Method, device and equipment for predicting critical heat flux of fluid and storage medium

Abstract

The invention provides a fluid critical heat flux prediction method and device, equipment and a storage medium. The method comprises the steps that a first simulation model of the in-channel boiling heat transfer process of a target channel is acquired; a plurality of heat flow densities are sequentially input into the first simulation model, and the maximum wall surface superheat degree and the maximum wall surface void fraction corresponding to each heat flow density are obtained; the heat flow density with the change rate of the corresponding maximum wall surface superheat degree larger than or equal to a first threshold value in the multiple heat flow densities is determined as the first heat flow density; comparing a wall surface maximum void fraction corresponding to the first heat flux with a wall surface boundary void fraction; and under the condition that the wall surface maximum void fraction corresponding to the first heat flow density is larger than or equal to the wall surface boundary void fraction, the critical heat flow density is determined according to the first heat flow density. According to the prediction method for the critical heat flux of the fluid, efficient and accurate prediction of the critical heat flux can be realized.

Description

Technical Field

[0001] The present application relates to the field of nuclear power technology, and in particular to a method, device, equipment and storage medium for predicting critical heat flux density of a fluid. Background Art

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

[0003] When the reactor is operating normally, it is usually necessary to avoid boiling phase change in the core rod bundle channel and maintain a single-phase flow state. Once boiling heat transfer occurs, a large number of boiling bubbles will appear in the core rod bundle channel and transform into a vapor-liquid two-phase flow state. In particular, when the heat flux density of the fuel element exceeds the critical heat flux density, a large number of boiling bubbles will be attached to the surface of the fuel element and difficult to detach, which will lead to a significant deterioration in the heat transfer capacity and a sharp increase in the surface temperature. The fuel element cladding may be damaged, accidents may occur, and serious consequences may occur.

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

[0005] In the existing critical heat flux determination methods, the experimental method is often used. However, since the critical heat flux experimental conditions of fuel elements in pressurized water reactors are very harsh, there are problems such as high difficulty, high cost and long cycle. Summary of the invention

[0006] The present application aims to propose a method, device, equipment and storage medium for predicting the critical heat flux density of a fluid, which can predict the critical heat flux density by a simulation method, thereby achieving a more efficient and accurate prediction of the critical heat flux density.

[0007] According to the first aspect of the present application, a method for predicting a fluid critical heat flux density comprises the following steps:

[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, wherein the boiling heat transfer process includes a gas-liquid two-phase flow behavior process, a boiling phase change process, and an interaction process between the gas-liquid two-phases;

[0009] Inputting a plurality of heat flux densities into the first simulation model in sequence to obtain a maximum wall superheat and a maximum wall cavitation fraction corresponding to each of the heat flux densities;

[0010] Determine, among the multiple heat flux densities, a heat flux density corresponding to which the change rate of the maximum wall superheat is greater than or equal to a first threshold as a first heat flux density; wherein the change rate represents a change in the maximum wall superheat relative to a change in the heat flux density;

[0011] comparing the maximum cavitation fraction of the wall corresponding to the first heat flux with the critical cavitation fraction of the wall;

[0012] When the maximum wall cavitation fraction corresponding to the first heat flux is greater than or equal to the critical wall cavitation fraction, a critical heat flux is determined according to the first heat flux.

[0013] According to some embodiments of the present application, after comparing the maximum wall cavitation fraction corresponding to the first heat flux density with the critical wall cavitation fraction, the method further includes:

[0014] In the case where the maximum wall cavitation fraction corresponding to the first heat flux is less than the wall boundary cavitation fraction, the second heat flux is used as the first heat flux, and the comparison between the maximum wall cavitation fraction corresponding to the first heat flux and the wall boundary cavitation fraction is returned to be performed until the critical heat flux is determined;

[0015] Wherein, when the plurality of heat flux densities are arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density.

[0016] According to some embodiments of the present application, determining, among the multiple heat flux densities, a heat flux density corresponding to a change rate of the maximum wall superheat that is greater than or equal to the first threshold as the first heat flux density includes:

[0017] Draw a boiling curve according to the plurality of heat flux densities and the corresponding plurality of maximum wall superheats;

[0018] Determine the interval where the inflection point appears in the boiling curve;

[0019] Obtaining a first growth rate corresponding to each of the heat flux densities in the interval, wherein the first growth rate represents the degree to which the maximum wall superheat increases with the increase of the heat flux density;

[0020] The heat flux density corresponding to when the first growth rate is greater than or equal to a second threshold is determined as the first heat flux density.

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

[0022] Obtaining the flow rate in the target channel;

[0023] The wall boundary cavitation fraction is obtained by a first formula;

[0024] Among them, the first formula is:

[0025] α crit =D1·G+D2

[0026] Among them, α crit represents the wall boundary cavitation fraction, G represents the flow rate in the target channel, D1 is the first coefficient, and D2 is the first constant.

[0027] According to some embodiments of the present application, the step of obtaining a first simulation model of a boiling heat transfer process in a target channel includes:

[0028] Obtaining a two-phase flow model and a turbulence model for simulating the flow behavior process of the gas-liquid two-phase;

[0029] 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 transition process of the gas-liquid two phases;

[0030] Obtaining 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;

[0031] The first simulation model is determined 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 model, the turbulence dissipation force model, the lift model and the wall lubrication force model.

[0032] According to some embodiments of the present application, obtaining the turbulent dissipation force model includes:

[0033] obtaining the pressure in the target channel;

[0034] The turbulent Prandtl number is obtained by the second formula;

[0035] According to the obtained turbulent Prandtl number, the turbulent dissipation force model is obtained;

[0036] Wherein, the second formula is:

[0037] σ h=D3·P 3 +D4·P 2 +D5·P+D6

[0038] Among them, 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.

[0039] According to some embodiments of the present application, the determining of 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 model, the turbulence dissipation force model, the lift model and the wall lubrication force model includes:

[0040] Determine the geometric features of the target channel and obtain a geometric model by modeling;

[0041] Meshing the geometric model to obtain a plurality of mesh nodes;

[0042] determining boundary conditions of the geometric model;

[0043] Selecting the grid nodes located on the surface of the heating wall as measurement points;

[0044] The first simulation model is determined using the geometric model, the grid nodes, the boundary conditions and the measurement points, as well as 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 model, the turbulence dissipation force model, the lift model and the wall lubrication force model.

[0045] According to the second aspect of the present application, a device for predicting the critical heat flux density of a fluid includes:

[0046] A first acquisition module is used to acquire 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 gas-liquid two-phase flow behavior process, a boiling phase change process, and a gas-liquid two-phase interaction process;

[0047] A second acquisition module is used to sequentially input a plurality of heat flux densities into the first simulation model to obtain a maximum wall superheat and a maximum wall cavitation fraction corresponding to each of the heat flux densities;

[0048] A first determination module is used to determine, among the multiple heat flux densities, a heat flux density corresponding to which the change rate of the maximum wall superheat is greater than or equal to a first threshold as a first heat flux density; wherein the change rate represents a change in the maximum wall superheat relative to a change in the heat flux density;

[0049] A comparison module, configured to compare the maximum wall cavitation fraction corresponding to the first heat flux with the critical wall cavitation fraction;

[0050] The second determination module is configured to determine the first heat flux as a critical heat flux when the maximum wall cavitation fraction corresponding to the first heat flux is greater than or equal to the critical wall cavitation fraction.

[0051] According to an electronic device of an embodiment of the third aspect of the present application, the device includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method for predicting the critical heat flux density of a fluid as described in any one of the embodiments of the first aspect are implemented.

[0052] According to the computer-readable storage medium of the fourth aspect embodiment of the present application, computer-executable instructions are stored, and the computer-executable instructions are used to execute the method for predicting the critical heat flux density of the fluid as described in the above-mentioned first aspect embodiment.

[0053] In the embodiment of the present application, the boiling heat transfer process in the channel can be simulated through the first simulation model. The model simulation method can effectively improve efficiency, and can simulate a variety of heat flux conditions, quickly find the corresponding heat flux condition when the maximum wall superheat is significantly increased, that is, the first heat flux. It can also simulate a variety of different experimental design schemes and accident conditions in large quantities, shorten the experimental cycle, and reduce experimental costs. In addition, the present application adds a step of judging the size of the maximum wall cavitation fraction value corresponding to the first heat flux condition. The maximum wall cavitation fraction value reaches the critical wall cavitation fraction before the first heat flux can be confirmed as the critical heat flux, which improves the accuracy of critical heat flux prediction and judgment.

[0054] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0056] Figure 1 It is a schematic flow chart of an embodiment of a method for predicting critical heat flux density of a fluid of the present application;

[0057] Figure 2 It is a schematic diagram of constructing a first simulation model of an embodiment of a method for predicting critical heat flux density of a fluid of the present application;

[0058] Figure 3 It is a schematic diagram of a boiling curve of an embodiment of a method for predicting critical heat flux density of a fluid of the present application;

[0059] Figure 4 A detailed parameter list of the interval where the inflection point of the boiling curve is located in one embodiment of the method for predicting the critical heat flux density of a fluid of the present application;

[0060] Figure 5 It is a schematic diagram comparing the predicted value of critical heat flux density and the experimental value of one embodiment of the method for predicting the critical heat flux density of a fluid of the present application;

[0061] Figure 6 It is a structural schematic diagram of an embodiment of a device for predicting critical heat flux density of a fluid of the present application;

[0062] Figure 7 It is a schematic diagram of the hardware structure of an embodiment of the electronic device of the present application. DETAILED DESCRIPTION

[0063] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0064] In the description of this 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 relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0065] In the description of the present application, it should be understood that the descriptions involving orientation, such as the orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0066] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0067] The technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described below are only part of the embodiments of the present application, not all of the embodiments.

[0068] Figure 1 This is a flow chart of a method for predicting the critical heat flux density of a fluid according to an embodiment of the present application. Figure 1 , the embodiments of the present application are further described.

[0069] The present application embodiment provides a method for predicting the critical heat flux density of a fluid, and the method for predicting the critical heat flux density of a fluid comprises the following steps:

[0070] Step 101, 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 gas-liquid two-phase flow behavior process, a boiling phase change process, and an interaction process between the gas-liquid two-phases;

[0071] Step 102: input a plurality of heat flux densities into the first simulation model in sequence to obtain a maximum wall superheat and a maximum wall cavitation fraction corresponding to each heat flux density;

[0072] Step 103: Determine, among the multiple heat flux densities, a heat flux density corresponding to a change rate of the maximum wall superheat that is greater than or equal to a first threshold value as a first heat flux density; wherein the change rate represents a change in the maximum wall superheat relative to a change in the heat flux density;

[0073] Step 104, comparing the maximum cavitation fraction of the wall corresponding to the first heat flux density with the critical cavitation fraction of the wall;

[0074] Step 105 : determining a critical heat flux density according to the first heat flux density when the maximum wall cavitation fraction corresponding to the first heat flux density is greater than or equal to the critical wall cavitation fraction.

[0075] In the embodiment of the present application, the boiling heat transfer process in the channel can be simulated through the first simulation model. The model simulation method can effectively improve efficiency, and can simulate a variety of heat flux conditions, quickly find the corresponding heat flux condition when the maximum wall superheat is significantly increased, that is, the first heat flux. It can also simulate a variety of different experimental design schemes and accident conditions in large quantities, shorten the experimental cycle, and reduce experimental costs. In addition, the present application adds a step of judging the size of the maximum wall cavitation fraction value corresponding to the first heat flux condition. The maximum wall cavitation fraction value reaches the critical wall cavitation fraction before the first heat flux can be confirmed as the critical heat flux, which improves the accuracy of critical heat flux prediction and judgment.

[0076] In the above step 101, a first simulation model of the boiling heat transfer process in the target channel is obtained; 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 the flow behavior process of the gas-liquid two phases, the boiling phase change process and the interaction process between the gas-liquid two phases.

[0077] The target channel is a channel to be simulated, and the channel may be but is not limited to a core rod bundle channel of a reactor. When the reactor is operating normally, it is usually necessary to avoid boiling phase change in the core rod bundle channel and maintain a single-phase flow state.

[0078] Under certain conditions, boiling heat transfer will still occur in the above-mentioned channels. When the above-mentioned channels need to strengthen local heat transfer, boiling heat transfer will occur on the surface of the fuel element; when the above-mentioned channels are in accident conditions, due to changes in the reactivity and thermal hydraulic parameters in the core, local supercooling boiling will occur on the surface of the fuel element. In the above-described situation, a large number of boiling bubbles will appear in the core bundle channel and transform into a vapor-liquid two-phase flow state.

[0079] The above-mentioned 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.

[0080] In the above step 102, a plurality of heat flux densities are sequentially input into the first simulation model to obtain the maximum wall superheat and the maximum wall cavitation fraction corresponding to each heat flux density.

[0081] After completing the construction of the above-mentioned first simulation model, the heat flux at different times can be used to solve the equations in the model to obtain the implementation parameters of the fluid in the channel and the heated wall at the corresponding time. The above-mentioned solution process can be directly run in the simulation analysis software, such as the fluid calculation software STAR-CCM+, to complete the automatic solution. The data that need to be monitored in the above-mentioned implementation parameters include the maximum wall superheat and the maximum wall cavitation fraction of the heated wall. Specifically, by inputting the heat flux into the first simulation model, the maximum wall superheat and the maximum wall cavitation fraction corresponding to the heat flux can be obtained.

[0082] The above heat flux density is the heat flux density on the surface of the fuel element in the channel.

[0083] The above-mentioned maximum wall superheat and maximum wall cavitation fraction can characterize the boiling state where boiling heat transfer occurs on the surface of the fuel element.

[0084] The step of sequentially inputting a plurality of heat flux densities into the first simulation model may be sequentially inputting N heat flux densities into the first simulation model, wherein N is an integer greater than 1.

[0085] Inputting N heat flux densities into the first simulation model in sequence may be N input steps, wherein each input step is to input a heat flux density into the first simulation model at the beginning of the simulation, and when the simulation result reaches a steady state, the maximum wall superheat and the maximum wall cavitation fraction corresponding to the currently input heat flux density are obtained.

[0086] For example, N heat fluxes are sequentially input into the first simulation model. The heat flux q1 is input into the first simulation model at the beginning of the simulation for the first time. When the simulation result reaches a steady state, the maximum wall superheat T1 and the maximum wall cavitation fraction α1 corresponding to q1 are obtained. The heat flux q2 is input into the first simulation model at the beginning of the simulation for the second time. When the simulation result reaches a steady state, the maximum wall superheat T2 and the maximum wall cavitation fraction α2 corresponding to q2 are obtained. ..., the heat flux q is input into the first simulation model at the beginning of the simulation for the Nth time. n , when the simulation results reach a steady state, we get q n The corresponding maximum wall superheat T n and the maximum cavitation fraction α of the wall n .

[0087] In the above, a plurality of heat flux densities are sequentially input into the first simulation model, wherein the plurality of heat flux densities may be arranged from small to large to form a sequence which may be an arithmetic sequence.

[0088] For example, N heat flux densities are sequentially input into the first simulation model, and the N heat flux densities are arranged from small to large to form an arithmetic progression. Among them, the first heat flux density is q1 with a smaller value, and then the heat flux density is increased with a certain gradient value Δq, that is, the second heat flux density q2 is q1+Δq, the third heat flux density q3 is q2+Δq, ..., the Nth heat flux density q n The value of q n-1 +Δq.

[0089] The step of sequentially inputting a plurality of heat flux densities into the first simulation model may be a multiple input step in which the heat flux densities are sequentially input into the first simulation model in an ascending order of the heat flux values.

[0090] For example, the step of sequentially inputting N heat flux densities into the first simulation model may be N input steps in which the N heat flux densities are sequentially input into the first simulation model in an ascending order of the heat flux values.

[0091] In the above step 103, the heat flux density whose corresponding maximum wall superheat change rate is greater than or equal to the first threshold value among multiple heat flux densities is determined as the first heat flux density; wherein the change rate represents the change of the maximum wall superheat relative to the change of the heat flux density.

[0092] The above-mentioned rate of change of the maximum wall superheat can characterize the change of the maximum wall superheat relative to the change of the heat flux density.

[0093] For example, when N heat flux densities are sequentially input into the first simulation model, the above-mentioned change rate can represent that when multiple heat flux densities are arranged in ascending order, the i+1th heat flux density q i+1 The corresponding maximum wall superheat T i+1 Relative to the i-th heat flux q i The corresponding maximum wall superheat T i , where 1≤i≤N, and N is an integer greater than 1.

[0094] For example, the change rate of the maximum wall superheat can be a measure of the change in the maximum wall superheat relative to the change in the heat flux Δq, that is, the change rate can be ΔT / Δq. Specifically, the i-th heat flux q i The corresponding rate of change can be (T i+1 -T i ) / (q i+1 -q i ), further, when Δq is a fixed value, the i-th heat flux q i The corresponding rate of change can be (T i+1 -T i ) / Δq.

[0095] The change rate of the above-mentioned maximum wall superheat can also be used to characterize the change of the next maximum wall superheat relative to the current maximum wall superheat.

[0096] For example, the change rate of the maximum wall superheat can be expressed as i+1 Relative to the change of the current maximum wall superheat Ti, that is, the change rate corresponding to the i-th heat flux qi can be (T i+1 -T i ) / T i .

[0097] The first threshold may be an experimental reference value obtained by statistics of experimental measurement data, or N change rates corresponding to the N heat flux densities may be calculated, with the maximum value being taken as the first threshold.

[0098] In the above step 104, the maximum wall cavitation fraction corresponding to the first heat flux density is compared with the critical wall cavitation fraction;

[0099] The above-mentioned critical cavitation fraction of the wall surface corresponds to the critical cavitation fraction under the current working conditions of the target channel. The specific value of the critical cavitation fraction can be obtained by statistically analyzing experimental measurement data to obtain an experimental reference value, or by substituting the current working conditions of the target channel into the calculation formula to obtain a theoretical reference value.

[0100] In the above step 105, when the maximum wall cavitation fraction corresponding to the first heat flux is greater than or equal to the critical wall cavitation fraction, the critical heat flux is determined according to the first heat flux.

[0101] After boiling phase transition occurs in the core bundle channel and boiling heat transfer occurs for a period of time, when the maximum wall superheat suddenly rises, it means that the heat transfer capacity has decreased and is close to the critical heat flux density. However, since it still has a certain heat transfer capacity, it has not reached the actual critical value; as the heat flux density continues to increase, once the maximum cavitation share of the wall of the boiling bubbles reaches the critical cavitation share of the wall, a large number of boiling bubbles will be difficult to detach from the surface of the fuel element, which will cause a significant deterioration in the heat transfer capacity. At this time, the actual critical heat flux density is reached. Once the heat flux density in the channel is higher than the critical heat flux density, the fuel element cladding may be damaged, resulting in a serious accident.

[0102] Therefore, the prediction method of the critical heat flux density of the fluid in the present application determines the heat flux density corresponding to the sudden increase in the maximum wall superheat as the first heat flux density; the above-mentioned determination of the critical heat flux density based on the first heat flux density can be that when the maximum wall cavitation fraction corresponding to the first heat flux density is greater than or equal to the critical wall cavitation fraction, the first heat flux density is determined as the critical heat flux density.

[0103] The above step 103 is actually to determine the corresponding heat flux density as the first heat flux density when the maximum wall superheat suddenly increases, and to judge whether the maximum wall superheat suddenly increases by the condition of whether the rate of change of the maximum wall superheat is greater than or equal to the first threshold, so as to determine the first heat flux density.

[0104] The above steps 104 and 105 are actually a judgment process of whether the maximum cavitation fraction of the wall has reached a critical value. Only when the maximum cavitation fraction of the wall has reached the critical cavitation fraction of the wall can the first heat flux be confirmed as the critical heat flux, which effectively improves the accuracy of critical heat flux prediction and judgment.

[0105] In some embodiments, after comparing the maximum wall cavitation fraction corresponding to the first heat flux density with the wall boundary cavitation fraction, the method further includes:

[0106] When the maximum wall cavitation fraction corresponding to the first heat flux is less than the boundary cavitation fraction of the wall, the first heat flux is updated to the second heat flux, and the maximum wall cavitation fraction corresponding to the first heat flux is compared with the boundary cavitation fraction of the wall, until the critical heat flux is determined;

[0107] In which, when a plurality of heat flux densities are arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density.

[0108] In this embodiment, as the heat flux density increases, the corresponding maximum wall cavitation fraction will gradually increase. Therefore, it is no longer necessary to compare and judge the maximum wall cavitation fraction corresponding to other heat flux densities less than the first heat flux density with the critical wall cavitation fraction, thereby reducing the comparison and judgment time and improving the prediction efficiency of the critical heat flux density.

[0109] In the above-mentioned case where at least one heat flux density is arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density, and the case where multiple heat flux densities are arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density; it can also be that when multiple heat flux densities greater than a certain value are arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density.

[0110] For example, N heat flux densities are sequentially input into the first simulation model. When the N heat flux densities are arranged in ascending order, the next heat flux density greater than the first heat flux density is the second heat flux density.

[0111] For example, only the first heat flux density and other heat flux densities greater than the first heat flux density are arranged in order from small to large, and the next heat flux density of the first heat flux density is the second heat flux density.

[0112] In some cases, other heat flux densities greater than the first heat flux density may be arranged in order from small to large, with the first one being the second heat flux density.

[0113] The above-mentioned determination of the critical heat flux density can be completed by, when the maximum wall cavitation fraction corresponding to the first heat flux density is smaller than the boundary cavitation fraction of the wall, continuing to compare the corresponding maximum wall cavitation fraction with the boundary cavitation fraction of the wall of the second heat flux density, until a heat flux density that satisfies the condition that the corresponding maximum wall cavitation fraction is greater than or equal to the boundary cavitation fraction of the wall is obtained, and is determined as the critical heat flux density.

[0114] In some embodiments, determining a heat flux density whose corresponding maximum wall superheat change rate is greater than or equal to a first threshold value among a plurality of heat flux densities as a first heat flux density includes:

[0115] Draw a boiling curve according to multiple heat flux densities and corresponding multiple maximum wall superheats;

[0116] Determine the interval where the inflection point occurs in the boiling curve;

[0117] Obtaining the first growth rate corresponding to each heat flux density in the interval, the first growth rate represents the degree to which the maximum wall superheat increases with the increase of the heat flux density;

[0118] The heat flux density corresponding to when the first growth rate is greater than or equal to the second threshold is determined as the first heat flux density.

[0119] In this embodiment, by drawing the boiling curve, the interval where the inflection point appears in the boiling curve can be quickly determined according to the graph, which makes it easier to judge whether the maximum wall superheat appears to rise suddenly, and facilitates the rapid and accurate determination of the first heat flux density.

[0120] The boiling curve may be a relationship curve qT between the heat flux q and the maximum wall superheat T, wherein the heat flux q is the abscissa and the maximum wall superheat T is the ordinate.

[0121] The interval where the inflection point appears in the boiling curve can be used to find the interval where the slope of the curve changes.

[0122] The first growth rate may be ΔT / Δq, where ΔT is the change in the maximum wall superheat, and Δq is the change in the heat flux density. Specifically, the i-th heat flux density q i The corresponding rate of change can be (T i+1 -T i ) / (q i+1 -q i ).

[0123] The plurality of heat flux density component series may be an arithmetic progression.

[0124] For example, N heat flux densities, where the sequence of N heat flux densities can be an arithmetic progression. The first heat flux density is q1 with a smaller value, and then the heat flux density is increased with a certain gradient value Δq, that is, the value of q2 is q1+Δq, the value of q3 is q2+Δq, ..., q n The value of q n-1 +Δq. At this time, Δq is a fixed value, and ΔT / Δq only changes with the change of ΔT, which makes it easier to judge the size of ΔT / Δq. Specifically, the i-th heat flux density q i The corresponding rate of change can be (T i+1 -Ti ) / Δq.

[0125] The second threshold may be an experimental reference value obtained by statistics of experimental measurement data, or the first growth rates corresponding to the heat flux densities in the interval where the inflection point appears in the boiling curve may be calculated, with the maximum value being the second threshold.

[0126] In order to determine the heat flux density corresponding to the sudden increase in the maximum wall superheat as the first heat flux density, the first heat flux density can be determined by judging whether the maximum wall superheat has a sudden increase by judging 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.

[0127] In some embodiments, before comparing the maximum wall cavitation fraction corresponding to the first heat flux density with the wall boundary cavitation fraction, the method further includes:

[0128] Get the traffic in the target channel;

[0129] The wall boundary cavitation fraction is obtained by the first formula;

[0130] Among them, the first formula is:

[0131] α crit =D1·G+D2

[0132] Among them, α crit represents the wall boundary cavitation fraction, G represents the flow rate in the target channel, D1 is the first coefficient, and D2 is the first constant.

[0133] In this embodiment, the wall boundary cavitation fraction is calculated by the first formula, so that a more accurate wall boundary cavitation fraction value can be obtained, which is helpful for further accurate judgment of the critical heat flux density.

[0134] Regarding the above-mentioned wall-facing interfacial cavitation fraction, in the existing literature, there are certain differences in the values ​​of the wall-facing interfacial cavitation fraction, and no unified setting scheme has been formed, which causes great uncertainty in the calculation results of the critical heat flux density. Therefore, in this application, based on a large number of calculation results of the critical heat flux density under different pressures and flow rates, and comparing and correcting the calculation results with the experimental results, a setting method for the wall-facing interfacial cavitation fraction suitable for the channel is given, namely, the above-mentioned first formula.

[0135] To obtain the above-mentioned wall-facing interfacial cavitation fraction, it is first necessary to obtain the flow rate in the target channel, and then according to the first formula, by substituting the flow rate data in the target channel, the wall-facing interfacial cavitation fraction of the target channel can be calculated.

[0136] The specific values ​​of the coefficients in the first formula can be set to achieve accurate calculation of the wall boundary cavitation fraction.

[0137] The first formula above can be:

[0138]

[0139] The specific setting method of each coefficient in the formula can realize the accurate calculation of the wall boundary cavitation fraction of the target channel.

[0140] The unit of the above flow rate G is kg·m -2 ·s -1 .

[0141] It should be noted that the flow rate is required to calculate the critical cavitation fraction of the wall in this strategy. Since the calculation method of the first formula above is obtained by comparing and correcting the calculated data with the experimental data, the flow rate of the simulated working condition of this strategy is limited by the working condition range selected in the comparison and correction process, and the flow rate can be between 2000-4000kg·m -2 ·s -1 Inside.

[0142] In some embodiments, obtaining a first simulation model of a boiling heat transfer process in a target channel includes:

[0143] Obtain two-phase flow models and turbulence models to simulate the flow behavior of gas and liquid phases;

[0144] Obtain boiling model, activation core density model, bubble detachment diameter model, bubble detachment frequency model and bubble size distribution model to simulate the boiling phase transition process of gas-liquid two phases;

[0145] Obtain drag force model, turbulence dissipation force model, lift force model and wall lubrication force model to simulate the interaction process between gas and liquid phases;

[0146] The first simulation model is determined using a two-phase flow model, a turbulence model, a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, a bubble size distribution model, a drag model, a turbulent dissipation force model, a lift model and a wall lubrication force model.

[0147] In this embodiment, a plurality of specific models are obtained to construct a simulation model including the flow behavior process of the gas-liquid two-phase during the boiling heat transfer process, the boiling phase change process and the gas-liquid two-phase interaction process, so as to better simulate the boiling heat transfer process of the fluid in the channel, achieve a more comprehensive and realistic simulation effect, and thus help to realize the accurate prediction of the critical heat flux density of the fluid in the channel.

[0148] The above-mentioned two-phase flow model, turbulence model, boiling model, activated core density model, bubble detachment diameter model, bubble detachment frequency model, bubble size distribution model, drag model, turbulence dissipation force model, lift model and wall lubrication force model can be developed using simulation analysis software, such as fluid calculation software STAR-CCM+. The above-mentioned related models can be established in the software, and the specific models used to call each related model can be selected in the software to finally determine the first simulation model.

[0149] The above two-phase flow model is used to describe the gas-liquid two-phase flow. Specifically, the above two-phase flow model can adopt the Euler two-phase flow model, which is widely used and can more 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 using this model, the physical properties of the gas and liquid phases need to be clarified separately, that is, the physical property parameters of the gas and liquid phases need to be input separately.

[0150] For example, the two-phase flow model adopts the Euler two-phase flow model. In STAR-CCM+, the two-phase flow model selects the Euler two-phase flow model, and the physical properties of the gas and liquid phases are input separately, including density, saturation temperature, thermal conductivity, dynamic viscosity coefficient, specific heat, internal energy and molecular weight (gas phase only).

[0151] The above turbulence model is used to describe turbulence. Specifically, the above turbulence model can adopt the realizable K-Epsilon two-layer model, which is widely used in turbulence calculations and has good stability and accuracy when calculating turbulence problems. When using this model, the input coefficients can adopt the commonly used recommended values ​​of the model.

[0152] For example, the turbulence model uses the realizable K-Epsilon two-layer model. In STAR-CCM+, the turbulence model selects the realizable K-Epsilon two-layer model, and the default values ​​are used for each coefficient.

[0153] Based on the above two-phase flow model and turbulence model, a closed Navier-Stokes equations can be constructed and used to solve the flow behavior of the gas-liquid two-phase in the channel.

[0154] The above boiling model is used to simulate the phase change process of vapor-liquid two-phase boiling. Specifically, the above boiling model can adopt the RPI boiling model, and the specific form of the model is shown in the following formula:

[0155] q=(q conv +q evap +q quench )(1-K dry )+K dry q dry

[0156] Where, q is the heat flux, q convis the convective heat flux, q evap is the evaporation heat flux, q quench is the quenching heat flux, q dry is the steam heat flux. The above convection heat flux, evaporation heat flux, quenching heat flux and steam heat flux all have mature thermodynamic formulas to calculate according to the fluid state and wall temperature at the heating wall. For example, in STAR-CCM+, the default settings can be used. In addition, the steam effective wall contact area fraction K dry The calculation method is as follows:

[0157]

[0158] Among them, the coefficient β = (α-α crit ) / (1-α crit ), α is the maximum cavitation fraction on the wall, α crit is the wall-boundary cavitation fraction. It can be seen that the wall-boundary cavitation fraction α crit The calculation of wall heat flux is very important. In the existing literature, there are certain differences in the values ​​of the wall boundary cavitation fraction, and there is no unified setting scheme, which causes great uncertainty in the calculation results of the critical heat flux density. Therefore, in the present invention, based on a large number of calculation results of the critical heat flux density under different pressures and flow rates, and comparing and correcting the calculation results with the experimental results, a setting method for the wall boundary cavitation fraction suitable for the channel is given, that is, the above-mentioned first formula:

[0159] α crit =D1·G+D2

[0160] Among them, α crit represents the boundary cavitation fraction on the wall, G represents the flow rate in the channel, in kg·m -2 ·s -1 , D1 is the first coefficient, and D2 is the first constant. Specifically, the first formula above can be:

[0161]

[0162] Among them, α crit represents the boundary cavitation fraction on the wall, G represents the flow rate in the channel, in kg·m -2 ·s -1 When setting, the flow rate in the channel should be confirmed first, and the wall-facing intermediary cavitation fraction should be calculated according to the above formula. Then the obtained wall-facing intermediary cavitation fraction can be input into the RPI boiling model for simulation.

[0163] The above activation core density model is used to describe the density of the boiling activation core of the heated surface. Specifically, the above activation core density model can adopt the Hibiki-Ishii activation core density model, which can be applied to high pressure (19.8MPa and below) and has a wide range of applications.

[0164] For example, the above activation core density model selects the Hibiki-Ishii activation core density model. When used, the average hole density is set to 472,000 points / m 2 The wall contact angle and the wall contact angle scale are both 0.722 radians, and the hole 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.

[0165] The above-mentioned bubble detachment diameter model is used to calculate the diameter of the boiling bubble when it detaches from the heating surface. Specifically, the above-mentioned bubble detachment diameter model can adopt the Kocamustafaogullari model, which is usually used in conjunction with the Hibiki-Ishii model.

[0166] For example, the bubble detachment diameter model may adopt the Kocamustafaogullari model. When used, the calibration parameters are set to 0.0015126 m / radian, and the wall contact angle is 0.722 radians.

[0167] The bubble detachment frequency model is used to calculate the number of boiling bubbles detached per unit time. Specifically, the bubble detachment frequency model can adopt the Cole model, which is widely used in boiling simulation and has good stability and accuracy. When using it, the model can be introduced without any special settings.

[0168] 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 a pre-integrated S-Gamma model, while considering S-Gamma breakage and S-Gamma coalescence. The model has high calculation efficiency and is suitable for engineering calculations.

[0169] For example, the bubble size distribution model may adopt a pre-integrated S-Gamma model, when used, the coalescence factor is 0.01 and the breakup factor is 1.0.

[0170] Based on the above boiling model, activation core density model, bubble detachment diameter model, bubble detachment frequency model and bubble size distribution model, the boiling process, including the nucleation, growth and detachment process of bubbles, can be calculated, thereby describing the gas-liquid two-phase boiling phase change process.

[0171] After completing the construction of the above-mentioned two-phase flow model, turbulence model, boiling model, activation core density model, bubble detachment diameter model, bubble detachment frequency model and bubble size distribution model, although the equations can be guaranteed to be closed and calculated and solved, the calculation results are still not accurate because the interaction between the gas and liquid phases is not considered. Therefore, it is also necessary to consider the phase interaction and add it to the Navier-Stokes equations to ensure the accuracy of the calculation results. In specific implementation, an additional force term is added to the momentum transfer term in the momentum conservation equation.

[0172] The interaction forces between the gas and liquid phases can be drag, turbulent dissipation, lift and wall lubrication. In fact, there are many types of interactions between the two phases, but in the process of calculating the boiling process and determining the critical heat flux, the influence of some interactions is not obvious and can be ignored. Through sensitivity analysis, it is finally determined that drag, turbulent dissipation, lift and wall lubrication need to be considered.

[0173] The drag force is the force exerted by the fluid on the solid with relative velocity. This force is opposite to the velocity of the solid relative to the fluid, which is the relative motion resistance. The interphase drag is usually calculated as a function of the drag coefficient. Specifically, the drag coefficient can be calculated using the contaminated Tomiyama relation, which is very mature in the simulation of water vapor-water terms and can achieve accurate calculation of the drag force.

[0174] The above-mentioned turbulent dissipation force, turbulence is an irregular motion state in fluid motion. The energy conversion and turbulent structure formation in turbulent motion are realized through the turbulent dissipation process. Turbulent dissipation refers to the energy loss process caused by the viscosity and turbulent motion of the fluid. In the process of turbulent dissipation, the kinetic energy and thermal energy in the fluid will be converted into each other and finally converted into thermal energy on the microscopic scale, so that the turbulent structure gradually becomes smaller and finally dissipated. The turbulent dissipation force has a significant impact 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 turbulent Prandtl number calculation method is difficult to be applied to large bubbles and small bubbles at the same time. Therefore, the above-mentioned turbulent dissipation force is calculated by obtaining an empirical formula through a large number of calculation results to calculate the turbulent Prandtl number, and then using it to calculate the turbulent dissipation force.

[0175] The lift mentioned above is a force perpendicular to the direction of fluid flow. The lift can be calculated using the Sugrue lift coefficient model and corrected using the Podowski near-wall adjustment.

[0176] The wall lubrication force mentioned above is another important interphase force in the bubble flow state. The bubbles are concentrated near the wall area and do not adhere to the wall surface. Therefore, the volume fraction of the bubbles decreases in the near-wall area, which will generate a lubrication force caused by surface tension, pushing the bubbles away from the wall surface and preventing the secondary phase from adhering to the wall surface. The Antal model can be used to calculate the wall lubrication force.

[0177] Among the above-mentioned interaction forces between gas and liquid phases and interaction force models, except for the turbulent dissipation force, all 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, it is necessary to calculate the turbulent Prandtl number and then use it to calculate the turbulent dissipation force.

[0178] For example, in STAR-CCM+, the contaminated Tomiyama relationship is directly used 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.

[0179] The above-mentioned first simulation model of the boiling heat transfer process in the channel of 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 adopted by each related model in the software, and finally determining the first simulation model. The specific models adopted by the above-mentioned related models are all commonly used models in the field of fluid calculation. Therefore, they can be found and directly called in fluid calculation software, such as STAR-CCM+ software; self-programming can also be used for calculation, and the equations in the model are solved by mathematical methods through code.

[0180] In some embodiments, obtaining a turbulent dissipative force model includes:

[0181] Obtaining the pressure in the target channel;

[0182] The turbulent Prandtl number is obtained by the second formula;

[0183] According to the obtained turbulent Prandtl number, the turbulent dissipative force model is obtained;

[0184] Among them, the second formula is:

[0185] σ h =D3·P 3 +D4·P 2 +D5·P+D6

[0186] Among them, σ hrepresents 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.

[0187] In this embodiment, a more accurate turbulent Prandtl number can be obtained by calculating the turbulent Prandtl number using the second formula, and the turbulent dissipation force can be calculated more accurately using the obtained turbulent Prandtl number, thereby facilitating accurate prediction of the critical heat flux density of the fluid in the channel.

[0188] The above-mentioned turbulent Prandtl number is an important input parameter when calculating the turbulent dissipative force. There are many ways to obtain the value in the existing literature, but under different flow rates and pressure conditions, the existing methods of obtaining the value will cause the critical heat flux density calculation value inaccurate in some conditions. Therefore, in this application, based on a large number of calculation results of the critical heat flux density under different pressures and flow rates, and comparing and correcting the calculation results with the experimental results, the calculation formula for the turbulent Prandtl number in the channel is obtained, that is, the above-mentioned second formula.

[0189] To obtain the above turbulent Prandtl number, it is first necessary to obtain the pressure in the target channel, and then according to the second formula, by substituting the pressure data in the target channel, the turbulent Prandtl number of the target channel can be calculated.

[0190] The specific values ​​of the coefficients in the second formula above can be set to achieve accurate calculation of the turbulent Prandtl number.

[0191] The second formula above can be:

[0192] σ h =0.0125P 3 -0.5563P 2 +8.3375P-40.94

[0193] Among them, the specific setting method of each coefficient in the formula can realize the accurate calculation of the turbulent Prandtl number of the target channel.

[0194] The unit of the above pressure is MPa.

[0195] It should be noted that pressure is required when calculating the turbulent Prandtl number in this strategy. Since the calculation method in the present invention is obtained by comparing and correcting the calculated data with the experimental data, the pressure of the simulated working condition of this strategy is limited by the working condition range selected during the comparison and correction process, and the pressure can be within 12-18MPa.

[0196] In some embodiments, a first simulation model is determined using a two-phase flow model, a turbulence model, a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, a bubble size distribution model, a drag model, a turbulent dissipation force model, a lift model, and a wall lubrication force model, including:

[0197] Determine the geometric features of the target channel and obtain a geometric model by modeling;

[0198] Meshing the geometric model to obtain multiple mesh nodes;

[0199] Determine the boundary conditions of the geometric model;

[0200] The grid nodes located on the surface of the heated wall are selected as measurement points;

[0201] The first simulation model is determined using the geometric model, mesh nodes, boundary conditions and measurement points, as well as the two-phase flow model, turbulence model, boiling model, activation core density model, bubble detachment diameter model, bubble detachment frequency model, bubble size distribution model, drag model, turbulence dissipation force model, lift model and wall lubrication force model.

[0202] In this embodiment, by further setting a geometric model, grid nodes, boundary conditions and measurement points suitable for the target channel, a simulation model suitable for the target channel can be obtained, which can better simulate the boiling heat transfer process of the fluid in the current target channel, and achieve a more specific and accurate simulation effect, thereby helping to achieve accurate prediction of the critical heat flux density of the fluid in the target channel.

[0203] The geometric features of the target channel are determined and the geometric model is obtained by modeling. Various geometric features of the target channel to be simulated, such as length, cross-sectional shape, etc., can be measured and input into the geometric modeling.

[0204] The target channel may be a pipe or a single-rod channel. The pipe may be a straight pipe with a circular or polygonal cross section, the single-rod channel may be a channel with a straight heating rod disposed in the pipe, and the interface of the heating rod may be circular or polygonal.

[0205] The above-mentioned meshing of the geometric model may be performed by using a structured grid, because the geometric structure of the channel is relatively simple.

[0206] The above boundary conditions include the inlet flow rate, inlet flow velocity, outlet pressure of the pipeline, the area where the heating wall is located and its heating power, etc.

[0207] The above inlet flow rate can be used to substitute into the first formula to calculate the wall boundary cavitation fraction, and the above outlet pressure can be used to substitute into the second formula to calculate the turbulent Prandtl number.

[0208] When selecting and arranging the above measurement points, 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.

[0209] The first simulation model can be a general simulation model that is pre-created and widely applicable to the simulation of boiling heat transfer processes in various rod bundle channels, and then further set or input characteristic parameters such as geometric models, grid nodes, boundary conditions and measurement points applicable to the target channel to obtain a simulation model applicable to the current target channel.

[0210] It should be noted that the various implementation methods provided in the embodiments of the present application can be implemented independently or in combination without conflicting with each other. The specific implementation method can be determined based on actual needs, and the embodiments of the present application are not limited to this.

[0211] To facilitate understanding by those skilled in the art, a group of specific embodiments are provided below. The present application is further described in detail below in conjunction with the specific embodiments.

[0212] The above technical solution is now used to simulate and predict the critical heat flux density in the circular tube channel under high pressure forced circulation conditions. Assume that the length of the heating section in the channel is 1m, the pressure is 14MPa, and the inlet flow rate is 2000kg / m 2 ·s, and the inlet subcooling is 108.39℃.

[0213] Firstly, the circular tube channel is geometrically modeled and then meshed using a structured network. Subsequently, the parameters in the channel are determined as boundary conditions, and all mesh nodes on the heated wall surface are used as measurement points.

[0214] In the simulation, the following model is constructed: Figure 2 As shown:

[0215] Construct an Euler two-phase flow model; construct a realizable K-Epsilon two-layer model.

[0216] The RPI boiling model was constructed, and the wall boundary cavitation fraction was calculated according to the inlet flow rate, and its value was 0.89. The Hibiki-Ishii activation core density model was constructed, in which the average cavitation density was 472,000 points / m 2 The wall contact angle and the wall contact angle scale are both 0.722 radians, and the hole length scale is 2.5×10 -6 m, density function constants C0, C1, C2, and C3 are -0.01064, 0.48246, -0.22712, and 0.05468, respectively; the Kocamustafaogullari model is constructed, and the calibration constant is 1.5126×10 -3m / radian, and the wall contact angle is 0.722 radians; the Cole model is constructed; the pre-integrated S-Gamma model is constructed, including S-Gamma crushing and S-Gamma coalescence, with a crushing factor of 1.0 and a coalescence factor of 0.01.

[0217] After completing the above model construction and key parameter setting, the following phase interactions need to be constructed, such as Figure 2 As shown:

[0218] A drag model was constructed, in which the drag coefficient was calculated using the contaminated Tomiyama relation. A turbulent dissipation force model was constructed, in which the calibration constant was taken as 1, and the turbulent Prandtl number was calculated based on the pressure, and its value was taken as 1.05. A lift model was constructed, in which the lift coefficient was calculated using the Sugrue lift coefficient model, and the lift was corrected by considering the Podowski near-wall adjustment. A wall lubrication force model was constructed, in which the wall lubrication force was calculated using the Antal model, and the calibration coefficient C was w1 is -0.01, the calibration coefficient C w2 is 0.05.

[0219] After completing the construction of the above phase interactions, we can start simulating the boiling heat transfer process in the channel. The preset initial heat flux is 2200kW / m 2 , the lifting 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.

[0220] Use the data recorded in the simulation to draw a boiling curve diagram, such as Figure 3 Next, find the inflection point, that is, the heat flux density is 2450~2650kW / m 2 The detailed parameters of the interval where the inflection point is located are listed, and the change rate ΔT / Δq is calculated at the same time, specifically (T i+1 -T i ) / Δq, such as Figure 4 As shown. Figure 4 It can be seen from the parameter list that when the heat flux density increases from 2600kW / m 2 Increased to 2650kW / m 2 After that, the maximum wall superheat increased from 8.60℃ to 92.67℃, and the maximum wall superheat value increased by more than 50℃. ΔT / Δq increased from 2.42×10 -6 Increased to 1681.36×10 -6 , it can be determined that 2600kW / m 2is the first heat flux density, and at this time the maximum cavitation fraction on the wall surface is 0.91, which has exceeded the critical cavitation fraction of 0.89 obtained by the above calculation. Therefore, it can be determined that the predicted value of the critical heat flux density is 2600kW / m 2 The predicted value was compared with the experimental value, and the calculated relative error was 3.20%.

[0221] First, to further illustrate the accuracy of the critical heat flux predicted by the present application scheme, Figure 5 As shown in the figure, the comparison results between the predicted values ​​and the experimental values ​​in 20 working conditions are given. The solid line in the figure is the experimental value of the critical heat flux density, the dotted line is the ±10% error line, and the points are the predicted values ​​of the critical heat flux density of the 20 working conditions. The test results of the 20 working conditions in the figure are all distributed near the experimental values ​​and are all within a reasonable error range. Figure 5 It can be seen that the present application scheme has a good prediction capability for the critical heat flux density of the fluid in the channel.

[0222] The prediction method of the critical heat flux density of a fluid provided in the embodiment of the present application can be performed by the prediction device 200 of the critical heat flux density of a fluid. In the embodiment of the present application, the prediction method of the critical heat flux density of a fluid performed by the prediction device 200 of the critical heat flux density of a fluid is taken as an example to illustrate the prediction device 200 of the critical heat flux density of a fluid provided in the embodiment of the present application.

[0223] See also Figure 6 , is a schematic diagram of the structure of a fluid critical heat flux prediction device 200 provided in an embodiment of the present application. Figure 6 As shown, the prediction device 200 for the critical heat flux density of the fluid includes:

[0224] A first acquisition module 201 is used to acquire 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 gas-liquid two-phase flow behavior process, a boiling phase change process, and a gas-liquid two-phase interaction process;

[0225] The second acquisition module 202 is used to sequentially input a plurality of heat fluxes into the first simulation model to obtain a maximum wall superheat and a maximum wall cavitation fraction corresponding to each heat flux;

[0226] The first determination module 203 is used to determine the heat flux density whose corresponding maximum wall superheat change rate is greater than or equal to the first threshold value among the multiple heat flux densities as the first heat flux density; wherein the change rate represents the change of the maximum wall superheat relative to the change of the heat flux density;

[0227] A comparison module 204, for comparing the maximum wall cavitation fraction corresponding to the first heat flux density with the critical wall cavitation fraction;

[0228] The second determination module 205 is used to determine the critical heat flux density according to the first heat flux density when the maximum wall cavitation fraction corresponding to the first heat flux density is greater than or equal to the critical wall cavitation fraction.

[0229] In some implementations, the second determining module 205 may also be used to:

[0230] When the maximum wall cavitation fraction corresponding to the first heat flux is less than the boundary cavitation fraction of the wall, the second heat flux is used as the first heat flux, and the maximum wall cavitation fraction corresponding to the first heat flux is compared with the boundary cavitation fraction of the wall, until the critical heat flux is determined;

[0231] In which, when a plurality of heat flux densities are arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density.

[0232] In some implementations, the first determining module 203 may be configured to:

[0233] Draw a boiling curve according to multiple heat flux densities and corresponding multiple maximum wall superheats;

[0234] Determine the interval where the inflection point occurs in the boiling curve;

[0235] Obtaining the first growth rate corresponding to each heat flux density in the interval, the first growth rate represents the degree to which the maximum wall superheat increases with the increase of the heat flux density;

[0236] The heat flux density corresponding to when the first growth rate is greater than or equal to the second threshold is determined as the first heat flux density.

[0237] In some implementations, the comparison module 204 may also be used to:

[0238] Get the traffic in the target channel;

[0239] The wall boundary cavitation fraction is obtained by the first formula;

[0240] Among them, the first formula is:

[0241] α crit =D1·G+D2

[0242] Among them, α crit represents the wall boundary cavitation fraction, G represents the flow rate in the target channel, D1 is the first coefficient, and D2 is the first constant.

[0243] In some implementations, the first acquisition module 201 may be used to:

[0244] Obtain two-phase flow models and turbulence models to simulate the flow behavior of gas and liquid phases;

[0245] Obtain boiling model, activation core density model, bubble detachment diameter model, bubble detachment frequency model and bubble size distribution model to simulate the boiling phase transition process of gas-liquid two phases;

[0246] Obtain drag force model, turbulence dissipation force model, lift force model and wall lubrication force model to simulate the interaction process between gas and liquid phases;

[0247] The first simulation model is determined using a two-phase flow model, a turbulence model, a boiling model, an activated core density model, a bubble detachment diameter model, a bubble detachment frequency model, a bubble size distribution model, a drag model, a turbulent dissipation force model, a lift model and a wall lubrication force model.

[0248] In some implementations, the first acquisition module 201 may be used to:

[0249] Obtaining the pressure in the target channel;

[0250] The turbulent Prandtl number is obtained by the second formula;

[0251] According to the obtained turbulent Prandtl number, the turbulent dissipative force model is obtained;

[0252] Among them, the second formula is:

[0253] σ h =D3·P 3 +D4·P 2 +D5·P+D6

[0254] Among them, 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.

[0255] In some implementations, the first acquisition module 201 may also be used to:

[0256] Determine the geometric features of the target channel and obtain a geometric model by modeling;

[0257] Meshing the geometric model to obtain multiple mesh nodes;

[0258] Determine the boundary conditions of the geometric model;

[0259] The grid nodes located on the surface of the heated wall are selected as measurement points;

[0260] The first simulation model is updated according to the geometric model, mesh nodes, boundary conditions and measurement points.

[0261] Since the prediction device 200 for the critical heat flux density of a fluid adopts all the technical solutions of the prediction method for the critical heat flux density of a fluid in the above-mentioned embodiment, it has at least all the beneficial effects brought by the technical solutions of the above-mentioned embodiment, which will not be described in detail here.

[0262] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0263] The electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0264] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0265] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0266] In some embodiments, the memory 302 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an 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 including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0267] The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement any one of the methods for predicting the critical heat flux density of the fluid in the above embodiments.

[0268] In one example, the electronic device may further include a communication interface 303 and a bus 310. Figure 7As shown, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0269] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0270] Bus 310 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0271] The electronic device can execute the prediction method of the fluid critical heat flux density in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 6 A method and apparatus for predicting the critical heat flux of a fluid are described.

[0272] In addition, in combination with the prediction method of the critical heat flux density of the fluid in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the prediction methods of the critical heat flux density of the fluid in the above embodiment is implemented.

[0273] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0274] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in 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, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0275] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. 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 embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0276] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram 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 device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. 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 can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0277] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for predicting critical heat flux of a fluid, characterized in that: The following steps are involved: 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, wherein the boiling heat transfer process includes a gas-liquid two-phase flow behavior process, a boiling phase change process, and an interaction process between the gas-liquid two-phases; Inputting a plurality of heat flux densities into the first simulation model in sequence to obtain a maximum wall superheat and a maximum wall cavitation fraction corresponding to each of the heat flux densities; Determine, among the multiple heat flux densities, a heat flux density corresponding to which the change rate of the maximum wall superheat is greater than or equal to a first threshold as a first heat flux density; wherein the change rate represents a change in the maximum wall superheat relative to a change in the heat flux density; comparing the maximum cavitation fraction of the wall corresponding to the first heat flux with the critical cavitation fraction of the wall; When the maximum wall cavitation fraction corresponding to the first heat flux is greater than or equal to the critical wall cavitation fraction, a critical heat flux is determined according to the first heat flux.

2. The method according to claim 1, characterized in that After comparing the maximum wall cavitation fraction corresponding to the first heat flux with the critical wall cavitation fraction, the method further includes: In the case where the maximum wall cavitation fraction corresponding to the first heat flux is less than the wall boundary cavitation fraction, the second heat flux is used as the first heat flux, and the comparison between the maximum wall cavitation fraction corresponding to the first heat flux and the wall boundary cavitation fraction is returned to be performed until the critical heat flux is determined; Wherein, when the plurality of heat flux densities are arranged in order from small to large, the second heat flux density is the next heat flux density of the first heat flux density.

3. The method according to claim 1, characterized in that: The step of determining, among the plurality of heat flux densities, a heat flux density corresponding to which the rate of change of the maximum wall superheat is greater than or equal to the first threshold as the first heat flux density comprises: Draw a boiling curve according to the plurality of heat flux densities and the corresponding plurality of maximum wall superheats; Determine the interval where the inflection point appears in the boiling curve; Obtaining a first growth rate corresponding to each of the heat flux densities in the interval, wherein the first growth rate represents the degree to which the maximum wall superheat increases with the increase of the heat flux density; The heat flux density corresponding to when the first growth rate is greater than or equal to a second threshold is determined as the first heat flux density.

4. The method according to claim 1, characterized in that: Before comparing the maximum wall cavitation fraction corresponding to the first heat flux with the critical wall cavitation fraction, the method further includes: Obtaining the flow rate in the target channel; The wall boundary cavitation fraction is obtained by a first formula; Among them, the first formula is: α crit =D1·G+D2 Among them, α crit represents the wall boundary cavitation fraction, G represents the flow rate in the target channel, D1 is the first coefficient, and D2 is the first constant.

5. The method according to claim 1, characterized in that: The step of obtaining a first simulation model of a boiling heat transfer process in a target channel includes: Obtaining a two-phase flow model and a turbulence model for simulating the flow behavior process of the gas-liquid two-phase; 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 transition process of the gas-liquid two phases; Obtaining a drag model, a turbulent dissipation force model, a lift model and a wall lubrication force model for simulating the interaction process between the gas-liquid two phases; The first simulation model is determined 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 model, the turbulence dissipation force model, the lift model and the wall lubrication force model.

6. The method according to claim 5, characterized in that The turbulence dissipation force model is obtained by: obtaining the pressure in the target channel; The turbulent Prandtl number is obtained by the second formula; According to the obtained turbulent Prandtl number, the turbulent dissipation force model is obtained; Wherein, the second formula is: σ h =D3·P 3 +D4·P 2 +D5·P+D6 Among them, 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.

7. The method according to claim 5, characterized in that The method of 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 model, the turbulence dissipation force model, the lift model and the wall lubrication force model comprises: Determine the geometric features of the target channel and obtain a geometric model by modeling; Meshing the geometric model to obtain a plurality of mesh nodes; determining boundary conditions of the geometric model; Selecting the grid nodes located on the surface of the heating wall as measurement points; The first simulation model is determined using the geometric model, the grid nodes, the boundary conditions and the measurement points, as well as 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 model, the turbulence dissipation force model, the lift model and the wall lubrication force model.

8. A device for predicting critical heat flux density of a fluid, characterized in that: include: A first acquisition module is used to acquire 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 gas-liquid two-phase flow behavior process, a boiling phase change process, and a gas-liquid two-phase interaction process; A second acquisition module is used to sequentially input a plurality of heat flux densities into the first simulation model to obtain a maximum wall superheat and a maximum wall cavitation fraction corresponding to each of the heat flux densities; A first determination module is used to determine, among the multiple heat flux densities, a heat flux density corresponding to which the change rate of the maximum wall superheat is greater than or equal to a first threshold as a first heat flux density; wherein the change rate represents a change in the maximum wall superheat relative to a change in the heat flux density; A comparison module, configured to compare the maximum wall cavitation fraction corresponding to the first heat flux with the critical wall cavitation fraction; The second determination module is used to determine the first heat flux as a critical heat flux when the maximum wall cavitation fraction corresponding to the first heat flux is greater than or equal to the critical wall cavitation fraction.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method for predicting the critical heat flux density of a fluid as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for predicting the critical heat flux density of a fluid as described in any one of claims 1 to 7.

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