Critical heat flux density relationship determination method, apparatus, and computer device

Abstract

The application relates to a critical heat flux density relationship determination method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining critical heat flux density test data of a nuclear fuel assembly test piece; performing burnout point local parameter calculation according to the critical heat flux density test data to obtain local parameter data corresponding to the burnout point local parameter; and performing regression analysis on the critical heat flux density test data, a relationship factor and local parameter data corresponding to the relationship factor according to the type of the nuclear fuel assembly test piece in sequence to determine a target critical heat flux density relationship, wherein the relationship factor is obtained according to the burnout point local parameter. The critical heat flux density relationship is determined by performing regression analysis on the data selected according to the type of the nuclear fuel assembly test piece in sequence, the process of determining the critical heat flux density relationship is simplified, experience of a relationship developer is not needed, and the method can be universally applied to newly developed reactor nuclear fuel assemblies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a critical heat flux relationship determination method and device, computer equipment and a storage medium. BACKGROUND

[0002] Heat flux, also known as heat flux, refers to the heat per unit cross-sectional area of the heating surface per unit time. When the heat flux changes from the value corresponding to nucleate boiling to the value corresponding to transition boiling, there are many bubbles on the heating surface, so that many bubbles are connected together and cover part of the heating surface. Due to the low heat transfer coefficient of the gas film, the temperature of the heating surface will rise quickly, and the heating surface will be burned, at which time the value corresponding to the heat flux is also called the boiling critical point or critical heat flux (CHF).

[0003] At present, in the technical fields of nuclear reactor fuel design, nuclear fuel assembly thermal hydraulic test, nuclear reactor thermal hydraulic analysis and safety analysis, critical heat flux is an important limiting thermal hydraulic parameter, and the critical heat flux relationship must be developed to realize accurate prediction of the critical heat flux. In the determination method of the critical heat flux relationship, a multivariate nonlinear regression method is generally used to fit the experimental data, so as to obtain an applicable critical heat flux relationship. For reactor nuclear fuel assemblies, the composition factors and model forms of the critical heat flux relationships used at home and abroad are different, and for many critical heat flux relationships, the factor selection and composition form are determined by the relationship developer according to his own experience, which is difficult to be universally applicable to newly developed reactor nuclear fuel assemblies. SUMMARY

[0004] Therefore, it is necessary to provide a critical heat flux relationship determination method, device, computer equipment and storage medium to solve the defects that the determination method of the CHF relationship in the prior art is empirical and difficult to be universally applicable to newly developed reactor nuclear fuel assemblies.

[0005] In a first aspect, the present application provides a critical heat flux relationship determination method, which comprises:

[0006] Obtaining critical heat flux test data of a nuclear fuel assembly test piece;

[0007] According to the critical heat flux test data, the burnout point local parameter calculation is performed to obtain local parameter data corresponding to the burnout point local parameter;

[0008] The critical heat flux test data, the relational expression factor and the local parameter data corresponding to the relational expression factor are selected according to the type of the nuclear fuel assembly test piece in sequence, and regression analysis is performed to determine a target critical heat flux relational expression; wherein the relational expression factor is obtained according to the burnout point local parameter.

[0009] In one embodiment, the type of the nuclear fuel assembly test piece includes an axial power distribution type; the critical heat flux test data, the relational expression factor and the local parameter data corresponding to the relational expression factor are selected according to the type of the nuclear fuel assembly test piece in sequence, and regression analysis is performed to determine a target critical heat flux relational expression, including:

[0010] The critical heat flux test data, the relational expression factor and the local parameter data corresponding to the relational expression factor of the test piece with uniform heating of the axial power distribution type are selected and regression analysis is performed to determine a uniform heating term of the critical heat flux relational expression;

[0011] Based on the uniform heating term, the critical heat flux test data and the local parameter data of the test piece with non-uniform heating of the axial power distribution type are selected and analyzed to determine a non-uniform heating term of the critical heat flux relational expression;

[0012] The target critical heat flux relational expression is determined in combination with the uniform heating term and the non-uniform heating term.

[0013] In one embodiment, the type of the nuclear fuel assembly test piece also includes a structure type; the critical heat flux test data, the relational expression factor and the local parameter data corresponding to the relational expression factor of the test piece with uniform heating of the axial power distribution type are selected and regression analysis is performed to determine a uniform heating term of the critical heat flux relational expression, including:

[0014] The first critical heat flux test data, the first relational expression factor and the first local parameter data corresponding to the first relational expression factor of the test piece with no guide tube and the same grid layout of the structure type are selected and regression analysis is performed to determine a base term of the critical heat flux relational expression;

[0015] The second critical heat flux test data, the second relational expression factor and the second local parameter data corresponding to the second relational expression factor of the test piece with no guide tube and different grid layouts of the structure type are selected to modify the base term to determine a grid layout influence term of the critical heat flux relational expression;

[0016] The third critical heat flux test data, the third relational expression factor and the third local parameter data corresponding to the third relational expression factor of the test piece with a guide tube of the structure type are selected to modify the grid layout influence term to determine a cold wall effect term of the critical heat flux relational expression.

[0017] The cold wall effect term of the determined critical heat flux density relationship is taken as a uniform heating term of the critical heat flux density relationship.

[0018] In one embodiment, the first critical heat flux density test data, the first relationship factor and the first local parameter data corresponding to the first relationship factor of the test piece of the structure type of no guide tube and the same grid layout are selected for regression analysis to determine a basic term of the critical heat flux density relationship.

[0019] The first critical heat flux density test data of the test piece of the structure type of no guide tube and the same grid layout is selected to form a first CHF value matrix as a dependent variable of a first critical heat flux density regression equation.

[0020] The first relationship factor and the first local parameter data corresponding to the first relationship factor are selected to form a first factor matrix as an independent variable of the first critical heat flux density regression equation.

[0021] The first factor coefficient matrix of the first critical heat flux density regression equation is determined by regression analysis based on the first CHF value matrix and the first factor matrix.

[0022] The first critical heat flux density regression equation formed based on the first factor coefficient matrix is taken as a basic term of the critical heat flux density relationship.

[0023] In one embodiment, the second critical heat flux density test data, the second relationship factor and the second local parameter data corresponding to the second relationship factor of the test piece of the structure type of no guide tube and different grid layouts are selected to modify the basic term to determine a grid layout influence term of the critical heat flux density relationship.

[0024] The second relationship factor is selected and added to the first factor matrix one by one to form a second factor matrix.

[0025] Based on the second factor matrix, the second critical heat flux density test data of the test piece of the structure type of no guide tube and different grid layouts and the second local parameter data corresponding to the second relationship factor are selected to modify the first critical heat flux density regression equation to obtain a second critical heat flux density regression equation.

[0026] Based on the second critical heat flux density regression equation, the nested model comparison method is used to screen the second relationship factor added one by one until the screening of all selected second relationship factors is completed to determine the grid layout influence term of the critical heat flux density relationship.

[0027] In one of the embodiments, based on the uniform heating term, the critical heat flux test data and the local parameter data of the test piece with the axial power distribution type of non-uniform heating are analyzed to determine the non-uniform heating term of the critical heat flux relationship, comprising:

[0028] The local parameter data of the test piece with the axial power distribution type of non-uniform heating is substituted into the uniform heating term to obtain a critical heat flux prediction value;

[0029] According to the critical heat flux prediction value and the critical heat flux test data of the test piece with the axial power distribution type of non-uniform heating, a non-uniform heating factor value is obtained;

[0030] Based on the non-uniform heating factor value and the local parameter data of the test piece with the axial power distribution type of non-uniform heating, regression analysis is performed to determine the non-uniform heating term of the critical heat flux relationship.

[0031] In one of the embodiments, the burnout point local parameters include local thermal parameters and local geometric parameters; and the manner of obtaining the relationship factor according to the burnout point local parameters comprises:

[0032] The local thermal parameters and the local geometric parameters are subjected to interaction term processing, high-order term processing, and exponential term processing to obtain interaction variable, high-order variable, and exponential variable;

[0033] A relationship factor library is constructed based on the interaction variable, the high-order variable, the exponential variable, and a virtual variable; and the relationship factor library is used to select a relationship factor in the determination process of the target critical heat flux relationship.

[0034] In a second aspect, the application further provides a critical heat flux relationship determination device, comprising:

[0035] A test data acquisition module is configured to acquire critical heat flux test data of a nuclear fuel assembly test piece;

[0036] A local parameter calculation module is configured to calculate burnout point local parameters according to the critical heat flux test data to obtain local parameter data corresponding to the burnout point local parameters;

[0037] A relationship determination module is configured to sequentially select critical heat flux test data, relationship factors, and local parameter data corresponding to the relationship factors according to the type of the nuclear fuel assembly test piece to perform regression analysis and determine a target critical heat flux relationship; wherein the relationship factors are obtained according to the burnout point local parameters.

[0038] In a third aspect, the present application provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0039] In a fourth aspect, the present application provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method described above.

[0040] The critical heat flux density determining method, device, computer device and storage medium described above determine the critical heat flux density relationship by selecting the critical heat flux density test data, relationship factor and local parameter data corresponding to the relationship factor according to the type of the nuclear fuel assembly test piece in sequence and performing regression analysis, thereby simplifying the process of determining the critical heat flux density relationship and making it applicable to newly developed reactor nuclear fuel assemblies without relying on the experience of relationship developers. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 It is a flowchart of the application environment of the critical heat flux density relationship determining method in one embodiment;

[0042] Figure 2 It is a flowchart of the critical heat flux density relationship determining method in one embodiment;

[0043] Figure 3 It is a flowchart of the step of determining the target critical heat flux density relationship in one embodiment;

[0044] Figure 4 It is a flowchart of the step of determining the uniform heating term in one embodiment;

[0045] Figure 5 It is a flowchart of the step of determining the base term in one embodiment;

[0046] Figure 6 It is a flowchart of the step of determining the grid layout influence term in one embodiment;

[0047] Figure 7 It is a flowchart of the step of determining the non-uniform heating term in one embodiment;

[0048] Figure 8 It is a flowchart of the critical heat flux density relationship determining method in another embodiment;

[0049] Figure 9 It is a structure block diagram of the critical heat flux density relationship determining device in one embodiment;

[0050] Figure 10Fig. 1 is a schematic diagram of an internal structure of a computer device according to an embodiment. DETAILED DESCRIPTION

[0051] For the purpose, technical solutions and advantages of the present application to be clearer, further detailed description will be made to the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0052] The critical heat flux density relationship determination method provided by the embodiments of the present application can be applied in the application environment as shown in Figure 1 . Specifically, the server 104 communicates with the nuclear fuel assembly test device 102 through the network. The critical heat flux density test data of the nuclear fuel assembly test device 102 for testing the nuclear fuel assembly test piece is obtained. The burnout point local parameter calculation is performed according to the critical heat flux density test data, and the local parameter data corresponding to the burnout point local parameter is obtained. The critical heat flux density test data, the relationship factor and the local parameter data corresponding to the relationship factor are selected in turn according to the type of the nuclear fuel assembly test piece, and the regression analysis is performed to determine the target critical heat flux density relationship. The relationship factor is obtained according to the burnout point local parameter. The data storage system can store the data required to be processed by the server 104. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The server 104 can be realized by an independent server or a server cluster composed of multiple servers.

[0053] In one embodiment, as shown in Figure 2 , a critical heat flux density relationship determination method is provided. Taking the server 104 in Figure 1 as an example, the method includes the following steps:

[0054] S202: Obtain the critical heat flux density test data of the nuclear fuel assembly test piece.

[0055] The nuclear fuel assembly test piece is designed according to the structure of the actual nuclear fuel assembly, and is used to perform the critical heat flux density experiment to obtain the critical heat flux density test data. The actual nuclear fuel assembly may, for example, be a newly developed nuclear fuel assembly for which the critical heat flux density relationship needs to be developed. It can be understood that the nuclear fuel test piece needs to reflect the structural characteristics of the actual nuclear fuel assembly and its application mode in the reactor. For example, multiple nuclear fuel assembly test pieces can be designed according to different structural types and different axial power distribution types of the actual nuclear fuel assembly. In this embodiment, nine nuclear fuel assembly test pieces of different structural types and different axial power distribution types are designed, and the critical heat flux density experiment is performed on the nine nuclear fuel assembly test pieces to obtain nine groups of corresponding critical heat flux density test data.

[0056] Specifically, the critical heat flux test data are a plurality of sets of test data obtained by performing a critical heat flux experiment on each nuclear fuel assembly test piece under different test conditions, which can include test boundary condition parameter data under different test conditions, such as test inlet temperature, test inlet flow rate, and test outlet pressure, and can also include critical heat flux values (CHF values) under corresponding test conditions obtained by measurement.

[0057] S204: Burnout point local parameter calculation is performed according to the critical heat flux test data to obtain local parameter data corresponding to the burnout point local parameters.

[0058] According to the critical heat flux correlation applied to the nuclear fuel assembly of the reactor, by analyzing the main factors affecting the critical heat flux value in the critical heat flux correlation, physical parameters related to the critical heat flux are selected, which are generally local parameters of the burnout point. The burnout point (BO point) is generally the position where the nuclear fuel assembly of the reactor deviates from the phenomenon of nucleate boiling, which can be obtained from the critical heat flux test data. Therefore, the burnout point local parameters can be determined according to the critical heat flux test data, and the local parameter data corresponding to the burnout point local parameters can be calculated to determine the target critical heat flux correlation.

[0059] Specifically, the burnout point local parameters generally include local thermal parameters and local geometric parameters. The specific parameters included in the local thermal parameters and the local geometric parameters are not fixed and can be selected according to the test condition setting of the actual critical heat flux test, for example, in this embodiment, the local thermal parameters can specifically include the pressure P at the BO point, the sub-channel flow rate G, and the steam quality X; and the local geometric parameters can specifically include the heating length l from the heating starting point, the distance (grid span) gsp between the two grids upstream of the BO point, the distance dg between the BO point and the downstream surface of the upstream grid, the BO point sub-channel hydraulic diameter De, and the BO point sub-channel heating diameter Dh. The local parameter data corresponding to the local thermal parameters can be calculated based on the test boundary condition parameter data under different test conditions input into the sub-channel analysis software, and the local geometric parameters can be analyzed based on the positions corresponding to the BO point under different test conditions. DNB

[0060] S206: Regression analysis is performed on the critical heat flux test data, the correlation factor, and the local parameter data corresponding to the correlation factor according to the type of the nuclear fuel assembly test piece in sequence to determine the target critical heat flux correlation; wherein the correlation factor is obtained according to the burnout point local parameters.

[0061] ​Wherein, the relational expression factor is each factor term used for sequentially constructing the target critical heat flux relational expression, the form of the factor term is not unique, and can be variously deformed according to the burnout point local parameter to obtain, for example, interactive term processing, high-order term processing, and exponential term processing, etc. Further, the local parameter data corresponding to the relational expression factor can also be calculated according to the deformation mode of the local parameter data corresponding to the burnout point local parameter.

[0062] Specifically, after obtaining the relational expression factor based on the burnout point parameter, the regression model can be constructed by sequentially selecting the relational expression factor according to the type of the nuclear fuel assembly test piece in each relational expression factor, and then based on the critical heat flux test data and the local parameter data corresponding to each burnout point local parameter, the regression model is analyzed by using the multiple linear regression method, and then the target critical heat flux relational expression is determined. Wherein, the type of the nuclear fuel assembly test piece can be the type of the axial power distribution, or the structure type, which can be sequentially selected according to the type of the axial power distribution to construct the regression model, or according to the structure type to construct the regression model, or according to the type of the axial power distribution and the structure type to construct the regression model.

[0063] Specifically, the target critical heat flux relational expression determination process can be based on the principle of "from simple to complex", and sequentially in the multiple linear regression model, first add the relational expression factor selected according to the uniform heating and the basic structure, then add the relational expression factor selected according to the different grid layout, then add the relational expression factor selected according to the cold wall effect, and finally add the non-uniform heating factor calculated according to the non-uniform heating test data and the local parameter data, and gradually form the relational expression. In this process, the coefficient determination process of the first added relational expression factor can obtain the coefficients of each factor term by least square fitting, and then the stepwise regression method is used to select and determine each relational expression factor. In the process of introducing new factors, the nested model comparison method can be used to judge whether the newly introduced factor can be retained, until all the factors are evaluated, so as to finally determine the target critical heat flux relational expression.

[0064] The above-mentioned critical heat flux relational expression determination method determines the critical heat flux relational expression by sequentially selecting the critical heat flux test data, the relational expression factor and the local parameter data corresponding to the relational expression factor according to the type of the nuclear fuel assembly test piece, simplifies the process of determining the critical heat flux relational expression, and is universally applicable to newly developed reactor nuclear fuel assemblies without relying on the experience of relational expression developers.

[0065] In one embodiment, the manner of obtaining the relational expression factor according to the local parameter of the burnout point comprises: performing interaction term processing, high order term processing, and exponential term processing on each local thermal parameter and each local geometric parameter to obtain interaction term variables, high order term variables, and exponential term variables; constructing a relational expression factor library based on the interaction term variables, the high order term variables, the exponential term variables, and virtual variables; and the relational expression factor library is used for selecting a relational expression factor in the determination process of the target critical heat flux density relational expression.

[0066] Specifically, the interaction term processing on each local thermal parameter and each local geometric parameter can obtain interaction term variables, which refer to variables formed by multiplication of two or three independent parameters. For example, PX represents the product of pressure and steam quality, and other interaction term variables can also be PG, GX, Pdg*, PGX, etc. The high order term processing on each local thermal parameter and each local geometric parameter can obtain high order term variables, which mainly refer to variables formed by square terms and cubic terms of independent parameters. For example, P 2 , G 2 , P 3 , X 2 , dg* 2 , etc. C gsp 2 , etc. In this embodiment, to avoid too large fluctuation of the trend of the critical heat flux density value, the maximum power is not more than the third power, such as P 3 , and the square term is preferred. The exponential term processing on each local thermal parameter and each local geometric parameter can obtain exponential term variables, which mainly refer to variables formed by exponential forms of independent parameters. For example, e P , e G , e X , etc. can reflect the exponential change characteristics of the critical heat flux density in the local range of some parameters.

[0067] Further, based on the obtained interaction term variables, the high order term variables, and the exponential term variables, interaction term factors, high order term factors, and exponential term factors in the relational expression factor library can be constructed. In addition, the relational expression factor library can also include virtual factors obtained based on virtual variables, and the virtual variables can represent the structural characteristics of the reactor nuclear fuel assembly, for example, the virtual variable can be through 1 and 0 to represent whether the reactor nuclear fuel assembly contains a guide tube, which can be used to reflect the influence of the cold wall effect. It can be understood that in the determination process of the target critical heat flux density relational expression, after a relational expression factor is selected from the relational expression factor library, the local parameter data corresponding to the relational expression factor can be calculated based on the corresponding variable form, for example, if the selected relational expression factor is PX, then the product calculation of the local parameter data corresponding to the pressure P and the local parameter data corresponding to the steam quality X is performed, and the result of the calculation is taken as the local parameter data corresponding to the relational expression factor PX.

[0068] In addition, due to the different physical units of each local parameter affecting the critical heat flux density, too large or too small local parameter data can cause the coefficients of some factor terms in the finally determined critical heat flux density relationship to be too large or too small, resulting in calculation difficulty in the actual critical heat flux density prediction process. Therefore, to avoid the above problems, the local parameters can be normalized or dimensionless.

[0069] In one embodiment, before the interaction term variable, the high-order term variable and the exponential term variable are obtained by processing each local thermal parameter and each local geometric parameter, the method further comprises: normalizing and dimensionless processing the local parameter data corresponding to each local thermal parameter and each local geometric parameter.

[0070] Specifically, the normalization processing method is not unique, which can be min-max normalization method, z-score normalization method or range normalization method. In the embodiment, the range normalization method is used to normalize the local parameter data corresponding to each local thermal parameter and each local geometric parameter. Taking the distance (grid span) gsp between the two grids upstream of the BO point as an example, the range normalization method can be defined as:

[0071] C gsp = (gsp0-gsp) / gsp0

[0072] Where gsp0 is the maximum grid span of the nuclear fuel assembly test piece under different test conditions.

[0073] Further, dimensionless processing can parameterize the local parameter changing in a certain numerical range to dimensionless expression. Taking the distance dg between the BO point and the downstream surface of the upstream grid as an example, it reflects the influence of the relative distance between the BO point and the upstream grid on the critical heat flux density, so the distance changes in the range [0, dg max ], where dg max is the maximum grid spacing in the test group. The dimensionless method can be defined as: dg * = dg / dg max . Similarly, the dimensionless method for the pressure P at the BO point can be defined as: P * = P / P 0 .

[0074] It can be understood that the axial power distribution type of the nuclear fuel assembly test piece can include uniform heating and non-uniform heating, so the corresponding part of the critical heat flux density relationship can be determined according to the two types of test pieces in turn, and then combined to form a complete critical heat flux density relationship. In one embodiment, as Figure 3As shown, S206 includes S302 to S306, wherein:

[0075] S302: regression analysis is performed on the critical heat flux density test data, the relational expression factor and the local parameter data corresponding to the relational expression factor of the test piece with the axial power distribution type of uniform heating to determine the uniform heating term of the critical heat flux density relational expression.

[0076] In the process of determining the critical heat flux density relational expression according to the test data, several nuclear fuel assembly test pieces corresponding to the uniform heating and non-uniform heating types can be designed respectively, and then a plurality of sets of critical heat flux density test data obtained by performing critical heat flux density experiments on each nuclear fuel assembly test piece under different test conditions are obtained, and then the local parameter data obtained by performing burnup point local parameter calculation are used for fitting of the target critical heat flux density relational expression. For example, in the present embodiment, a total of 9 nuclear fuel assembly test pieces are designed, of which 5 sets are uniformly heated in the axial direction, and the other 4 sets are non-uniformly heated in the axial direction, and the non-uniform heating can be non-uniform heating by using a cosine distribution type axial power distribution.

[0077] Further, in the process of determining the uniform heating term of the critical heat flux density relational expression in S302, data can also be selected and determined according to the structure type of the nuclear fuel assembly test piece. The structure type of the nuclear fuel assembly test piece can include whether the test piece has a guide tube, and can also include test pieces with different grid layouts. In one embodiment, as shown in Figure 4 S302 includes S402 to S408, wherein:

[0078] S402: regression analysis is performed on the first critical heat flux density test data, the first relational expression factor and the first local parameter data corresponding to the first relational expression factor of the test piece with the structure type of no guide tube and the same grid layout to determine the basic term of the critical heat flux density relational expression.

[0079] Specifically, the test piece with the structure type of no guide tube and the same grid layout belongs to the basic structure of the test piece, and can be used to analyze the basic term of the critical heat flux density relational expression. In the present embodiment, at least one set of critical heat flux density test data of the test piece with no guide tube and the same grid layout can be selected from the 5 sets of test pieces uniformly heated in the axial direction as the first critical heat flux density test data.

[0080] Further, when the first relationship factor is determined based on the critical heat flux relationship of the test piece with the structure type of no guide tube and the same grid layout, the relationship factor obtained from the relationship factor library can be understood as the relationship factor related to the base term. In the embodiment, the first relationship factor is mainly selected to have the pressure P at the BO point, the sub-channel flow G, and the steam quality X, and the interaction factor obtained based on the above selected relationship factor is processed to obtain the interaction factor. It can be understood that the first local parameter data is the local parameter data corresponding to the first relationship factor selected above.

[0081] Further, the selected first critical heat flux test data, the first relationship factor and the first local parameter data corresponding to the first relationship factor can be used for regression analysis to determine the base term of the critical heat flux relationship. The determination method is not unique. In an embodiment, as shown in Figure 5 S402 includes S502 to S508, wherein:

[0082] S502: Select the first critical heat flux test data of the test piece with the structure type of no guide tube and the same grid layout to form the first CHF value matrix y as the dependent variable of the first critical heat flux regression equation.

[0083]

[0084] Specifically, each element in the first CHF value matrix y is the CHF value obtained under each test condition when the critical heat flux test is performed.

[0085] S504: Select the first relationship factor and the first local parameter data corresponding to the first relationship factor to form the first factor matrix x as the independent variable of the first critical heat flux regression equation.

[0086]

[0087] wherein x ni represents the first local parameter data of the i-th factor corresponding to the n-th test condition, and 1 is a constant term (0-th) factor.

[0088] S506: According to the first CHF value matrix y and the first factor matrix x, the first factor coefficient matrix of the first critical heat flux regression equation is determined by regression analysis.

[0089] Specifically, a regression analysis model is constructed according to the first CHF value matrix y and the first factor matrix x: y = xa. The CHF values obtained under each test condition in the critical heat flux test are taken as dependent variables, and each column factor in the factor matrix is taken as an independent variable. The factor term coefficients are obtained by least square fitting to form an initial factor coefficient matrix a1.

[0090] Further, each factor in the first factor matrix x is screened by a preset confidence level combined with a stepwise regression method until a new first factor matrix x1 of the first critical heat flux regression equation is finally determined. The method for screening factors can be to perform significance checking on each factor in the first factor matrix x one by one, remove the factors that are not significant, and finally determine the factor coefficient matrix corresponding to the new first factor matrix x1 as the first factor coefficient matrix a of the first critical heat flux regression equation.

[0091] S508: The first critical heat flux regression equation formed based on the first factor coefficient matrix a is taken as a basic term of the critical heat flux relationship.

[0092] CHF = a0 + a1x1 + a2x2 + a3x3 +... + aixi j x j

[0093] wherein a j is the coefficient of the jth relationship factor, x j is the jth relationship factor. And because the relationship factors have been screened by stepwise regression, j≤i (i is the number of the first relationship factors selected in S402).

[0094] S404: The second critical heat flux test data of the test piece of the structure type of no guide tube and different grid layout, the second relationship factor and the second local parameter data corresponding to the second relationship factor are selected to modify the basic term to determine the grid layout influence term of the critical heat flux relationship.

[0095] Specifically, the test piece of the structure type of no guide tube and different grid layout is a test piece of a different grid layout from the test piece selected in S402, which can be used to analyze and determine the grid layout influence term in the critical heat flux relationship. In this embodiment, at least one set of critical heat flux test data of the test piece of the structure type of no guide tube and different grid layout can be selected from the five sets of test pieces using axial uniform heating as the second critical heat flux test data.

[0096] Further, when the second relationship factor is determined based on the structure type of the test piece without a guide tube and different grid layouts, the relationship factor obtained by selecting from the relationship factor library can be understood as a relationship factor related to the grid layout influence term. In this embodiment, the second relationship factor mainly selects the pressure P at the BO point, the sub-channel flow G, the steam quality X, the distance between the two grids upstream of the BO point (grid span) gsp, the distance dg between the BO point and the downstream surface of the upstream grid, and the high-order term factor and the interaction term factor obtained by processing the high-order term and the interaction term based on the selected relationship factor. It can be understood that the second local parameter data is the local parameter data corresponding to the selected second relationship factor.

[0097] Further, the selected second critical heat flux test data, the second relationship factor, and the second local parameter data corresponding to the second relationship factor can be used for regression analysis to determine the grid layout influence term of the critical heat flux relationship. The determination method is not unique. In one embodiment, as shown in S404, S404 includes S602 to S606, wherein: Figure 6

[0098] S602: The selected second relationship factor is added to the first factor matrix x one by one to form a second factor matrix x1. Wherein, adding one by one can be understood as arranging the selected second relationship factor to the factor column of the first factor matrix x one by one, for example, arranging to the ith factor of the first factor matrix x.

[0099] S604: Based on the second factor matrix x1, the second critical heat flux test data of the test piece with the structure type of no guide tube and different grid layouts, and the second local parameter data corresponding to the second relationship factor are used to modify the first critical heat flux regression equation to obtain a second critical heat flux regression equation. It can be understood that the selected second relationship factor is introduced one by one after the first critical heat flux regression equation (base term) by adding method to obtain the second critical heat flux regression equation.

[0100] S606: Based on the second critical heat flux regression equation, the nested model comparison method is used to screen the second relationship factor added one by one until the screening of all selected second relationship factors is completed to determine the grid layout influence term of the critical heat flux relationship.

[0101] ​Specifically, in the process of introducing the second relationship factors one by one, a nested model comparison method is used to screen the second relationship factors added one by one. The nested model comparison method is used to determine whether the newly introduced second relationship factor makes the second critical heat flux regression equation obtain a larger sum of squares regression (SSR). It can be understood that if the newly introduced second relationship factor can make the second critical heat flux regression equation obtain a larger SSR, the second relationship factor is retained; if the newly introduced second relationship factor cannot make the second critical heat flux regression equation obtain a larger SSR, the second relationship factor needs to be removed. Through this process, until the screening of all selected second relationship factors is completed, the grid layout influence term of the critical heat flux relationship is determined.

[0102] The principle of the nested model comparison method is as follows. The first equation CHF1 has independent variables (1, x1, x2, x3), and the second equation CHF2 has independent variables (1, x1, x2, x3, x4). As can be seen, the first equation CHF1 is a subset or linear combination of the subset of the second equation CHF2. The regression model formed by the two equations is called a nested model, wherein:

[0103] CHF1=a0+a1×x1+a2×x2+a3×x3

[0104] CHF2=a0+a1×x1+a2×x2+a3×x3+a4×x4

[0105] Further, the calculation method of the regression sum of squares SSR is as follows:

[0106]

[0107] wherein x i =(1x1…x i ) is the i-th row of the factor matrix x; is the coefficient matrix, a i is the coefficient of the i-th relationship factor; is the average of the measured CHF values.

[0108] S406: The third critical heat flux test data of the test piece with a guide tube, the third relationship factor, and the third local parameter data corresponding to the third relationship factor are selected to correct the grid layout influence term, and the cold wall effect term of the critical heat flux relationship is determined.

[0109] Specifically, the structure type is a guided tube test piece, which can be used to analyze and determine the cold wall effect term in the critical heat flux density relationship. In this embodiment, generally, among the 5 groups of test pieces with axial uniform heating, the critical heat flux density test data of 1-2 groups of test pieces with guided tubes can be selected as the third critical heat flux density test data.

[0110] Further, the third relationship factor is a relationship factor obtained by selecting from the relationship factor library when determining the cold wall effect term in the critical heat flux density relationship based on the structure type of the guided tube test piece. It can be understood that the relationship factor related to the cold wall effect term. In this embodiment, the third relationship factor is mainly selected as a virtual factor obtained based on a virtual variable, and a factor formed by multiplying the virtual variable and other primary variables (including but not limited to the pressure P at the BO point, the sub-channel flow G, the steam quality X, the distance between the two grids upstream of the BO point (grid span) gsp, the distance between the BO point and the downstream surface of the upstream grid dg, etc.), the interaction term variable, and the high-order term variable. In addition, if the pipe diameters of the guided tubes of different test pieces in the above structure type of the guided tube test piece are different, the third relationship factor can also select the BO point sub-channel hydraulic diameter De, the BO point sub-channel heating diameter Dh, and other relationship factors, and the factors obtained by processing the interaction term, the high-order term, or the exponential term based on the two relationship factors. It can be understood that the third local parameter data is the local parameter data corresponding to the third relationship factor selected above.

[0111] Further, the selected third critical heat flux density test data, the third relationship factor, and the third local parameter data corresponding to the third relationship factor can be used for regression analysis to determine the cold wall effect term in the critical heat flux density relationship. The determination method can include: selecting the third relationship factor and adding it to the second factor matrix x1 to form a third factor matrix x2; based on the third factor matrix x2, the third critical heat flux density test data of the structure type of the guided tube test piece and the third local parameter data corresponding to the third relationship factor are used to modify the second critical heat flux density regression equation to obtain a third critical heat flux density regression equation; based on the third critical heat flux density regression equation, the nested model comparison method is used to screen the third relationship factor added one by one until the screening of all selected third relationship factors is completed, and the cold wall effect term in the critical heat flux density relationship is determined.

[0112] As the same as the process of determining the grid layout impact term, the step of adding one by one can be understood as arranging the selected third relational expression factors one by one to the factor column of the second factor matrix x1. Then, the third critical heat flux regression equation is obtained by introducing the selected third relational expression factors one by one after the second critical heat flux regression equation (grid layout impact term) through the additive method. Similarly, the nested model comparison method is used to screen the third relational expression factors added one by one until the screening of all selected third relational expression factors is completed, and the cold wall effect term of the critical heat flux relationship is determined. The detailed principle process is consistent with the above-mentioned process of determining the grid layout impact term, and will not be repeated here.

[0113] S408: The determined cold wall effect term of the critical heat flux relationship is taken as the uniform heating term CHF of the critical heat flux relationship. uni . Wherein, CHF uni The method is suitable for predicting the critical heat flux of the nuclear fuel assembly of the reactor using the uniform heating method.

[0114] S304: Based on the uniform heating term, the critical heat flux test data and the local parameter data of the test piece with the axial power distribution type of non-uniform heating are analyzed to determine the non-uniform heating term of the critical heat flux relationship.

[0115] It can be understood that, on the basis of the relationship of the uniform heating term determined in S302, the preliminary calculated critical heat flux is obtained, and then the preliminary calculated critical heat flux is combined with the actual CHF value in the critical heat flux test data to analyze the influence degree of non-uniform heating on the target critical heat flux relationship, that is, the non-uniform heating term of the critical heat flux relationship.

[0116] In one embodiment, as shown in Figure 7 S304 includes S702 to S706, wherein:

[0117] S702: The local parameter data of the test piece with the axial power distribution type of non-uniform heating is substituted into the uniform heating term to obtain the critical heat flux prediction value.

[0118] It can be understood that the critical heat flux prediction value is the critical heat flux calculated according to the determined critical heat flux relationship. The critical heat flux prediction value calculated according to the uniform heating term can be understood as the preliminary calculated critical heat flux on the basis of the relationship determined in the current S302 step. The preliminary calculated critical heat flux is combined with the actual CHF value in the critical heat flux test data, and the influence degree of non-uniform heating on the target critical heat flux relationship can be analyzed to be corrected.

[0119] Specifically, the local parameter data of the test piece with the axial power distribution type of non-uniform heating can be substituted into the factor matrix of the uniform heating term, and then combined with the coefficient matrix, so that the critical heat flux prediction value CHF corresponding to each test condition can be calculated uni .

[0120] S704: Calculate according to the critical heat flux prediction value and the critical heat flux test data of the test piece with the axial power distribution type of non-uniform heating to obtain the non-uniform heating factor.

[0121] Specifically, the critical heat flux prediction value CHF uni characterizes the critical heat flux calculated by the uniform heating term, and the CHF value (CHF non-uni , non-uni is the abbreviation of Non-Uniform) characterizes the critical heat flux suitable for both uniform heating and non-uniform heating. Then, the non-uniform heating factor FUN corresponding to each test condition is calculated according to the following formula:

[0122]

[0123] S706: Based on the non-uniform heating factor value and the local parameter data of the test piece with the axial power distribution type of non-uniform heating, regression analysis is performed to determine the non-uniform heating term of the critical heat flux relationship.

[0124] Specifically, based on the non-uniform heating factor value and the local parameter data of the test piece with the axial power distribution type of non-uniform heating, for example, including the steam quality X, the sub-channel flow G, the heating length l DNB , etc., the regression model of the non-uniform term is constructed:

[0125]

[0126] wherein, b1, b2 and b3 are coefficients to be fitted; q(z) is the axial power distribution function. Then, regression analysis is performed using the above formula to fit b1, b2 and b3, thereby determining the non-uniform heating term FNU of the critical heat flux relationship.

[0127] S306: Combine the uniform heating term and the non-uniform heating term to determine the target critical heat flux relationship. Specifically, the obtained uniform heating term and non-uniform heating term can be combined according to the following formula to determine the target critical heat flux relationship:

[0128]

[0129] Where, the left side of the equal sign is the CHF value (applicable to uniform heating and non-uniform heating), and the right side of the equal sign is the molecule CHF uni is the uniform heating term, and the denominator FNU is the non-uniform heating term.

[0130] The following takes the flowchart shown in FIG. 1 as an example to describe the critical heat flux density relationship determination method provided in the present application in detail, including the following steps: Figure 8

[0131] Step 1: Obtain CHF test data of nuclear fuel assembly test pieces with different structures and different axial power distributions. Each test piece will be subjected to CHF tests under different inlet temperature, flow rate, outlet pressure and other working conditions, and the critical heat flux density under the corresponding working conditions will be obtained by measurement, and the test results will be used as the input of the sub-channel analysis software modeling. The test pieces are generally designed according to the structure of the new nuclear fuel assembly, and reflect the structural characteristics of the fuel assembly and its application mode in the reactor. For example, the test party designs 9 nuclear fuel assembly test pieces with different structures and different axial power distributions, and obtains 9 groups of CHF test data, of which 5 groups are axial uniform heating, and 4 groups are axial non-uniform heating (generally can be cosine distribution). This method will select 5 groups of axial uniform heating test data to fit the uniform heating term of the CHF relationship, and the remaining 4 groups of axial non-uniform heating test data will be used to fit the non-uniform heating factor of the CHF relationship.

[0132] Step 2: Calculate the local thermal parameters of the burnout point (BO point) where CHF occurs under different test working conditions by using the sub-channel analysis software. These local thermal parameters specifically include: the pressure P at the BO point, the sub-channel flow rate G, and the steam quality X. Generally, it is believed that these thermal parameters have a certain correlation with the CHF value.

[0133] Step 3: Obtain the local geometric parameters of the BO point, specifically including: the heating length l DNB from the heating starting point, the distance (grid span) gsp between the two grids upstream of the BO point, the distance dg between the BO point and the downstream surface of the upstream grid, the BO point sub-channel hydraulic diameter De, and the BO point sub-channel heating diameter Dh. Generally, it is believed that these geometric parameters also have a certain correlation with the CHF value.

[0134] Step 4: Calculate the CHF relationship factor according to the above local thermal parameters and local geometric parameters, and form a CHF relationship factor library.

[0135] ​Step 5: Determine the CHF relationship formula base term 1. Select the CHF data of the test pieces with uniform heating, no guide tube, and the same grid layout (generally select at least one of the aforementioned 5 groups of non-uniform heating data) and its corresponding factors, and use multiple linear regression method to carry out regression analysis on the regression model to determine the CHF relationship formula base term. The related factors in this step are mainly selected as the 1st order terms and interaction terms of P, G, and X.

[0136] Step 6: Determine the grid layout influence term. Keep the existing relationship formula base term 1, and select the CHF data of the test pieces with uniform heating, no guide tube, and different grid layouts (generally at least one group is designed in the experimental design). Introduce the factors not used in step 5 one by one after the base term 1 by additive method, including the 1st and 2nd order terms and interaction terms of P, G, X, dg, and gsp. In the process of introducing new factors one by one, the nested model comparison method is used to judge whether the newly introduced factor can obtain a larger regression sum of squares (SSR). If it can, the factor is retained. If it cannot, the factor is removed. Until all factors are evaluated, the factors constituting the relationship formula form are obtained. The relationship formula base term 2 obtained in this step is obtained.

[0137] Step 7: Determine the cold wall effect term. Keep the existing relationship formula base term 2, add the uniform heating, guide tube test piece data in the above regression analysis matrix (generally 1-2 groups of experimental pieces reflecting this effect are designed in the experimental design), and increase the virtual variable related factor column in the regression analysis matrix, mainly including virtual variable, virtual variable and 1st order term, interaction term, 2nd order term product term, if there is a difference in the guide tube diameter of different test pieces, De and Dh related factors can be introduced. Introduce new factors one by one in the relationship formula base term 2, and in the process of introducing new factors one by one, the nested model method is used to judge whether the newly introduced factor can obtain a larger regression sum of squares. If it can, the factor is retained. If it cannot, the factor is removed. Until all previously unused factors are evaluated, the factors constituting the relationship formula form are obtained. The CHF relationship formula uniform heating term CHF uni (subscript uni is the abbreviation of Uniform, indicating uniform heating) is obtained in this step.

[0138] Step 8: Fit and determine the non-uniform heating factor FNU. The local thermal parameters and local set parameters of the BO point of the axial non-uniform heating experimental group are used to calculate the uniform heating term CHF uni in the CHF relationship formula. Further, according to the calculated uniform heating term CHF uniThe non-uniform heating factor of each working point is calculated according to the CHF measurement value. Finally, the coefficient of FUN is fitted by using the regression analysis method according to the calculated non-uniform heating factor and related parameters, so as to determine the form and coefficient of FUN.

[0139] Step 9: determining the form of the relationship from the uniform heating term and the non-uniform heating factor term.

[0140] In the embodiment, the CHF relationship determined by the BO point parameters is obtained. The form of the relationship established by the method can reflect the characteristics of the test results based on the CHF point data observed in the test, and can provide a CHF relationship form with better prediction effect for subsequent coefficient optimization and data test evaluation of the CHF relationship. By constructing a factor library and using the regression analysis method to fit the coefficients of the relationship, since the relationship is formed by the multiplication and addition of each factor and its coefficient, the method is equivalent to multiple linear regression, and the fitting process is simpler than the multiple nonlinear regression method.

[0141] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0142] Based on the same inventive concept, the embodiments of the present application also provide a critical heat flux relationship determination device for implementing the critical heat flux relationship determination method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more critical heat flux relationship determination device embodiments provided below can refer to the limitations of the critical heat flux relationship determination method described above, and will not be repeated here.

[0143] In one embodiment, as shown in Figure 9 A critical heat flux relationship determination device is provided, comprising: a test data acquisition module 910, a local parameter calculation module 920, and a relationship determination module 930, wherein:

[0144] The test data acquisition module 910 is configured to acquire critical heat flux test data of a nuclear fuel assembly test piece.

[0145] The local parameter calculation module 920 is configured to perform burnout point local parameter calculation according to the critical heat flux test data, and obtain local parameter data corresponding to the burnout point local parameter.

[0146] The relationship determination module 930 is configured to perform regression analysis on the critical heat flux test data, the relationship factor and the local parameter data corresponding to the relationship factor of the nuclear fuel assembly test piece of each type in sequence, and determine a target critical heat flux relationship, wherein the relationship factor is obtained according to the burnout point local parameter.

[0147] In an embodiment, the types of the nuclear fuel assembly test piece include an axial power distribution type.

[0148] The relationship determination module 930 is further configured to perform regression analysis on the critical heat flux test data, the relationship factor and the local parameter data corresponding to the relationship factor of the test piece of the axial power distribution type of uniform heating, and determine a uniform heating term of the critical heat flux relationship; based on the uniform heating term, perform analysis on the critical heat flux test data and the local parameter data of the test piece of the axial power distribution type of non-uniform heating, and determine a non-uniform heating term of the critical heat flux relationship; and combine the uniform heating term and the non-uniform heating term to determine the target critical heat flux relationship.

[0149] In an embodiment, the types of the nuclear fuel assembly test piece further include a structure type.

[0150] The relationship determination module 930 is further configured to perform regression analysis on the first critical heat flux test data, the first relationship factor and the first local parameter data corresponding to the first relationship factor of the test piece of the structure type of no guide tube and same grid layout, and determine a base term of the critical heat flux relationship; perform correction on the base term by using the second critical heat flux test data, the second relationship factor and the second local parameter data corresponding to the second relationship factor of the test piece of the structure type of no guide tube and different grid layout, and determine a grid layout influence term of the critical heat flux relationship; perform correction on the grid layout influence term by using the third critical heat flux test data, the third relationship factor and the third local parameter data corresponding to the third relationship factor of the test piece of the structure type of having guide tube, and determine a cold wall effect term of the critical heat flux relationship; and take the determined cold wall effect term of the critical heat flux relationship as a uniform heating term of the critical heat flux relationship.

[0151] In an embodiment, the relationship determining module 930 is further configured to select first critical heat flux test data of test pieces of the structure type of no guide tube and the same grid layout to form a first CHF value matrix as a dependent variable of a first critical heat flux regression equation; select the first relationship factor and first local parameter data corresponding to the first relationship factor to form a first factor matrix as an independent variable of the first critical heat flux regression equation; perform regression analysis based on the first CHF value matrix and the first factor matrix to determine a first factor coefficient matrix of the first critical heat flux regression equation; and take the first critical heat flux regression equation formed based on the first factor coefficient matrix as a basic term of the critical heat flux relationship.

[0152] In an embodiment, the relationship determining module 930 is further configured to add the second relationship factor to the first factor matrix one by one to form a second factor matrix; perform correction on the first critical heat flux regression equation based on the second factor matrix, the second critical heat flux test data of test pieces of the structure type of no guide tube and different grid layouts, and the second local parameter data corresponding to the second relationship factor to obtain a second critical heat flux regression equation; and perform screening on the second relationship factor added one by one based on the second critical heat flux regression equation by using a nested model comparison method until screening of all selected second relationship factors is completed to determine a grid layout influence term of the critical heat flux relationship.

[0153] In an embodiment, the relationship determining module 930 is further configured to substitute the local parameter data of test pieces of the axial power distribution type of non-uniform heating into the uniform heating term to obtain a critical heat flux prediction value; perform calculation based on the critical heat flux prediction value and the critical heat flux test data of test pieces of the axial power distribution type of non-uniform heating to obtain a non-uniform heating factor value; and perform regression analysis based on the non-uniform heating factor value and the local parameter data of test pieces of the axial power distribution type of non-uniform heating to determine a non-uniform heating term of the critical heat flux relationship.

[0154] In an embodiment, the burnout point local parameter includes a local thermal parameter and a local geometric parameter; and the critical heat flux relationship determining apparatus further includes a factor library constructing module, in which:

[0155] The factor library constructing module is configured to perform interaction term processing, high-order term processing, and exponential term processing on each local thermal parameter and each local geometric parameter to obtain an interaction variable, a high-order variable, and an exponential variable; and construct a relationship factor library based on the interaction variable, the high-order variable, the exponential variable, and a virtual variable; and the relationship factor library is used to select a relationship factor in a process of determining a target critical heat flux relationship.

[0156] In the embodiment, the critical heat flux density relationship is determined by sequentially selecting the critical heat flux density test data, the relational expression factor and the local parameter data corresponding to the relational expression factor according to the type of the nuclear fuel assembly test piece, the process of determining the critical heat flux density relationship is simplified, and the critical heat flux density relationship can be universally applied to newly developed reactor nuclear fuel assemblies without relying on the experience of relational expression developers.

[0157] Each module in the critical heat flux density relationship determining apparatus can be realized by software, hardware or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0158] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 10 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store critical heat flux density test data and related data in the process of determining the critical heat flux density relationship. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with terminals outside through a network connection. The computer program is executed by the processor to implement a critical heat flux density relationship determining method.

[0159] Those skilled in the art can understand that Figure 10 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0160] In one embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above method.

[0161] In one embodiment, a computer readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.

[0162] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method described above.

[0163] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0165] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0166] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A critical heat flux relationship determination method, characterized by, The method comprises: obtaining critical heat flux test data of a nuclear fuel assembly test piece; calculating a burnout point local parameter according to the critical heat flux test data to obtain local parameter data corresponding to the burnout point local parameter; selecting critical heat flux test data, a relationship factor and local parameter data corresponding to the relationship factor according to the type of the nuclear fuel assembly test piece in sequence to perform regression analysis and determine a uniform heating term and a non-uniform heating term, and then determine a target critical heat flux relationship formula by combining the uniform heating term and the non-uniform heating term; wherein the relationship factor is obtained according to the burnout point local parameter, and the type of the nuclear fuel assembly test piece includes an axial power distribution type and a structure type; the determination of the uniform heating term comprises: selecting first critical heat flux test data, a first relationship factor and first local parameter data corresponding to the first relationship factor of a test piece with the axial power distribution type of uniform heating and the structure type of no guide tube and same grid layout to perform regression analysis and determine a base term of the critical heat flux relationship formula; selecting second critical heat flux test data, a second relationship factor and second local parameter data corresponding to the second relationship factor of a test piece with the axial power distribution type of uniform heating and the structure type of no guide tube and different grid layout to modify the base term and determine a grid layout influence term of the critical heat flux relationship formula; the second relationship factor is selected as the pressure, the sub-channel flow rate, the quality ratio, the distance between two grids upstream of the BO point, the distance dg between the BO point and the downstream surface of the upstream grid, and high-order term factors and interaction factors obtained by processing high-order terms and interaction terms based on the selected relationship factors; selecting third critical heat flux test data, a third relationship factor and third local parameter data corresponding to the third relationship factor of a test piece with the axial power distribution type of uniform heating and the structure type of having a guide tube to modify the grid layout influence term and determine a cold wall effect term of the critical heat flux relationship formula; the third relationship factor is selected as a virtual factor obtained based on a virtual variable, and factors formed by multiplying the virtual variable with other first-order variables, interaction variables and high-order variables; the cold wall effect term of the determined critical heat flux relationship formula is taken as the uniform heating term.

2. The method of claim 1, wherein, the determination of the non-uniform heating term comprises: based on the uniform heating term, selecting critical heat flux test data and local parameter data of a test piece with the axial power distribution type of non-uniform heating to perform analysis and determine a non-uniform heating term of the critical heat flux relationship formula.

3. The method of claim 1, wherein, selecting first critical heat flux test data, a first relationship factor and first local parameter data corresponding to the first relationship factor of a test piece with the structure type of no guide tube and same grid layout to perform regression analysis and determine a base term of the critical heat flux relationship formula, which comprises: The first critical heat flux value matrix is formed as the dependent variable of the first critical heat flux regression equation by selecting the first critical heat flux test data of the test piece of the structure type of no guide tube and same grid layout; The first factor matrix is formed as the independent variable of the first critical heat flux regression equation by selecting the first relationship factor and the first local parameter data corresponding to the first relationship factor; The first factor coefficient matrix of the first critical heat flux regression equation is determined by regression analysis of the first CHF value matrix and the first factor matrix through the preset confidence level combined with the stepwise regression method. The first critical heat flux regression equation formed based on the first factor coefficient matrix is used as the basic term of the critical heat flux relationship.

4. The method of claim 3, wherein, The grid layout influence term of the critical heat flux relationship is determined by modifying the basic term by selecting the second critical heat flux test data of the test piece of the structure type of no guide tube and different grid layout, the second relationship factor and the second local parameter data corresponding to the second relationship factor, including: The second factor matrix is formed by adding the second relationship factor to the first factor matrix one by one; The second critical heat flux regression equation is obtained by modifying the first critical heat flux regression equation based on the second factor matrix, the second critical heat flux test data of the test piece of the structure type of no guide tube and different grid layout, and the second local parameter data corresponding to the second relationship factor; The grid layout influence term of the critical heat flux relationship is determined by screening the second relationship factor added one by one based on the second critical heat flux regression equation by using the nested model comparison method until the screening of all selected second relationship factors is completed.

5. The method of claim 4, wherein, The grid layout influence term of the critical heat flux relationship is determined by screening the second relationship factor added one by one based on the second critical heat flux regression equation by using the nested model comparison method until the screening of all selected second relationship factors is completed, including: If the newly introduced second relationship factor in the second critical heat flux regression equation makes the second critical heat flux regression equation obtain a larger regression sum of squares, the second relationship factor is retained; If the newly introduced second relationship factor in the second critical heat flux regression equation cannot make the second critical heat flux regression equation obtain a larger regression sum of squares, the second relationship factor is removed.

6. The method of claim 2, wherein, The non-uniform heating term of the critical heat flux relationship is determined by analyzing the critical heat flux test data and local parameter data of the test piece of the axial power distribution type of non-uniform heating based on the uniform heating term, including: The critical heat flux prediction value is obtained by substituting the local parameter data of the test piece of the axial power distribution type of non-uniform heating into the uniform heating term; The non-uniform heating factor value is obtained by calculating the critical heat flux prediction value and the critical heat flux test data of the test piece of the axial power distribution type of non-uniform heating. The non-uniform heating term of the critical heat flux correlation is determined based on the non-uniform heating factor value and local parameter data of the test piece of the axial power distribution type being non-uniform heating.

7. The method according to any one of claims 1 to 6, characterized in that, The burnout point local parameters include local thermal parameters and local geometric parameters; and the manner of obtaining the correlation factor according to the burnout point local parameters comprises: The local thermal parameters and the local geometric parameters are subjected to interaction term processing, high-order term processing and exponential term processing to obtain interaction variable, high-order variable and exponential variable; A correlation factor library is constructed based on the interaction variable, the high-order variable, the exponential variable and a dummy variable; and the correlation factor library is used for selecting a correlation factor in the determination process of the target critical heat flux correlation.

8. A device for determining the critical heat flux density relationship, characterized in that, The device comprises: a test data acquisition module configured to acquire critical heat flux test data of a nuclear fuel assembly test piece; a local parameter calculation module configured to calculate burnout point local parameters according to the critical heat flux test data to obtain local parameter data corresponding to the burnout point local parameters; a correlation determination module configured to sequentially select critical heat flux test data, a correlation factor and local parameter data corresponding to the correlation factor according to the type of the nuclear fuel assembly test piece to perform regression analysis, determine a uniform heating term and a non-uniform heating term, and then determine a target critical heat flux correlation in combination with the uniform heating term and the non-uniform heating term; wherein the correlation factor is obtained according to the burnout point local parameters, and the type of the nuclear fuel assembly test piece includes an axial power distribution type and a structure type. The relationship determining module is further configured to: select first critical heat flux density test data, a first relationship factor, and first local parameter data corresponding to the first relationship factor of a test piece with an axial power distribution type of uniform heating and a structure type of no guide tube and same grid layout, perform regression analysis on the first critical heat flux density test data, the first relationship factor, and the first local parameter data, and determine a basic term of the critical heat flux density relationship; select second critical heat flux density test data, a second relationship factor, and second local parameter data corresponding to the second relationship factor of a test piece with the axial power distribution type of uniform heating and a structure type of no guide tube and different grid layout, modify the basic term by using the second critical heat flux density test data, the second relationship factor, and the second local parameter data, and determine a grid layout influence term of the critical heat flux density relationship; the second relationship factor is selected as pressure at a BO point, sub-channel flow rate, steam quality, a distance between two grids upstream of the BO point, a distance dg between the BO point and a downstream surface of an upstream grid, and high-order term factors and interaction term factors obtained by performing high-order term processing and interaction term processing based on the selected relationship factors; select third critical heat flux density test data, a third relationship factor, and third local parameter data corresponding to the third relationship factor of a test piece with the axial power distribution type of uniform heating and a structure type of having a guide tube, modify the grid layout influence term by using the third critical heat flux density test data, the third relationship factor, and the third local parameter data, and determine a cold wall effect term of the critical heat flux density relationship; the third relationship factor is selected as a virtual factor obtained based on a virtual variable, and factors formed by multiplying the virtual variable with other first-order variables, interaction term variables, and high-order term variables; and take the determined cold wall effect term of the critical heat flux density relationship as the uniform heating term. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 7.

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