A large-density-ratio two-phase boiling heat transfer simulation method based on lattice boltzmann algorithm

Abstract

The application discloses a large-density-ratio two-phase boiling heat transfer simulation method based on a lattice Boltzmann algorithm, and comprises the following steps: solving corresponding pseudo-potentials according to densities and corresponding non-ideal state equations, calculating various forces in a multiphase system, and calculating source terms in a gas-liquid phase change model; calculating relaxation factors corresponding to different orders of moments in a multiphase flow field and a temperature field according to entropy stability conditions and specific properties of the fluid; substituting the calculated results into evolution equations of the multiphase flow field and the temperature field, and performing collision and migration according to the evolution equations; and calculating macro-physical quantities such as densities, velocities and temperatures at each lattice point according to density and temperature distribution functions, and judging whether iteration is ended or not. The application can effectively improve the two-phase density ratio in the simulation of gas-liquid boiling problems, realize the simulation of the phase change conversion process of water and water vapor under real conditions, and solve the simulation problem of complex multiphase flow and heat transfer.

Description

Technical Field

[0001] This invention relates to the field of multiphase flow heat transfer analysis technology, and specifically to a high-density-ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm. Background Technology

[0002] Due to its extremely high heat exchange efficiency, boiling flow heat transfer has been widely used in various heat dissipation systems and industrial fields, such as nuclear power reactor cooling, chip cooling in computer data centers, avionics cooling, satellite electronics cooling, and cooling of various other advanced electronic devices. However, in practical applications, boiling heat transfer still has some limitations. For example, once the transition from nucleus boiling to film boiling occurs, the boiling heat transfer efficiency drops sharply, endangering equipment operation safety and, in severe cases, causing huge economic losses and casualties. Therefore, in-depth research into the boiling heat transfer mechanism is of great significance. However, due to the complex and diverse processes of bubble nucleation, growth, and merging, the motion changes of the multiphase interface are complex and diverse, and these processes usually occur within a relatively small time and space scale. Therefore, studying such a complex multiphase flow heat transfer system is not an easy task. In addition, due to the complex and rich dynamic behavior of bubbles inside the liquid, it remains extremely difficult to observe the entire process of bubble growth and expansion and obtain detailed information about the multiphase flow field through experimental means. Furthermore, it is difficult to decouple different variables in experiments. Therefore, it is essential 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 (CFD) methods require additional models and artificially arranged initial nucleation points to simulate boiling heat transfer, thus failing to delve into the bubble nucleation mechanism. In contrast, based on mesoscopic physics, the lattice Boltzmann method can establish different models to simulate complex multiphase heat transfer phenomena that naturally combine microscopic and macroscopic physics, such as phase interface breakage, merging, and large deformation.

[0004] The invention disclosed in CN118673830A is a method for freely setting initial nucleation points based on a pseudo-potential lattice Boltzmann model to simulate boiling processes. Based on boiling simulation of a flat, uniform wall surface, the method includes the following steps: (1) determining characteristic grid points and corresponding wetting parameters within the wall grid point range according to the number and wetting properties of the nucleation points, and setting the initial position of the nucleation points through the extreme points of the wetting parameters; (2) obtaining the parameters of other grid points by interpolation between the characteristic grid points, thereby initializing the wetting parameters of the wall grid points, and keeping them unchanged for a certain initial time step; (3) updating the wetting parameters of the wall grid points according to the desired setting form and wetting requirements after a certain time step. This invention does not rely on stimulation from factors such as temperature and surface structure to activate nucleation points, and allows setting the number, distribution, and wetting properties of nucleation points. After nucleation, the wall wetting state can be easily controlled according to the desired format.

[0005] However, limited by numerical stability, current multiphase lattice Boltzmann methods still struggle to simulate two-phase boiling problems with high density ratios. In recent years, many scholars have proposed methods such as expanding the computational template for interaction forces and increasing the transition width of the interface to increase the density ratio in gas-liquid phase transition simulations. However, the density ratios these methods can simulate are still difficult to reach above 1000, far lower than the density ratio of two-phase boiling with water as the medium under real-world conditions. Therefore, further developing the multiphase lattice Boltzmann method to simulate boiling heat transfer phenomena under real-world conditions, exploring the influence of different factors such as surface wettability, surface structure, and surface materials on boiling heat transfer, and revealing the boiling heat transfer mechanism are of great significance for further improving the application efficiency and applicability of boiling heat transfer in various practical industrial fields. Summary of the Invention

[0006] The purpose of this invention is to provide a high-density-ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm. It introduces multiple entropy stabilization operators to improve the computational stability of the multiphase flow field, and adopts a multi-relaxation scheme to solve the gas-liquid phase change model, which effectively improves the density ratio of the two-phase boiling problem simulation and realizes the simulation of the phase change transformation 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-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

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

[0009] S1, solve for the corresponding pseudopotential ψ based on the density ρ and the corresponding nonideal equation of state, and calculate the interparticle interaction force F in the multiphase system. int and gravity F g=ρg, and solve for the source term φ in the gas-liquid phase change model based on the velocity and temperature of the flow field. T Based on the entropy stability condition and the fluid's own properties, the relaxation matrices S and S' corresponding to different moments in the multiphase flow field and temperature field are calculated. T ;

[0010] S2, the relaxation matrices S and S T Interparticle interaction force F int Gravity F g =ρg and source term φ T Substituting each into the density distribution function f i and temperature distribution function g i Collisions and migrations are performed within the evolution equations to obtain the density distribution function and temperature distribution function;

[0011] S3 calculates the macroscopic physical quantities, including density, velocity, and temperature, at each grid point based on the density distribution function and temperature distribution function, and determines whether to end the iteration.

[0012] Further, in step S1, the corresponding pseudopotential ψ is solved based on the density ρ and the corresponding nonideal state equation:

[0013]

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

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

[0016] Further, in step S1, the interparticle interaction force F is calculated using the following formula. int :

[0017]

[0018] Where A is an adjustment parameter used to adjust the mechanical stability conditions, G is the interaction force intensity, x represents the current position, t represents the current time, and e i Let δt represent the discrete velocity along the i-th direction, and δt be the time step. Here, ψ(x,t) represents the weighting coefficients, and ψ(x,t) represents the pseudopotential. Wall wettability can be achieved by adjusting the pseudo density of the boundary nodes.

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

[0020]

[0021] Where λ is the thermal conductivity, and c v ρ is the specific heat capacity, k is the constant of the lattice Boltzmann production, and ρ is the fluid density. For the temperature gradient, For the partial derivative of the equation of state with respect to temperature under constant density conditions, P EOS U represents the pressure obtained through the non-ideal state equations, and U represents 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 Let λ0, λ1, and λ2 be the relaxation factors corresponding to the viscosity coefficient v, and let λ0, λ1, and λ2 be the corresponding entropy stabilization operators. The specific formulas for the entropy stabilization operators used are as follows:

[0025]

[0026] Where C -1 It is a correlation matrix The reverse, relaxation parameter s v It is related to the viscosity coefficient v, and the relationship between the two is as follows: Ts is the sheared part of the density distribution function and h n For the higher-order moments; m and n are integers, both ranging from 0, 1, to 2, corresponding to the parts corresponding to the second, third, and fourth higher-order moments, respectively; and These are the deviations of the shear and higher-order moments from their equilibrium states, respectively. The superscript eq represents the equilibrium function, and i indicates the i-direction.

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

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

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

[0030]

[0031]

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

[0033] Further, in step S3, the flow field density ρ, velocity U, and temperature T at each grid point are calculated based on the density distribution function and the temperature distribution function:

[0034]

[0035] Where ρ is density, f i and g i Let i and q represent the density and temperature distribution functions, respectively, where i is an integer, i = 0, 1, ..., q-1, q is the number of discrete velocities, and e is the density and temperature distribution functions, respectively. i Let U represent the discrete velocity along the i-th direction, where U is the velocity and F is the sum of the forces acting on the vector, including the inter-density interaction force F. int and gravity F g =ρg; g is the acceleration due to gravity.

[0036] Furthermore, in step S3, it is determined whether the density change of the flow field is less than 10. -6 Alternatively, if the preset maximum number of calculation steps has been reached, the iteration will stop; otherwise, return to step S1 to proceed to the next iteration.

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

[0038] The present invention provides a high-density-ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm. By introducing multiple entropy stabilization operators and the parameter k in the source term, the stability of numerical calculation is effectively improved, and the simulation of high-density-ratio two-phase boiling heat transfer problem is realized. This further expands the simulation capability of the lattice Boltzmann method for complex multiphase flow heat transfer problems in practical engineering. Attached Figure Description

[0039] Figure 1 A schematic diagram of the thermodynamic consistency verification results provided in an embodiment of the present invention;

[0040] Figure 2 The diagram illustrates the formation, breakup, and separation of a single bubble under different gravity conditions, as provided in the embodiments of the present invention; wherein (a) corresponds to microgravity and (b) corresponds to hypergravity.

[0041] Figure 3 A schematic diagram illustrating the variation of bubble separation diameter with gravity according to an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram illustrating the formation, breakup, and detachment of a single bubble under the same gravity but different density ratios, provided for an embodiment of the present invention; wherein (a) corresponds to a low density ratio and (b) corresponds to a high density ratio.

[0043] Figure 5 This is a flowchart of the high-density-ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm of the present invention. Detailed Implementation

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

[0045] like Figure 5 As shown, this invention discloses a method for simulating high-density-ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm. The method includes the following steps:

[0046] S1, solve for the corresponding pseudopotential ψ based on the density ρ and the corresponding nonideal equation of state, and calculate the interparticle interaction force F in the multiphase system. int and gravity F g =ρg, and solve for the source term φ in the gas-liquid phase change model based on the velocity and temperature of the flow field. T Based on the entropy stability condition and the fluid's own properties, the relaxation matrices S and S' corresponding to different moments in the multiphase flow field and temperature field are calculated. T .

[0047] In this invention, the corresponding pseudopotential obtained by solving the density ρ and the corresponding equation of state is:

[0048]

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

[0050]

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

[0052] The interparticle interaction forces at grid points can be solved using the following formula:

[0053]

[0054] Where A is an adjustment parameter used to adjust the mechanical stability conditions to achieve high density ratio two-phase simulation, G is the interaction force intensity, x represents the current position, t represents the current time, and e is the current position. i Let δt represent the discrete velocity along the i-th direction, and δt be the time step. The weighting coefficients have values ​​of w(1) = 1 / 6 and w(2) = 1 / 12 on the D3Q19 lattice, and ψ(x,t) is a pseudopotential. The source term φ T It can be calculated using the following formula:

[0055]

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

[0057] When a solid surface exists in the simulation and its wettability needs to be considered, a dummy 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 factor. In this invention, different relaxation factors are determined based on the entropy stability condition and fluid properties. The magnitude of the entropy stability operator can be calculated first using the entropy stability condition.

[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 These are the density distribution function and the equilibrium distribution function, respectively, g i and These are the temperature distribution function and the equilibrium distribution function, S and S, respectively. T These are the relaxation matrices for the flow field and temperature field evolution equations, respectively; M and N are the transformation matrices related to the lattice template; e i Let I be the particle velocity, and R be the identity matrix. i For the corresponding force term, M T This is the transformation matrix for the temperature field evolution equation. In this invention, the flow field evolution equation uses the enhanced central moment scheme, while the temperature field evolution equation uses the multi-relaxation scheme.

[0066] This will be illustrated using the D3Q19 template as an example. The discrete velocity in the D3Q19 template can be expressed as:

[0067]

[0068] The central moment can be selected as

[0069] in It is an integer.

[0070] The shear portion and higher-order moments in these moments are selected as follows:

[0071]

[0072]

[0073] The force term can be expressed as:

[0074]

[0075] According to the entropy stability condition, we can obtain:

[0076]

[0077] Where C -1 It is a correlation matrix The reverse, Ts is the corresponding sheared part of the density distribution function, while h n For the higher-order moments, n = 0, 1, 2, which correspond to the second, third, and fourth moments, respectively. and λ0, λ1, and λ2 represent the deviations from their equilibrium states for the corresponding shear components and higher-order moments, respectively, with the superscript eq indicating the equilibrium function. λ0, λ1, and λ2 are the entropy stabilization operators corresponding to the second, third, and fourth moments, respectively. When the parameter k = 0.1 in the source term of the gas-liquid phase change model, the relaxation matrices of the flow and temperature fields 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 The relaxation factor corresponding to the viscosity coefficient v is given by the relationship v = 3(1 / s). v The relationship is -0.5), where λ0, λ1, and λ2 are the corresponding entropy stabilizing operators. The selection of the shearing part and various higher-order terms can vary; the selection method shown here is used for subsequent result calculations. This example uses the D3Q19 template for expansion, and this method is also applicable to two-dimensional templates based on D2Q9 and three-dimensional templates such as D3Q15 and D3Q27.

[0081] Let the relaxation matrices S and S T Interparticle interaction force F int Gravity F g =ρg and source term φ T Substituting each into the density distribution function f iand temperature distribution function g i Collisions and migrations are performed within the evolution equations to obtain the density distribution function and temperature distribution function.

[0082] The relaxation factor is determined using the entropy stabilizing operator calculated in step S1. Specifically, the relaxation factor corresponding to the corresponding moment can be obtained by multiplying the corresponding entropy stabilizing operator by the relaxation factor corresponding to the moment corresponding to the viscous term in the Navier-Stokes equation. The relaxation matrices S and S' are then calculated using step S1. T (All are q×q two-dimensional diagonal matrices), and substitute them and the forces calculated in step S1 into the density distribution function f respectively. i The evolution equation and the source term calculated by step S1 are substituted into the temperature distribution function g. i In the evolution equation, collisions and migrations are performed; where the relaxation parameter s v It is related to the viscosity coefficient v, and the relationship between the two is as follows:

[0083] S3 calculates the macroscopic physical quantities, including density, velocity, and temperature, at each grid point based on the density distribution function and temperature distribution function, and determines whether to end the iteration.

[0084] The fluid density ρ and velocity U are calculated based on the density distribution function obtained in step S2. The calculation formula is as follows: ρ=∑ i f i ,ρU=∑ i f i e i +F / 2, calculate the fluid temperature T = ∑ based on the temperature distribution function obtained in step S2. i g i .

[0085] Determine if the density change of the flow field is less than 10. -6 Alternatively, if the maximum number of computation steps is reached, the program stops iterating if the condition is met; otherwise, the program proceeds to the next iteration and repeats the above steps until the iteration convergence condition is met.

[0086] The following specific application examples serve as concrete embodiments of the method disclosed in this invention.

[0087] Example 1

[0088] Thermodynamic consistency verification. On high-temperature surfaces, the bubble dynamics within a liquid enhance thermal convection and intensify heat transport. Boiling heat transfer is widespread and plays a crucial role in energy, chemical, and aerospace industries, significantly impacting equipment safety and performance. Thermodynamic consistency is a prerequisite for accurately simulating boiling heat transfer problems. Figure 1Thermodynamic consistency verification was demonstrated. The computational domain size was set to 201×201×21, and the boundary was set to a periodic boundary. The initial density field was set to... Where W = 5δx. Calculate the coexistence density at different temperatures and compare it with the Maxwell distribution. Figure 1 As shown, when the parameter A in the interparticle interaction force is taken as -0.9185, the numerical simulation results (LBM simulation results) agree well with the Maxwell construction, verifying the thermodynamic consistency of the model.

[0089] Example 2

[0090] The formation, breakup, and separation of individual bubbles under different gravity conditions. With the development of technology, the operating environment of equipment has become more complex and variable. For example, spacecraft experience various gravity environments during operation, such as hypergravity during launch and orbit changes, and microgravity during orbital operation. Studying flow boiling under different gravity conditions can provide theoretical support for the thermal management system of spacecraft, ensuring 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 multiphase 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 computational domain size was set to 81×81×241, the upper and lower boundaries were set to no-slip boundaries, and the perimeter was set to symmetrical boundaries. To trigger bubble nucleation, a microheater with a size of 30×30×15 was placed at the bottom center. The upper surface temperature of the microheater was set to 1.35T. c The remaining fluid-solid interfaces were set as adiabatic boundaries with a contact angle of 102.5°. Initially, the lower three-quarters of the computational domain was filled with liquid, while the remainder was gas. The liquid density was set to 8.85, the gas density to 0.00623, the liquid viscosity to 0.030975, the gas viscosity to 0.00135, the Prandtl numbers to 1.768 and 1.0, and the specific heat capacities to 4.2 and 2.1, respectively. These settings correspond to a saturated water and water vapor two-phase system at 100°C under standard atmospheric pressure. Figure 2 The dimensionless time in is defined as Where H is the initial liquid film height. It can be observed that due to the locally high surface temperature, a bubble nucleates in the central region of the bottom surface. Then, the bubble gradually grows, and after reaching a certain size, under the influence of gravity, it eventually breaks into two parts. And from... Figure 2It can be observed that with increasing gravity, bubble deflection occurs earlier and the deflection diameter increases. The current model provides a powerful tool for exploring flow boiling in microgravity or hypergravity environments, which is of great significance for a deeper understanding of the physical mechanisms of boiling heat transfer, promoting the development of aerospace and deep space exploration technologies, and optimizing ground-based industrial applications.

[0091] Example 3

[0092] The bubble separation diameter varies with gravity. In previous studies, many scholars have attempted to link the macroscopic characteristics of boiling (such as bubble separation diameter) with various factors, the most common being the power-law relationship between bubble separation diameter and gravity. -0.5 Proportional. To quantitatively validate the current model, the settings in Example 2 were used to simulate the bubble breakup and detachment under different gravity conditions, and the change in bubble detachment diameter with gravity was statistically analyzed. Figure 3 As shown, the solid line represents the fitted curve D = 0.191g. -0.5 It can be observed that the bubble escape diameter predicted by the current model matches the fitted curve well and is consistent with the classical power-law relationship, verifying the accuracy of the model in simulating heat transfer in two-phase boiling flow with high density ratios and proving the feasibility of the current method for studying the boiling heat transfer mechanism.

[0093] Example 4

[0094] The formation, breakup, and detachment of a single bubble under the same gravity but different density ratios. Different density ratios lead to significant changes in the nucleation motion of the bubble. Maintaining a liquid-gas two-phase density ratio consistent with that of a real water-vapor system is key to simulating flow boiling under realistic conditions. Figure 4 The formation, breakup, and detachment of individual bubbles are illustrated under different density ratios. The saturation temperature corresponding to the low density ratio is T. s =0.86T c The densities of the two phases are 6.5 and 0.38, respectively. To describe the surface superheat, a dimensionless parameter Ja = c is introduced. v (T w -T s ) / h fg To minimize the impact of the contact angle, all contact angles were set to 90°. The remaining settings remained consistent with the above. When Ja = 0.4156, the entire bubble growth and detachment process was extremely similar. However, the difference was that the vapor bubble growth process was faster and the detachment diameter was larger at lower density ratios.

[0095] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this 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.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0096] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, causing a series of operational steps to be executed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that run on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0099] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0100] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for simulating high-density ratio two-phase boiling heat transfer based on the lattice Boltzmann algorithm, characterized in that, The method includes the following steps: S1, based on density Solving the corresponding pseudopotentials using the corresponding nonideal state equations Calculate the interparticle interaction forces in a multiphase system. and gravity The source terms in the gas-liquid phase change model are solved based on the velocity and temperature of the flow field. Based on the entropy stability condition and the fluid's inherent properties, the relaxation matrices corresponding to different moments in the multiphase flow field and temperature field are calculated. and ; S2, the relaxation matrix and Interparticle interaction forces ,gravity and source item Substituting into the density distribution function respectively and temperature distribution function Collisions and migrations are performed within the evolution equations to obtain the density distribution function and temperature distribution function; S3: Calculate the macroscopic physical quantities, including density, velocity, and temperature, at each grid point based on the density distribution function and temperature distribution function, and determine whether to end the iteration. In step S1, the source term is calculated using the following formula. : ； in It is thermal conductivity. It is specific heat capacity. These are constants generated by the lattice Boltzmann method. It is the fluid density. For the temperature gradient, For the partial derivative of the equation of state with respect to temperature under constant density conditions, The pressure is obtained through non-ideal state equations. For speed.

2. The high-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, based on density Solving the corresponding pseudopotentials using the corresponding nonideal state equations : ； In the formula, c is the lattice velocity, and P is the pressure obtained through the non-ideal state equations. It is the speed of sound in a grid. is the fluid density, x represents the current position, t represents the current time, and G is the interaction force intensity.

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

4. The high-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 interparticle interaction force is calculated using the following formula. : ； in, The adjustment parameters are used to adjust the mechanical stability conditions, where G is the intensity of the interaction force, x represents the current position, and t represents the current time. Represented as discrete velocity in the i-direction. For time step, These are the weighting coefficients. It is a false pretense.

5. The high-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 relaxation matrix of the flow field is expressed as follows: ； in For the corresponding viscosity coefficient relaxation factor, These are the corresponding entropy stabilizing operators; the specific formulas for the entropy stabilizing operators used are as follows: ； in It is a correlation matrix The reverse, relaxation parameters With viscosity coefficient Related, the relationship between the two is ; It is the sheared part of the density distribution function and This refers to the higher-order moments. m and n are integers, both ranging from 0, 1, to 2, corresponding to the parts corresponding to the second, third, and fourth higher-order moments, respectively. and These are the deviations of the shear and higher-order moments from their equilibrium states, respectively. The superscript eq represents the equilibrium function, and i indicates the i-direction. The relaxation matrix of the temperature field is expressed as follows: 。 6. The high-density-ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm according to claim 1, characterized in that, In step S3, the flow field density at each grid point is calculated based on the density distribution function and the temperature distribution function. ,speed Temperature T: ； in, For density, and Let these represent the density and temperature distribution functions, respectively. It is an integer. q represents the number of discrete velocities. Represented as discrete velocity in the i-direction. Let F be the velocity, and F be the sum of the forces acting on the system, including the interdensity interaction forces. and gravity g is the acceleration due to gravity.

7. The high-density-ratio two-phase boiling heat transfer simulation method based on the lattice Boltzmann algorithm according to claim 1, characterized in that, In step S3, it is determined whether the density change of the flow field is less than... Alternatively, if the preset maximum number of calculation steps has been reached, the iteration will stop; otherwise, return to step S1 to proceed to the next iteration.

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