A numerical simulation method for pool boiling with randomly distributed hydrophobic spots

Abstract

The present invention discloses a numerical simulation method for pool boiling with randomly distributed hydrophobic points, including: determining the size of the computational domain and operating parameters, determining the size of the computational domain with reference to the grid independence test structure, and determining parameters at the saturation temperature, such as but not limited to the gas-liquid coexistence density and viscosity; determining the numerical simulation strategy, clarifying the size and direction of the heater, the simulation conditions of the hydrophilic contact angle and hydrophobic contact angle, and constructing a pseudo-potential phase change lattice Boltzmann model; generating a mixed wettability surface; performing numerical simulation and outputting the results. By proposing a strategy for randomly and automatically generating the distribution of hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface, the present invention can generate hydrophobic points with specified numbers, sizes, and different spacings randomly, and automatically form a hydrophilic-hydrophobic mixed surface, which provides convenience for high-throughput pool boiling numerical simulation, and can quickly obtain the smooth surface boiling conditions with randomly distributed hydrophobic points arbitrarily, providing a theoretical basis and technical foundation for efficient direct numerical simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of computer simulation, and particularly to a numerical simulation method for pool boiling with randomly distributed hydrophobic points. Background Art

[0002] Boiling, as one of the efficient heat transfer methods, has very important application values in aspects such as nuclear power plants, desalination, heat dissipation of electronic devices, and aerospace thermal control systems. A large number of studies have shown that both experimental methods and numerical simulations can improve the critical heat flux (CHF) and the pool boiling heat transfer coefficient (HTC). The influencing factors explored mainly involve roughness, wettability, superheat, properties of the working fluid, size and direction of the heater, saturation temperature, etc. Among them, wettability (measured by the contact angle θ, 0° < θ < 90°, the wall surface is a hydrophilic surface; 90° < θ < 180°, the wall surface is a hydrophobic surface.) has a very complex and important influence on the pool boiling performance. A large number of previous studies have shown that hydrophobic surfaces are beneficial to promoting bubble nucleation and can increase the HTC; while hydrophilic surfaces mainly improve the CHF by promoting bubble detachment. In various applications, what is needed is an efficient heat transfer hybrid surface with low cost and long-term stability, which makes the construction of a hydrophilic-hydrophobic hybrid surface with excellent heat dissipation performance become one of the technologies that have received much attention. Numerical simulation technology has been applied to design the optimal area ratio of hybrid wettability surfaces due to its advantages such as low cost, high efficiency, and space saving.

[0003] Wettability and roughness are the key factors affecting the heat dissipation performance of pool boiling. A large number of existing experimental studies and numerical simulation results have shown that the change in the wettability of a smooth flat surface makes the boiling performance vary greatly. Compared with experimental studies, numerical simulations can exclude the interference of roughness to separately study the influence of wettability on boiling heat transfer. Placing a certain number and size of hydrophobic points on a hydrophilic surface to form a hydrophilic-hydrophobic hybrid wettability surface can enhance the nucleate boiling heat transfer performance; the number, size, and spacing of hydrophobic points all have an impact on the pool boiling heat transfer of the hydrophilic-hydrophobic hybrid wettability surface, but there is still no unified and detailed explanation for the mechanism of the influence of wettability on boiling heat transfer, and there are many simplified assumptions and empirical correlations in the vapor-liquid phase change model based on interface capturing.

[0004] Current numerical methods include the volume of fluid method (VOF) and the level set method (LSM) for solving the two-phase N - S equations, and the lattice Boltzmann method based on mesoscopic kinetic theory. VOF and LSM need to implement a complex interface reconstruction process and simulate the boiling phase change process by assuming nucleation sites, relying on too many assumptions and empirical correlations, and the calculation efficiency is low. The numerical simulation of pool boiling by LBM is to control a single variable (such as the size, number, spacing, etc. of hydrophobic points), manually input the information of hydrophobic points, and need to manually change it many times when performing the numerical simulation of the next state, and the process is complex and time-consuming. Summary of the Invention

[0005] In view of the above defects of the prior art, the technical problem to be solved by the present invention is to provide a numerical simulation method for pool boiling with randomly distributed hydrophobic points. The hydrophobic points can be randomly and automatically distributed on the boiling surface during the numerical simulation, which facilitates the high-throughput calculation of further numerical simulation of pool boiling. The boiling conditions of a smooth surface with randomly distributed hydrophobic points can be quickly obtained, and effective direct numerical simulation can be carried out.

[0006] To achieve the above object, the present invention provides a numerical simulation method for pool boiling with randomly distributed hydrophobic points, including the following steps:

[0007] Step 1: Determine the size of the computational domain and operating parameters, determine the size of the computational domain, and determine the parameters including but not limited to the gas-liquid coexistence density and viscosity at the saturation temperature;

[0008] Step 2: Determine the numerical simulation strategy. According to the simulation requirements, clarify the size and direction of the heater, the simulation conditions of the hydrophilic contact angle and hydrophobic contact angle sizes, and construct a pseudo-potential phase change lattice Boltzmann model;

[0009] Step 3: Generate a mixed wettability surface. Implement a strategy for randomly and automatically generating the distribution of hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface in the constructed pseudo-potential phase change lattice Boltzmann model to generate a hydrophilic-hydrophobic mixed surface;

[0010] Step 4: Perform numerical simulation and output the results. Solve the evolution equation of the single-relaxation particle distribution function or the multi-relaxation time collision model evolution equation with the BGK collision operator in the pseudo-potential gas-liquid phase change lattice Boltzmann model described in Step 3.

[0011] Further, the strategy for randomly and automatically generating the distribution of hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface in Step 3 is specifically as follows: According to the required hydrophilic contact angle and hydrophobic contact angle sizes, obtain the corresponding hydrophilic point wall contact angle with the fluid-solid interaction coefficient G x亲 and the hydrophobic point wall contact angle with the fluid-solid interaction coefficient G x疏 ; Use a random function to automatically generate hydrophobic points with specified quantity and size and random positions, and arrange them on the hydrophilic surface to form a hydrophilic-hydrophobic mixed surface; the contact angle of the hydrophobic points is greater than 90 degrees.

[0012] Further, the random function and its constraint conditions are as follows:

[0013] When N hydrophobic points need to be set, the size of the hydrophobic points is one lattice unit, and the number of lattices in the two-dimensional computational domain is NX*NY, the following function is used to generate randomly distributed hydrophobic points:

[0014] [N1 N2...N N-1 NN = randi([1,NX],1,N) (1)

[0015] N i+1 -N i > L b ; i = 1,2,...,N - 1 (2)

[0016] N1 + N N > (NX + 1 - L b ) (3)

[0017] Among them, randi([1,NX],1,N) in formula (1) is denoted as a function that can randomly generate N natural numbers within the range of 1 to NX. The natural numbers are arranged in ascending order and placed into the left row vector from left to right in sequence. The numerical value of the natural number represents the position of the hydrophobic point; formula (2) serves as the constraint condition for formula (1) to ensure that the N natural numbers randomly generated from N1 to N N are all different in size. The minimum distance between adjacent natural numbers is the natural number L b , which represents the minimum distance between adjacent hydrophobic points and is used to prevent the hydrophobic points from being too densely distributed. Its size can be set as needed; since the periodic boundary condition is adopted in the horizontal direction of the computational domain, formula (3) is used to ensure that the minimum distance between the hydrophobic points corresponding to the smallest natural number N1 and the largest natural number N N generated in formula (1) is L b , and thus hydrophobic points with arbitrary distributions of a given quantity can be obtained; assign the constant G x亲 to each point on the fluid-solid wall surface, and then assign G x疏 to [N1 N2... N N-1 N N . These N hydrophobic points automatically cover the hydrophilic points at the corresponding positions, and a hydrophilic-hydrophobic mixed surface that can represent the contact angle size is obtained in the numerical simulation.

[0018] Furthermore, in step 4, solving the temperature field distribution includes solving the microscopic temperature distribution function by the lattice Boltzmann method or solving the macroscopic temperature field distribution by the finite difference method.

[0019] Furthermore, the evolution equation of the single-relaxation particle distribution function of the BGK collision operator in step 4 is:

[0020]

[0021] f i (x,t) is the particle distribution function in the i direction at position x at time t, τ is the relaxation time, Δf i (x,t) is the volume force term such as gravity, δ t is the time step, is the corresponding equilibrium distribution function, and the expression is as follows:

[0022]

[0023] where ω i is the weight coefficient, e i is the discrete velocity along the i direction, and u is the macroscopic velocity.

[0024] Furthermore, the evolution equations of the multi-relaxation time collision and migration models in step 4 are respectively:

[0025]

[0026]

[0027] In the formula, F i represents the external force acting on the particles in the i direction, where M is the orthogonal transformation matrix, and f * = M -1 m * . Λ is a diagonal matrix, and the particle distribution function and the equilibrium distribution function are transformed from the velocity space to the matrix space through the orthogonal transformation matrix M.

[0028] The beneficial effects of the present invention are:

[0029] By proposing a strategy for randomly and automatically generating the distribution of hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface, the present invention can generate hydrophobic points with specified numbers, sizes, and different spacings, and automatically form a hydrophilic-hydrophobic mixed surface, which provides convenience for high-throughput calculations of pool boiling numerical simulations. The boiling conditions of a smooth surface with randomly distributed hydrophobic points can be quickly obtained, and effective direct numerical simulations can be carried out.

[0030] The concept, specific structure, and technical effects of the present invention will be further described below in conjunction with the drawings to fully understand the purpose, features, and effects of the present invention. Description of the Drawings

[0031] Figure 1 D2Q9 velocity model.

[0032] Figure 2 Schematic diagrams of two random distribution cases and the uniform distribution of hydrophobic point positions.

[0033] Figure 3 Schematic diagram of the calculation region.

[0034] Figure 4 Boiling curves for pure hydrophilic and two random distributions and one uniform distribution of hydrophobic points.

[0035] Figure 5ΔT = 8.44 K, density fields at different times for randomly distributed and uniformly distributed hydrophobic points.

[0036] Figure 6 The transient heat flux density at a superheat ΔT = 4.68 K.

[0037] Figure 7 Flowchart of direct numerical simulation of pool boiling with randomly distributed hydrophobic points. Detailed implementation

[0038] In the prior art, the lattice Boltzmann method describes the behavior of fluids through the particle distribution function. The evolution equation of the fluid particle distribution function with the BGK collision operator is:

[0039]

[0040] f i (x, t) is the particle distribution function in the i - direction at position x at time t, τ is the relaxation time, Δf i (x, t) is the volume force term such as gravity, δ t is the time step, is the corresponding equilibrium distribution function, and the expression is as follows:

[0041]

[0042] where ω i is the weight coefficient, e i is the discrete velocity along the i - direction, u is the macroscopic velocity. For two - dimensional simulations, the commonly used discrete velocity models are D2Q7 and D2Q9 models; the schematic diagram of the D2Q9 velocity model is as Figure 1 shown.

[0043] c s is the lattice sound speed. In D2Q9, c is the lattice velocity, c = 1.

[0044] For the D2Q9 lattice, the weight coefficient ω i is ω i = 4 / 9 (i = 0), ω i = 1 / 9 (i = 1 - 4), ω i = 1 / 36 (i = 5 - 8), and the expression of the discrete velocity e i is as follows:

[0045]

[0046] The macroscopic density and macroscopic velocity are obtained from the following formulas:

[0047] ρ = ∑f i (3)

[0048]

[0049] Equation (1) is the single-relaxation lattice Boltzmann method model. The multi-relaxation model has better numerical stability and adjustability compared with the single-relaxation model. The evolution equations of the multi-relaxation time collision and migration models are respectively:

[0050]

[0051]

[0052] In the formula, F i represents the external force acting on the particle in the i direction, where M is the orthogonal transformation matrix and Λ is the diagonal matrix. The particle distribution function and the equilibrium distribution function are transformed from the velocity space to the matrix space through the orthogonal transformation matrix M.

[0053] The collision process simulates various interactions between fluid particles. The above two equations can be rewritten in the following form to represent the collision process:

[0054]

[0055] where I is the identity matrix, the force term comes from m and m eq are respectively the particle distribution function and the equilibrium distribution function in the moment space.

[0056] For the D2Q9 velocity model, m eq and M are given as follows:

[0057]

[0058]

[0059] For the D2Q9 velocity model, the diagonal matrix Λ is:

[0060]

[0061] Different formats of the external force term have a great influence on the numerical stability and numerical accuracy of the pseudo-potential model. The introduction of the acting force can be roughly divided into three categories: the velocity correction method, the discrete acting force method, and the exact difference method. After continuous improvement, the expression of one stable acting force format converted to the moment space is given by the following formula:

[0062]

[0063] Denoted as a constant, used to adjust the mechanical stability. Where F represents the total force, F = F m + Fabs +F g , ψ represents the effective mass.

[0064]

[0065] P EOS is obtained from the Peng-Robision equation of state:

[0066]

[0067] ω = 0.344 is the acentric factor, and the critical parameters are obtained from the following formula: b = 0.0778RT c / p c . T represents the temperature of the entire computational domain, and p c , T c represent the critical pressure and critical temperature.

[0068] F m , F abs , F g are the intermolecular force between fluid particles, the adhesion force between the fluid and the solid wall, and the gravity, respectively.

[0069] One of the intermolecular forces F m is as follows:

[0070]

[0071] G is the action intensity (represented by an integer). ψ(x) is the effective mass at spatial position x, and ψ(x + e i δ t ) is the effective mass of the neighboring lattice point in the direction.

[0072] The wettability of the solid wall is realized by introducing the force between the solid and the fluid in the lattice Boltzmann pseudopotential two-phase flow model. The expression of one of the fluid-solid forces is as follows:

[0073]

[0074] s(x) is the indicator function of the fluid-solid phase. When s(x) = 1, x is a solid; when s(x) = 0, x is a fluid. G x is the intensity coefficient of the fluid-solid force, and the wall contact angle is adjusted by manually assigning the G x value at each point on the wall.

[0075] The expression of one of the gravity is as follows:

[0076] F g =(ρ - ρ ave)g (15)

[0077] ρ ave is the average density of the computational domain, and g is the gravitational acceleration.

[0078] The above equation is the lattice Boltzmann method pseudo-potential two-phase flow model. Combined with the energy equation, it constitutes the lattice Boltzmann pseudo-potential vapor-liquid phase change model. The energy equation can be solved by the lattice Boltzmann method to obtain the microscopic temperature distribution function or directly calculated by the finite difference method. The expression is as follows:

[0079]

[0080] λ is the thermal conductivity, c v is the heat capacity.

[0081] Boundary conditions can be divided into heuristic, dynamic, and interpolation and extrapolation formats according to the specific processing methods of density distribution function. Heuristic means that the boundary conditions of density distribution function are directly obtained according to the flow characteristics, including periodicity and rebound formats commonly used to deal with no-slip boundary conditions. The rebound format is suitable for complex boundary shapes, and its expression is:

[0082]

[0083] Where x is the fluid grid point and i represents the opposite direction of i.

[0084] The dynamic format requires knowing the macroscopic physical quantities of the boundary and inversely calculating the density distribution function based on the macroscopic physical parameters, such as the Zou-He boundary condition.

[0085] The characteristic length l0 and characteristic time t0 are expressed by the following formula:

[0086]

[0087] Where σ represents the surface tension, ρ l , v denote the density of liquid and gas respectively, and g denotes the acceleration due to gravity. Thus, dimensionless length and dimensionless time can be obtained.

[0088] The local heat flux density of the boiling surface is used to represent the heat flux density at various locations on the boiling surface and is calculated using the following formula:

[0089]

[0090] λ is the thermal conductivity and j is the lattice position vertical to the solid. w (x) is the heat flux density at space x.

[0091] The transient heat flux is calculated by the following formula:

[0092]

[0093] Currently, when using the pseudo-potential phase change lattice Boltzmann method to simulate pool boiling phase change, the contact angle is adjusted by manually inputting the fluid-solid interaction strength coefficient G at the wall surface x in an input manner. This is very inconvenient when studying the influence of wettability on pool boiling, especially for rough surfaces with various microstructures and when high-throughput calculations of dozens or hundreds of cases are required, as each case needs to be modified one by one

[0094] Based on the defects of the existing technologies in the above-mentioned technologies, the present invention aims to provide a direct numerical simulation technology for pool boiling with random automatic distribution of hydrophobic points on a hydrophilic-hydrophobic mixed surface based on the lattice Boltzmann method. By proposing a strategy for randomly and automatically generating the distribution of hydrophobic points on a hydrophilic-hydrophobic mixed wettability surface, hydrophobic points with specified numbers, sizes, and different spacings can be generated, automatically forming a hydrophilic-hydrophobic mixed surface, which provides convenience for high-throughput calculations in pool boiling numerical simulations

[0095] To achieve the above object, the technical solution provided by the present invention is as follows

[0096] A direct numerical simulation technology for pool boiling with random automatic distribution of hydrophobic points on a hydrophilic-hydrophobic mixed surface based on the lattice Boltzmann method, and the operation steps of the method are as follows

[0097] S1 Determine the size of the computational domain and operating parameters

[0098] Note: Determine the size of the computational domain and parameters such as the gas-liquid coexistence density and viscosity at the saturation temperature

[0099] S2 Determine the numerical simulation strategy

[0100] Note: According to the simulation requirements, clarify simulation conditions such as the size and direction of the heater, the sizes of the hydrophilic contact angle and the hydrophobic contact angle, etc. Construct a pseudo-potential phase change lattice Boltzmann model

[0101] S3 Generate a mixed wettability surface

[0102] Note: Implement a strategy for randomly and automatically generating the distribution of hydrophobic points on a hydrophilic-hydrophobic mixed wettability surface in the constructed pseudo-potential phase change lattice Boltzmann model to generate a hydrophilic-hydrophobic mixed surface

[0103] Strategy for randomly and automatically generating the distribution of hydrophobic points on a hydrophilic-hydrophobic mixed wettability surface

[0104] Since the wall contact angle is adjusted by the fluid-solid interaction force coefficient G x therefore, according to the required sizes of the hydrophilic contact angle and the hydrophobic contact angle, the corresponding G values are obtained respectivelyx亲 and G x疏 。

[0105] Use a random function to automatically generate hydrophobic points (contact angle greater than 90 degrees) with specified quantity and size and random positions, and arrange them on the hydrophilic surface to form a hydrophilic-hydrophobic mixed surface. The random function and its constraints are as follows:

[0106] When N hydrophobic points need to be set, the size of the hydrophobic points is one grid unit, and the number of grids in the two-dimensional computational domain is NX*NY, the following function is used in this invention to generate randomly distributed hydrophobic points.

[0107] [N1 N2... N N-1 N N = randi([1,NX],1,N) (21)

[0108] N i+1 -N i > L b ; i = 1,2,...,N-1 (22)

[0109] N1+N N > (NX+1-L b ) (23)

[0110] Among them, randi([1,NX],1,N) in formula (21) is denoted as a function that can randomly generate N natural numbers in the range of 1 to NX. Arrange the natural numbers in ascending order and put them into the left row vector from left to right in turn. The numerical size of the natural numbers represents the positions of the hydrophobic points. Formula (22) is used as a constraint condition for formula (21) to ensure that the N1 to N N A total of N natural numbers are of different sizes, and the minimum distance between adjacent natural numbers is the natural number L b , indicating the minimum distance between adjacent hydrophobic points, which is used to prevent the hydrophobic points from being too densely distributed, and its size can be set as needed. Since the periodic boundary condition is adopted in the horizontal direction of the computational domain, formula (23) is needed to ensure that the minimum distance between the hydrophobic points corresponding to the smallest natural number N1 and the largest natural number N N generated in formula (22) is L b . Thus, hydrophobic points with arbitrary distributions of a given quantity can be obtained.

[0111] Assign the constant G x亲 to each point (NX points) on the fluid-solid wall surface.

[0112] Then assign G x疏 to [N1 N2... N N-1 N N, these N hydrophobic points automatically cover the hydrophilic points at the corresponding positions. Thus, a hydrophilic-hydrophobic mixed surface that can represent the contact angle size can be obtained in numerical simulation.

[0113] S4 Numerical simulation and output the results;

[0114] Note: Solve the evolution equations of the single-relaxation or multi-relaxation particle distribution function and the temperature equation in the pseudo-potential gas-liquid phase change lattice Boltzmann model of S3.

[0115] The evolution equation of the single-relaxation particle distribution function using the BGK collision operator is:

[0116]

[0117] f i (x, t) is the particle distribution function in the i direction at position x at time t, τ is the relaxation time, and Δf i (x, t) is the volume force term such as gravity, and δ t is the time step, is the corresponding equilibrium distribution function, and the expression is as follows:

[0118]

[0119] where ω i is the weight coefficient, e i is the discrete velocity along the i direction, and u is the macroscopic velocity

[0120] The evolution equations of the multi-relaxation time collision and migration models are respectively:

[0121]

[0122]

[0123] In the formula, F i represents the external force acting on the particle in the i direction, where M is the orthogonal transformation matrix and Λ is the diagonal matrix. The particle distribution function and the equilibrium distribution function are transformed from the velocity space to the matrix space through the orthogonal transformation matrix M.

[0124] The temperature field distribution can be solved by the lattice Boltzmann method to solve the microscopic temperature distribution function or by the finite difference method to solve the macroscopic temperature field distribution.

[0125] Data such as the temperature field, velocity field, pressure field, local heat flux density, and transient heat flux density can be output at regular time steps as needed.

[0126] The principle of the present invention is described below in conjunction with specific embodiments:

[0127] Example 1:

[0128] This example introduces the specific implementation method according to the most original technical solution.

[0129] (1) Determine the size of the computational domain and operating conditions parameters

[0130] For the grid, the number of grids NX = 700 and NY = 350. All lattice units are used during the simulation. Initially, the fluid in the computational domain is saturated liquid and saturated gas. The coexistence density of the liquid phase and gas phase under the saturated state is obtained through the Maxwell distribution of the equation of state, and its saturation temperature is set to T s = 0.86T c , ρ l = 6.5, ρ v = 0.38.

[0131] (2) Determine the numerical simulation strategy

[0132] Periodic boundary conditions are implemented in the horizontal direction of the computational domain, and the zou-he boundary conditions are applied to the solid wall. The bottom wall heater is heated at a constant temperature, and the direction is horizontal and upward. The temperature of the heating wall is T b = T s + ΔT, where ΔT represents the superheat. The top is kept at a constant temperature T s . A multi-relaxation hybrid thermal phase change lattice Boltzmann model is constructed.

[0133] (3) Generate a mixed wettability surface

[0134] In the construction of the multi-relaxation hybrid thermal phase change lattice Boltzmann model, a strategy for automatically generating randomly distributed hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface is added to automatically generate information such as the position, size, and spacing of the hydrophobic points.

[0135] When performing numerical simulations for multiple operating conditions, this automated process will facilitate high-throughput calculations without the need for separate manual changes. In this example, N is taken as 7 and L b is taken as 10, that is, 7 hydrophobic points, and the minimum spacing of the hydrophobic points is 10Δx.

[0136] (4) Perform numerical simulation and output results

[0137] After starting the numerical simulation, the system first divides the grid, reads the parameters, and determines G corresponding to the hydrophilic contact angle x亲 and G corresponding to the hydrophobic contact angle x疏 . Through the strategy of automatically generating randomly distributed hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface, a hydrophobic point with a quantity of 7, a size of Δx (i.e., one lattice point), randomly distributed positions, and a minimum spacing of 10Δx is automatically generated on the hydrophilic surface to form a hydrophilic-hydrophobic mixed wettability surface. In this example, each case is calculated for a total of 50001δ t, every 100δ t Output data once. Numerical simulations are carried out respectively for two working conditions of generating randomly distributed hydrophobic points through the strategy of automatically generating randomly distributed hydrophobic points on the hydrophilic mixed-wettability surface (hereinafter referred to as random distribution for short), and the hydrophilic-hydrophobic mixed-wettability working condition of manually inputting the size (Δx), quantity (7) and position (spacing of 100Δx) of hydrophobic points (hereinafter referred to as uniform distribution because the spacing of hydrophobic points is the same), and the pure hydrophilic working condition without applying hydrophobic points. The working conditions of the position distribution of hydrophobic points are as Figure 2 shown.

[0138] After converting the lattice unit to the physical unit, it can be understood that 7 hydrophobic points are randomly arranged on the heater surface with a length of 0.413 m, and the size of each hydrophobic point is 5.9×10 -4 m, and the rest are hydrophilic surfaces. The simulation working conditions are liquid density 65 kg / m 3 , gas density 3.8 kg / m 3 , gravity 9.8 m / s 2 , surface tension 0.1 N / m, liquid-phase dynamic viscosity 8.2×10 -4 m 2 / s, thermal diffusivity 3.827×10 -5 m 2 / s, latent heat of vaporization 254.34 J / (kg·K), liquid-phase thermal conductivity 6.33 W / (m·K), gas-phase thermal conductivity 0.037 W / (m·K), and simulation duration 2.125 s. The computational domain after converting to the physical unit is as Figure 3 shown.

[0139] The results obtained from the simulation are as follows. Among them, the boiling curves under the working conditions of randomly distributed hydrophobic points A, randomly distributed hydrophobic points B, uniformly distributed hydrophobic points C and pure hydrophilic working condition on the surface are as Figure 4 shown. It can be seen that the boiling curves of the two schemes of randomly generating hydrophobic points are generally similar. The uniformly distributed hydrophobic points working condition C can improve the heat flux density and enhance nucleate boiling at low superheat. The density maps of the simulation results of three working conditions (randomly distributed hydrophobic points A, randomly distributed hydrophobic points B, uniformly distributed hydrophobic points C) at different times under the superheat ΔT = 8.44 K are as Figure 5 shown. It can be seen that the bubble nucleation points start from near the hydrophobic points, which is consistent with the actual situation.

[0140] The transient heat flux densities under the pure hydrophilic and two randomly distributed hydrophobic points and one uniformly distributed hydrophobic point working conditions are as Figure 6As shown, it can be found that at a low superheat degree (ΔT = 4.68 K), the transient heat flux density of the hydrophilic-hydrophobic mixed surface under the random distribution condition A of hydrophobic points and the uniform distribution condition C of hydrophobic points is higher than that of the pure hydrophilic surface. However, the mixed surface scheme under the random distribution condition B of hydrophobic points does not show an improvement, indicating that not all mixed wettability surface design schemes can enhance the nucleate boiling process. Therefore, the present invention proposes a technique of directly numerically simulating by randomly generating hydrophobic points with arbitrary numbers, sizes, and spacings on a hydrophilic heating surface, which is of great significance for the future industrial preparation of heat transfer structure surfaces with an appropriate hydrophobic / hydrophilic optimal area ratio and for reducing unnecessary cost research and development and tests.

[0141] The present invention proposes a numerical simulation method for pool boiling with randomly distributed hydrophobic points. In Example 1, by specifying the size and number of hydrophobic points and the minimum spacing between them, a random distribution strategy of hydrophobic points is automatically generated using a hydrophilic-hydrophobic mixed wettability surface, and a random automatic distribution scheme of hydrophobic point positions is generated. Then, numerical simulations are carried out respectively and compared with the mixed wettability conditions of uniformly distributed hydrophobic points manually input and the pure hydrophilic condition. The results show that the simulation results of the random automatic distribution of hydrophobic points are similar to those of the manually input uniform distribution, indicating that the technique proposed by the present invention can effectively simulate the direct numerical simulation of pool boiling with randomly distributed hydrophobic points. This automated process provides convenience for the high-throughput calculation of numerical simulations and is of great significance for the future industrial manufacture of heat transfer materials with an optimal hydrophobic / hydrophilic area ratio and for reducing unnecessary high-cost research and development and tests.

[0142] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field according to the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. A numerical simulation method for pool boiling with randomly distributed hydrophobic points, characterized in that, It includes the following steps: Step 1: Determine the size of the computational domain and operating conditions parameters, determine the size of the computational domain, and determine the parameters including but not limited to the gas-liquid coexistence density and viscosity at the saturation temperature; Step 2: Determine the numerical simulation strategy. According to the simulation requirements, clarify the size and direction of the heater, the simulation conditions of the hydrophilic contact angle and hydrophobic contact angle sizes, and construct a pseudo-potential phase change lattice Boltzmann model; Step 3: Generate a mixed wettability surface. Implement a strategy for randomly and automatically generating the distribution of hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface in the construction of the pseudo-potential phase change lattice Boltzmann model to generate a hydrophilic-hydrophobic mixed surface; Step 4: Perform numerical simulation and output the results. Solve the pseudo-potential phase change lattice Boltzmann model described in Step 3, and use the evolution equation of the single-relaxation particle distribution function with the BGK collision operator or the evolution equation of the multi-relaxation time collision and migration model; In Step 4, solving the temperature field distribution includes solving the microscopic temperature distribution function by the lattice Boltzmann method or solving the macroscopic temperature field distribution by the finite difference method; The evolution equation of the single-relaxation particle distribution function with the BGK collision operator in Step 4 is: f i (x, t) is the particle distribution function in the i - direction at position x at time t, τ is the relaxation time, and Δf i (x, t) is the volume force term such as gravity, and δ t is the time step, and f i eq (x, t) is the corresponding equilibrium distribution function, and the expression is as follows: where ω i is the weighting coefficient, e i is the discrete velocity along the i direction, and u is the macroscopic velocity; The evolution equations of the multi-relaxation time collision and migration model in Step 4 are respectively: f i (x + e i δ t , t + δ t ) = f i * (x, t) (4) where F i represents the external force on the particle in the i direction, where M is an orthogonal transformation matrix, and f * = M -1 m * ; Λ is a diagonal matrix. The particle distribution function and the equilibrium distribution function are transformed from the velocity space to the matrix space through the orthogonal transformation matrix M.

2. The numerical simulation method for pool boiling with randomly distributed hydrophobic points according to claim 1, wherein, In step 3, the strategy for randomly and automatically generating the distribution of hydrophobic points on the hydrophilic-hydrophobic mixed wettability surface is specifically as follows: according to the magnitudes of the required hydrophilic contact angle and hydrophobic contact angle, the corresponding hydrophilic point wall contact angle and the fluid-solid interaction force coefficient G x亲 and the hydrophobic point wall contact angle and the fluid-solid interaction force coefficient G x疏 are obtained; a random function is used to automatically generate hydrophobic points with specified quantity, size, and random positions, which are arranged on the hydrophilic surface to form a hydrophilic-hydrophobic mixed surface; the contact angle of the hydrophobic points is greater than 90 degrees.

3. A numerical simulation method for pool boiling with randomly distributed hydrophobic points according to claim 2, characterized in that, The random function and its constraint conditions are: When N hydrophobic points need to be set, the size of the hydrophobic point is one lattice unit, and the number of lattices in the two-dimensional computational domain is NX*NY, the following function is used to generate randomly distributed hydrophobic points: [N1 N2... N N-1 N N = randi([1,NX],1,N) (5) N i+1 -N i >L b ; i = 1, 2, ..., N - 1 (6) N1+N N >(NX+1-L b ) (7) Among them, randi([1, NX], 1, N) in formula (5) is denoted as a function that can randomly generate N natural numbers within the range of 1 to NX. The natural numbers are arranged in ascending order and placed in the left row vector from left to right in sequence. The numerical value of the natural number represents the position of the hydrophobic point. Formula (6) serves as the constraint condition for formula (5) to ensure that the N1 to N N a total of N natural numbers are of different sizes, and the minimum distance between adjacent natural numbers is the natural number L b , which represents the minimum distance between adjacent hydrophobic points and is used to prevent the hydrophobic points from being too densely distributed. Its size can be set according to needs. Since the periodic boundary condition is adopted in the horizontal direction of the computational domain, formula (7) is used to ensure that the minimum natural number N1 and the maximum natural number N generated in formula (5) N The minimum distance between the corresponding hydrophobic points is L b , and thus hydrophobic points with arbitrary distributions of a given quantity can be obtained. The constant G x亲 is assigned to each point on the fluid-solid wall surface, and then G x疏 is assigned to [N1 N2... N N-1 N N . These N hydrophobic points automatically cover the hydrophilic points at the corresponding positions, and a hydrophilic-hydrophobic mixed surface that can represent the contact angle size is obtained in the numerical simulation.

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