Large-density-ratio two-phase boiling heat transfer simulation method based on lattice Boltzmann algorithm

Abstract

The invention discloses a large-density-ratio two-phase boiling heat transfer simulation method based on a lattice Boltzmann algorithm. The method comprises the following steps: solving a corresponding pseudo potential according to density and a corresponding non-ideal state equation, calculating various forces in a multiphase system, and calculating a source item in a gas-liquid phase change model; calculating relaxation factors corresponding to different moments in the multiphase flow field and the temperature field according to entropy stability conditions and specific attributes of fluid, and executing collision and migration according to an evolution equation obtained by calculation and substituted into the multiphase flow field and the temperature field; and calculating macroscopic physical quantities such as density, speed and temperature at each grid point according to the density and temperature distribution function, and judging whether iteration is ended or not. The two-phase density ratio in simulation of the gas-liquid boiling problem can be effectively improved, simulation of the phase change conversion process of water and water vapor under the real condition is achieved, and the simulation problem of complex multi-phase flow heat transfer is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of multiphase flow heat transfer analysis, and particularly relates to a simulation method for two-phase boiling heat transfer with a large density ratio based on the lattice Boltzmann algorithm. Background Art

[0002] Due to its extremely high heat exchange efficiency, boiling flow heat transfer has been widely applied in various heat dissipation systems and industrial fields, such as the cooling of nuclear power reactors, the cooling of chips in computer data centers, the cooling of avionics equipment, the cooling of satellite electronics, and the cooling of various other advanced electronic devices. However, in practical applications, there are still some limitations in boiling heat transfer. For example, once the transition from nucleate boiling to film boiling occurs, the boiling heat transfer efficiency will drop sharply, endangering the safe operation of equipment, and in severe cases, it will also cause huge economic losses and casualties. Therefore, it is of great significance to deeply explore the boiling heat transfer mechanism. However, due to processes such as the generation, growth, and coalescence of internal bubble nuclei, the movement and change of the multiphase interface are complex and diverse, and these processes usually occur within small time and space scales. Therefore, it is not an easy task to study such a complex multiphase flow heat transfer system. In addition, due to the complex and rich bubble dynamics behavior inside the liquid, it is still extremely difficult to observe the entire process of bubble growth and obtain detailed information on the multiphase flow field through experimental means. And it is also difficult to decouple and study different variables through experiments. Therefore, it is extremely necessary to develop a robust and accurate numerical simulation method for two-phase boiling heat transfer to capture such a complex multiphase flow heat transfer phenomenon.

[0003] Traditional macroscopic computational fluid dynamics methods need to rely on additional models and artificially arranged initial nucleation points to achieve the simulation of boiling heat transfer, so they cannot deeply explore the bubble nucleation mechanism. Different from traditional computational fluid dynamics methods, due to its mesoscopic physical basis, the lattice Boltzmann method can establish different models to simulate complex multiphase heat transfer phenomena that naturally combine microscale and macroscale physics, such as the breakup, coalescence, and large deformation of phase interfaces.

[0004] An invention with the publication number CN118673830A discloses a method for freely setting the initial nucleation sites in the simulation of the boiling process based on the pseudo-potential lattice Boltzmann model. The boiling simulation based on a flat and uniform wall surface includes the following steps: (1) Determine the characteristic lattice points and the corresponding wetting parameters within the wall lattice point range according to the number of nucleation points and the wetting properties of the nucleation points, and set the initial positions of the nucleation points through the extreme value positions of the wetting parameters; (2) Interpolate between the characteristic lattice points to obtain the parameters of other lattice points, thereby initializing and setting the wetting parameters of the wall lattice points, and keeping them unchanged within a certain initial time step; (3) Set that after a certain time step, update the wetting parameters of the wall lattice points according to the desired setting form and wetting property requirements. This invention does not rely on stimuli such as temperature and surface structure to activate the nucleation points, can set the number, distribution, and wetting properties of the nucleation points, and can conveniently control the wall wetting state in the desired format after nucleation.

[0005] However, limited by numerical stability, the current multiphase lattice Boltzmann method is still difficult to handle the simulation of two-phase boiling problems with a large density ratio. In recent years, many scholars have proposed methods such as expanding the calculation template of the interaction force and increasing the transition width of the interface to increase the density ratio in the simulation of gas-liquid phase change. However, the density ratios that these methods can simulate are still difficult to reach above 1000, which is much smaller than the density ratio of two-phase boiling with water as the medium under real conditions. Therefore, it is of great significance to further develop the multiphase lattice Boltzmann method so that it can simulate the boiling heat transfer phenomenon under real conditions, explore the influence laws of different factors such as surface wettability, surface structure, and surface materials on boiling heat transfer, reveal the boiling heat transfer mechanism, and further improve the application efficiency and scope of boiling heat transfer in various industrial fields. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for simulating two-phase boiling heat transfer with a large density ratio based on the lattice Boltzmann algorithm, introducing multiple entropy-stable operators to improve the computational stability of the multiphase flow field, and at the same time using the multi-relaxation format to solve the gas-liquid phase change model, effectively increasing the density ratio of the simulation of two-phase boiling problems, and realizing the simulation of the phase change process of water and water vapor under real conditions, thereby solving the simulation problem of complex multiphase flow heat transfer problems.

[0007] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for simulating two-phase boiling heat transfer with a large density ratio based on the lattice Boltzmann algorithm, the method includes the following steps:

[0009] S1, Solve the corresponding pseudo-potential ψ according to the density ρ and the corresponding non-ideal state equation, and calculate the inter-particle interaction force F in the multiphase system int and the gravity F g= ρg, and solve the source term φ in the gas-liquid phase change model according to the velocity and temperature of the flow field T ; Calculate the relaxation matrices S and S corresponding to different order moments in the multiphase flow field and temperature field according to the entropy stability condition and the properties of the fluid itself T ;

[0010] S2, and substitute the relaxation matrices S and S T 、the inter-particle interaction force F int 、the gravity F g = ρg and the source term φ T into the evolution equations of the density distribution function f i and the temperature distribution function g i respectively to perform collision and migration, and obtain the density distribution function and the temperature distribution function;

[0011] S3, calculate the macroscopic physical quantities including density, velocity and temperature at each grid point according to the density distribution function and the temperature distribution function, and judge whether to end the iteration.

[0012] Further, in step S1, solve the corresponding pseudo-potential ψ according to the density ρ and the corresponding non-ideal state equation:

[0013]

[0014] In the formula, c is the lattice velocity, P is the pressure obtained through the non-ideal state equation, is the lattice sound speed, ρ is the fluid density, x represents the current position, t represents the current time, and G is the interaction force strength.

[0015] Further, the non-ideal state equation includes the van der Waals equation, the Peng-Robinson equation, the Carnahan-Starling equation, and the piecewise linear equation.

[0016] Further, in step S1, calculate the inter-particle interaction force F using the following formula int :

[0017]

[0018] Among them, A is an adjustment parameter used to adjust the mechanical stability condition, G is the interaction force strength, x represents the current position, t represents the current time, e i represents the discrete velocity in the i direction, δt is the time step, is the weight coefficient, ψ(x,t) is the pseudo-potential, and the wall wettability can be realized by adjusting the false density of the boundary nodes.

[0019] Further, in step S1, calculate the source term φ using the following formula T:

[0020]

[0021] where λ is the thermal conductivity, c v is the specific heat capacity, k is the constant generated by the lattice Boltzmann, ρ is the fluid density, is the temperature gradient, is the partial derivative of the equation of state with respect to temperature under the constant density condition, P EOS is the pressure obtained through the non - ideal equation of state, and U is the velocity.

[0022] Furthermore, in step S1, the relaxation matrix of the flow field is expressed as follows:

[0023] S = diag{0,1,1,1,s v ,s v ,s v ,λ0s v ,s v ,s v ,λ1s v ,λ1s v ,λ1s v ,λ1s v ,λ1s v ,λ1s v ,λ2s v ,λ2s v ,λ2s v}

[0024] where s v is the relaxation factor corresponding to the viscosity coefficient v, and λ0, λ1, λ2 are the corresponding entropy - stable operators respectively; the specific formulas of the adopted entropy - stable operators are as follows:

[0025]

[0026] where C -1 is the inverse of the correlation matrix , the relaxation parameter s v is related to the viscosity coefficient v, and their relationship is Ts is the shear part of the density distribution function and h n is the high - order moment part; m, n are integers, and their value ranges are both 0, 1, 2, corresponding to the parts corresponding to the second - order high - order moment, the third - order high - order moment, and the fourth - order high - order moment respectively; and are the deviations of the shear and high - order moments from their equilibrium states respectively, the superscript eq represents the equilibrium - state function, and i represents the i - direction;

[0027] The relaxation matrix of the temperature field is expressed as follows:

[0028] S T = diag{1, 1, 1, 1.25, 1, 1.25, 1, 1.25, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}。

[0029] Further, in step S2, the evolution equations of the density distribution function f i and the temperature distribution function g i are respectively expressed as:

[0030]

[0031]

[0032] where f i and are respectively the density distribution function and the equilibrium distribution function, g j and are respectively the temperature distribution function and the equilibrium distribution function, S and S T are respectively the relaxation matrices of the flow field and temperature field evolution equations, M and N are transformation matrices related to the lattice template, e i is the discrete velocity in the i direction, I is the identity matrix, R i is the corresponding force term, M T is the transformation matrix of the temperature field evolution equation.

[0033] Further, in step S3, calculate the flow field density ρ, velocity U, and temperature T at each grid point according to the density distribution function and the temperature distribution function:

[0034]

[0035] where ρ is the density, f i and g i respectively represent the density and temperature distribution functions, i is an integer, i = 0, 1, …, q - 1, q is the number of discrete velocities, e i represents the discrete velocity in the i direction, U is the velocity, F is the sum of the forces received, including the interaction force F int between densities and the gravity F g = ρg; g is the gravitational acceleration.

[0036] Further, in step S3, determine whether the density change of the flow field is less than 10 -6 or whether the preset maximum number of calculation steps is reached. If so, stop the iteration; otherwise, return to step S1 for the next iteration.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] The large density ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm of the present invention introduces multiple entropy stable operators and the parameter k in the source term, effectively improving the stability of numerical calculations, realizing the simulation of the large density ratio two-phase boiling heat transfer problem, and further expanding the simulation ability of the lattice Boltzmann method for the complex multiphase flow and heat transfer problems in practical engineering. Description of the Drawings

[0039] Figure 1 Schematic diagram of the thermodynamic consistency verification result provided by the embodiment of the present invention;

[0040] Figure 2 Schematic diagrams of the formation, breakup, and separation of a single bubble under different gravity conditions provided by the embodiment of the present invention; where (a) corresponds to microgravity and (b) corresponds to hypergravity;

[0041] Figure 3 Schematic diagram of the variation result of the separation diameter of the bubble with gravity provided by the embodiment of the present invention;

[0042] Figure 4 Schematic diagrams of the formation, breakup, and detachment processes of a single bubble under the same gravity and different density ratios provided by the embodiment of the present invention; where (a) corresponds to a low density ratio and (b) corresponds to a high density ratio;

[0043] Figure 5 Flow chart of the large density ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm of the present invention. Detailed Embodiment

[0044] The embodiments of the present invention will be further described in detail below with reference to the drawings.

[0045] As Figure 5 shown, the present invention discloses a large density ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm, and the method includes the following steps:

[0046] S1, solving the corresponding pseudo-potential ψ according to the density ρ and the corresponding non-ideal state equation, calculating the inter-particle interaction force F in the multiphase system int and the gravity F g = ρg, and solving the source term φ in the gas-liquid phase change model according to the velocity and temperature of the flow field T ; calculating the relaxation matrices S and S corresponding to different order moments in the multiphase flow field and temperature field according to the entropy stability condition and the properties of the fluid itself T .

[0047] In the present invention, the corresponding pseudo-potential obtained by solving according to the density ρ and the state equation is:

[0048]

[0049] In the formula, G is the acting force intensity, which can take the value of -1 when using the non-ideal equation of state to ensure that the term inside the square root is greater than 0; c is the lattice velocity, P is the pressure which can be obtained from the non-ideal equation of state. Commonly used equations of state include the van der Waals equation, the Peng-Robinson equation, the Carnahan-Starling equation, and the piecewise linear equation, etc. In the simulation of two-phase boiling with water as the medium, the Peng-Robinson equation is usually adopted. The non-ideal equation of state, the Peng-Robinson equation, can be expressed as:

[0050]

[0051] Where For water, ω = 0.344, P c is the critical pressure, T c is the critical temperature.

[0052] The inter-particle interaction force at the grid points can be solved by the following formula:

[0053]

[0054] Where A is the adjustment parameter used to adjust the mechanical stability condition to achieve two-phase simulation with a large density ratio, G is the interaction force intensity, x represents the current position, t represents the current time, e i represents the discrete velocity in the i direction, δt is the time step, is the weight coefficient. On the D3Q19 lattice, its values are w(1) = 1 / 6, w(2) = 1 / 12, and ψ(x, t) is the pseudo-potential. The source term φ T can be calculated by the following formula:

[0055]

[0056] Where λ is the thermal conductivity, c v is the specific heat capacity, k is a constant generated by the lattice Boltzmann method and has no specific physical meaning, and can be fixed at 0.1.

[0057] When there is a solid surface in the simulation and the wettability of the surface needs to be considered, a false density can be set for the solid boundary grid points to adjust the surface wettability:

[0058]

[0059] Gravity can be expressed as: F g = ρg.

[0060] To solve the evolution equations of the density distribution function and the temperature distribution function, it is also necessary to determine the relaxation factors. In the present invention, different relaxation factors are determined according to the entropy stability condition and the fluid properties. Through the entropy stability condition, the magnitude of the entropy stable operator can be first calculated.

[0061] The evolution equation of the density distribution function can be expressed as:

[0062]

[0063] The evolution equation of the temperature distribution function can be expressed as:

[0064]

[0065] where f i and are the density distribution function and the equilibrium distribution function respectively, g i and are the temperature distribution function and the equilibrium distribution function respectively, S and S T are the relaxation matrices of the evolution equations of the flow field and the temperature field respectively, M and N are the transformation matrices related to the lattice template, e i is the particle velocity, I is the identity matrix, R i is the corresponding force term, M T is the transformation matrix of the evolution equation of the temperature field. In the present invention, the enhanced central moment format is adopted for the evolution equation of the flow field, and the multi-relaxation format is adopted for the evolution equation of the temperature field.

[0066] Taking the D3Q19 template as an example for illustration. The discrete velocities in the D3Q19 template can be expressed as:

[0067]

[0068] The central moments can be selected as

[0069] where is an integer.

[0070] The shear part and the high-order moment part in these moments are selected as:

[0071]

[0072]

[0073] The force term can be expressed as:

[0074]

[0075] According to the entropy stability condition, it can be obtained that:

[0076]

[0077] Among them C -1 is the correlation matrix The inverse, Ts is the corresponding shear part of the density distribution function and h n is the high-order moment part, where n=0, 1, 2, corresponding to the second-order moment, third-order moment, and fourth-order moment, respectively. and are the deviations of the corresponding shear part and the high-order moment part from their equilibrium state, respectively, and the superscript eq represents the equilibrium state function. λ0,λ1,λ2 are the entropy stabilizing operators corresponding to the second-order, third-order and fourth-order moments, respectively. When the parameter k in the source term of the gas-liquid phase change model is 0.1, the relaxation matrix of the flow field and temperature field can be determined as follows:

[0078] S = diag{0,1,1,1,s v ,s v ,s v ,λ0s v ,s v ,s v ,λ1s v ,λ1s v ,λ1s v ,λ1s v ,λ1s v ,λ1s v ,λ2s v ,λ2s v ,λ2s v}

[0079] S2=diag{1,1,1,1.25,1,1.25,1,1.25,1,1,1,1,1,1,1,1,1,1,1}

[0080] where s v is the relaxation factor corresponding to the viscosity coefficient v, and the specific relationship is v = 3 (1 / s v -0.5), λ0,λ1,λ2 are the corresponding entropy stabilizing operators. The selection of the shearing part and various high-order terms can be changed. The selection method shown here is used for subsequent result calculations. This example is based on the D3Q19 template. This method is also applicable to two-dimensional templates based on D2Q9 and three-dimensional templates such as D3Q15 and D3Q27.

[0081] The relaxation matrices S and S T , the interaction force between particles F int , gravity F g =ρg and source term φ T Substitute into the density distribution function f iand the temperature distribution function g i Perform collisions and migrations in the evolution equations of i to obtain the density distribution function and the temperature distribution function.

[0082] Use the entropy-stable operator calculated in step S1 to determine the relaxation factor, that is, the relaxation factor corresponding to the corresponding moment can be obtained as the corresponding entropy-stable operator multiplied by the relaxation factor of the moment corresponding to the viscous term in the corresponding N - S equation. Use the relaxation matrices S and S T (both are 2D diagonal matrices of q×q), and substitute them and the various forces calculated in step S1 into the density distribution function f i Substitute the source term calculated in step S1 into the evolution equation of the temperature distribution function g i in the evolution equation, and perform collisions and migrations; where the relaxation parameter s v is related to the viscosity coefficient v, and their relationship is

[0083] S3. Calculate the macroscopic physical quantities including density, velocity, and temperature at each grid point according to the density distribution function and the temperature distribution function, and determine whether to end the iteration.

[0084] Calculate the density ρ and velocity U of the fluid according to the density distribution function obtained in step S2. The calculation formulas are as follows: ρ = ∑ i f i , ρU = ∑ i f i e i +F / 2. Calculate the temperature T of the fluid according to the temperature distribution function obtained in step S2: T = ∑ i g i .

[0085] Judge whether the density change of the flow field is less than 10 -6 or whether the maximum number of calculation steps to be calculated is reached. If the judgment condition is met, the program stops iterating; otherwise, the program enters the next iteration and repeats the above steps until the iteration convergence condition is met.

[0086] The following gives a specific application example as a specific embodiment of the method disclosed in the present invention.

[0087] Embodiment 1

[0088] Verification of thermodynamic consistency. On a high-temperature surface, the bubble dynamics process inside the liquid will enhance the heat convection effect, strengthen heat transport, and the boiling heat transfer phenomenon widely exists and plays an important role in the fields of energy and chemical engineering, aerospace, etc., and has an important impact on the operation safety and performance of equipment. Thermodynamic consistency is the premise for accurately simulating the boiling heat transfer problem. Figure 1Shows the verification of thermodynamic consistency. The size of the computational domain is set to 201×201×21, and the boundary is set to a periodic boundary. The initial density field is set to where W = 5δx. Calculate the coexistence density at different temperatures and compare it with the Maxwell distribution. As Figure 1 shown, when the parameter A in the inter-particle interaction force is taken as -0.9185, the numerical simulation results (LBM simulation results) are in good agreement with the Maxwell distribution (Maxwell construction), verifying the thermodynamic consistency of the model.

[0089] Example 2

[0090] The formation, breakup, and separation of a single bubble under different gravity conditions. With the development of technology, the operating environment of equipment has become more complex and changeable. For example, spacecraft will experience various gravity environments during operation, such as hypergravity during launch and orbit transfer, and microgravity during orbital operation. Studying flow boiling under different gravities can provide theoretical support for the thermal management system of spacecraft and ensure its efficient operation in complex environments. Simulating the complete process of bubble nucleation, growth, breakup, and separation is of great significance for deepening the understanding of such a complex multi-phase heat transfer phenomenon and can provide important theoretical guidance and new design ideas for improving the operating efficiency and safety of equipment. In the simulation, the size of the computational domain is set to 81×81×241, the upper and lower boundaries are set to no-slip boundaries, and the four sides are set to symmetric boundaries. To trigger bubble nucleation, a micro-heater with a size of 30×30×15 is set at the bottom center. The upper surface temperature of the micro-heater is set to 1.35T c , and the remaining fluid-solid interfaces are set to adiabatic boundaries, and the contact angle is set to 102.5°. At the initial moment, the lower three-quarters of the computational domain is filled with liquid, and the rest is gas. The liquid density is set to 8.85, the gas density is set to 0.00623, the viscosity coefficient of the liquid is 0.030975, the viscosity coefficient of the gas is 0.00135, the two-phase Prandtl numbers are 1.768 and 1.0 respectively, and the specific heat capacities are 4.2 and 2.1 respectively. The above settings correspond to the saturated water and steam two-phase system at 100 °C under standard atmospheric pressure. Figure 2 The dimensionless time in is defined as Figure 2It can be found that as the gravity increases, the time of bubble deviation occurs earlier and the deviation diameter becomes larger. The current model provides a powerful tool for exploring flow boiling in microgravity or hypergravity environments, which is of great significance for deeply understanding the physical mechanism of boiling heat transfer, promoting the development of aerospace and deep space exploration technologies, and optimizing ground industrial applications.

[0091] Example 3

[0092] The variation of the separation diameter of bubbles with gravity. In previous studies, many scholars have tried to relate the macroscopic characteristics of boiling (such as the bubble departure diameter) to different factors. The most common one is the power-law relationship between the bubble departure diameter and gravity, where the bubble departure diameter is proportional to g -0.5 To quantitatively verify the current model, the settings in Example 2 were adopted to simulate the breakup and departure process of bubbles under different gravity conditions and to statistically analyze the variation of the bubble departure diameter with gravity. As Figure 3 shown, the solid line represents the fitting curve D = 0.191g -0.5 . It can be found that the predicted values of the bubble departure diameter by the current model are in good agreement with the fitting curve, and are consistent with the classical power-law relationship, verifying the accuracy of the model in simulating heat transfer in two-phase boiling flows with a large density ratio and proving the feasibility of the current method for studying the mechanism of boiling heat transfer.

[0093] Example 4

[0094] The formation, breakup, and departure processes of a single bubble under the same gravity but different density ratios. Different density ratios will cause significant changes in the nucleation motion of bubbles. Keeping the liquid-vapor two-phase density ratio consistent with the real water-steam system is the key to realizing the simulation of flow boiling under real conditions. Figure 4 Shows the formation, breakup, and departure processes of a single bubble under different density ratios. The low-density ratio case corresponds to a saturation temperature of T s = 0.86T c , and the two-phase densities are 6.5 and 0.38 respectively. To describe the surface superheat, a dimensionless parameter Ja = c v (T w -T s ) / h fg is introduced. To reduce the influence of the contact angle, the contact angle is set to 90°. The remaining settings are the same as above. When Ja = 0.4156, the entire growth and departure process of the bubbles is extremely similar. However, the difference is that the growth process of the vapor bubbles at low density ratios is faster and the departure diameter is also larger.

[0095] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions run by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are run on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions running on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0099] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0100] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to cover these changes and modifications.

Claims

1. A simulation method for two-phase boiling heat transfer with a large density ratio based on the lattice Boltzmann algorithm, characterized in that The said method comprises the following steps: S1. Solve the corresponding pseudo-potential ψ according to the density ρ and the corresponding non-ideal state equation, and calculate the inter-particle interaction force F in the multiphase system int and the gravity F g = ρg, and solve the source term φ in the gas-liquid phase change model according to the velocity and temperature of the flow field T ; Calculate the relaxation matrices S and S corresponding to different orders of moments in the multiphase flow field and temperature field according to the entropy stability condition and the properties of the fluid itself T ; S2, substitute the relaxation matrix S and S T , the inter-particle interaction force F int , the gravity F g = ρg and the source term φ T into the evolution equations of the density distribution function f i and the temperature distribution function g i respectively to perform collision and migration, and obtain the density distribution function and the temperature distribution function; S3. Calculate the macroscopic physical quantities including density, velocity and temperature at each grid point according to the density distribution function and the temperature distribution function, and determine whether to end the iteration.

2. The method for simulating large density ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm according to claim 1, characterized in that, In step S1, solve the corresponding pseudo-potential ψ according to the density ρ and the corresponding non-ideal state equation: where c is the lattice velocity, P is the pressure obtained from the non-ideal equation of state, is the lattice sound speed, ρ is the fluid density, x represents the current position, t represents the current time, and G is the strength of the interaction force.

3. The method for simulating large density ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm according to claim 1 or 2, characterized in that The said non-ideal state equation includes the van der Waals equation, the Peng-Robinson equation, the Carnahan-Starling equation and the piecewise linear equation.

4. The method for simulating large density ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm according to claim 1, characterized in that, In step S1, the following formula is used to calculate the inter-particle interaction force F int : Among them, A is an adjustment parameter used to adjust the mechanical stability condition, G is the strength of the interaction force, x represents the current position, t represents the current time, and e i represents the discrete velocity in the i direction, δt is the time step, is the weight coefficient, and ψ(x, t) is the pseudo-potential.

5. The large density ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm according to claim 1, characterized in that In step S1, the source term φ is calculated using the following formula T : where λ is the thermal conductivity, c v is the specific heat capacity, k is the constant generated by the lattice Boltzmann method, ρ is the fluid density, is the gradient of temperature, is the partial derivative of the equation of state with respect to temperature under the condition of constant density, P EOS is the pressure obtained by the non-ideal equation of state, and U is the velocity.

6. The method for simulating large density ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm according to claim 1, characterized in that In step S1, the relaxation matrix of the flow field is expressed as follows: S = diag{0, 1, 1, 1, s v , s v , s v , λ0s v , s v , s v , λ1s v , λ1s v , λ1s v , λ1s v , λ1s v , λ1s v , λ2s v , λ2s v , λ2s v} where s v is the relaxation factor corresponding to the viscosity coefficient v, and λ0, λ1, and λ2 are the corresponding entropy stability operators respectively; the specific formulas of the adopted entropy stability operators are as follows: where C -1 is the inverse of the correlation matrix , the relaxation parameter s v is related to the viscosity coefficient v, and the relationship between them is Ts is the shear part of the density distribution function and h n is the high-order moment part; m and n are integers, and their value ranges are both 0, 1, 2, corresponding to the parts corresponding to the second-order high-order moment, the third-order high-order moment, and the fourth-order high-order moment respectively; and are the deviations of the shear and the high-order moment from their equilibrium states respectively. The superscript eq represents the equilibrium state function, and i represents the i direction; The relaxation matrix of the temperature field is expressed as follows: S T = diag{1, 1, 1, 1.25, 1, 1.25, 1, 1.25, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}。 7. The method for simulating large density ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm according to claim 1, wherein In step S2, the evolution equations of the density distribution function f i and the temperature distribution function g i are respectively expressed as: where f i and are the density distribution function and the equilibrium distribution function respectively, g j and are the temperature distribution function and the equilibrium distribution function respectively, S and S T are the relaxation matrices of the evolution equations of the flow field and the temperature field respectively, M and N are the transformation matrices related to the lattice template, e i is the discrete velocity in the i direction, I is the identity matrix, R i is the corresponding force term, M T is the transformation matrix of the temperature field evolution equation.

8. The simulation method for two-phase boiling heat transfer with a large density ratio based on the lattice Boltzmann algorithm according to claim 1, characterized in that In step S3, calculate the flow field density ρ, velocity U and temperature T at each grid point according to the density distribution function and the temperature distribution function. where ρ is the density, f i and g i represent the density and temperature distribution functions respectively, i is an integer, i = 0, 1, …, q - 1, q is the number of discrete velocities, e i represents the discrete velocity in the i - direction, U is the velocity, F is the sum of the forces received, including the interaction force F int between densities and the gravity F g = ρg; g is the gravitational acceleration.

9. The method for simulating two-phase boiling heat transfer with a large density ratio based on the lattice Boltzmann algorithm according to claim 1, wherein In step S3, it is determined whether the density change of the flow field is less than 10 -6 or whether the preset maximum number of calculation steps is reached. If so, the iteration is stopped; otherwise, return to step S1 to enter the next iteration.

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