A CHF numerical prediction method based on dryout-dry patch dynamics and conjugate heat conduction
By employing a numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction, the problem of insufficient accuracy and efficiency in CHF prediction in existing technologies is solved, achieving accurate prediction of CHF and improving its physical interpretability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NUCLEAR POWER INSTITUTE OF CHINA
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-26
AI Technical Summary
Existing CHF numerical prediction techniques have limitations in describing microscopic physical processes, making it difficult to simultaneously meet the dual requirements of prediction accuracy and computational efficiency in engineering design. This is especially true in the development of high-power-density next-generation nuclear reactors, where existing methods struggle to accurately describe microscopic physical mechanisms.
A numerical prediction method based on dry spot-dry spot dynamics and conjugate heat conduction is adopted. A bubble dynamics parameter model is established through regression analysis of experimental data, a dry spot-dry spot dynamics model is constructed, and combined with the three-dimensional conjugate heat conduction equation, accurate prediction of CHF is achieved.
It improves the physical interpretability and computational efficiency of CHF prediction, simplifies the calculation process, reduces the use of empirical parameters, and achieves a quantitative correlation of CHF triggering mechanisms.
Smart Images
Figure CN122287254A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of nuclear reactor thermal-hydraulic safety analysis technology, specifically to a numerical prediction method for CHF based on dry spot-dry patch dynamics and conjugate heat conduction. Background Technology
[0002] In the field of nuclear reactor thermal-hydraulic safety analysis, accurate prediction of critical heat flux density (CHF) is a core technical challenge for ensuring the safe and economical operation of reactors. As a key parameter characterizing deviation from nucleus boiling (DNB), CHF's physical essence lies in the fact that when the heat flux density exceeds the critical threshold, a stable vapor film will form on the heated surface, causing a sharp deterioration in the heat transfer coefficient and a rapid rise in fuel cladding temperature. If this process gets out of control, it can lead to serious accidents such as fuel element meltdown. Recent research reveals that the formation mechanism of DNB-type CHF is mainly governed by the microscale hydrodynamic behavior in the near-wall region, with the nucleation, growth, and stabilization processes of wall-side dry spots being the decisive factors triggering the boiling crisis.
[0003] In engineering practice, current CHF prediction techniques mainly rely on two types of methods: experimental correlation and numerical simulation. While experimental methods offer high reliability, their high cost and long development cycle severely limit their application in engineering design. Existing numerical simulation methods generally suffer from insufficient characterization of the microscopic physical processes of dry speckle dynamics, particularly the limitations of two-fluid models in describing gas-liquid interface evolution and local heat transfer characteristics. This makes it difficult to simultaneously meet the dual requirements of prediction accuracy and computational efficiency in engineering design. Given the technological trend of next-generation nuclear reactors towards higher power densities, developing efficient prediction methods that can accurately describe microscopic physical mechanisms has become a critical technical problem that urgently needs to be solved. Summary of the Invention
[0004] The purpose of this invention is to overcome the limitations of existing CHF numerical prediction techniques and provide a CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction.
[0005] The technical solution of this invention is as follows: A numerical prediction method for CHF based on dry spot-dry speckle dynamics and conjugate thermal conduction, comprising the following steps: S1: Through regression analysis of experimental data, a functional relationship model between wall superheat and bubble dynamics parameters is established, namely, the bubble dynamics parameter model; S2: Based on the bubble dynamics parameter model, establish the dry spot-dry patch dynamics model; S3: Based on the distribution characteristics of dry spots and dry patches on the wall surface and their corresponding thermal resistance characteristics, establish a heat transfer boundary condition model; S4: The dry spot-dry speckle dynamics model established through particle dynamics simulation step 2; S5: Construct a three-dimensional conjugate heat conduction equation, couple it with the dynamically updated heat transfer boundary condition model established in S3, obtain the wall temperature and heat flow distribution through numerical solution, and feed the results back to the bubble dynamics parameter model established in S3. S6: Visualize the wall temperature and heat flux distribution in S5, calculate the average wall temperature and average heat flux, and plot the boiling curve near the CHF stage. The extreme value of the boiling curve is the predicted CHF value.
[0006] In S1, the bubble dynamics parameters include nucleation point density, bubble detachment diameter, bubble growth time, bubble waiting time, bubble detachment frequency, and bubble growth rate. A functional relationship model between wall superheat and bubble kinetic parameters was established using nonlinear regression analysis, including the following steps: a) Perform curve fitting on the experimental data to obtain the basic correlation formula; b) To correct the thermodynamic characteristics of the near-CHF stage, the asymptotic matching method is used to extrapolate the model; c) Verify the monotonicity of the dynamic parameters; d) Establish an extended dynamic parameter model applicable to near-critical states.
[0007] In S2, the dynamic model for the formation and development of dry spots and dry patches includes dry spot nucleation, dry spot growth, dry spot-dry patch contraction, dry spot-dry patch merging, and dry spot-dry patch sliding. Dry nucleation satisfies the vaporization nucleus density; Dry point growth is described by equations (1) to (5): In the formula, , , and These are the bubble kinetic parameters: bubble departure diameter, growth rate constant, bubble growth time, and bubble contact angle. It is the contact diameter. It is the diameter of the dry point. It is the dry point expansion rate; The shrinkage of dry spots and dry patches is described by equations (6) to (8): In the formula, It is the basic shrinkage rate of the dry point. This is the shrinkage rate after taking into account the influence of surrounding dry points. It's a dry, open perspective. It is the percentage of wetting fluid around the dry point; The sliding speed of the dry spot / dry patch is consistent with the sliding speed of the bubble.
[0008] In S3, the distribution of dry spots and dry patches on the wall includes areas that are never covered by dry spots or dry patches, areas covered by dry spots or dry patches, areas that are covered by dry spots or dry patches but then become wet again, and areas around dry spots that are covered by a micro-liquid layer; and the areas that are never covered by dry spots or dry patches are liquid-to-gas flow boundaries, the areas covered by dry spots or dry patches are gas-to-gas flow boundaries, the areas covered by dry spots or dry patches but then become wet again are transient cooling flow boundaries, and the areas around dry spots that are covered by a micro-liquid layer are micro-liquid layer boundaries. The heat transfer boundary conditions are described by the model equations (9) to (12): In the formula, , , and These correspond to liquid convective heat flow, transient quenching heat flow, micro-liquid layer evaporation heat flow, and gas convective heat flow, respectively. and It refers to the wall superheat and the mainstream subcooling. It is the volume of the bubble, according to the formula for a truncated sphere. calculate; , , , , and These are liquid phase thermal conductivity, liquid phase thermal diffusivity, gas phase density, latent heat of vaporization, liquid phase relative heat transfer coefficient, and gas phase relative heat transfer coefficient.
[0009] S4 includes the following steps: S41: Initialize wall superheat and heat flux, with a turn-on time step of [time step value]. The loop; S42: Update the gas-liquid physical properties, bubble dynamics model parameters, and number of nucleation points based on the current average wall temperature; S43: Traverse the nucleation points in S42 and wait for idle nucleation points to generate new dry points; S44: Traverse the dry points on the wall and mark their states; S45: Increased duration of dry spot presence Then, adjust the status marker of the dry point based on its existence time and radius to determine whether the dry point is expanding or contracting. S46: Repeat steps (2) to (6) until the specified calculation time is reached and then terminate the calculation.
[0010] In S44, the dry point state is marked by a dry point expansion function or a dry point contraction function.
[0011] In S5, the three-dimensional conjugate heat conduction equation is: (13) In the formula, , , , , , , These are the density, specific heat capacity, thermal conductivity, heating power per unit volume, heating power per unit area, wall thickness, and wall heat flux of the wall material.
[0012] When the wall thickness ( )<< Lateral dimensions of the wall ( When the three-dimensional conjugate heat conduction equation is simplified to the two-dimensional conjugate heat conduction equation, the equation becomes: S5 includes the following steps: S51: Grid the wall surface; S52: Initialize the wall temperature and heat flux at the grid points, and update the gas-liquid physical properties and thermophysical properties; S53: Based on the distribution of wall liquid, chilled region, micro-liquid layer and dry region in the current time step, assign the heat flow boundary described by equations (9) to (12) in S3; then, after the dynamic state of each time step is completed, proceed to the calculation of the three-dimensional conjugate heat conduction equation; S54: Based on the heat flux boundary and the current grid point temperature, the implicit finite volume method is used to numerically iterate and solve for the grid point temperature in the next event step; S55: Update wall temperature and heat flux In step S55, if the iteration time has not been reached, the process returns to update the gas-liquid properties, bubble dynamics parameters, and nucleation point distribution.
[0013] The significant advantages of this invention are: (1) The innovative particle-mesh hybrid computing method effectively avoids the full-field solution of the traditional two-phase flow heat transfer equation by focusing on the core physical processes of dry spot dynamics and wall conjugate heat transfer, thereby improving the computational efficiency. (2) By accurately modeling the near-wall bubble dynamics and the evolution of dry spots and dry patches, the physical essence of the CHF triggering mechanism is described, which improves the physical interpretability and reliability of the simulation results and makes the predictions have a clear physical basis. (3) The irreversible dry spot formation mechanism was used as the DNB triggering criterion to establish a quantitative correlation between dry spot stability and CHF; (4) By simplifying the complex calculation process of two-phase flow heat transfer and adopting a mechanism-driven modeling method, the number of empirical parameters used is effectively reduced. Attached Figure Description
[0014] Appendix Figure 1 The implementation flow of the CHF numerical prediction method of the present invention is as follows; Appendix Figure 2 This relates to the principle of coupling dynamic calculations with conjugate heat transfer in this invention. Appendix Figure 3 This is the main algorithm flow and particle information storage structure of the dynamics module in the numerical algorithm of this invention; Appendix Figure 4 The algorithm flow for coupling the dynamic module with the conjugate heat conduction module in the numerical algorithm of this invention is shown below. Detailed Implementation
[0015] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.
[0016] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.
[0017] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first.
[0018] The following is a summary of the contents covered in this instruction manual: The part where the vapor bubble comes into contact with the wall through the vapor phase is called the dry point. The dry point is part of the vapor bubble, and the generation of the dry point means the generation of the vapor bubble. Nucleation points indicate the locations where bubbles are generated; Dry point nucleation means that a dry point is generated at the nucleation point, that is, a bubble is generated; Multiple dry spots merge to form dry patches; There are currently no sites where nucleation has occurred; these are idle nucleation sites. These sites may have previously experienced nucleation. The specific technical content of the present invention will now be described with reference to the accompanying drawings; A numerical prediction method for CHF based on dry spot dynamics and conjugate thermal conduction coupling includes the following steps: 1) Bubble kinetic parameter modeling: Through regression analysis of experimental data, a quantitative correlation model is established between key thermal parameters (including wall superheat, mainstream subcooling, and mass flow rate) and bubble kinetic parameters (including nucleation point density, bubble detachment diameter, bubble growth time, bubble waiting time, bubble detachment frequency, and bubble growth rate). 2) Construction of the Dry Spot-Dry Spot Dynamics Model: Based on the bubble dynamics parameter model and combined with the microscopic mechanism of dry spot formation and development, a dynamic model describing the nucleation, growth, contraction, merging, and sliding of dry spots and dry spots is established. This model fully considers the influence mechanism of bubble dynamics on the evolution from dry spots to dry spots.
[0019] 3) Construction of the wall conjugate heat transfer model: Based on the distribution characteristics of dry spots and dry patches on the wall and their corresponding thermal resistance characteristics, a heat transfer boundary condition model is established (referring to Equations 9-12). The heat transfer characteristics of different regions are mapped to the corresponding heat flux boundary equations to achieve accurate characterization of local heat transfer characteristics; 4) Dynamic simulation of dry spots and dry spots: Based on the dynamic model of dry spots and dry spots established in step 2, the two-dimensional particle simulation method is used to construct core point and dry spot particles, and the time stepping method is used to simulate the spatiotemporal evolution process of dry spots and dry spots to accurately capture the dynamic behavior of dry spots. 5) Wall conjugate heat transfer simulation: Construct two-dimensional / three-dimensional numerical heat transfer equations, couple dynamically updated multi-scale heat transfer boundary conditions, obtain wall temperature and heat flux distribution through numerical solution, and feed the calculation results back to the bubble dynamics parameter model to achieve closed-loop update; 6) CHF determination: Visualize the wall temperature and heat flux distribution in S5, calculate the average wall temperature and average heat flux, and plot the boiling curve near the CHF stage. The extreme value of the boiling curve is the predicted CHF value.
[0020] A numerical prediction method for CHF based on dry spot dynamics and conjugate heat conduction is implemented through the following steps: S1: Obtain the wall superheat before boiling criticality through experimental measurement or literature review. T sup The corresponding set of bubble dynamics parameters includes: nucleation point density ( N'' [m -2 ]), bubble growth rate ( K [m·s -0 · 5 ]), bubble growth time ( t g [s]), bubble waiting time ( t w[s]), bubble separation diameter ( D d [m]) and bubble detachment frequency ( f [s -1 ]).
[0021] A nonlinear regression analysis method was used to establish a functional relationship model between wall superheat and various bubble kinetic parameters, specifically including: a) Perform curve fitting on the experimental data to obtain the basic correlation formula; b) To correct the thermodynamic characteristics of the near-CHF stage, the asymptotic matching method is used to extrapolate the model; c) Verify the monotonicity of the dynamic parameters; d) Establish an extended dynamic parameter model applicable to near-critical states.
[0022] S2: Based on the bubble dynamics parameter model obtained in S1, a multi-scale dynamic framework for the evolution of dry spots is constructed, including: dry spot nucleation, dry spot growth, dry spot-dry spot contraction, dry spot-dry spot merging, and dry spot-dry spot sliding.
[0023] Specifically, dry nucleation behavior is related to bubble nucleation, requiring the existence of activated but uncovered nucleation sites. The activated nucleation sites should meet the vaporization nucleus density requirement. N'' [m -2 ]).
[0024] Dry point growth behavior is related to bubble growth and is described by equations (1) to (5): In the formula, , , and These are the bubble kinetic parameters: bubble departure diameter, growth rate constant, bubble growth time, and bubble contact angle. It is the contact diameter. It is the diameter of the dry point. It is the dry point expansion rate, which is approximately linear instantaneously.
[0025] The shrinkage behavior of dry spots and dry patches is related to bubble detachment and is described by equations (6) to (8): In the formula, It is the basic shrinkage rate of the dry point. This is the shrinkage rate after taking into account the influence of surrounding dry points. It's a dry, open perspective. It is the proportion of wetting fluid around the dry point, calculated using particle simulation methods.
[0026] The dry spot-dry patch merging mechanism is superposition rather than fusion and does not require a mathematical model description.
[0027] The sliding speed of the dry spot / dry patch should be consistent with the sliding speed of the bubble.
[0028] S3: Based on the bubble structure on the wall, the dynamic boundary of the bubble on the wall is mapped to the four heat transfer boundaries of the liquid described by equations (9) to (12).
[0029] Specifically, the region that is never covered by dry spots or dry patches is the liquid-to-convection heat flow boundary; the region covered by dry spots or dry patches is the gas-to-convection boundary; the region covered by dry spots or dry patches and then re-wetted is the transient quenching heat flow boundary; and the region around the dry spot that is covered by a micro-liquid layer is the micro-liquid layer boundary. In the formula, , , and These correspond to liquid-to-liquid convective heat flux, transient quenching heat flux, micro-liquid layer evaporation heat flux, and gas-to-liquid convective heat flux, respectively. and It refers to the wall superheat and the mainstream subcooling. It is the volume of the bubble, which can be determined using the formula for a truncated sphere. Calculations can be obtained from formulas (1)-(8). , , , , and These are, respectively, the liquid phase thermal conductivity, the liquid phase thermal diffusivity, the gas phase density, the latent heat of vaporization, the liquid phase relative heat transfer coefficient, and the gas phase relative heat transfer coefficient. (These can be found in tables.) S4: Modeling the physical behavior described in mathematical models (1) to (8) using particle dynamics algorithms, with appendix Figure 3 This refers to the corresponding particle simulation algorithm flow and storage structure. (According to the appendix...) Figure 3 The gas-liquid information on the wall surface is stored in data structures of nucleation point class and dry point class.
[0030] S41: Initialize wall superheat and heat flux, with a turn-on time step of [time step value]. The loop; S42: Update gas-liquid properties (including density and thermal conductivity), bubble kinetic model parameters, and number of nucleation points based on the current average wall temperature; S43: Traverse the nucleation points in S42 and wait for idle nucleation points to generate new dry points; S44: Traverse the dry points existing on the wall surface. Based on the dry point status flags, use the dry point expansion function or dry point contraction function (i.e., formulas 5 and 6). For example, if the dry point exists for less than... / 2, the dry point is still in an expanding state and should continue to grow; S45: Increased duration of dry spot presence Then, the state flag of the dry point is adjusted based on its existence time and radius, that is, the flag clearly indicates whether the dry point is expanding or contracting. For example, if the existence time of a dry point within the iteration cycle of this time step is just greater than... / 2, the dry point should change from an expanding state to a contracting state. If the radius of a dry point decreases to 0 within the iteration period of this time step, the dry point should change from a contracting state to a disappearing state.
[0031] S46: Delete the dry nodes that are in a disappearing state and release memory.
[0032] S47: Repeat steps S42 to S46 until the specified calculation time is reached, then terminate the calculation.
[0033] S5: Corresponding Appendix Figure 2 Establish the three-dimensional heat conduction equation of the wall (13), when the wall thickness ( )<< Lateral dimensions of the wall ( When ), the three-dimensional heat conduction calculation (13) can be simplified to the two-dimensional heat conduction calculation (14). In the formula, , , , , , , These are the density, specific heat capacity, thermal conductivity, heating power per unit volume, heating power per unit area, wall thickness, and wall heat flux of the wall material.
[0034] Based on the conjugate coupling of the dynamic behavior of dry spots and dry patches on the wall surface, the implicit finite volume method is used to numerically discretize equation (13) or equation (14) to obtain the three-dimensional or two-dimensional wall temperature field.
[0035] Appendix Figure 4 This is the algorithm flow after embedding the conjugate heat conduction module.
[0036] S51: Grid the wall surface; S52: Initialize the wall temperature and heat flux at the grid points, and update the gas-liquid physical properties, especially the thermophysical properties, such as specific heat. S53: Based on the distribution of wall liquid, chilled region, micro-liquid layer and dry region at the current time step, allocate the heat flow boundary described by equations (9) to (12); subsequently, after the dynamic state of each time step is completed, enter the conjugate heat conduction calculation module, that is, the three-dimensional heat conduction equation (13) or formula (14) of the wall: S54: Based on the heat flux boundary and the current grid point temperature, the implicit finite volume method is used to numerically iterate and solve for the grid point temperature in the next event step; S55: Update wall temperature and heat flux. If the iteration time has not been reached, return to update gas-liquid properties, bubble kinetic parameters, and nucleation point distribution.
[0037] This process enables bidirectional coupling and transfer between the kinetic module and the heat transfer module. Kinetic boundaries such as dry spots and dry patches are mapped to heat transfer boundaries, and the calculated wall temperature can update the gas-liquid phase properties, kinetic parameters, and the number of nucleation points.
[0038] S6: Visualize the wall temperature and heat flux distribution in S5, calculate the average wall temperature and average heat flux, and plot the boiling curve near the CHF stage. The extreme value of the boiling curve is the predicted CHF value.
[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
[0040] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0041] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0042] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of this application, thereby enabling those skilled in the art to better understand and utilize this application.
Claims
1. A numerical prediction method for CHF based on dry spot-dry speckle dynamics and conjugate thermal conduction, characterized in that: Includes the following steps: S1: Through regression analysis of experimental data, a functional relationship model between wall superheat and bubble dynamics parameters is established, namely, the bubble dynamics parameter model; S2: Based on the bubble dynamics parameter model, establish the dry spot-dry patch dynamics model; S3: Based on the distribution characteristics of dry spots and dry patches on the wall surface and their corresponding thermal resistance characteristics, establish a heat transfer boundary condition model; S4: The dry spot-dry speckle dynamics model established through particle dynamics simulation step 2; S5: Construct a three-dimensional conjugate heat conduction equation, couple it with the dynamically updated heat transfer boundary condition model established in S3, obtain the wall temperature and heat flow distribution through numerical solution, and feed the results back to the bubble dynamics parameter model established in S3. S6: Visualize the wall temperature and heat flux distribution in S5, calculate the average wall temperature and average heat flux, and plot the boiling curve near the CHF stage. The extreme value of the boiling curve is the predicted CHF value.
2. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 1, characterized in that: In S1, the bubble dynamics parameters include nucleation point density, bubble detachment diameter, bubble growth time, bubble waiting time, bubble detachment frequency, and bubble growth rate.
3. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 2, characterized in that: A functional relationship model between wall superheat and bubble kinetic parameters was established using nonlinear regression analysis, including the following steps: a) Perform curve fitting on the experimental data to obtain the basic correlation formula; b) To correct the thermodynamic characteristics of the near-CHF stage, the asymptotic matching method is used to extrapolate the model; c) Verify the monotonicity of the dynamic parameters; d) Establish an extended dynamic parameter model applicable to near-critical states.
4. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 1, characterized in that: In S2, the dynamic model for the formation and development of dry spots and dry patches includes dry spot nucleation, dry spot growth, dry spot-dry patch contraction, dry spot-dry patch merging, and dry spot-dry patch sliding. Dry nucleation satisfies the vaporization nucleus density; Dry point growth is described by equations (1) to (5): In the formula, , , and These are the bubble kinetic parameters: bubble departure diameter, growth rate constant, bubble growth time, and bubble contact angle. It is the contact diameter. It is the diameter of the dry point. It is the dry point expansion rate; The shrinkage of dry spots and dry patches is described by equations (6) to (8): In the formula, It is the basic shrinkage rate of the dry point. This is the shrinkage rate after taking into account the influence of surrounding dry points. It's a dry, open perspective. It is the percentage of wetting fluid around the dry point; The sliding speed of the dry spot / dry patch is consistent with the sliding speed of the bubble.
5. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 4, characterized in that: In S3, the distribution of dry spots and dry patches on the wall includes areas that are never covered by dry spots or dry patches, areas covered by dry spots or dry patches, areas that are covered by dry spots or dry patches but then become wet again, and areas around dry spots that are covered by a micro-liquid layer; and the areas that are never covered by dry spots or dry patches are liquid-to-gas flow boundaries, the areas covered by dry spots or dry patches are gas-to-gas flow boundaries, the areas covered by dry spots or dry patches but then become wet again are transient cooling flow boundaries, and the areas around dry spots that are covered by a micro-liquid layer are micro-liquid layer boundaries. The heat transfer boundary conditions are described by the model equations (9) to (12): In the formula, , , and These correspond to liquid convective heat flow, transient quenching heat flow, micro-liquid layer evaporation heat flow, and gas convective heat flow, respectively. and It refers to the wall superheat and the mainstream subcooling. It is the volume of the bubble, according to the formula for a truncated sphere. calculate; , , , , and These are liquid phase thermal conductivity, liquid phase thermal diffusivity, gas phase density, latent heat of vaporization, liquid phase relative heat transfer coefficient, and gas phase relative heat transfer coefficient.
6. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 5, characterized in that: S4 includes the following steps: S41: Initialize wall superheat and heat flux, with a turn-on time step of [time step value]. The loop; S42: Update the gas-liquid physical properties, bubble dynamics model parameters, and number of nucleation points based on the current average wall temperature; S43: Traverse the nucleation points in S42 and wait for idle nucleation points to generate new dry points; S44: Traverse the dry points on the wall and mark their states; S45: Increased duration of dry spot presence Then, adjust the status marker of the dry point based on its existence time and radius to determine whether the dry point is expanding or contracting. S46: Repeat steps (2) to (6) until the specified calculation time is reached and then terminate the calculation.
7. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 6, characterized in that: In S44, the dry point state is marked by a dry point expansion function or a dry point contraction function.
8. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 6, characterized in that: In S5, the three-dimensional conjugate heat conduction equation is: （13） In the formula, , , , , , , These are the density, specific heat capacity, thermal conductivity, heating power per unit volume, heating power per unit area, wall thickness, and wall heat flux of the wall material.
9. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 8, characterized in that: When the wall thickness ( )<< Lateral dimensions of the wall ( When the three-dimensional conjugate heat conduction equation is simplified to the two-dimensional conjugate heat conduction equation, the equation becomes:
10. The CHF numerical prediction method based on dry spot-dry speckle dynamics and conjugate thermal conduction according to claim 8, characterized in that: S5 includes the following steps: S51: Grid the wall surface; S52: Initialize the wall temperature and heat flux at the grid points, and update the gas-liquid physical properties and thermophysical properties; S53: Based on the distribution of wall liquid, chilled region, micro-liquid layer and dry region in the current time step, assign the heat flow boundary described by equations (9) to (12) in S3; then, after the dynamic state of each time step is completed, proceed to the calculation of the three-dimensional conjugate heat conduction equation; S54: Based on the heat flux boundary and the current grid point temperature, the implicit finite volume method is used to numerically iterate and solve for the grid point temperature in the next event step; S55: Update wall temperature and heat flux.