on the solid surface temperature for simulation. According to the method, the simulation precision of the temperature of the fluid-solid interface near the complex solid surface when the lattice Boltzmann method is used for simulating the large-spatial-scale thermal fluid is improved to a great extent.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid mechanics, and in particular to a method and system for simulating the temperature at a fluid-solid interface based on a lattice Boltzmann method. Background Art

[0002] In the field of fluid mechanics computer simulation, the lattice Boltzmann method (LBM) is a relatively advanced fluid mechanics simulation method based on discrete lattices and molecular dynamics. The basic idea of ​​the lattice Boltzmann method is to solve the macroscopic motion equation of the fluid by simulating the collision and migration behavior of fluid microscopic particles. Due to its advantages such as simple calculation and high parallel efficiency, it is widely used in many fields. However, in the case of large-scale spatial structures such as the wind and heat environment of urban buildings and the atmospheric environment, which are simultaneously facing strong turbulent wind and heat effects, the accuracy of LBM simulation results is usually poor, especially the temperature simulation value at the "fluid-solid interface" near the complex solid surface is less consistent with the experimental value or measured value, and the accuracy is poor.

[0003] like Figure 1 As shown in the figure, when performing simulation, first determine the simulation area and turbulence simulation method, obtain grid information and thermal fluid physical parameters; then directly use LBM to simulate the thermal fluid, calculate temperature distribution, turbulent thermal diffusion coefficient and other heat-related physical quantities; finally solve the temperature lattice Boltzmann equation (LBE). In order to solve the problem of poor temperature simulation accuracy at the "fluid-solid interface" near the complex solid surface, a finer grid and discrete velocity model are usually used to improve the spatial resolution of the simulation, thereby improving the simulation accuracy.

[0004] However, this will cause the simulation load to increase exponentially, especially for cities, buildings and even atmospheric environment scales, the computing time and hardware costs caused by this load are unrealistic. Summary of the invention

[0005] Based on this, it is necessary to provide a simulation method and system for the fluid-solid interface temperature based on the lattice Boltzmann method to address the above technical problems, which can enable LBM to greatly improve the temperature accuracy at the "fluid-solid interface" near the complex solid surface without significantly encrypting the calculation grid and increasing the calculation load.

[0006] In one aspect, the present invention provides a method for simulating the temperature at a fluid-solid interface based on a lattice Boltzmann method, the method comprising: Determine the simulation area and turbulence simulation method, obtain grid information and thermal fluid physical parameters, use the lattice Boltzmann method to perform thermal fluid simulation, and obtain the grid point velocity and grid point turbulent dynamic viscosity of each grid point; The turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface is calculated based on the turbulent dynamic viscosity of the plurality of grid points; The thermal diffusion coefficient at a distance of 1 / 2 of the grid from the solid surface is calculated based on the turbulent dynamic viscosity; The dimensionless distance of the first layer of grid is calculated based on the turbulent dynamic viscosity, the thermal fluid physical parameters, the solid grid point velocity and the first grid point velocity; wherein the solid grid point velocity is the grid point velocity of the solid surface; and the first grid point velocity is the grid point velocity of the first layer of grid points adjacent to the solid surface; The solid surface temperature is obtained according to the dimensionless distance and the thermal diffusion coefficient correction; and the temperature lattice Boltzmann equation is solved based on the solid surface temperature to perform simulation.

[0007] Furthermore, the grid information includes: one or more of: grid shape, number of grids, number of fluid grid points, grid length, and grid coordinates.

[0008] Furthermore, the thermal fluid physical parameters include: fluid molecular dynamic viscosity, fluid density, fluid molecular Prandtl number, and fluid turbulence Prandtl number.

[0009] The solid surface temperature The calculation method is: ; in, represents the original solid boundary temperature calculated by the lattice Boltzmann method for thermal fluid simulation; represents the dimensionless distance; represents the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface; represents the Prandtl number of fluid molecules; It represents the temperature of the first layer of grid points adjacent to the solid boundary and pointing to the interior of the fluid perpendicular to the solid boundary surface; Represents the dynamic viscosity of fluid molecules.

[0010] Furthermore, the dimensionless distance The calculation method is: ; in, It represents the dynamic viscosity of fluid molecules; It represents the turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface; represents the velocity of the first grid point; represents the tangential velocity of the first grid point; represents the solid grid point velocity; represents the tangential velocity of the solid grid point; Represents the density of the fluid at the solid surface.

[0011] Furthermore, the turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface is calculated by using the Lagrangian extrapolation method based on the grid point turbulent dynamic viscosity of N adjacent fluid grid points.

[0012] Furthermore, the turbulent dynamic viscosity The calculation method is: ; in, Indicates the number of fluid grid points; represents the grid point turbulent dynamic viscosity of the i-th grid point adjacent to the solid surface.

[0013] Furthermore, the thermal diffusion coefficient including turbulence effect at 1 / 2 grid distance from the solid surface is The way to get is: ; in, It represents the dynamic viscosity of fluid molecules; represents the dimensionless distance; represents the turbulent Prandtl number; represents the von Karman constant; represents the wall roughness constant; represents the molecular Prandtl number.

[0014] Furthermore, the method further comprises: Restore the corrected solid surface temperature to the original solid boundary temperature; The temperature value of each grid point inside the fluid is calculated, and the buoyancy term is calculated according to the set external force model, and the fluid lattice Boltzmann equation is solved to perform fluid simulation.

[0015] On the other hand, the present invention also proposes a simulation system for fluid-solid interface temperature based on the lattice Boltzmann method, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0016] In general, the present invention provides a method and system for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method, which can achieve the following beneficial effects compared with the prior art: The present invention calculates the dimensionless distance of the first layer of grid based on the turbulent dynamic viscosity at 1 / 2 grid from the solid surface, the physical parameters of the thermal fluid, the solid grid point velocity and the first grid point velocity; and corrects the solid surface temperature according to the dimensionless distance and the thermal diffusion coefficient containing the turbulence effect at 1 / 2 grid from the solid surface, so as to solve the temperature lattice Boltzmann equation and the fluid lattice Boltzmann method. Through this method of the present invention, the accuracy of fluid and temperature related physical quantities near complex solid surfaces when using LBM for large-scale thermal fluid dynamics simulation can be enhanced, thereby improving the applicability of LBM in large-scale thermal fluid simulations such as building cities, building wind and heat environments, and atmospheric and ocean currents.

[0017] In addition, based on the characteristics of laminar and turbulent effects at the "fluid-solid interface", the wall temperature boundary value is adjusted based on the wall friction to obtain the correct heat flux at the wall, which overcomes the problem of heat flux distortion near the complex solid surface when the spatial scale is large, and effectively improves the simulation accuracy of the fluid simulation results and temperature at the "fluid-solid interface" when LBM simulates thermal fluid in a high turbulence environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of a method for simulating the temperature at the fluid-solid interface based on the original lattice Boltzmann method in the prior art provided by the present invention; Figure 2 It is a schematic diagram of a method flow of a method and system for simulating temperature at a fluid-solid interface based on a lattice Boltzmann method provided by the present invention; Figure 3 It is a schematic diagram of grid point distribution of a method for simulating temperature at a fluid-solid interface based on a lattice Boltzmann method and a system provided by the present invention; Figure 4 It is a schematic diagram of a fluid simulation method based on the original lattice Boltzmann method in the prior art provided by the present invention; Figure 5 It is a schematic diagram of a non-isothermal natural convection square cavity for LBM thermal fluid simulation provided by the present invention for verifying the effectiveness of the present invention; Figure 6 It is a comparison chart of simulation results of the temperature at the fluid-solid interface based on the original lattice Boltzmann method and a lattice Boltzmann method provided by the present invention, as well as experimental values. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the drawings and embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0021] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a method, step or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such method, step or system. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the method, step or system including the element.

[0022] like Figure 2 As shown, the present invention provides a method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method to improve the temperature accuracy of the "fluid-solid interface" near the complex solid surface when LBM simulates a large-scale thermal fluid. The method includes: Step 101: Determine the simulation area and turbulence simulation method, obtain grid information and thermal fluid physical parameters, use the lattice Boltzmann method to perform thermal fluid simulation, and obtain the grid point velocity and grid point turbulent dynamic viscosity of each grid point.

[0023] The thermal fluid simulation of LBM is to solve LBE and temperature LBE cyclically. When using LBM to simulate thermal fluid in high turbulence environments in large-scale fields such as cities, buildings, and the atmosphere, it is necessary to determine the simulation area and turbulence simulation method. Among them, turbulence simulation methods include: Reynolds time average (RANS), large eddy simulation (LES), etc.

[0024] It should be noted that the grid information includes: one or more of: grid shape, number of grids, number of fluid grid points, grid length, and grid coordinates.

[0025] As an embodiment, a structural grid system is used, that is, all grids are two-dimensional squares or three-dimensional cubes, and all grids have the same length. It should be noted that the grid length is determined according to the structural grid system.

[0026] More specifically, Figure 3The figure shows the mesh distribution at the "fluid-solid interface" in the LBM simulation with a 2D mesh system. Assuming that the boundary temperature is higher than the interior temperature, the heat flows from the boundary to the interior. The same principle applies when the temperature gradient is opposite. The temperature gradient causes the heat flux to flow from the boundary mesh (BC, solid surface) to the first layer of mesh.

[0027] The physical parameters of thermal fluids include at least: fluid molecular dynamic viscosity, fluid density, fluid molecular Prandtl number, and fluid turbulence Prandtl number. It can also include boundary conditions such as velocity and temperature of each boundary, and the solid surface velocity at the "fluid-solid interface". (solid grid point velocity) and solid surface temperature (Original temperature of the solid grid point).

[0028] The grid point velocity of each grid point includes the grid point velocity of the solid surface and the grid point velocity of the first layer of grid points adjacent to the solid surface.

[0029] It should be noted that the grid point velocity and grid point turbulent dynamic viscosity of each grid point of the thermal fluid can be obtained directly by solving the existing fluid lattice Boltzmann equation, which will not be repeated here.

[0030] Step 102: Calculate the turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface based on the turbulent dynamic viscosity at multiple grid points.

[0031] As an embodiment, since there is no 1 / 2 grid point in LBM, the turbulent dynamic viscosity can be calculated based on the grid point turbulent dynamic viscosity of N adjacent fluid grid points and calculated using the Lagrangian extrapolation method.

[0032] The turbulent dynamic viscosity is obtained by Lagrangian extrapolation of the N grid points from the first grid point adjacent to the solid surface to the Nth grid point and comparing it with 0 to take the larger value.

[0033] More specifically, the turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface is The calculation method is: ; in, Indicates the number of fluid grid points. An integer can be set according to the fluid simulation. Preferably, ; represents the grid point turbulent dynamic viscosity of the i-th grid point adjacent to the solid surface, which can be obtained by LBM simulation.

[0034] Step 103: Calculate the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface based on the turbulent dynamic viscosity.

[0035] According to the basic principles of heat transfer, heat flux and temperature The relationship between them is: ; in, Represents the heat flux from the boundary grid to the first layer of grid; represents the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface. In this embodiment, is the thermal diffusion coefficient between the boundary grid and the first layer of grid; represents the temperature at the boundary grid; represents the temperature at the first layer of grid; Indicates the grid length.

[0036] According to the basic theory of turbulent boundary layer, there is a viscous sublayer at the "fluid-solid interface" surface, in which heat is mainly transferred in the form of molecular diffusion. On the outside is the logarithmic layer, in which the propagation of heat is more obvious due to the turbulent effect. However, in the thermal fluid simulation of large-scale strong turbulence such as cities, buildings, and the atmosphere, the viscous sublayer is extremely thin (thickness ranges from several millimeters to several centimeters). In this case, due to computational load considerations, the first layer of grids at the "fluid-solid interface" surface is usually coarse (several centimeters to several meters), resulting in this layer of grids being located in the logarithmic layer. In this layer of grids, LBM still mainly transmits heat in the form of molecular diffusion, so the correct heat flux cannot be calculated.

[0037] To solve this problem, general fluid mechanics simulation methods, such as the finite volume method based on the Navier-Stokes equations, apply turbulence effects to the thermal diffusion coefficient near the "fluid-solid interface" to correct the heat flux. However, LBM uses a uniform square or cubic co-located grid system, that is, all physical quantities are obtained at the grid points. Therefore, it is impossible to use the above heat flux and temperature Correction of the thermal diffusivity at a distance of 1 / 2 grid from the solid surface To reflect the turbulence effect.

[0038] Therefore, based on the basic principle of heat transfer, the present invention temporarily applies the turbulence effect of heat diffusion to the boundary temperature to obtain a corrected heat flux, and restores it to the original value before the end of an iteration step. It can be considered that the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface is It is equal to the thermal diffusion coefficient of the molecules in the viscous sublayer.

[0039] Specifically, the original solid boundary temperature Change to solid surface temperature , so that it can maintain the same heat flux under turbulence, that is: .

[0040] According to the heat flux formula and heat flux formula , and obtain the corrected solid surface temperature ;in, Represents the molecular thermal diffusion coefficient, which is equal to the fluid molecular motion viscosity The ratio of the molecular Prandtl number Pr to obtain the thermal diffusion coefficient at a distance of 1 / 2 of the grid from the solid surface .

[0041] It should be noted that the effective thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface is The calculation of can be based on the difference between the viscous sublayer and the log layer, specifically: ; in, represents the molecular thermal diffusion coefficient, which is equal to the molecular dynamic viscosity of the fluid divided by the molecular Prandtl number; represents dimensionless distance; Represents the dimensionless distance threshold set; It represents the dynamic viscosity of fluid molecules; represents the Prandtl number of fluid molecules; represents the von Karman constant; represents the wall roughness constant; It represents the empirical formula to ensure the consistency with the actual distribution, which is generally: .

[0042] As an example, the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface is The specific way to obtain is: ; in, It represents the dynamic viscosity of fluid molecules; represents dimensionless distance; represents the fluid turbulence Prandtl number; represents the von Karman constant, which can take the value of 0.41; represents the wall roughness constant, which can be taken as 9.8; represents the Prandtl number of fluid molecules.

[0043] Step 104: Calculate the dimensionless distance of the first layer of grid based on the turbulent dynamic viscosity, the physical parameters of the thermal fluid, the solid grid point velocity and the first grid point velocity.

[0044] The solid grid point velocity is the grid point velocity of the solid surface; the first grid point velocity is the grid point velocity of the first layer of grid points adjacent to the solid surface. The first layer of grid is the grid part between the solid surface and the first layer of grid points.

[0045] It should be noted that both the solid grid point velocity and the first grid point velocity can be obtained by directly solving the existing fluid lattice Boltzmann equation, which will not be described in detail here.

[0046] As an example, the dimensionless distance The calculation method is: ; in, It represents the dynamic viscosity of fluid molecules; It represents the turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface; represents the velocity of the first grid point; represents the tangential velocity of the first grid point, obtained by solving the LBE of the fluid; represents the solid grid point velocity; Represents the tangential velocity of the solid grid point, usually 0; Represents the density of the fluid at the solid surface.

[0047] It should be noted that step 103 and step 104 may be performed in any order and are not limited thereto.

[0048] Step 105: The solid surface temperature is obtained according to the dimensionless distance and the thermal diffusion coefficient correction at the 1 / 2 grid distance from the solid surface; and the temperature lattice Boltzmann equation is solved based on the solid surface temperature to perform simulation.

[0049] As an example, the solid surface temperature The calculation method is: ; in, represents the original solid boundary temperature calculated by the lattice Boltzmann method for thermal fluid simulation; represents dimensionless distance; Represents the thermal diffusion coefficient at 1 / 2 grid distance from the solid surface; represents the Prandtl number of fluid molecules; It represents the temperature of the first layer of grid points adjacent to the solid boundary and pointing to the interior of the fluid perpendicular to the solid boundary surface; Represents the dynamic viscosity of fluid molecules.

[0050] As a specific embodiment, the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface is Substitute the solid surface temperature The solid surface temperature The calculation method can also be: ; in, represents the original solid boundary temperature calculated by the lattice Boltzmann method for thermal fluid simulation; represents the molecular Prandtl number; represents dimensionless distance; It represents the temperature of the first layer of grid points adjacent to the solid boundary and pointing to the interior of the fluid perpendicular to the solid boundary surface; represents the turbulent Prandtl number; represents the von Karman constant; represents the wall roughness constant.

[0051] In order to prevent the solid surface temperature The correct solid surface temperature is outputted. In addition, the method further comprises: restoring the corrected solid surface temperature to the original solid boundary temperature; calculating the temperature value of each grid point inside the fluid, calculating the buoyancy term according to the set external force model, and solving the fluid lattice Boltzmann equation for fluid simulation.

[0052] As a specific example, Figure 4 As shown, first, the fluid LBE is solved based on the turbulence simulation method to obtain the hot fluid grid point velocity at each grid point , and the turbulent dynamic viscosity at the grid point ; Then, according to the turbulent dynamic viscosity at each grid point , the Lagrangian extrapolation method is used to calculate the turbulent dynamic viscosity at a distance of 1 / 2 of the grid from the solid surface ; Next, based on the turbulent dynamic viscosity Calculate the thermal diffusion coefficient at a distance of 1 / 2 of the grid from the solid surface ; Then, combined with the turbulent dynamic viscosity , the grid point velocity of the solid surface and the grid point velocity of the first layer of mesh adjacent to the solid surface , calculate the dimensionless distance of the first layer of grid ; Next, based on the dimensionless distance and the thermal diffusivity at a distance of 1 / 2 grid from the solid surface , calculate the corrected solid surface temperature ; and based on the solid surface temperature Solve for temperature LBE; Finally, the corrected solid surface temperature Restore to original solid boundary temperature ; Calculate the temperature value of each grid point inside the fluid , and calculate the buoyancy term according to the set external force model Solve for the fluid LBE.

[0053] At this point, one cycle ends and enters the next calculation cycle.

[0054] On the other hand, the present invention provides a simulation system for fluid-solid interface temperature based on the lattice Boltzmann method, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods.

[0055] In order to verify the effectiveness of the simulation, a validity verification was carried out. Figure 5 As shown in FIG. 1 , a schematic diagram of a non-isothermal natural convection cavity for LBM thermal fluid simulation provided by the present invention is shown in FIG. Figure 5 The natural convection in the non-isothermal closed square cavity shown is simulated by LBM.

[0056] The simulation object is a closed square cavity with a length L, width D, and height H of 1.0 m, 2.0 m, and 1.0 m respectively, and the interior of the square cavity is filled with still air.

[0057] The left side of the square cavity is a high-temperature solid surface, and the wall surface has a fixed temperature of 51.3°C; the right side of the square cavity is a low-temperature solid surface, and the wall surface has a fixed temperature of 0°C. The top and bottom of the square cavity are both solid surfaces, with a polynomial temperature distribution from 51.3°C to 0°C from left to right; the temperature distribution of the top of the square cavity is set according to the experimental fitting data: ; The temperature distribution at the bottom of the square cavity is set according to the experimental fitting data: The front wall and the rear wall of the square cavity are both adiabatic solid surfaces, that is, the heat passing through the wall is 0.

[0058] The simulation method uses LBM to simulate the spontaneous convection movement of air in a closed cavity under the action of temperature difference and gravity. The turbulence simulation method uses large eddy simulation (LES), and the sub-grid model uses the standard Smagorinsky model.

[0059] The obtained grid information and thermal fluid physical parameters are: grid length ; Dynamic viscosity of fluid molecules ; Fluid density ; Molecular Prandtl number ; Turbulent Prandtl number .

[0060] The temperature simulation results are as follows Figure 6As shown, the original LBM temperature simulation results, the LBM temperature simulation results of the present invention and the experimental values ​​are compared; (a) is the temperature distribution in the vertical direction at the center of the square cavity; (b) is the temperature distribution in the horizontal direction at the center of the square cavity; the results show that the original LBM temperature simulation results show a large difference from the experimental values, while the LBM temperature simulation results of the present invention are more consistent with the experimental values, and the simulation accuracy of the temperature is greatly improved, which proves the effectiveness of the simulation method of the present invention.

[0061] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, and according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0062] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed method or system can be implemented in other ways. For example, the embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0063] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application.

[0066] Those skilled in the art will appreciate that all or part of the various circuits in the above embodiments may be completed by entering a program to instruct related hardware, and the program may be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0067] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0068] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method, characterized in that: The method comprises: Determine the simulation area and turbulence simulation method, obtain grid information and thermal fluid physical parameters, use the lattice Boltzmann method to perform thermal fluid simulation, and obtain the grid point velocity and grid point turbulent dynamic viscosity of each grid point; The turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface is calculated based on the turbulent dynamic viscosity of the plurality of grid points; The thermal diffusion coefficient at a distance of 1 / 2 of the grid from the solid surface is calculated based on the turbulent dynamic viscosity; The dimensionless distance of the first layer of grid is calculated based on the turbulent dynamic viscosity, the thermal fluid physical parameters, the solid grid point velocity and the first grid point velocity; wherein the solid grid point velocity is the grid point velocity of the solid surface; and the first grid point velocity is the grid point velocity of the first layer of grid points adjacent to the solid surface; The solid surface temperature is obtained according to the dimensionless distance and the thermal diffusion coefficient correction; and the temperature lattice Boltzmann equation is solved based on the solid surface temperature to perform simulation.

2. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 1, characterized in that: The grid information includes: one or more of: grid shape, number of grids, number of fluid grid points, grid length, and grid coordinates.

3. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 1, characterized in that: The thermal fluid physical parameters include: fluid molecular dynamic viscosity, fluid density, fluid molecular Prandtl number, and fluid turbulence Prandtl number.

4. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 1, characterized in that: The solid surface temperature The calculation method is: ； in, represents the original solid boundary temperature calculated by the lattice Boltzmann method for thermal fluid simulation; represents the dimensionless distance; represents the thermal diffusion coefficient at a distance of 1 / 2 grid from the solid surface; represents the Prandtl number of fluid molecules; It represents the temperature of the first layer of grid points adjacent to the solid boundary and pointing to the interior of the fluid perpendicular to the solid boundary surface; Represents the dynamic viscosity of fluid molecules.

5. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 4, characterized in that: The dimensionless distance The calculation method is: ； in, It represents the dynamic viscosity of fluid molecules; represents the turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface; represents the velocity of the first grid point; represents the tangential velocity of the first grid point; represents the solid grid point velocity; represents the tangential velocity of the solid grid point; Represents the density of the fluid at the solid surface.

6. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 5, characterized in that: The turbulent dynamic viscosity at a distance of 1 / 2 grid from the solid surface is calculated by using the Lagrangian extrapolation method based on the grid point turbulent dynamic viscosity of N adjacent fluid grid points.

7. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 6, characterized in that: The turbulent dynamic viscosity The calculation method is: ； in, Indicates the number of fluid grid points; represents the grid point turbulent dynamic viscosity of the i-th grid point adjacent to the solid surface.

8. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 1 or 4, characterized in that: Thermal diffusivity at a distance of 1 / 2 grid from the solid surface The way to get is: ； in, It represents the dynamic viscosity of fluid molecules; represents the dimensionless distance; represents the fluid turbulence Prandtl number; represents the von Karman constant; represents the wall roughness constant; represents the Prandtl number of fluid molecules.

9. The method for simulating the temperature at the fluid-solid interface based on the lattice Boltzmann method according to claim 1, characterized in that: The method further comprises: Restore the corrected solid surface temperature to the original solid boundary temperature; The temperature value of each grid point inside the fluid is calculated, and the buoyancy term is calculated according to the set external force model, and the fluid lattice Boltzmann equation is solved to perform fluid simulation.

10. A system for simulating the temperature at a fluid-solid interface based on the lattice Boltzmann method, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes a computer program to implement the steps of the method according to any one of claims 1 to 9.

Images