Method and apparatus for predicting critical heat flux of fluid, and device

Abstract

The present application proposes a method and an apparatus for predicting a critical heat flux of a fluid, and a device. The method comprises: acquiring a first simulation model for an in-channel boiling heat transfer process of a target channel; sequentially inputting a plurality of heat fluxes into the first simulation model to obtain, for each heat flux, corresponding maximum wall superheat and a corresponding maximum wall void fraction; determining, among the plurality of heat fluxes, a heat flux for which a rate of change of the corresponding maximum wall superheat is greater than or equal to a first threshold as a first heat flux; comparing the maximum wall void fraction corresponding to the first heat flux with a critical wall void fraction; and when the maximum wall void fraction corresponding to the first heat flux is greater than or equal to the critical wall void fraction, determining a critical heat flux on the basis of the first heat flux. The method for predicting a critical heat flux of a fluid in the present application enables efficient and accurate prediction of the critical heat flux.

Description

Method, device and equipment for predicting fluid critical heat flux TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power, in particular to a method, device and equipment for predicting fluid critical heat flux. BACKGROUND

[0002] The determination of the critical heat flux in a rod bundle channel is one of the reactor thermal design criteria and a key issue for fuel element design. As an important limiting thermal hydraulic parameter, the critical heat flux directly affects the allowable output power of the reactor core and the size of the safety margin of the reactor core.

[0003] During normal operation of the reactor, boiling phase transition in the rod bundle channel of the reactor core is usually avoided to maintain a single-phase flow state. Once boiling heat transfer occurs, a large number of boiling bubbles will appear in the rod bundle channel of the reactor core and the flow state will change to a vapor-liquid two-phase flow state. In particular, when the heat flux of the fuel element exceeds the critical heat flux, a large number of boiling bubbles will be difficult to detach from the surface of the fuel element, which will cause a significant deterioration of the heat transfer capacity and a sharp rise in the surface temperature, possibly leading to fuel element cladding rupture and accidents, resulting in serious consequences.

[0004] Therefore, in order to avoid such phenomena and ensure that the heat flux on the surface of the fuel element is always below the critical heat flux, it is necessary to accurately determine the critical heat flux of the fuel element. In the engineering design of the reactor, accurately obtaining the critical heat flux of the fuel element is beneficial to improving the safety and economy of the reactor.

[0005] In existing methods for determining the critical heat flux, experimental methods are often used. However, due to the harsh experimental conditions for determining the critical heat flux of the fuel element in a pressurized water reactor, there are problems such as great difficulty, high cost and long cycle. SUMMARY

[0006] The present application aims to provide a method, device, equipment and storage medium for predicting fluid critical heat flux, which can more efficiently and accurately predict the critical heat flux through simulation.

[0007] According to the method for predicting fluid critical heat flux according to the first aspect of the present application, the following steps are included:

[0008] Obtaining a first simulation model of a boiling heat transfer process in a target channel; wherein the first simulation model is used to simulate the boiling heat transfer process in the target channel, and the boiling heat transfer process includes a flow behavior process of vapor-liquid two-phase, a boiling phase transition process and an interaction process between vapor-liquid two-phase;

[0009] Inputting multiple heat fluxes into the first simulation model in sequence to obtain a maximum wall surface superheat and a maximum wall surface void fraction corresponding to each of the heat fluxes;

[0010] Determining, as a first heat flux, a heat flux corresponding to the maximum wall surface superheat with a change rate greater than or equal to a first threshold value among the multiple heat fluxes, wherein the change rate represents a change of the maximum wall surface superheat relative to a change of the heat flux;

[0011] Comparing the maximum wall surface void fraction corresponding to the first heat flux with a critical wall surface void fraction;

[0012] In a case where the maximum wall surface void fraction corresponding to the first heat flux is greater than or equal to the critical wall surface void fraction, determining a critical heat flux according to the first heat flux.

[0013] According to some embodiments of the present application, after the comparison of the maximum wall surface void fraction corresponding to the first heat flux with the critical wall surface void fraction, the method further comprises:

[0014] In a case where the maximum wall surface void fraction corresponding to the first heat flux is less than the critical wall surface void fraction, taking a second heat flux as the first heat flux, and returning to compare the maximum wall surface void fraction corresponding to the first heat flux with the critical wall surface void fraction until the determination of the critical heat flux is completed;

[0015] Wherein, in a case where the multiple heat fluxes are arranged in sequence from small to large, the second heat flux is a next heat flux of the first heat flux.

[0016] According to some embodiments of the present application, the determination of the first heat flux from the multiple heat fluxes with the change rate of the maximum wall surface superheat greater than or equal to the first threshold value comprises:

[0017] Drawing a boiling curve according to the multiple heat fluxes and the multiple maximum wall surface superheats corresponding thereto;

[0018] Determining an interval in which an inflection point appears in the boiling curve;

[0019] Obtaining a first growth rate corresponding to each of the heat fluxes in the interval, wherein the first growth rate represents a degree of growth of the maximum wall surface superheat with the growth of the heat flux;

[0020] Determining, as the first heat flux, the heat flux corresponding to the first growth rate greater than or equal to a second threshold value.

[0021] According to some embodiments of the present application, before the comparing the wall maximum void fraction corresponding to the first heat flux density with the wall critical void fraction, the method further comprises:

[0022] obtaining a flow rate in the target channel;

[0023] obtaining the wall critical void fraction by a first formula;

[0024] wherein the first formula is: α crit =D1·G+D2

[0025] wherein α crit represents the wall critical void fraction, G represents the flow rate in the target channel, D1 is a first coefficient, and D2 is a first constant.

[0026] According to some embodiments of the present application, the obtaining the first simulation model of the in-channel boiling heat transfer process of the target channel comprises:

[0027] obtaining a two-phase flow model and a turbulence model for simulating the flow behavior process of the gas-liquid two-phase;

[0028] obtaining a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, and a bubble size distribution model for simulating the boiling phase change process of the gas-liquid two-phase;

[0029] obtaining a drag force model, a turbulence dissipation force model, a lift force model, and a wall lubrication force model for simulating the interaction process between the gas-liquid two-phase;

[0030] determining the first simulation model by using the two-phase flow model, the turbulence model, the boiling model, the activated core density model, the bubble detachment diameter model, the bubble detachment frequency model, the bubble size distribution model, the drag force model, the turbulence dissipation force model, the lift force model, and the wall lubrication force model.

[0031] According to some embodiments of the present application, the obtaining the turbulence dissipation force model comprises:

[0032] obtaining a pressure in the target channel;

[0033] obtaining a turbulent Prandtl number by a second formula;

[0034] obtaining the turbulence dissipation force model according to the obtained turbulent Prandtl number;

[0035] wherein the second formula is: σ h =D3·P 3 +D4·P 2 +D5·P+D6

[0036] wherein σ h represents a turbulent Prandtl number, P represents the pressure in the target channel, D3 is a second coefficient, D4 is a third coefficient, D5 is a fourth coefficient, and D6 is a second constant.

[0037] According to some embodiments of the present application, the determination of the first simulation model using the two-phase flow model, the turbulent flow model, the boiling model, the activated core density model, the bubble departure diameter model, the bubble departure frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model comprises:

[0038] determining the geometric characteristics of the target channel to obtain a geometric model;

[0039] dividing the geometric model into a plurality of grid nodes;

[0040] determining the boundary conditions of the geometric model;

[0041] selecting the grid nodes on the surface of the heating wall as measurement points;

[0042] determining the first simulation model using the geometric model, the grid nodes, the boundary conditions, and the measurement points, and the two-phase flow model, the turbulent flow model, the boiling model, the activated core density model, the bubble departure diameter model, the bubble departure frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model.

[0043] According to the second aspect of the present application, the device for predicting the critical heat flux of a fluid comprises:

[0044] a first obtaining module configured to obtain a first simulation model of a boiling heat transfer process in a target channel; wherein the first simulation model is used to simulate the boiling heat transfer process in the target channel, and the boiling heat transfer process includes a flow behavior process of gas-liquid two-phase, a boiling phase change process, and a gas-liquid two-phase interaction process;

[0045] a second obtaining module configured to input a plurality of heat fluxes into the first simulation model in sequence to obtain a maximum wall superheat and a maximum wall void fraction corresponding to each heat flux;

[0046] The first determining module is configured to determine a heat flux density corresponding to a maximum wall surface superheat degree change rate greater than or equal to a first threshold value in the plurality of heat flux densities as a first heat flux density, wherein the change rate represents a change of the maximum wall surface superheat degree relative to the heat flux density change;

[0047] The comparing module is configured to compare the wall surface maximum void fraction corresponding to the first heat flux density with a wall surface critical void fraction.

[0048] The second determining module is configured to determine the first heat flux density as a critical heat flux density in a case where the wall surface maximum void fraction corresponding to the first heat flux density is greater than or equal to the wall surface critical void fraction.

[0049] The electronic device according to the third aspect of the present application comprises a processor and a memory, the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the fluid critical heat flux density prediction method according to any one of the first aspect of the present application.

[0050] The computer readable storage medium according to the fourth aspect of the present application stores computer executable instructions for executing the fluid critical heat flux density prediction method according to the first aspect of the present application.

[0051] In the embodiments of the present application, the first simulation model can simulate the boiling heat transfer process in the channel, the model simulation method can effectively improve the efficiency, can simulate multiple heat flux density conditions, quickly find the heat flux density condition corresponding to the significant increase of the maximum wall surface superheat degree, that is, the first heat flux density, and can also simulate a large number of different experimental design schemes and accident conditions, reduce the experimental period, and reduce the experimental cost. In addition, the present application adds a step of judging the size of the wall surface maximum void fraction value corresponding to the first heat flux density condition, and only when the wall surface maximum void fraction value reaches the wall surface critical void fraction, the first heat flux density can be confirmed as the critical heat flux density, thereby improving the accuracy of the critical heat flux density prediction and judgment.

[0052] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be understood from the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0053] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the description, taken in conjunction with the accompanying drawings, in which:

[0054] FIG. 1 is a flowchart of an embodiment of the fluid critical heat flux density prediction method of the present application;

[0055] FIG. 2 is a first simulation model construction diagram of an embodiment of the prediction method of the fluid critical heat flux density of the application;

[0056] FIG. 3 is a boiling curve diagram of an embodiment of the prediction method of the fluid critical heat flux density of the application;

[0057] FIG. 4 is a detailed parameter list of the interval where the inflection point of the boiling curve of an embodiment of the prediction method of the fluid critical heat flux density of the application;

[0058] FIG. 5 is a comparison diagram of the predicted value and the experimental value of the critical heat flux density of an embodiment of the prediction method of the fluid critical heat flux density of the application;

[0059] FIG. 6 is a structure diagram of an embodiment of the prediction device of the fluid critical heat flux density of the application;

[0060] FIG. 7 is a hardware structure diagram of an embodiment of the electronic device of the application. DETAILED DESCRIPTION

[0061] The embodiments of the application will be described in detail below with reference to the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the application, and cannot be understood as a limitation of the application.

[0062] In the description of the application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features or the sequence of the indicated technical features.

[0063] In the description of the application, it should be understood that the orientation description, such as the orientation or position relationship indicated by up, down, etc., is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the application and simplifying the description, and therefore cannot be understood as indicating or implying that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the application.

[0064] In the description of the application, it should be noted that, unless otherwise explicitly limited, the words such as setting, installing, connecting, etc. should be broadly understood, and the person skilled in the art can reasonably determine the specific meaning of the above words in the application in combination with the specific content of the technical solution.

[0065] The technical solutions of the application will be described in detail below with reference to the accompanying drawings. Obviously, the following described embodiments are only part of the embodiments of the application, not all embodiments.

[0066] FIG. 1 is a flowchart of a method for predicting a fluid critical heat flux according to an embodiment of the present application. The method for predicting a fluid critical heat flux according to an embodiment of the present application will be described in detail below with reference to FIG. 1.

[0067] The method for predicting a fluid critical heat flux according to an embodiment of the present application comprises the following steps:

[0068] In step 101, a first simulation model of a boiling heat transfer process in a target channel is obtained. The first simulation model is used to simulate the boiling heat transfer process in the target channel, and the boiling heat transfer process includes a flow behavior process of gas-liquid two-phase, a boiling phase change process, and an interaction process between gas-liquid two-phase.

[0069] In step 102, a plurality of heat fluxes are sequentially input into the first simulation model to obtain a maximum wall superheat and a maximum wall void fraction corresponding to each heat flux.

[0070] In step 103, a heat flux corresponding to a change rate of the maximum wall superheat greater than or equal to a first threshold value is determined as a first heat flux in the plurality of heat fluxes. The change rate represents a change of the maximum wall superheat relative to a change of the heat flux.

[0071] In step 104, the maximum wall void fraction corresponding to the first heat flux is compared with a wall critical void fraction.

[0072] In step 105, in a case where the maximum wall void fraction corresponding to the first heat flux is greater than or equal to the wall critical void fraction, a critical heat flux is determined according to the first heat flux.

[0073] In the embodiment of the present application, the first simulation model can be used to simulate the boiling heat transfer process in the channel. The model simulation method can effectively improve the efficiency, can simulate a plurality of heat flux conditions, can quickly find the heat flux condition corresponding to the maximum wall superheat when the maximum wall superheat significantly increases, i.e., the first heat flux, and can simulate a plurality of different experimental design schemes and accident conditions, thereby reducing the experimental period and the experimental cost. In addition, the method of the present application further comprises a step of judging the size of the maximum wall void fraction value corresponding to the first heat flux condition. The maximum wall void fraction value reaches the wall critical void fraction, and the first heat flux is determined as the critical heat flux, thereby improving the accuracy of the prediction and determination of the critical heat flux.

[0074] In step 101, a first simulation model of a boiling heat transfer process in a target channel is obtained. The first simulation model is used to simulate the boiling heat transfer process in the target channel, and the boiling heat transfer process includes a flow behavior process of gas-liquid two-phase, a boiling phase change process, and an interaction process between gas-liquid two-phase.

[0075] The target channel is a channel that needs to be simulated, and the channel can be but is not limited to a reactor core rod bundle channel. In normal operation of the reactor, boiling phase change in the reactor core rod bundle channel needs to be avoided, and a single-phase flow state needs to be maintained.

[0076] The channel described above can still have boiling heat transfer under some conditions. When the channel needs to be strengthened in local heat transfer, boiling heat transfer can occur on the surface of the fuel element; and when the channel is in an accident condition, due to changes in reactivity and thermal hydraulic parameters in the core, local subcooled boiling can occur on the surface of the fuel element. In the above described cases, a large number of boiling bubbles will appear in the reactor core rod bundle channel, and will be converted into a vapor-liquid two-phase flow state.

[0077] The first simulation model can be directly created to obtain a simulation model suitable for the target channel, or a general simulation model can be pre-created, which is widely applicable to the simulation of boiling heat transfer processes in various rod bundle channels, and then the characteristic parameters of the channel are input into the general simulation model to obtain a simulation model suitable for the current target channel.

[0078] In step 102, a plurality of heat fluxes are sequentially input into the first simulation model to obtain a maximum wall superheat and a maximum wall void fraction corresponding to each heat flux.

[0079] After the first simulation model is constructed, the equations in the model can be solved using heat fluxes at different times to obtain implementation parameters of the fluid in the channel and the heated wall at the corresponding time. The solving process can be directly run in a simulation analysis software, such as a fluid calculation software STAR-CCM+, to complete automatic solving. The data that need to be monitored in the implementation parameters include the maximum wall superheat and the maximum wall void fraction of the heated wall. Specifically, the heat flux is input into the first simulation model to obtain the maximum wall superheat and the maximum wall void fraction corresponding to the heat flux.

[0080] The heat flux is the heat flux of the surface of the fuel element in the channel.

[0081] The maximum wall superheat and the maximum wall void fraction can represent the boiling state of the boiling heat transfer phenomenon on the surface of the fuel element.

[0082] The plurality of heat fluxes are sequentially input into the first simulation model, which can be N heat fluxes input into the first simulation model sequentially, where N is an integer greater than 1.

[0083] The N heat flux densities are sequentially input into the first simulation model, which can be N input steps, wherein in each input step, a heat flux density is input into the first simulation model at the beginning of simulation, and when the simulation result reaches a steady state, the maximum wall surface superheat and the maximum wall surface void fraction corresponding to the current input heat flux density are obtained.

[0084] For example, the N heat flux densities are sequentially input into the first simulation model, which can be that a heat flux density q1 is input into the first simulation model at the beginning of simulation for the first time, when the simulation result reaches a steady state, the maximum wall surface superheat T1 and the maximum wall surface void fraction a1 corresponding to q1 are obtained, a heat flux density q2 is input into the first simulation model at the beginning of simulation for the second time, when the simulation result reaches a steady state, the maximum wall surface superheat T2 and the maximum wall surface void fraction a2 corresponding to q2 are obtained,..., a heat flux density qN is input into the first simulation model at the beginning of simulation for the Nth time, when the simulation result reaches a steady state, the maximum wall surface superheat TN and the maximum wall surface void fraction aN corresponding to qN are obtained. n , when the simulation result reaches a steady state, the maximum wall surface superheat T n and the maximum wall surface void fraction a n corresponding to q n are obtained.

[0085] The multiple heat flux densities sequentially input into the first simulation model can be arranged in ascending order to form an arithmetic sequence.

[0086] For example, the N heat flux densities sequentially input into the first simulation model can be arranged in ascending order to form an arithmetic sequence. The first heat flux density q1 has a smaller value, and then the heat flux density is increased by a certain gradient value Δq, that is, the second heat flux density q2 has a value of q1+Δq, the third heat flux density q3 has a value of q2+Δq,..., and the Nth heat flux density qN has a value of qN-1+Δq. n . n-1

[0087] The multiple heat flux densities sequentially input into the first simulation model can be multiple input steps, and the multiple heat flux densities are sequentially input into the first simulation model in ascending order of the heat flux density values.

[0088] For example, the N heat flux densities sequentially input into the first simulation model can be N input steps, and the N heat flux densities are sequentially input into the first simulation model in ascending order of the heat flux density values.

[0089] In the step 103, the heat flux density corresponding to the maximum wall surface superheat with a change rate greater than or equal to a first threshold value is determined as the first heat flux density in the multiple heat flux densities, wherein the change rate represents the change of the maximum wall surface superheat relative to the change of the heat flux density.

[0090] ​The change rate of the maximum wall surface superheat can represent the change of the maximum wall surface superheat relative to the change of the heat flux density.

[0091] For example, in the case of sequentially inputting N heat flux densities to the first simulation model, the change rate can represent the case that the i+1th heat flux density q i+1 corresponds to the change of the maximum wall surface superheat T i+1 relative to the i th heat flux density q i . i , where 1≤i≤N, and N is an integer greater than 1.

[0092] For example, the change rate of the maximum wall surface superheat can represent the change of the change amount ΔT of the maximum wall surface superheat relative to the change amount Δq of the heat flux density, that is, the change rate can be ΔT / Δq. Specifically, the change rate corresponding to the i th heat flux density q i may be (T i+1 -T i ) / (q i+1 -q i ), and further, when Δq is a fixed value, the change rate corresponding to the i th heat flux density q i may be (T i+1 -T i ) / Δq.

[0093] The change rate of the maximum wall surface superheat can also represent the change of the next maximum wall surface superheat relative to the current maximum wall surface superheat.

[0094] For example, the change rate of the maximum wall surface superheat can represent the change of the next maximum wall surface superheat T i+1 relative to the current maximum wall surface superheat T i , that is, the change rate corresponding to the i th heat flux density q i+1 may be (T i -T i ) / T i .

[0095] The first threshold value can be an experimental reference value obtained by statistical analysis of experimental measurement data, or the N change rates corresponding to the N heat flux densities can be calculated, and the maximum value is taken as the first threshold value.

[0096] In step 104, the maximum wall surface void fraction corresponding to the first heat flux density is compared with the critical wall surface void fraction.

[0097] The wall critical void fraction corresponds to a critical void fraction of the target channel under the current operating condition, and the specific value of the critical void fraction can be obtained from experimental reference values through statistical measurement data or theoretical reference values by substituting the current target channel operating condition into a calculation formula.

[0098] In the step 105, when the wall maximum void fraction corresponding to the first heat flux is greater than or equal to the wall critical void fraction, the critical heat flux is determined according to the first heat flux.

[0099] After the boiling phase transition occurs in the rod bundle channel of the reactor core and the boiling heat transfer phenomenon appears for a period of time, when the maximum wall superheat suddenly rises, it indicates that the heat transfer capacity has decreased and is close to the critical heat flux, but it still has a certain heat transfer capacity and has not reached the actual critical value. With the continuous increase of the heat flux, once the wall maximum void fraction of the boiling bubble reaches the wall critical void fraction, a large number of boiling bubbles will be attached to the surface of the fuel element and difficult to detach, thereby causing a significant deterioration of the heat transfer capacity, and at this time, the actual critical heat flux is reached. Once the heat flux in the channel is higher than the critical heat flux, the fuel element cladding may be damaged, and thus a serious accident may occur.

[0100] Therefore, the method for predicting the fluid critical heat flux determines the heat flux corresponding to the sudden rise of the maximum wall superheat as the first heat flux. The determination of the critical heat flux according to the first heat flux can be that when the wall maximum void fraction corresponding to the first heat flux is greater than or equal to the wall critical void fraction, the first heat flux is determined as the critical heat flux.

[0101] The step 103 is actually to determine the heat flux corresponding to the sudden rise of the maximum wall superheat as the first heat flux. By determining whether the maximum wall superheat suddenly rises through whether the change rate of the maximum wall superheat is greater than or equal to the first threshold, the first heat flux is determined.

[0102] The steps 104 and 105 are actually a judgment process of whether the wall maximum void fraction reaches the critical value. Only when the wall maximum void fraction reaches the wall critical void fraction, the first heat flux can be confirmed as the critical heat flux, thereby effectively improving the accuracy of the prediction and judgment of the critical heat flux.

[0103] In some embodiments, after comparing the wall maximum void fraction corresponding to the first heat flux with the wall critical void fraction, the method further comprises:

[0104] In a case where the maximum wall cavity fraction corresponding to the first heat flux density is less than the critical wall cavity fraction, the first heat flux density is updated to a second heat flux density, and the comparison between the maximum wall cavity fraction corresponding to the first heat flux density and the critical wall cavity fraction is performed again until the determination of the critical heat flux density is completed.

[0105] In a case where the plurality of heat flux densities are arranged in the order from small to large, the second heat flux density is the next heat flux density of the first heat flux density.

[0106] In the embodiment, since the maximum wall cavity fraction corresponding to the heat flux density gradually increases as the heat flux density increases, the comparison between the maximum wall cavity fraction corresponding to the heat flux density less than the first heat flux density and the critical wall cavity fraction is no longer performed, the time for comparison is reduced, and the prediction efficiency of the critical heat flux density is improved.

[0107] In the case where the at least one heat flux density is arranged in the order from small to large, the second heat flux density can be the next heat flux density of the first heat flux density, or the second heat flux density can be the next heat flux density of the first heat flux density in a case where a plurality of heat flux densities greater than a certain value are arranged in the order from small to large.

[0108] For example, N heat flux densities are sequentially input to the first simulation model, and in a case where the N heat flux densities are arranged in the order from small to large, the next heat flux density greater than the first heat flux density is the second heat flux density.

[0109] For example, only the first heat flux density and the other heat flux densities greater than the first heat flux density are arranged in the order from small to large, and the next heat flux density of the first heat flux density is the second heat flux density.

[0110] In some cases, only the other heat flux densities greater than the first heat flux density can be arranged in the order from small to large, and the first heat flux density is the second heat flux density.

[0111] The determination of the critical heat flux density can be that, in a case where the maximum wall cavity fraction corresponding to the first heat flux density is less than the critical wall cavity fraction, the comparison between the maximum wall cavity fraction corresponding to the second heat flux density and the critical wall cavity fraction is continued until a heat flux density satisfying the condition that the maximum wall cavity fraction corresponding to the heat flux density is greater than or equal to the critical wall cavity fraction is obtained, and the heat flux density is determined as the critical heat flux density.

[0112] In some embodiments, the heat flux density corresponding to a change rate of the maximum wall surface superheat greater than or equal to a first threshold value is determined as the first heat flux density, including:

[0113] drawing a boiling curve according to the plurality of heat flux densities and the corresponding plurality of maximum wall surface superheats;

[0114] determining an interval in which an inflection point appears in the boiling curve;

[0115] obtaining a first growth rate corresponding to each heat flux density in the interval, the first growth rate representing a degree of growth of the maximum wall surface superheat with the growth of the heat flux density;

[0116] determining the heat flux density corresponding to a first growth rate greater than or equal to a second threshold value as the first heat flux density.

[0117] In the present embodiment, by drawing the boiling curve, the interval in which the inflection point appears in the boiling curve can be quickly determined according to the graph, and it is easier to determine whether the maximum wall surface superheat appears a sudden rise phenomenon, and it is convenient to quickly and accurately determine the first heat flux density.

[0118] The boiling curve can be a relationship curve q-T of the heat flux density q and the maximum wall surface superheat T, the heat flux density q is the abscissa, and the maximum wall surface superheat T is the ordinate.

[0119] The interval in which the inflection point appears in the boiling curve can be an interval in which the slope of the curve changes.

[0120] The first growth rate can be ΔT / Δq, where ΔT is the change amount of the maximum wall surface superheat, and Δq is the change of the change amount of the heat flux density. Specifically, the change rate corresponding to the i-th heat flux density q can be (T i+1 -T i ) / (q i+1 -q i ).

[0121] The plurality of heat flux densities can form an arithmetic sequence.

[0122] For example, N heat flux densities, where the N heat flux densities form an arithmetic sequence. The first heat flux density has a smaller value q1, and then the heat flux density is increased by a certain gradient value Δq, that is, the value of q2 is q1+Δq, the value of q3 is q2+Δq,..., and the value of q n is q n-1 +Δq. At this time, Δq is a fixed value, and ΔT / Δq only changes with ΔT, which is easier to determine the size of ΔT / Δq. Specifically, the change rate corresponding to the i-th heat flux density q can be (T i+1 -T i ) / Δq.

[0123] The second threshold value can be an experimental reference value obtained by statistical analysis of experimental measurement data, or each first growth rate corresponding to each heat flux density in the interval where the inflection point appears in the boiling curve, wherein the maximum value is taken as the second threshold value.

[0124] The first heat flux density can be determined by judging whether the maximum wall surface superheat appears a sudden rise phenomenon through the condition that whether the first growth rate corresponding to each heat flux density in the interval where the inflection point appears in the boiling curve is greater than or equal to the second threshold value, and then determining the first heat flux density.

[0125] In some embodiments, before comparing the wall surface maximum void fraction corresponding to the first heat flux density with the wall surface critical void fraction, the method further comprises:

[0126] Obtaining the flow rate in the target channel;

[0127] Obtaining the wall surface critical void fraction through a first formula;

[0128] Wherein, the first formula is: α crit =D1·G+D2

[0129] Wherein, α crit represents the wall surface critical void fraction, G represents the flow rate in the target channel, D1 is the first coefficient, and D2 is the first constant.

[0130] In the embodiment, the wall surface critical void fraction is calculated through the first formula, which can obtain a more accurate wall surface critical void fraction value, and is helpful for further accurate judgment of the critical heat flux density.

[0131] The wall surface critical void fraction has certain differences in the existing literature, and there is no unified setting scheme, which causes great uncertainty to the calculation result of the critical heat flux density. Therefore, based on a large number of calculation results of the critical heat flux density under different pressures and flow rates, and by comparing and correcting the calculation results with experimental results, a setting method of the wall surface critical void fraction suitable for the channel is given, that is, the first formula.

[0132] To obtain the wall surface critical void fraction, first, the flow rate in the target channel is obtained, and then according to the first formula, the wall surface critical void fraction of the target channel can be calculated by substituting the flow rate data in the target channel.

[0133] The specific values of the coefficients in the first formula can be set to accurately calculate the wall surface critical void fraction.

[0134] The first formula can be:

[0135] The specific setting of each coefficient in the formula can realize accurate calculation of the wall critical void fraction of the target channel.

[0136] The unit of the flow G is kg·m -2 ·s -1 .

[0137] It should be noted that the flow rate is required when calculating the wall critical void fraction in this strategy. Since the calculation method of the first formula is to obtain the correction by comparing the calculation data with the experimental data, the flow rate of the simulated working condition is limited by the range of working conditions selected in the comparison and correction process. The flow rate can be within 2000-4000 kg·m -2 ·s -1 .

[0138] In some embodiments, a first simulation model of a boiling heat transfer process in a target channel is obtained, comprising:

[0139] A two-phase flow model and a turbulence model are obtained for simulating the flow behavior process of the gas-liquid two-phase flow;

[0140] A boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, and a bubble size distribution model are obtained for simulating the boiling phase change process of the gas-liquid two-phase flow;

[0141] A drag force model, a turbulence dissipation force model, a lift force model, and a wall lubrication force model are obtained for simulating the interaction process between the gas-liquid two-phase flow;

[0142] The two-phase flow model, the turbulence model, the boiling model, the activated core density model, the bubble detachment diameter model, the bubble detachment frequency model, the bubble size distribution model, the drag force model, the turbulence dissipation force model, the lift force model, and the wall lubrication force model are used to determine the first simulation model.

[0143] In this embodiment, the simulation model including the flow behavior process, the boiling phase change process, and the interaction process between the gas-liquid two-phase flow in the boiling heat transfer process is constructed by using multiple specific models, which better realizes the simulation of the boiling heat transfer process of the fluid in the channel, achieves a more comprehensive and realistic simulation effect, and further helps to accurately predict the critical heat flux density of the fluid in the channel.

[0144] The two-phase flow model, the turbulence model, the boiling model, the activated core density model, the bubble detachment diameter model, the bubble detachment frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model can be simulated by simulation analysis software, such as fluid calculation software STAR-CCM+, the related models are established in the software, and specific models used in the related models are selected and called in the software, and finally the first simulation model is determined.

[0145] The two-phase flow model is used to describe the flow of the gas-liquid two-phase. Specifically, the two-phase flow model can adopt the Euler two-phase flow model, which is widely used and can accurately capture the flow state of the gas-liquid two-phase flow. The Euler two-phase flow model includes mass, momentum, and energy conservation equations. When the model is used, the physical properties of the gas-liquid two-phase need to be determined respectively, that is, the gas-liquid two-phase physical property parameters are input respectively.

[0146] For example, the two-phase flow model adopts the Euler two-phase flow model, the two-phase flow model selects the Euler two-phase flow model in STAR-CCM+, and the gas-liquid two-phase physical property parameters including density, saturation temperature, thermal conductivity, dynamic viscosity coefficient, specific heat, internal energy, and molecular weight (only for gas phase) are input respectively.

[0147] The turbulence model is used to describe the turbulence. Specifically, the turbulence model can adopt the realizable K-Epsilon two-layer model, which is widely used in turbulence calculation and has good stability and accuracy in calculating turbulence problems. When the model is used, the coefficients input can adopt the commonly recommended values of the model.

[0148] For example, the turbulence model adopts the realizable K-Epsilon two-layer model, the turbulence model selects the realizable K-Epsilon two-layer model in STAR-CCM+, and the coefficients adopt default values.

[0149] Based on the two-phase flow model and the turbulence model, a closed Navier-Stokes equation set can be formed and used to solve the flow behavior of the gas-liquid two-phase in the channel.

[0150] The boiling model is used to simulate the boiling phase change process of the gas-liquid two-phase. Specifically, the boiling model can adopt the RPI boiling model, and the specific form of the model is as follows: q = (q conv + q evap + q quench )(1-K dry )+ K dry q dry

[0151] Wherein, q is the heat flux, q conv is the convective heat flux, q evapq is the evaporation heat flux quench q is the quenching heat flux dry q is the vapor heat flux dry The calculation method of the effective vapor wall contact area fraction K

[0152] wherein the coefficient β = (α-α crit ) / (1-α crit ), and α is the maximum wall void fraction, and α crit is the critical wall void fraction. It can be seen that the critical wall void fraction α crit is very important for the calculation of the wall heat flux. In the existing literature, the value of the critical wall void fraction is different, and there is no unified setting scheme, which causes great uncertainty to the calculation result of the critical heat flux. Therefore, in the present application, based on a large number of calculation results of the critical heat flux under different pressures and flow rates, and by comparing and correcting the calculation results with experimental results, a setting method of the critical wall void fraction suitable for the channel is given, i.e. the above first formula: α crit =D1·G+D2

[0153] wherein α crit represents the critical wall void fraction, G represents the flow rate in the channel, the unit is kg·m -2 ·s -1 , D1 is the first coefficient, and D2 is the first constant. Specifically, the above first formula can be:

[0154] wherein α crit represents the critical wall void fraction, G represents the flow rate in the channel, the unit is kg·m -2 ·s -1 . When setting, the flow rate in the channel should be confirmed first, and then the critical wall void fraction can be calculated according to the above formula, and then the obtained critical wall void fraction can be input into the RPI boiling model for simulation.

[0155] The above activation core density model is used to describe the density of the boiling activation core of the heating surface. Specifically, the above activation core density model can adopt the Hibiki-Ishii activation core density model, which can be applied to high pressure (19.8 MPa and below) conditions, and has a wide range of applications.

[0156] For example, the above-mentioned activated core density model selects the Hibiki-Ishii activated core density model, when used, the average cavity density is set to 472000 points / m 2 , the wall contact angle and the wall contact angle scale are both 0.722 radians, the cavity length scale is 2.5*10 -6 m, and the density function constants C0, C1, C2 and C3 are-0.01064, 0.48246, -0.22712 and 0.05468 respectively.

[0157] The above-mentioned bubble departure diameter model is used to calculate the diameter of a boiling bubble when it departs from the heating surface. Specifically, the above-mentioned bubble departure diameter model can adopt the Kocamustafaogullari model, which is usually used in combination with Hibiki-Ishii,

[0158] For example, the above-mentioned bubble departure diameter model can adopt the Kocamustafaogullari model, when used, the calibration parameter is set to 0.0015126 m / radian, and the wall contact angle is 0.722 radians.

[0159] The above-mentioned bubble departure frequency model is used to calculate the number of bubbles departing per unit time. Specifically, the above-mentioned bubble departure frequency model can adopt the Cole model, which is widely used in boiling simulation and has good stability and accuracy. When used, the model is introduced without special settings.

[0160] The above-mentioned bubble size distribution model is used to describe the bubble size in each region of the fluid. Specifically, the above-mentioned bubble size distribution model can adopt the pre-integrated S-Gamma model, which takes into account both S-Gamma fragmentation and S-Gamma coalescence. This model has high calculation efficiency and is suitable for engineering calculations.

[0161] For example, the above-mentioned bubble size distribution model can adopt the pre-integrated S-Gamma model, when used, the coalescence factor is 0.01 and the fragmentation factor is 1.0.

[0162] Based on the above-mentioned boiling model, activated core density model, bubble departure diameter model, bubble departure frequency model and bubble size distribution model, the boiling process, including bubble nucleation, growth and departure process, can be calculated, thereby describing the gas-liquid two-phase boiling phase change process.

[0163] After the construction of the two-phase flow model, the turbulence model, the boiling model, the activated core density model, the bubble departure diameter model, the bubble departure frequency model, and the bubble size distribution model, although the equations are closed and can be calculated and solved, the calculation results are still not accurate because the necessary consideration of the interaction between the gas-liquid two phases is not taken into account. Therefore, the interaction between the phases needs to be considered and added to the Navier-Stokes equation to ensure the accuracy of the calculation results. In actual implementation, an additional force term is added to the momentum transfer term in the momentum conservation equation.

[0164] The above-mentioned interaction force between the gas-liquid two phases can be drag force, turbulent dissipation force, lift force, and wall lubrication force. In fact, there are many types of interaction between two phases, but in the process of calculating the boiling process and determining the critical heat flux density, the influence of some interactions is not obvious and can be ignored. Through sensitivity analysis, it is finally determined that the drag force, turbulent dissipation force, lift force, and wall lubrication force need to be considered.

[0165] The above-mentioned drag force is the force exerted by the fluid on the solid with relative velocity, which is opposite to the direction of the relative velocity of the solid with respect to the fluid, that is, the relative motion resistance. The interphase drag force is usually calculated by a function of the drag coefficient. Specifically, the contaminated Tomiyama relationship can be used to calculate the drag coefficient. This model is very mature in simulating water-vapor-water two-phase flow and can accurately calculate the drag force.

[0166] The above-mentioned turbulent dissipation force is a kind of irregular motion state in fluid motion. The energy conversion and formation of turbulent structure in turbulent motion are realized through the process of turbulent dissipation. Turbulent dissipation refers to the process of energy loss due to fluid viscosity and turbulent motion. In the process of turbulent dissipation, kinetic energy and thermal energy in the fluid are converted into each other, and finally converted into thermal energy on a microscopic scale, so that the turbulent structure gradually becomes smaller and is eventually dissipated. The turbulent dissipation force has a significant influence on the calculation results of the critical heat flux density. Under different pressures, the size of the bubble is significantly different. When calculating the turbulent dissipation force, the turbulent Prandtl number is an important input parameter, but the existing calculation method of the turbulent Prandtl number is difficult to be applicable to both large bubbles and small bubbles. Therefore, the above-mentioned turbulent dissipation force is calculated by obtaining an empirical formula for the turbulent Prandtl number through a large number of calculation results, and then the turbulent dissipation force is calculated using the turbulent Prandtl number.

[0167] The above-mentioned lift force is the force perpendicular to the direction of fluid flow. The Sugrue lift coefficient model can be used to calculate the lift force, and the Podowski near-wall adjustment is used to correct the lift force.

[0168] The wall lubrication force is another important interfacial force in the bubble flow regime. Bubbles concentrate near the wall region without adhering to the wall, so the volume fraction of the bubbles decreases in the near-wall region, which generates a lubrication force caused by the surface tension, pushing the bubbles away from the wall and preventing the secondary phase from adhering to the wall. The Antal model can be used to calculate the wall lubrication force.

[0169] Among the above-mentioned gas-liquid two-phase interfacial interaction forces, except for the turbulent dissipation force, all the other forces are mature models that have been widely used, and can be directly selected in the simulation analysis software without special adjustment. When calculating the turbulent dissipation force, the turbulent Prandtl number needs to be calculated, and then the turbulent dissipation force is calculated by using the turbulent Prandtl number.

[0170] For example, in STAR-CCM+, the contaminated Tomiyama correlation is directly selected to calculate the drag coefficient; the Sugrue lift coefficient model is used to calculate the lift, and the Podowski near-wall adjustment is used to correct the lift; the Antal model is used to calculate the wall lubrication force; the turbulent dissipation force calculation method is manually modified, and other settings remain unchanged.

[0171] The above-mentioned first simulation model of the in-channel boiling heat transfer process in the target channel can be obtained by using simulation analysis software, establishing the above-mentioned related models in the software, determining and selecting the specific models used by each related model in the software, and finally determining the first simulation model. The specific models used by each related model are common models in the field of fluid calculation, so they can be found and directly called in fluid calculation software, such as STAR-CCM+ software. They can also be calculated by self-programming, and the equations in the model are solved by code using mathematical methods.

[0172] In some embodiments, obtaining the turbulent dissipation force model comprises:

[0173] Obtaining the pressure in the target channel;

[0174] Obtaining the turbulent Prandtl number by the second formula;

[0175] Obtaining the turbulent dissipation force model according to the obtained turbulent Prandtl number;

[0176] The second formula is: σ h = D3·P 3 + D4·P 2 + D5·P + D6

[0177] Wherein, σ h represents the turbulent Prandtl number, P represents the pressure in the target channel, D3 is the second coefficient, D4 is the third coefficient, D5 is the fourth coefficient, and D6 is the second constant.

[0178] In this embodiment, the turbulent Prandtl number calculated by the second formula can obtain more accurate turbulent Prandtl number, and the turbulent dissipation force can be more accurately calculated by the obtained turbulent Prandtl number, thereby helping to realize accurate prediction of the critical heat flux density of the fluid in the channel.

[0179] The turbulent Prandtl number is an important input parameter in calculating the turbulent dissipation force, and there are various ways of taking values in the existing literature. However, under different flow rates and pressure conditions, the existing ways of taking values will cause the calculated critical heat flux density to be inaccurate in some conditions. Therefore, based on a large number of calculation results of the critical heat flux density under different pressures and flow rates, and by comparing and correcting the calculation results with experimental results, the calculation formula of the turbulent Prandtl number suitable for the channel is obtained, that is, the second formula.

[0180] To obtain the turbulent Prandtl number, the pressure in the target channel is first obtained, and then the pressure data in the target channel is substituted into the second formula to calculate the turbulent Prandtl number of the target channel.

[0181] The specific values of the coefficients in the second formula can be set to accurately calculate the turbulent Prandtl number.

[0182] The second formula can be: σ h = 0.0125P 3 - 0.5563P 2 + 8.3375P - 40.94

[0183] The specific setting method of each coefficient in the formula can realize accurate calculation of the turbulent Prandtl number of the target channel.

[0184] The unit of the pressure is MPa.

[0185] It should be noted that the pressure is required when calculating the turbulent Prandtl number in this strategy. Since the calculation method in this application is obtained by comparing and correcting the calculation data with the experimental data, the pressure of the simulated condition in this strategy is limited by the range of selected conditions in the comparison and correction process, and the pressure can be within 12-18 MPa.

[0186] In some embodiments, a first simulation model is determined by using a two-phase flow model, a turbulent flow model, a boiling model, an activation core density model, a bubble detachment diameter model, a bubble detachment frequency model, a bubble size distribution model, a drag force model, a turbulent dissipation force model, a lift force model, and a wall lubrication force model, including:

[0187] The geometric characteristics of the target channel are determined, and a geometric model is established.

[0188] grid the geometric model to obtain a plurality of grid nodes;

[0189] determine boundary conditions of the geometric model;

[0190] select grid nodes located on the surface of the heating wall as measurement points;

[0191] determine the first simulation model by using the geometric model, the grid nodes, the boundary conditions and the measurement points, and the two-phase flow model, the turbulent flow model, the boiling model, the activated core density model, the bubble detachment diameter model, the bubble detachment frequency model, the bubble size distribution model, the drag force model, the turbulent flow dissipation force model, the lift force model and the wall lubrication force model.

[0192] In the embodiment, by further setting the geometric model, the grid nodes, the boundary conditions and the measurement points suitable for the target channel, the simulation model suitable for the target channel can be obtained, the simulation of the boiling heat transfer process of the fluid in the current target channel can be better realized, a more specific and accurate simulation effect can be achieved, and the accurate prediction of the critical heat flux density of the fluid in the target channel can be further facilitated.

[0193] The above determination of the geometric characteristics of the target channel and the modeling to obtain the geometric model can measure the geometric characteristics of the target channel to be simulated, such as length, cross-sectional shape, etc., and input the geometric characteristics when modeling.

[0194] The above target channel can be a pipe, and can also be a single rod channel. The above pipe can be a straight pipe with a circular or polygonal cross section, and the above single rod channel can be a channel provided with a straight heating rod in the pipe. The interface of the above heating rod can be circular or polygonal.

[0195] The above grid division of the geometric model can be structural grid, because the geometric structure of the channel is relatively simple.

[0196] The above boundary conditions include the inlet flow rate, the inlet flow velocity, the outlet pressure of the pipe, the region where the heating wall is located and the heating power thereof, etc.

[0197] The above inlet flow rate can be used to calculate the wall critical void fraction by substituting it into the first formula, and the above outlet pressure can be used to calculate the turbulent Prandtl number by substituting it into the second formula.

[0198] When the above measurement points are selected and arranged, in order to accurately measure the critical heat flux density, all grid nodes located on the surface of the heating wall are arranged as measurement points.

[0199] The first simulation model can be a general simulation model that is widely applicable to the simulation of the boiling heat transfer process in various rod bundle channels, and further setting or inputting characteristic parameters such as a geometry model, a grid node, a boundary condition and a measurement point applicable to a target channel to obtain a simulation model applicable to the current target channel.

[0200] It should be noted that the implementation manners provided in the embodiments of the present application can be independently implemented, or can be implemented in combination without conflict, and the specific implementation can be determined according to actual needs, which is not limited in the embodiments of the present application.

[0201] For the convenience of those skilled in the art, a set of specific embodiments is provided below. The present application is further described in detail below in combination with specific embodiments.

[0202] The above technical solutions are used to simulate and predict the critical heat flux density in the circular tube channel under high-pressure forced circulation conditions. It is assumed that the length of the heating section in the channel is 1 m, the pressure is 14 MPa, the inlet flow rate is 2000 kg / m 2 s, and the inlet subcooling degree is 108.39℃.

[0203] First, the circular tube channel is geometrically modeled, then the structured network is used for grid division, and then the above parameters in the channel are determined as boundary conditions, and all grid nodes located on the surface of the heating wall are taken as measurement points.

[0204] In the simulation, the following model is constructed, as shown in FIG. 2:

[0205] An Euler two-phase flow model is constructed; a K-Epsilon two-layer model that can be implemented is constructed.

[0206] An RPI boiling model is constructed, and the wall critical void fraction is calculated according to the inlet flow rate, and the value is 0.89; a Hibiki-Ishii activation core density model is constructed, wherein the average cavity density is 472000 points / m 2 , the wall contact angle and the wall contact angle scale are both 0.722 radians, the cavity length scale is 2.5×10 -6 m, and the density function constants C0, C1, C2 and C3 are -0.01064, 0.48246, -0.22712 and 0.05468, respectively; a Kocamustafaogullari model is constructed, and the calibration constant is 1.5126×10 -3 m / radian, and the wall contact angle is 0.722 radian; a Cole model is constructed; a pre-integrated S-Gamma model is constructed, including S-Gamma breakup and S-Gamma coalescence, and the breakup factor is 1.0 and the coalescence factor is 0.01.

[0207] After completing the above model construction and key parameter setting, the following interphase interactions also need to be constructed, as shown in Figure 2:

[0208] A drag force model is constructed, where the drag coefficient is calculated using the contaminated Tomiyama relation; a turbulent dissipative force model is constructed, with a calibration constant of 1, and the Prandtl number is calculated based on pressure, yielding a value of 1.05; a lift force model is constructed, using the Sugrue lift coefficient model to calculate the lift coefficient, and incorporating Podowski near-wall adjustments to correct for the lift; a wall lubrication force model is constructed, using the Antal model to calculate the wall lubrication force, with a calibration coefficient C. w1 The calibration coefficient C is -0.01. w2 It is 0.05.

[0209] After constructing the interphase interactions described above, the simulation of the boiling heat transfer process within the channel can begin. The initial heat flux density is preset to 2200 kW / m³. 2 The boost gradient Δq is 50kW / m 2 After each simulation result stabilizes, the maximum wall superheat and the maximum cavitation fraction on the wall surface are recorded.

[0210] The boiling curve was plotted using the data recorded in the simulation, as shown in Figure 3. Next, the inflection point was located in the range of heat flux density 2450–2650 kW / m³. 2 Between these points, a detailed parameter list of the interval containing the inflection point is provided, along with the calculation of the rate of change ΔT / Δq, specifically (T... i+1 -T i ) / Δq, as shown in Figure 4. From the parameter list in Figure 4, it can be seen that when the heat flux density increases from 2600 kW / m³... 2 Increased to 2650kW / m 2 Subsequently, the maximum wall superheat increased from 8.60℃ to 92.67℃, exhibiting a sudden increase exceeding 50℃, and ΔT / Δq increased from 2.42×10 -6 Increased to 1681.36×10 -6 It can be determined that the power density is 2600 kW / m³. 2 The first heat flux density is given, and the maximum cavitation fraction on the wall surface is 0.91, which exceeds the critical cavitation fraction of 0.89 calculated above. Therefore, the predicted value of the critical heat flux density can be determined to be 2600 kW / m². 2 The predicted values ​​were compared with the experimental values, and the relative error was calculated to be 3.20%.

[0211] To further illustrate the accuracy of the critical heat flux density prediction scheme, Fig. 5 shows a comparison between the predicted values and the experimental values of the critical heat flux density of 20 working conditions. The solid line in the figure is the experimental value of the critical heat flux density, the dashed line is the ±10% error line, and the dots are the predicted values of the critical heat flux density of the 20 working conditions. The test results of the 20 working conditions are distributed near the experimental values and are within the reasonable error range. As can be seen from Fig. 5, the scheme has good prediction ability for the critical heat flux density of the fluid in the channel.

[0212] The execution subject of the fluid critical heat flux density prediction method provided in the embodiments of the present application can be a fluid critical heat flux density prediction device 200. In the embodiments of the present application, the fluid critical heat flux density prediction method is executed by the fluid critical heat flux density prediction device 200, and the fluid critical heat flux density prediction device 200 provided in the embodiments of the present application is described.

[0213] Fig. 6 is a structural schematic diagram of a fluid critical heat flux density prediction device 200 provided in the embodiments of the present application. As shown in Fig. 6, the fluid critical heat flux density prediction device 200 includes:

[0214] A first acquisition module 201 is configured to acquire a first simulation model of a boiling heat transfer process in a target channel. The first simulation model is used to simulate the boiling heat transfer process in the target channel, and the boiling heat transfer process includes a flow behavior process of gas-liquid two-phase, a boiling phase change process, and a gas-liquid two-phase interaction process.

[0215] A second acquisition module 202 is configured to input a plurality of heat flux densities to the first simulation model in sequence to obtain a maximum wall superheat and a maximum wall void fraction corresponding to each heat flux density.

[0216] A first determination module 203 is configured to determine a heat flux density as a first heat flux density if the change rate of the maximum wall superheat corresponding to the heat flux density is greater than or equal to a first threshold value in the plurality of heat flux densities. The change rate represents the change of the maximum wall superheat relative to the change of the heat flux density.

[0217] A comparison module 204 is configured to compare the maximum wall void fraction corresponding to the first heat flux density with a wall critical void fraction.

[0218] A second determination module 205 is configured to determine the critical heat flux density according to the first heat flux density if the maximum wall void fraction corresponding to the first heat flux density is greater than or equal to the wall critical void fraction.

[0219] In some embodiments, the second determination module 205 can be further configured to:

[0220] In a case where the maximum wall cavity fraction corresponding to the first heat flux is less than the critical wall cavity fraction, the second heat flux is taken as the first heat flux, and the comparison between the maximum wall cavity fraction corresponding to the first heat flux and the critical wall cavity fraction is performed again until the determination of the critical heat flux is completed.

[0221] In a case where the plurality of heat fluxes are arranged in ascending order, the second heat flux is the next heat flux of the first heat flux.

[0222] In some embodiments, the first determination module 203 can be configured to:

[0223] draw a boiling curve according to the plurality of heat fluxes and the corresponding plurality of maximum wall superheats;

[0224] determine an interval in which an inflection point appears in the boiling curve;

[0225] obtain a first growth rate corresponding to each heat flux in the interval, the first growth rate representing a degree of growth of the maximum wall superheat with the growth of the heat flux;

[0226] determine the heat flux corresponding to a case where the first growth rate is greater than or equal to a second threshold value as the first heat flux.

[0227] In some embodiments, the comparison module 204 can be further configured to:

[0228] obtain a flow rate in the target channel;

[0229] obtain the critical wall cavity fraction through a first formula;

[0230] wherein the first formula is: α crit =D1·G+D2

[0231] wherein α crit represents the critical wall cavity fraction, G represents the flow rate in the target channel, D1 is a first coefficient, and D2 is a first constant.

[0232] In some embodiments, the first obtaining module 201 can be configured to:

[0233] obtain a two-phase flow model and a turbulent flow model for simulating a flow behavior process of the gas-liquid two-phase flow;

[0234] obtain a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, and a bubble size distribution model for simulating a boiling phase change process of the gas-liquid two-phase flow;

[0235] obtain a drag force model, a turbulent flow dissipation force model, a lift force model, and a wall lubrication force model for simulating an interaction process between the gas-liquid two-phase flows;

[0236] The first simulation model is determined by using a two-phase flow model, a turbulence model, a boiling model, an activation core density model, a bubble detachment diameter model, a bubble detachment frequency model, a bubble size distribution model, a drag force model, a turbulence dissipation force model, a lift force model, and a wall lubrication force model.

[0237] In some embodiments, the first obtaining module 201 can be configured to:

[0238] obtain the pressure in the target channel;

[0239] obtain the turbulent Prandtl number by using a second formula;

[0240] obtain the turbulence dissipation force model according to the obtained turbulent Prandtl number;

[0241] wherein the second formula is: σ h =D3·P 3 +D4·P 2 +D5·P+D6

[0242] wherein σ h represents the turbulent Prandtl number, P represents the pressure in the target channel, D3 is a second coefficient, D4 is a third coefficient, D5 is a fourth coefficient, and D6 is a second constant.

[0243] In some embodiments, the first obtaining module 201 can be further configured to:

[0244] determine the geometric characteristics of the target channel, and obtain a geometric model by modeling;

[0245] divide the geometric model into a plurality of grid nodes;

[0246] determine the boundary conditions of the geometric model;

[0247] select a grid node located on the surface of the heated wall as a measurement point;

[0248] update the first simulation model according to the geometric model, the grid nodes, the boundary conditions, and the measurement point.

[0249] Since the fluid critical heat flux density prediction device 200 adopts all the technical solutions of the fluid critical heat flux density prediction method of the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be repeated here.

[0250] FIG. 7 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application.

[0251] The electronic device can include a processor 301 and a memory 302 having computer program instructions stored therein.

[0252] In particular, the processor 301 can include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to perform the operations of the embodiments of the application.

[0253] The memory 302 can include mass storage for data or instructions. As an example and not by way of limitation, the memory 302 can include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc (e.g., a compact disc (CD) or a digital versatile disc (DVD)), a solid-state drive (SSD), a USB drive, or a combination of two or more of these. Where appropriate, the memory 302 can include removable or non-removable (or fixed) media, where appropriate. Where appropriate, the memory 302 can be internal or external to the integrated gateway disaster recovery device. In some embodiments, the memory 302 is non-volatile, solid-state memory.

[0254] In some embodiments, the memory 302 can include read-only memory (ROM), random-access memory (RAM), a disk storage medium, an optical storage medium, a flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (by one or more processors), is operable to perform operations described with reference to the methods according to an aspect of the application.

[0255] The processor 301 implements any one of the above-described fluid critical heat flux density prediction methods by reading and executing computer program instructions stored in the memory 302.

[0256] In one example, the electronic device can further include a communication interface 303 and a bus 310. As shown in FIG. 7, the processor 301, the memory 302, and the communication interface 303 are connected by the bus 310 and complete communication with each other.

[0257] The communication interface 303 is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the application.

[0258] Bus 310 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0259] The electronic device can execute the method for predicting the critical heat flux density of a fluid in the embodiments of this application, thereby realizing the method and apparatus for predicting the critical heat flux density of a fluid as described in conjunction with Figures 1 and 6.

[0260] Furthermore, in conjunction with the fluid critical heat flux prediction method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the fluid critical heat flux prediction methods in the above embodiments.

[0261] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0262] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0263] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0264] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in 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, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0265] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method of predicting the critical heat flux of a fluid, characterized in that, The method comprises the following steps: obtaining a first simulation model of a boiling heat transfer process in a target channel; wherein the first simulation model is used to simulate the boiling heat transfer process in the target channel, and the boiling heat transfer process comprises a flow behavior process of gas-liquid two-phase flow, a boiling phase change process, and an interaction process between gas-liquid two-phase flow; inputting a plurality of heat fluxes into the first simulation model in sequence to obtain a maximum wall surface superheat and a maximum wall surface void fraction corresponding to each heat flux; determining a first heat flux from the plurality of heat fluxes, wherein a change rate of the maximum wall surface superheat corresponding to the first heat flux is greater than or equal to a first threshold value; wherein the change rate represents a change of the maximum wall surface superheat relative to a change of the heat flux; comparing the maximum wall surface void fraction corresponding to the first heat flux with a wall surface critical void fraction; in a case where the maximum wall surface void fraction corresponding to the first heat flux is greater than or equal to the wall surface critical void fraction, determining a critical heat flux according to the first heat flux.

2. The method of claim 1, wherein, After the comparison of the maximum wall surface void fraction corresponding to the first heat flux with the wall surface critical void fraction, the method further comprises: in a case where the maximum wall surface void fraction corresponding to the first heat flux is less than the wall surface critical void fraction, taking a second heat flux as the first heat flux, and returning to compare the maximum wall surface void fraction corresponding to the first heat flux with the wall surface critical void fraction until the determination of the critical heat flux is completed; wherein, in a case where the plurality of heat fluxes are arranged in sequence from small to large, the second heat flux is a next heat flux of the first heat flux.

3. The method of claim 1, wherein, The determination of the first heat flux from the plurality of heat fluxes, wherein a change rate of the maximum wall surface superheat corresponding to the first heat flux is greater than or equal to the first threshold value, comprises: drawing a boiling curve according to the plurality of heat fluxes and a plurality of maximum wall surface superheats corresponding to the plurality of heat fluxes; determining an interval in which an inflection point appears in the boiling curve; obtaining a first growth rate corresponding to each heat flux in the interval, wherein the first growth rate represents a degree of growth of the maximum wall surface superheat with the growth of the heat flux; determining the first heat flux corresponding to the heat flux when the first growth rate is greater than or equal to a second threshold value.

4. The method of claim 1, wherein, Before the comparison of the maximum wall surface void fraction corresponding to the first heat flux with the wall surface critical void fraction, the method further comprises: obtaining a flow rate in the target channel; obtaining the wall surface critical void fraction through a first formula; wherein the first formula is: a crit = D1 G + D2 wherein α crit represents a wall critical cavity fraction, G represents the flow rate in the target channel, D1 is a first coefficient, and D2 is a first constant.

5. The method of claim 1, wherein, The obtaining of the first simulation model of the boiling heat transfer process in the target channel comprises: obtaining a two-phase flow model and a turbulent flow model to simulate the flow behavior process of gas-liquid two-phase flow; obtaining a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, and a bubble size distribution model to simulate the boiling phase change process of gas-liquid two-phase flow; obtain a drag force model, a turbulent dissipation force model, a lift force model, and a wall lubrication force model for simulating the interaction process between the gas-liquid two phases; determine the first simulation model by using the two-phase flow model, the turbulent model, the boiling model, the activated core density model, the bubble departure diameter model, the bubble departure frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model.

6. The method of claim 5, wherein, The obtaining the turbulent dissipation force model comprises: obtain a pressure in the target channel; obtain a turbulent Prandtl number by a second formula; obtain the turbulent dissipation force model according to the obtained turbulent Prandtl number; The second formula is: σ h = D3*P 3 + D4*P 2 + D5*P + D6 where σ h represents a turbulent Prandtl number, P represents the pressure within the target channel, D3 is a second coefficient, D4 is a third coefficient, D5 is a fourth coefficient, and D6 is a second constant.

7. The method of claim 5, wherein, The determination of the first simulation model by using the two-phase flow model, the turbulent model, the boiling model, the activated core density model, the bubble departure diameter model, the bubble departure frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model comprises: determine a geometric feature of the target channel to obtain a geometric model; divide a grid of the geometric model to obtain a plurality of grid nodes; determine a boundary condition of the geometric model; select the grid node located on the surface of the heating wall as a measurement point; determine the first simulation model by using the geometric model, the grid node, the boundary condition, and the measurement point, and the two-phase flow model, the turbulent model, the boiling model, the activated core density model, the bubble departure diameter model, the bubble departure frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model.

8. A device for predicting the critical heat flux of a fluid, characterized in that comprise: a first obtaining module configured to obtain a first simulation model of a channel boiling heat transfer process of a target channel; wherein the first simulation model is used to simulate the channel boiling heat transfer process in the target channel, and the channel boiling heat transfer process comprises a flow behavior process of gas-liquid two phases, a boiling phase change process, and an interaction process between the gas-liquid two phases; a second obtaining module configured to input a plurality of heat fluxes to the first simulation model in sequence to obtain a maximum wall superheat and a maximum wall void fraction corresponding to each heat flux; a first determining module configured to determine a first heat flux as a heat flux corresponding to a maximum wall superheat whose change rate is greater than or equal to a first threshold value in the plurality of heat fluxes; wherein the change rate represents a change of the maximum wall superheat relative to a change of the heat flux; a comparing module configured to compare the maximum wall void fraction corresponding to the first heat flux with a critical wall void fraction; a second determining module configured to determine the first heat flux as a critical heat flux in a case that the maximum wall void fraction corresponding to the first heat flux is greater than or equal to the critical wall void fraction.

9. The prediction device of claim 8, wherein, The second determining module is further configured to: In a case where the maximum wall cavity fraction corresponding to the first heat flux is less than the critical wall cavity fraction, a second heat flux is taken as the first heat flux, and a comparison between the maximum wall cavity fraction corresponding to the first heat flux and the critical wall cavity fraction is performed again until the determination of the critical heat flux is completed. In a case where the plurality of heat fluxes are arranged in ascending order, the second heat flux is a next heat flux of the first heat flux.

10. The prediction device of claim 8, wherein, The first determination module is further configured to: draw a boiling curve according to the plurality of heat fluxes and the plurality of maximum wall superheats corresponding to the plurality of heat fluxes; determine an interval in which an inflection point appears in the boiling curve; obtain a first growth rate corresponding to each heat flux in the interval, the first growth rate representing a degree of growth of the maximum wall superheat with the growth of the heat flux; determine the heat flux corresponding to a case where the first growth rate is greater than or equal to a second threshold value as the first heat flux.

11. The prediction device of claim 8, wherein, The comparison module is further configured to: obtain a flow rate in the target channel; obtain the critical wall cavity fraction by a first formula; wherein the first formula is: a crit = D1 G + D2 wherein α crit represents the wall critical cavity fraction, G represents the flow rate in the target passage, D1 is a first coefficient, and D2 is a first constant.

12. The prediction device of claim 8, wherein, The first obtaining module is configured to: obtain a two-phase flow model and a turbulent flow model for simulating the flow behavior process of the gas-liquid two-phase flow; obtain a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, and a bubble size distribution model for simulating the boiling phase change process of the gas-liquid two-phase flow; obtain a drag force model, a turbulent dissipation force model, a lift force model, and a wall lubrication force model for simulating the interaction process between the gas-liquid two-phase flow; determine the first simulation model by using the two-phase flow model, the turbulent flow model, the boiling model, the activated core density model, the bubble detachment diameter model, the bubble detachment frequency model, the bubble size distribution model, the drag force model, the turbulent dissipation force model, the lift force model, and the wall lubrication force model.

13. The prediction device of claim 12, wherein, The first obtaining module is further configured to: obtain a pressure in the target channel; obtain a turbulent Prandtl number by a second formula; obtain the turbulent dissipation force model according to the obtained turbulent Prandtl number; wherein the second formula is: σ h = D3*P 3 + D4*P 2 + D5*P + D6 where σ h represents a turbulent Prandtl number, P represents the pressure within the target channel, D3 is a second coefficient, D4 is a third coefficient, D5 is a fourth coefficient, and D6 is a second constant.

14. The prediction device of claim 12, wherein, The first obtaining module is further configured to: determine a geometric feature of the target channel to obtain a geometric model; divide the geometric model into a plurality of grid nodes; determine a boundary condition of the geometric model; select a grid node located on the surface of the heating wall as a measurement point; update the first simulation model according to the geometric model, the grid node, the boundary condition, and the measurement point.

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