Method and device for evaluating nuclear reactor critical heat flux density data

Abstract

The invention relates to a nuclear reactor critical heat flux density data evaluation method and device. The method comprises the following steps: determining data points of critical heat flux density data and a type of variables required for evaluating the critical heat flux density data; determining a preset continuous range of one type of variables; generating a first-class variable statistical table according to the data points, the first-class variables and a preset continuous range of the first-class variables; and evaluating the integrity of the critical heat flux density data according to the one-class variable statistical table to obtain an integrity evaluation result. By adopting the method, the reliability of an integrity evaluation result can be improved.

Description

Technical Field

[0001] This application relates to the field of nuclear power technology, and in particular to a method for evaluating critical heat flux density data of a nuclear reactor and a device for evaluating critical heat flux density data of a nuclear reactor. Background Technology

[0002] Critical heat flux (CHF) is a crucial parameter in nuclear reactor core safety assessments. To ensure safe operation of the reactor core, the heat flux density on the surface of the fuel assemblies must consistently remain below the critical heat flux density. In pressurized water reactor safety analysis, the critical heat flux relationship is typically used to calculate and evaluate the core condition. This relationship is derived from critical heat flux data; therefore, the accuracy of the safety analysis results is significantly influenced by whether the critical heat flux data can fully describe the critical heat flux characteristics of the fuel assemblies.

[0003] For the reasons mentioned above, the completeness of critical heat flux density data needs to be confirmed before developing the critical heat flux density formula. Currently, the evaluation methods for whether critical heat flux density data meets the completeness requirements are still unclear in the published literature, and are mostly based on subjective factors, which may lead to insufficient reliability of the evaluation results. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and apparatus for evaluating the critical heat flux density data of a nuclear reactor that can clearly determine the integrity of the critical heat flux density data, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for evaluating critical heat flux density data of a nuclear reactor, comprising: determining data points of critical heat flux density data and a class of variables required for evaluating the critical heat flux density data; determining a preset continuous range of the class of variables; generating a statistical table of the class of variables based on the data points, the class of variables, and the preset continuous range of the class of variables; and evaluating the integrity of the critical heat flux density data based on the statistical table of the class of variables to obtain an integrity evaluation result.

[0006] In one embodiment, the method further includes: determining two types of variables required to evaluate the critical heat flux density data, wherein the first type of variable has a higher priority than the second type of variable; determining a preset type of the second type of variable; generating a statistical table of the second type of variable based on the data points, the second type of variable, and the preset type of the second type of variable; and evaluating the integrity of the critical heat flux density data based on the statistical table of the first type of variable and the statistical table of the second type of variable to obtain an integrity evaluation result.

[0007] In one embodiment, generating a statistical table of variables based on the data points, the type of variables, and a preset continuous range of the type of variables includes: dividing each preset continuous range into at least one continuous interval; determining a type of region for the statistical table of variables based on the continuous intervals of the type of variables; selecting key regions from the type of regions based on the nuclear reactor operating conditions; determining the proportion of data points in each key region; marking the type of regions based on the data points; and marking the key regions based on the proportion of data points in each key region to obtain the statistical table of variables.

[0008] In one embodiment, the type of variables includes the reactor outlet pressure, inlet mass flow rate, and local equilibrium steam content. The step of determining a type of region of the statistical table of the type of variables based on the continuous intervals of the type of variables includes: mapping any continuous interval of outlet pressure to any continuous interval of inlet mass flow rate to obtain corresponding intervals, and dividing the corresponding intervals according to all continuous intervals of local equilibrium steam content to obtain multiple type of regions.

[0009] In one embodiment, selecting a key region from the first type of region based on the nuclear reactor operating condition includes: determining a first type of region that includes the thermal-hydraulic state of the nuclear reactor operating condition and the minimum deviation bubble-nucleus boiling ratio corresponding to each thermal-hydraulic state; obtaining the nuclear fuel location corresponding to the minimum deviation bubble-nucleus boiling ratio and recording the data at the nuclear fuel location; and determining the first type of region where the recorded data at the nuclear fuel location is located as the key region.

[0010] In one embodiment, marking the type of region based on the data points includes: when a corresponding data point exists in the type of region, making a first mark in the type of region; when a corresponding data point does not exist in the type of region, making a second mark in the type of region, wherein the second mark is different from the first mark.

[0011] In one embodiment, marking the key region according to the proportion of data points in the key region includes: marking the key region a third time when the proportion of data points in the key region is greater than a preset proportion; and marking the key region a fourth time when the proportion of data points in the key region is less than or equal to the preset proportion, wherein the fourth mark is different from the third mark.

[0012] In one embodiment, evaluating the integrity of the critical heat flux density data based on the statistical table of a class of variables to obtain an integrity evaluation result includes: determining the number of a class of regions, the marking status of the class of regions, the number of key regions, and the marking status of key regions in the statistical table of a class of variables; calculating a first integrity index based on the number of a class of regions, the marking status of the class of regions, the number of key regions, and the marking status of key regions; and evaluating the integrity of the critical heat flux density data based on the first integrity index to obtain an integrity evaluation result.

[0013] In one embodiment, generating a binary variable statistical table based on the data points, the binary variables, and the preset types of the binary variables includes: determining the quantity of each preset type; determining the binary regions of the binary variable statistical table based on the quantity of each preset type of binary variable, wherein the quantity of the binary regions of each binary variable statistical table is the same as the quantity of the corresponding preset type of binary variable; and marking each binary region based on the data points to obtain the binary variable statistical table.

[0014] In one embodiment, marking each of the two types of regions according to the data points includes: when a corresponding data point exists in the two types of regions, making a first mark in the two types of regions; when a corresponding data point does not exist in the two types of regions, making a second mark in the two types of regions, wherein the second mark is different from the first mark.

[0015] In one embodiment, evaluating the integrity of the critical heat flux density data based on the first-class variable statistical table and the second-class variable statistical table to obtain an integrity evaluation result includes: determining the number of first-class regions, the marking status of first-class regions, the number of key regions, and the marking status of key regions in the first-class variable statistical table; determining the number of second-class variable statistical tables and the number of second-class regions and the marking status of second-class regions in each second-class variable statistical table; calculating a second integrity index based on the number of first-class regions, the marking status of first-class regions, the number of key regions, the marking status of key regions, the number of second-class variable statistical tables, the number of second-class regions, and the marking status of second-class regions; and evaluating the integrity of the critical heat flux density data based on the second integrity index to obtain an integrity evaluation result.

[0016] In one embodiment, evaluating the integrity of the critical heat flux density data based on the second integrity index includes: determining that the critical heat flux density data has sufficient integrity when the second integrity index is greater than or equal to a first integrity preset value; determining that the critical heat flux density data has basic integrity when the second integrity index is less than the first integrity preset value but greater than or equal to a second integrity preset value, wherein the second integrity preset value is less than the first integrity preset value; and determining that the critical heat flux density data does not have integrity when the second integrity index is less than the second integrity preset value.

[0017] In one embodiment, the two types of variables include the gate element type and the heating power distribution.

[0018] Secondly, this application also provides an evaluation device for critical heat flux density data of a nuclear reactor, comprising: a first determining module for determining data points of the critical heat flux density data and a class of variables required for evaluating the critical heat flux density data; a second determining module for determining a preset continuous range of the class of variables; a first generating module for generating a statistical table of the class of variables based on the data points, the class of variables, and the preset continuous range of the class of variables; and a first evaluation module for evaluating the integrity of the critical heat flux density data based on the statistical table of the class of variables to obtain an integrity evaluation result.

[0019] The technical solution described in this application first determines the data points for evaluating critical heat flux density data and a class of variables required for the evaluation, and then determines the preset continuous range of the class of variables. Next, a statistical table of the class of variables is generated based on the data points of the critical heat flux density data, the class of variables, and their preset continuous range. Finally, the completeness and degree of completeness of the critical heat flux density data are judged based on the statistical table of the class of variables. Since the class of variables and their range, as well as the actual critical heat flux density data points, are both objective and authentic, the statistical table of the class of variables generated from them is also objective. Therefore, evaluating the completeness of critical heat flux density data based on the statistical table of the class of variables clarifies the process of completeness evaluation, achieving a systematic, specific, and feasible evaluation. Furthermore, it evaluates based on objective factors, eliminating the need for evaluation based on subjective factors or reducing subjective factors, thus avoiding or reducing the phenomenon of insufficient reliability of evaluation results due to subjective evaluation, thereby improving the reliability of the completeness evaluation results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for evaluating the critical heat flux density data of a nuclear reactor in an embodiment of this application.

[0022] Figure 2 This is a flowchart of a method for evaluating the critical heat flux density data of a nuclear reactor in one embodiment.

[0023] Figure 3 for Figure 1 The flowchart for generating a statistical table of variables in step 103.

[0024] Figure 4 for Figure 3 The flowchart for determining a type of region in step 302.

[0025] Figure 5 for Figure 3 The flowchart for selecting key areas in step 303.

[0026] Figure 6 for Figure 1 The flowchart for the integrity evaluation in step 104.

[0027] Figure 7 for Figure 2 The flowchart for generating the statistical table of two types of variables in step 203.

[0028] Figure 8 for Figure 3 The flowchart for the integrity evaluation in step 204.

[0029] Figure 9 This is a flowchart of a method for evaluating the critical heat flux density data of a nuclear reactor in a specific example.

[0030] Figure 10 This is a structural block diagram of the device for evaluating the critical heat flux density data of a nuclear reactor in an embodiment of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0033] Figure 1 This is a flowchart illustrating the evaluation method for critical heat flux density data of a nuclear reactor in an embodiment of this application. This embodiment uses the application of this method to a terminal as an example; it is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. Figure 1 As shown, the method includes the following steps:

[0034] Step 101: Determine the data points for the critical heat flux density data and the class of variables required to evaluate the critical heat flux density data.

[0035] Critical heat flux density (CFL) data is obtained through experiments on the critical heat flux density, including data on pressure, temperature, and flow rate. The data points consist of a set of CFL data points. For each data point, the dependent variable is the CFL value, and there are several independent variables corresponding to this CFL value. The important variables among these independent variables that must be considered to evaluate the completeness of the critical heat flux density are called a class of variables. These can be pre-defined based on engineering experience and actual needs; for example, class of variables such as pressure and temperature are usually numerical variables (expressed as numerical values). A class of variables must include at least three different variables.

[0036] Critical heat flux density data may correspond to one type of variable, or they may not correspond to another type of variable.

[0037] Specifically, when the terminal and / or server receive a user-inputted evaluation instruction for the critical heat flux density data or detects that its integrity needs to be evaluated, it can first parse out each data point and the critical heat flux density data for each data point, and then determine multiple class-I variables required for evaluating the integrity based on specific needs. This can be obtained by reading a database or through user input. Specifically, the critical heat flux density data can be obtained by receiving the evaluation instruction, meaning the evaluation instruction includes the critical heat flux density data, or the actual data can be obtained by acquiring the critical heat flux density data separately.

[0038] Step 102: Determine the preset continuous range of a class of variables.

[0039] One type of variable may include the reactor outlet pressure, inlet mass flow rate, and local equilibrium steam content. The preset continuous range is a continuous range of values, which can be determined in advance based on engineering experience with pressurized water reactors and input into the terminal and / or server.

[0040] It should be noted that the expected distribution of critical heat flux density data should be strongly correlated with the corresponding nuclear reactor operating state. Therefore, when determining the preset continuous range of its variables, it should be consistent with the range of key thermal-hydraulic parameters and nuclear fuel structural parameters during nuclear reactor operation.

[0041] Specifically, after obtaining a class of variables, the terminal and / or server can determine its preset continuous range through historical data and user input. To illustrate, let's take the example of a class of variables including the reactor outlet pressure, inlet mass flow rate, and local equilibrium steam content. After the terminal and / or server determine that the class of variables consists of outlet pressure, inlet mass flow rate, and local equilibrium steam content, they each determine their preset continuous range.

[0042] For example, the preset continuous range of outlet pressure can be 3MPa to 17.5MPa; the preset continuous range of inlet mass flow rate can be 0.5t / m³. 2 s~4.0t / m 2 The preset continuous range of local equilibrium vapor content can be -0.25 to 0.40.

[0043] Step 103: Generate a statistical table of the first type of variables based on the data points, the first type of variables, and the preset continuous range of the first type of variables.

[0044] A single-variable statistical table refers to a statistical table that records at least one type of variable, the corresponding data points, and the relationship between the variables and the data. There can be at least one single-variable statistical table, and such tables can be multidimensional tables.

[0045] Specifically, the terminal and / or server generate at least one statistical table as a class of variable statistical table based on the data points, a class of variables, and a preset continuous range of the class of variables obtained in steps 101 and 102, and considering the coupling relationships between the various classes of variables. Specifically, the length, width, and / or height of the variable statistical table can be determined first based on the class of variables and their ranges to obtain the various regions of the table, which represent the coupling relationships between the corresponding variables. Then, the various regions of the variable statistical table are filled or marked according to the actual data points to obtain the class of variable statistical table.

[0046] Step 104: Evaluate the integrity of the critical heat flux density data based on a statistical table of a class of variables to obtain the integrity evaluation result.

[0047] Specifically, after the terminal and / or server fills or marks the area of ​​a statistical table of a type of variable, they quantify the integrity of the critical heat flux density data based on the content of the filled or marked statistical table of a type of variable and based on preset quantification rules. Based on the quantification results, they determine whether the data has integrity and the degree of integrity, thereby achieving integrity evaluation.

[0048] In practical applications, the completeness evaluation results can be quantified directly based on a single variable statistical table, or the preliminary completeness evaluation results can be quantified first based on multiple variable statistical tables, and then the final completeness evaluation results can be obtained by combining the multiple preliminary completeness evaluation results, such as by selecting the minimum value or calculating the mean from multiple preliminary completeness evaluation results.

[0049] Among them, the preset quantification rules can be rules determined in advance based on actual needs, historical evaluations, expert experience, etc., for quantifying the integrity evaluation results of critical heat flux density data. They can be dynamically adjusted according to actual needs and evaluation experience to ensure the accuracy of the rules.

[0050] The aforementioned method for evaluating critical heat flux density data in nuclear reactors first determines the data points and a class of variables required for the evaluation, and then defines a preset continuous range for these variables. Next, a statistical table of these variables is generated based on the data points, the class of variables, and their preset continuous range. Finally, the completeness and degree of completeness of the critical heat flux density data are judged based on this statistical table. Since the class of variables and their ranges, as well as the actual critical heat flux density data points, are objective and authentic, the generated statistical table is also objective. Therefore, evaluating the completeness of critical heat flux density data based on this statistical table clarifies the evaluation process, achieving a systematic, specific, and feasible evaluation. Furthermore, it is based on objective factors, eliminating the need for subjective evaluation or reducing subjective factors, thus avoiding or minimizing the unreliability of evaluation results due to subjective evaluation, thereby improving the reliability of the completeness evaluation results.

[0051] In practical applications, in addition to the important Class I variables, there are also secondary important variables. These secondary variables can be considered in the integrity evaluation according to actual needs, that is, integrity evaluation is based on Class I and Class II variables.

[0052] That is, in one embodiment, such as Figure 2 As shown, the evaluation method for nuclear reactor critical heat flux density data also includes steps 201 to 204. Wherein:

[0053] Step 201: Determine the two types of variables required to evaluate the critical heat flux density data, wherein the first type of variable has higher priority than the second type of variable.

[0054] Among them, the second-class variables refer to the less important independent variables that can be considered when evaluating the completeness of the critical heat flux density data, such as heating power distribution and the location where the critical heat flux density occurs. These can be preset according to the actual needs of the field. The second-class variables include at least two different variables.

[0055] Variables not falling into the above categories can be referred to as other variables. When identifying the types of variables required for completeness assessment, Category I variables must be included, Category II variables are recommended to be included, and other variables should be considered for inclusion based on the actual situation. This embodiment uses the inclusion of Category I and Category II variables as examples for illustration.

[0056] Step 202: Determine the preset type of the two types of variables.

[0057] The second type of variable may include grid type and heating power distribution, and may also include at least one of grid type, grid spacing and location of critical heat flux density.

[0058] It should be noted that binary variables are usually not continuous variables, but categorical variables. Their range includes multiple optional subtypes. In order to generate a reliable statistical table of binary variables, it is necessary to consider all possible types of each binary variable, which are called preset types. The preset type is the possible subtype of each binary variable. The number can be at least one and can be determined in advance according to actual needs, historical data and engineering experience, and then entered into the terminal and / or server.

[0059] Its preset type can be determined through historical data and user input.

[0060] Specifically, we will take two types of variables, namely grid type and heating power distribution, as an example. After determining that the two types of variables are grid type and heating power distribution, we will determine the preset type of grid type and the preset type of heating power distribution, respectively.

[0061] For example, the default type of the following two types of variables is a common type based on engineering experience with nuclear reactor pressurized water reactors:

[0062] Cell types: typical cell, guide tube cell;

[0063] Grid type: Unmixed grid, Mixed grid;

[0064] Grid spacing: Spacing between nuclear fuel prototypes;

[0065] Heating power distribution: uniform heating, non-uniform heating with the peak value located at the center, and non-uniform heating with the peak value not located at the center;

[0066] Location of critical heat flux density: mixed region, unmixed region.

[0067] Step 203: Generate a statistical table of binary variables based on the data points, binary variables, and the preset types of binary variables.

[0068] A binary variable statistical table refers to a statistical table that records at least two types of variables, corresponding critical heat flux density data points, and the relationship between variables and data. There can be multiple binary variable statistical tables; they can be one-dimensional tables; and there can be a one-to-one correspondence between binary variables and their statistical tables.

[0069] Specifically, based on the critical heat flux density data points, the two types of variables, and their preset types obtained in steps 201 and 202, a statistical table for each type of variable is generated, serving as the statistical table for the two types of variables. More specifically, the regions of the variable statistical table can be determined first based on the two types of variables and their types, and then each region of the variable statistical table can be filled or marked based on the actual critical heat flux density data to obtain the statistical table for the two types of variables.

[0070] Step 204: Evaluate the integrity of the critical heat flux density data based on the statistical tables of Class I and Class II variables to obtain the integrity evaluation results.

[0071] Specifically, the statistical tables of Class I and Class II variables can be combined, and the integrity of the critical heat flux density data can be quantified based on preset quantification rules. The integrity of the data can be evaluated by determining whether it is complete and the degree of completeness based on the quantification results. Alternatively, the quantitative evaluation results of Class I and Class II variables can be obtained separately, and then the two evaluation results can be considered together to obtain the final evaluation result.

[0072] Therefore, in this embodiment, variable statistical tables of different importance are generated based on the data of Class I and Class II variables of different importance, and the completeness evaluation is performed based on the variable statistical tables. This ensures the refinement and systematic nature of the variable statistical tables and further improves their reliability.

[0073] The following describes the specific process for generating statistical tables for Class I and Class II variables.

[0074] In one embodiment, such as Figure 3 As shown, step 103 (generating a statistical table of a class of variables based on data points, a class of variables, and a preset continuous range of the class of variables) includes steps 301 to 306. Wherein:

[0075] Step 301: Divide each preset continuous range into at least one continuous interval.

[0076] Specifically, each preset continuous range is divided into regions at certain intervals to obtain multiple continuous intervals, which are then used to determine the regions of the statistical table for a class of variables. The size of the interval affects the number of continuous intervals, and thus the number of regions in the statistical table for a class of variables. Therefore, the interval can be determined according to the actual number of regions required. It is also understandable that the interval can be determined in other ways. The smaller the interval, the more detailed the division, and the more reliable the statistical table for a class of variables generated later.

[0077] For example, the preset continuous range for outlet pressure of each variable is 3MPa~17.5MPa, and the preset continuous range for inlet mass flow rate is 0.5t / m³. 2 s~4.0 t / m 2 The preset continuous range of s and local equilibrium vapor content is -0.25 to 0.40, which can be divided into the following intervals:

[0078] Export pressure: from the lower limit of the range P min Initially, each interval ΔP is considered as one interval, with ΔP ranging from 1 to 2 MPa, and a possible value of 1.5 MPa.

[0079] Inlet quality flow rate: from the lower limit of range G min Initially, each interval ΔG represents a range, with ΔG ranging from 0.25 to 1.0 t / m. 2 s can take a value of 1.0t / m 2 s;

[0080] Local equilibrium vapor content: from the lower limit of the range X min Initially, each interval ΔX is a separate interval, with ΔX ranging from 0.02 to 0.2, and a possible value of 0.1.

[0081] Step 302: Determine a region of the statistical table of a class of variables based on the continuous intervals of a class of variables.

[0082] Among them, a type of region refers to the table regions obtained after determining the length, width, and / or height of a type of variable statistical table.

[0083] Specifically, taking a class of variables consisting of three factors—outlet pressure, inlet mass flow rate, and local equilibrium vapor content—as an example, a class of regions can be determined in the following two ways:

[0084] Method 1: Determine the table length based on the number of consecutive intervals of the first variable, the table width based on the number of consecutive intervals of the second variable, and the table height based on the number of consecutive intervals of the third variable. This results in multiple regions, each corresponding to three consecutive intervals. This region is a first-class region of the first-class variable statistical table.

[0085] Method 2: Determine the table length based on the number of consecutive intervals for the first and second variables, and the table width based on the number of consecutive intervals for the third variable, resulting in multiple regions. Each region corresponds to three consecutive intervals, and this region is a first-class region of the first-class variable statistical table.

[0086] Step 303: Select key areas from Class I areas based on the nuclear reactor operating conditions.

[0087] Among them, key areas refer to areas in a category that have at least one area that requires special attention and should be marked.

[0088] Step 304: Determine the percentage of data points in each key region.

[0089] Specifically, the proportion ε of data points in a key area is obtained by calculating the ratio between the number of data points in the key area and the total number of data points. The formula is as follows:

[0090] ε = Number of data points in this critical region / Total number of data points

[0091] Step 305: Label a type of region based on the data points.

[0092] Specifically, after generating a statistical table of a type of variable for a type of region, all types of regions (including key regions) are labeled once based on the data points of the actual critical heat flux density data and using certain labeling rules, including different first labels and second labels.

[0093] Step 306: Mark the key regions according to the proportion of data points in the key regions to obtain a statistical table of variables.

[0094] Specifically, after obtaining the key regions, all key regions are re-marked according to the proportion of data points in that region and using certain marking rules, including different third and fourth markings.

[0095] Therefore, in this embodiment, after dividing the preset continuous range of a type of variable into continuous intervals, a type of region is determined based on all continuous intervals. This not only considers all types of variables, but also the coupling relationship between continuous intervals of different variables. Based on this, key regions are identified, a type of region is marked once, and key regions within it are marked a second time. The resulting statistical table of a type of variable has stronger coupling.

[0096] The following example illustrates step 302 (determining a region of the statistical table of a type of variable based on the continuous interval of a type of variable).

[0097] In one example, such as Figure 4As shown, step 302 includes step 401. Wherein:

[0098] Step 401: Match any continuous interval of outlet pressure with any continuous interval of inlet mass flow rate to obtain corresponding intervals, and divide the corresponding intervals according to all continuous intervals of local equilibrium vapor content to obtain multiple Class I regions.

[0099] Among them, the continuous intervals of outlet pressure and the continuous intervals of inlet mass flow rate have a many-to-many correspondence, and each corresponding interval has a one-to-many correspondence with all continuous intervals of local equilibrium steam content.

[0100] Specifically, a class of variables should be considered in conjunction with each other. For example, within any continuous interval of outlet pressure and any continuous interval of mass flow rate, all continuous intervals of equilibrium vapor content should be considered to obtain multiple blank regions of this class. For intuitive representation, a three-dimensional statistical table of this class of variables can be listed, as shown in Table 1, where the number of regions is denoted as N. ClassI .

[0101]

[0102] It should be noted that the length and width of a type of variable statistical table are not limited to the format of Table 1. For example, the length can also be the range of mass flow rate and local equilibrium vapor content, and the width can be the range of pressure; or the length can be the range of mass flow rate and pressure, and the width can be the range of local equilibrium vapor content. This application does not impose any restrictions on this.

[0103] Therefore, this example generates rows and columns of a statistical table for a class of variables based on the continuous intervals of outlet pressure, inlet mass flow rate, and local equilibrium steam content. This allows for a more refined consideration of the coupling relationships between different variable intervals, ensuring the systematic nature and comprehensiveness of the statistical table for a class of variables.

[0104] In one example, such as Figure 5 As shown, step 303 (selecting key regions from a class of regions based on nuclear reactor operating conditions) includes steps 501 to 503. Wherein:

[0105] Step 501: Determine the thermal-hydraulic state of a region, including the nuclear reactor operating condition, and the minimum deviation nucleus boiling ratio corresponding to each thermal-hydraulic state.

[0106] Step 502: Obtain the nuclear fuel position corresponding to the minimum deviation nucleus boiling ratio and record the data at the nuclear fuel position.

[0107] The recorded data includes the reactor outlet pressure, inlet mass flow rate, and local equilibrium steam content.

[0108] Step 503: Identify a region where the data at the recorded nuclear fuel location is located as a critical region.

[0109] Specifically, each thermohydraulic state of a nuclear reactor operating condition has a corresponding minimum departure from nucleate boiling ratio (DNBR), and this value corresponds to a specific nuclear fuel location. Data at this location is recorded, including outlet pressure, inlet mass flow rate, and equilibrium steam content. The corresponding region is designated as the critical region k. n All key regions k n The number is N Key .

[0110] Therefore, this example considers the thermal-hydraulic state of the nuclear reactor and the corresponding minimum deviation bubble-nucleation boiling ratio to determine the key areas. This can effectively identify key areas that need special attention and make secondary markings. By conducting more in-depth data analysis, the reliability of the statistical table of a class of variables is further improved.

[0111] In one example, step 305, which is to mark a type of region based on data points, may include: when a corresponding data point exists in a type of region, performing a first mark in that type of region; when a corresponding data point does not exist in a type of region, performing a second mark in that type of region, the second mark being different from the first mark.

[0112] The first marker can be "present", and the second marker can be "absent".

[0113] For example, when statistically analyzing a class of variables, for a class of variables in a statistical table, if there are corresponding data points (outlet pressure, flow rate range, and local equilibrium steam content range) in that class of variables, then mark "present" in that class of variables; otherwise, mark "absent" in that class of variables.

[0114] Further, step 306, which is to mark the key area according to the proportion of data points in the key area, may include: marking the key area a third time when the proportion of data points in the key area is greater than a preset proportion; and marking the key area a fourth time when the proportion of data points in the key area is less than or equal to the preset proportion.

[0115] The preset percentage is determined in advance based on engineering experience, for example, it can be around 0.05. The first to fourth markers are all different, the third marker can be marked with "+", and the fourth marker can be marked with "-".

[0116] The key areas correspond to the thermal-hydraulic states of some nuclear reactor operating conditions. These states should include: steady-state operation at full power; and expected operating events, including control rod assembly malfunction, main steam pipe rupture, main coolant pipe rupture, abnormal coolant temperature change, abnormal coolant flow rate change, load shedding, abnormal boron dilution, loss of external power supply, excessive change in secondary flow rate of steam generator, and pressurizer malfunction.

[0117] Specifically, in each key region k n In the calculation, the ratio ε of the number of data in the region to the total number of data is calculated. If ε ≥ 0.05, a "+" is marked in the corresponding key region, otherwise a "-" is marked.

[0118] After completing the above marking, the number of all regions marked only "none" is N. ClassI-none The number of all regions marked only as "yes" is N. ClassI-exist The number of regions that are simultaneously marked with "yes" and "+" is N. Key-pass The number of regions that are simultaneously marked with "yes" and "-" is N. Key-fail The number of regions simultaneously marked with "none" and "-" is N. Key-none This is for subsequent integrity evaluation.

[0119] Therefore, this example marks one type of region as "present" or "absent" based on the actual data situation, and then marks the key regions as "+" or "-". This considers both the presence or absence of data and the magnitude of data in the key regions, improving the intuitiveness, systematicness, and comprehensiveness of the marking. This ensures that the statistical table of one type of variable comprehensively records the actual data situation, thereby guaranteeing the reliability of subsequent integrity evaluation.

[0120] After generating a statistical table of variables through the above steps, step 104 is executed, which is to evaluate the integrity of the critical heat flux density data based on the statistical table of variables in order to obtain the integrity evaluation result.

[0121] In one exemplary embodiment, such as Figure 6 As shown, step 104 includes steps 601 to 603. Wherein:

[0122] Step 601: Determine the number of regions in a category, the labeling status of regions in a category, the number of key regions, and the labeling status of key regions in a category of variable statistics table.

[0123] Among them, the first category of region marking includes the number of regions that are marked as first in all first category regions, and the key region marking includes the number of regions that are marked as first and third at the same time and the number of regions that are marked as first and fourth at the same time.

[0124] Step 602: Calculate the first integrity index based on the number of Class I areas, the marking status of Class I areas, the number of key areas, and the marking status of key areas.

[0125] The first integrity index refers to the quantitative result of integrity obtained from a statistical table of a class of variables, and its value can be between 0 and 1.

[0126] Specifically, the first integrity index can be calculated using the following formula:

[0127]

[0128] In the formula, c Integrity1 The first integrity index is N, where a1 and a2 are statistical coefficients. ClassI-exist N is the number of regions in a class that are only marked in the first step. Key-pass N represents the number of regions in a class that are simultaneously labeled with the first and third labels. Key-fail N represents the number of regions in a class that are simultaneously labeled with the first and fourth labels. ClassI N represents the number of regions in a given category. Key Number of key areas.

[0129] Step 603: Evaluate the integrity of the critical heat flux density data based on the first integrity index to obtain the integrity evaluation result.

[0130] Specifically, this step may include: determining that the critical heat flux density data has sufficient integrity when the first integrity index is greater than or equal to the first integrity preset value; determining that the critical heat flux density data has basic integrity when the first integrity index is less than the first integrity preset value and greater than or equal to the second integrity preset value, wherein the second integrity preset value is less than the first integrity preset value; and determining that the critical heat flux density data does not have integrity or has poor integrity when the first integrity index is less than the second integrity preset value.

[0131] The first integrity preset value and the second integrity preset value can be determined in advance based on engineering experience and actual needs. For example, the first integrity preset value can be 0.95 and the second integrity preset value can be 0.90.

[0132] Therefore, this example can determine whether a variable has sufficient integrity, basic integrity, or no integrity or poor integrity based on the interval of the integrity index obtained from a statistical table of variables, thus achieving an objective evaluation of the degree of integrity and improving reliability.

[0133] The above describes the specific process of generating a statistical table of one type of variable and performing a completeness evaluation based on it. In practical applications, a completeness evaluation can be performed based solely on the statistical table of one type of variable, or it can be performed by combining the statistical tables of one type of variable and the statistical tables of two types of variables. The specific process is described below.

[0134] In one exemplary embodiment, such as Figure 7 As shown, step 203 (generating a statistical table of binary variables based on data points, binary variables, and preset types of binary variables) includes steps 701 to 703. Wherein:

[0135] Step 701: Determine the quantity of each preset type.

[0136] For example, there are two preset types for the second type of variable cell: typical cell and guide tube cell.

[0137] Specifically, the number of each preset type is determined to account for the number of regions in the subsequent binary variable statistical table. The number of preset types affects the number of regions in the binary variable statistical table; therefore, the preset types can be determined based on the actual number of regions required. It is understood that the more detailed the preset type division, the more reliable the subsequently generated binary variable statistical table will be.

[0138] Step 702: Determine the second-class regions of the second-class variable statistical table based on the number of preset types of each second-class variable, wherein the number of second-class regions of each second-class variable statistical table is the same as the number of preset types of the corresponding second-class variable.

[0139] Each binary variable corresponds to a one-dimensional binary variable statistical table, the length of which can be the preset number of binary variable types, and the width is one.

[0140] Specifically, for binary variables, there is no need to consider coupling; we only need to consider the statistical results of each predefined type separately. A one-dimensional statistical table is then created, as shown in Table 2. The number of binary regions in each binary variable table is denoted as N. ClassII,i There are I statistical tables for two types of variables.

[0141]

[0142] Step 703: Mark each type II region according to the data points to obtain a statistical table of type II variables.

[0143] Specifically, after the terminal and / or server generate the Class II regions of the Class II variable statistical table, they mark all Class II regions once based on the actual critical heat flux density data and using certain rules.

[0144] Further, step 703 may include: when a corresponding data point exists in the second type of region, performing a first marking in the second type of region; when a corresponding data point does not exist in the second type of region, performing a second marking in the second type of region, wherein the second marking is different from the first marking.

[0145] The first marker can be "present" and the second marker can be "absent".

[0146] For example, when statistically analyzing the cell types of binary variables, the binary variable statistics table has two binary regions, corresponding to the typical cell and the guide tube cell, respectively. For one of the regions, if there are data points in that region, it is marked as "present"; if there are no data points in that region, it is marked as "absent".

[0147] Therefore, this example marks the two types of regions as "present" or "absent" based on the actual data situation. By considering both the presence and absence of data, the marking becomes more intuitive, systematic, and comprehensive, ensuring that the statistical table of the two types of variables records the actual data situation in all aspects, thereby guaranteeing the reliability of subsequent integrity evaluation.

[0148] The above describes the specific process for generating the two types of variable statistical tables. After generating the two statistical tables, step 204 is executed, which evaluates the integrity of the critical heat flux density data based on the first and second type variable statistical tables to obtain the integrity evaluation results.

[0149] It should be noted that when conducting integrity assessments based on Class I and Class II variable statistical tables, one approach is to combine the two tables for the assessment; another approach is to conduct integrity assessments separately for each table, obtaining two separate assessment results, and then combining these two results to arrive at the final integrity assessment result. The former approach will be used as an example below.

[0150] That is, in an exemplary embodiment, such as Figure 8 As shown, step 204 (evaluating the completeness of the critical heat flux density data based on the statistical tables of Class I and Class II variables) includes steps 801 to 804. Wherein:

[0151] Step 801: Determine the number of regions in a category, the labeling status of regions in a category, the number of key regions, and the labeling status of key regions in a category of variable statistics table.

[0152] Among them, the first type of region marking includes the number of regions marked with the first mark in all first type regions, and the key region marking includes the number of regions marked with the first mark and the third mark in all key regions, as well as the number of regions marked with the first mark and the fourth mark in all key regions. The first to fourth marks are different for each region.

[0153] In the statistical table of a single variable, all regions of a single variable are marked with either a first label "Yes" (indicating data exists) or a second label "No" (indicating no data). Furthermore, all key regions are marked with a third label "+" (indicating the percentage of data is greater than a preset percentage) and a fourth label "-" (indicating the percentage of data is less than or equal to a preset percentage). Within the same single variable region, the first and second labels cannot coexist, nor can the third and fourth labels coexist.

[0154] Step 802: Determine the number of binary variable statistical tables, the number of binary regions in each binary variable statistical table, and the labeling of the binary regions.

[0155] The labeling of the second-class regions includes the number of regions marked with the first label in each second-class variable statistical table. Each second-class region is either marked with the first label "Yes" (indicating that there is data) or the second label "No" (indicating that there is no data). The first label and the second label cannot exist simultaneously in the same second-class region.

[0156] Step 803: Calculate the second integrity index based on the number of Class I areas, the marking status of Class I areas, the number of key areas, the marking status of key areas, the number of Class II variable statistical tables, the number of Class II areas, and the marking status of Class II areas.

[0157] The second completeness index refers to the completeness quantification result obtained from the statistical tables of first-class and second-class variables, and its value can be between 0 and 1.

[0158] Specifically, the second integrity index can be calculated using the following formula:

[0159]

[0160] In the formula, c Integrity2 The second integrity index, a1, a2, and a3 are statistical coefficients, and N ClassI-exist N is the number of regions in a class that are only marked in the first step. Key-pass N represents the number of regions in a class that are simultaneously labeled with the first and third labels. Key-fail N represents the number of regions in a class that are simultaneously labeled with the first and fourth labels. ClassII-exist N represents the number of regions in the second category that are first-labeled. ClassI N represents the number of regions in a given category. Key N represents the number of critical regions. ClassII,i Let I represent the number of binary regions in the i-th binary variable statistics table, and let I represent the number of binary variable statistics tables.

[0161] Step 804: Evaluate the integrity of the critical heat flux density data based on the second integrity index to obtain the integrity evaluation result.

[0162] Specifically, this step may include: determining that the critical heat flux density data has sufficient integrity when the second integrity index is greater than or equal to the first integrity preset value; determining that the critical heat flux density data has basic integrity when the second integrity index is less than the first integrity preset value but greater than or equal to the second integrity preset value, wherein the second integrity preset value is less than the first integrity preset value; and determining that the critical heat flux density data does not have integrity or has poor integrity when the second integrity index is less than the second integrity preset value.

[0163] The first integrity preset value and the second integrity preset value can be determined in advance based on engineering experience and actual needs. For example, the first integrity preset value can be 0.95 and the second integrity preset value can be 0.90.

[0164] Therefore, this example can determine whether a variable has sufficient integrity, basic integrity, or no integrity or poor integrity based on the interval of the integrity index obtained from the statistical tables of Class I and Class II variables. This achieves a more comprehensive and systematic objective evaluation of the degree of integrity, further improving reliability.

[0165] The evaluation method for nuclear reactor critical heat flux density data of the present invention is described below through a specific example, considering both Class I and Class II variables, such as... Figure 9 As shown, the specific process is as follows:

[0166] (1) Determine the data points for the critical heat flux density data and the first-class and second-class variables required to evaluate the critical heat flux density data, wherein the first-class variables have higher priority than the second-class variables.

[0167] For each critical heat flux density data point, the dependent variable is the critical heat flux density value. In addition, there are several independent variables corresponding to this critical heat flux density value. This step classifies the independent variables into Class I variables and Class II variables.

[0168] For the critical heat flux density (CHF) data corresponding to the reactor core fuel assemblies, important independent variables include outlet pressure, inlet mass flow rate, and local equilibrium steam holdup; these variables are referred to as Class I variables. Secondary important independent variables include cell type, grid type, grid spacing, heating power distribution, and CHF occurrence location; these variables are referred to as Class II variables. Variables not listed above are referred to as other variables. It is worth noting that Class I variables should at least include the above three, while Class II variables, depending on the actual situation, may exclude some, but should at least include cell type and heating power distribution.

[0169] When determining the variables to be involved in the integrity assessment, Category I variables must be included, Category II variables are recommended to be included, and other variables should be considered for inclusion based on the actual situation.

[0170] (2) Determine the preset continuous range of the first type of variable and the preset type of the second type of variable.

[0171] The expected distribution of critical heat flux density data should be strongly correlated with the corresponding nuclear reactor operating state. Therefore, when identifying its variable range, it should be consistent with the range of key thermal-hydraulic parameters during nuclear reactor operation and the structural parameters of nuclear fuel.

[0172] Category 1 variables: These variables are all continuous variables, and their range is a continuous range of values. The following range is a commonly assumed continuous range based on experience in pressurized water reactor engineering:

[0173] Outlet pressure range: 3~17.5MPa; inlet mass flow rate range: 0.5~4.0t / m³ 2 s, local equilibrium vapor content: -0.25~0.40.

[0174] Category II variables: These variables are all categorical variables and have multiple selectable types. The following range is a typical preset type based on pressurized water reactor engineering experience:

[0175] Cell types: typical cell, guide tube cell;

[0176] Grid type: Unmixed grid, Mixed grid;

[0177] Grid spacing: Spacing between nuclear fuel prototypes;

[0178] Heating power distribution: uniform heating, non-uniform heating with the peak value located at the center, and non-uniform heating with the peak value not located at the center;

[0179] Location of critical heat flux density: mixed region, unmixed region.

[0180] (3) Divide each preset continuous range into at least one continuous interval.

[0181] For a given continuous range of a class of variables, it is necessary to divide it, as follows:

[0182] Export pressure: from the lower limit of the range P min Initially, each interval ΔP is considered as one interval, with the value of ΔP ranging from 1 to 2 MPa, and a value of 1.5 MPa is recommended.

[0183] Inlet quality flow rate: from the lower limit of range G min Initially, each interval ΔG represents a range, with ΔG ranging from 0.25 to 1.0 t / m. 2s is recommended to be 1.0 t / m 2 s;

[0184] Local equilibrium vapor content: from the lower limit of the range X min Initially, each interval ΔX is considered as one interval, with the value of ΔX ranging from 0.02 to 0.2, and a value of 0.1 is recommended.

[0185] (4) Determine the first type of region of the statistical table of a type of variable based on the continuous interval of a type of variable.

[0186] One type of variable should be considered in conjunction with other variables. For example, within any given outlet pressure range and inlet mass flow rate range, all local equilibrium vapor content ranges should be considered. For intuitive representation, a three-dimensional statistical table should be provided, as shown in Table 1 above. The number of one type of region in the table is denoted as N. ClassI .

[0187] (5) Select key areas from Class I areas based on the nuclear reactor operating conditions.

[0188] For a class of variables, it is necessary to select key regions, which include the thermo-hydraulic states of certain nuclear reactor operating conditions. These states should include:

[0189] Steady-state operating conditions at full power;

[0190] Expected operational events include control rod assembly malfunction, main steam pipe rupture, main coolant pipe rupture, abnormal coolant temperature changes, abnormal coolant flow rate changes, load shedding, abnormal boron dilution, loss of external power supply, excessive changes in secondary flow rate of steam generator, and pressurizer malfunction.

[0191] Each of the above states has a corresponding minimum deviation nucleus boiling ratio (DNBR) value, and each value corresponds to a nuclear fuel location. The core pressure, mass flow rate, and equilibrium vapor content at that location are recorded, and the corresponding region is denoted as critical region k. n All key regions k n The number is N Key .

[0192] (6) Determine the percentage of data points in each key area.

[0193] It is necessary to calculate the percentage of data points in key regions, that is, in each key region k n In the calculation, the ratio ε of the number of data points in this region to the total number of data points is calculated:

[0194] ε = Number of data points in this critical region / Total number of data points

[0195] (7) Mark a type of region based on the data points.

[0196] When performing statistics on a type of variable, the first step is to check whether there are data points in each type of region. If there are data points in a certain region, mark it as "present" in that type of region; otherwise, mark it as "absent".

[0197] (8) Mark the key areas according to the proportion of data points in the key areas to obtain a statistical table of variables.

[0198] If the critical region's ε ≥ 0.05, mark it with a "+"; otherwise, mark it with a "-". After completing the marking, count the number of regions marked only with "none" as N. ClassI-none The number of all regions marked only as "yes" is N. ClassI-exist The number of regions that are simultaneously marked with "yes" and "+" is N. Key-pass The number of regions that are simultaneously marked with "yes" and "-" is N. Key-fail The number of regions simultaneously marked with "none" and "-" is N. Key-none .

[0199] (9) Determine the second-class region of the statistical table of second-class variables based on the number of preset types of each second-class variable.

[0200] For binary variables, there is no need to consider coupling; we only need to consider the statistical results of each preset type separately. A one-dimensional statistical table is then created, as shown in Table 2 above. Each region is a binary region, and the number of binary regions is the same as the preset type of the corresponding binary variable. The number of regions in each binary variable table is denoted as N. ClassII,i There are two types of variable tables, I in total.

[0201] (10) Mark each type of region according to the data points to obtain a statistical table of type II variables.

[0202] When performing binary variable statistics, it is only necessary to count whether data points exist in each region. If a data point exists in a certain region, mark it as "present" in the one-dimensional statistical table, otherwise mark it as "absent".

[0203] After completing the above marking, the number of regions marked "none" is N. ClassII-none The number of regions marked "yes" is N. ClassII-exist .

[0204] (11) Calculate the second completeness index c based on the statistical tables of the first-class and second-class variables. Integrity2 The calculation formula is:

[0205]

[0206] In the formula, a1, a2 and a3 are statistical coefficients, and it is recommended that they be 1, 4 and 5 respectively.

[0207] (12) Evaluate data integrity based on the second integrity index.

[0208] If the second integrity index c Integrity If the value is not lower than 0.95, then sufficient integrity is determined.

[0209] If the second integrity index c Integrity If the value is not lower than 0.90, then the basic integrity is determined.

[0210] If the second integrity index c Integrity A score below 0.90 indicates poor or no integrity.

[0211] In summary, the embodiments of this application can systematically determine the integrity of critical heat flux density data. Based on the technical approach given in this application, a fixed, feasible, and complete means can be used to evaluate the integrity, thereby effectively avoiding errors and omissions caused by subjective evaluation, ensuring the reliability of the integrity evaluation of critical heat flux density data, and thus ensuring the integrity of the critical heat flux density data relation development.

[0212] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0213] Based on the same inventive concept, this application also provides an evaluation device for nuclear reactor critical heat flux density data, used to implement the evaluation method for nuclear reactor critical heat flux density data described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of an embodiment of the evaluation device for nuclear reactor critical heat flux density data provided below can be found in the limitations of the evaluation method for nuclear reactor critical heat flux density data described above, and will not be repeated here.

[0214] In the embodiments of this application, such as Figure 10As shown, an evaluation device for critical heat flux density data of a nuclear reactor is provided, comprising: a first determining module 1001, a second determining module 1002, a first generating module 1003, and a first evaluation module 1004, wherein:

[0215] The first determining module 1001 is used to determine the data points of the critical heat flux density data and a class of variables required to evaluate the critical heat flux density data.

[0216] The second determining module 1002 is used to determine the preset continuous range of the aforementioned type of variable;

[0217] The first generation module 1003 is used to generate a statistical table of variables based on the data points, the type of variables, and the preset continuous range of the type of variables.

[0218] The first evaluation module 1004 is used to evaluate the integrity of the critical heat flux density data based on the statistical table of the first type of variables, so as to obtain the integrity evaluation result.

[0219] In an exemplary embodiment, the evaluation device for nuclear reactor critical heat flux density data further includes: a third determination module, a fourth determination module, a second generation module, and a second evaluation module. Wherein:

[0220] The third determining module is used to determine two types of variables required to evaluate the critical heat flux density data, wherein the first type of variable has a higher priority than the second type of variable;

[0221] The fourth determining module is used to determine the preset type of the two types of variables;

[0222] The second generation module is used to generate a statistical table of binary variables based on the data points, the binary variables, and the preset types of the binary variables;

[0223] The second evaluation module is used to evaluate the integrity of the critical heat flux density data based on the first-class variable statistical table and the second-class variable statistical table, so as to obtain the integrity evaluation result.

[0224] In one example, the first generation module 1003 includes a partitioning unit, a first determining unit, a selection unit, a second determining unit, a first marking unit, and a second marking unit. Wherein:

[0225] The partitioning unit divides each of the preset continuous ranges into at least one continuous interval;

[0226] The first determining unit is used to determine a region of the statistical table of a class of variables based on the continuous interval of the class of variables.

[0227] The selection unit is used to select a key area from the aforementioned type of area based on the nuclear reactor operating conditions.

[0228] The second determining unit is used to determine the proportion of data points in each key area;

[0229] The first marking unit is used to mark the type of region based on the data points;

[0230] The second marking unit is used to mark the key area according to the proportion of data points in the key area to obtain a type of variable statistical table.

[0231] The first type of variable includes the outlet pressure, inlet mass flow rate, and local equilibrium steam content of the nuclear reactor. The selection unit is specifically used to: map any continuous interval of outlet pressure to any continuous interval of inlet mass flow rate to obtain corresponding intervals, and divide the corresponding intervals according to all continuous intervals of local equilibrium steam content to obtain multiple first-type regions.

[0232] Specifically, the selection unit is used for:

[0233] Determine the thermohydraulic state of a region including the nuclear reactor operating condition and the minimum deviation nucleus boiling ratio corresponding to each thermohydraulic state;

[0234] Obtain the nuclear fuel position corresponding to the minimum deviation nucleus boiling ratio, and record the data at the nuclear fuel position;

[0235] A region containing the recorded data at the location of the nuclear fuel is identified as the critical region.

[0236] Furthermore, the first marking unit is specifically used for:

[0237] When a corresponding data point exists in the first type of region, a first mark is made in the first type of region;

[0238] When no corresponding data point exists in a certain type of region, a second label is applied to that type of region, wherein the second label is different from the first label.

[0239] Furthermore, the second marking unit is specifically used to: mark the key area a third time when the proportion of data points in the key area is greater than a preset proportion;

[0240] When the proportion of data points in the key area is less than or equal to a preset proportion, the key area is marked with a fourth mark, which is different from the third mark.

[0241] In another exemplary embodiment, the first evaluation module 1004 includes a fifth determining unit, a first calculation unit, and a first evaluation unit. Wherein:

[0242] The fifth determining unit is used to determine the number of regions of a certain type, the marking status of regions of a certain type, the number of key regions, and the marking status of key regions in the statistical table of the variable of a certain type.

[0243] The first calculation unit is used to calculate a first integrity index based on the number of regions of the first type, the marking status of the regions of the first type, the number of key regions, and the marking status of the key regions;

[0244] The first evaluation unit is used to evaluate the integrity of the critical heat flux density data based on the first integrity index to obtain an integrity evaluation result.

[0245] In another example, the second generation module includes a sixth determining unit, a seventh determining unit, and a third marking unit. Wherein:

[0246] The sixth determining unit is used to determine the quantity of each of the preset types;

[0247] The seventh determining unit is used to determine the second-class regions of the second-class variable statistical table according to the number of each preset type of second-class variable, wherein the number of second-class regions of each second-class variable statistical table is the same as the number of the corresponding preset types of second-class variables;

[0248] The third labeling unit is used to label each of the two types of regions according to the data points to obtain a statistical table of two types of variables.

[0249] The third marking unit is specifically used for:

[0250] When a corresponding data point exists in the second type of region, a first mark is made in the second type of region;

[0251] When no corresponding data point exists in the second type of region, a second mark is made in the second type of region, wherein the second mark is different from the first mark.

[0252] In another exemplary embodiment, the second evaluation module includes an eighth determining unit, a ninth determining unit, a second calculation unit, and a second evaluation unit. Wherein:

[0253] The eighth determining unit is used to determine the number of regions of a certain type, the marking status of regions of a certain type, the number of key regions, and the marking status of key regions in the statistical table of the variable of a certain type.

[0254] The ninth determining unit is used to determine the number of the two-category variable statistical tables and the number of two-category regions and the labeling of the two-category regions in each of the two-category variable statistical tables;

[0255] The second calculation unit is used to calculate the second integrity index based on the number of the first type of regions, the marking status of the first type of regions, the number of key regions, the marking status of the key regions, the number of the second type of variable statistical tables, the number of the second type of regions, and the marking status of the second type of regions;

[0256] The second evaluation unit is used to evaluate the integrity of the critical heat flux density data according to the second integrity index, so as to obtain the integrity evaluation result.

[0257] The second evaluation unit is specifically used for:

[0258] When the second integrity index is greater than or equal to the first integrity preset value, it is determined that the critical heat flux density data has sufficient integrity;

[0259] When the second integrity index is less than the first integrity preset value and greater than or equal to the second integrity preset value, the critical heat flux density data is determined to have basic integrity, wherein the second integrity preset value is less than the first integrity preset value;

[0260] When the second integrity index is less than the second integrity preset value, the critical heat flux density data is determined to lack integrity.

[0261] Each module in the aforementioned critical heat flux density data evaluation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0262] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0263] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0264] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of evaluating nuclear reactor critical heat flux data, characterized by, The method comprises: determining data points of critical heat flux density data and a type of variable required for evaluating the critical heat flux density data; determining a preset continuous range of the type of variable; generating a type of variable statistical table according to the data points, the type of variable and the preset continuous range of the type of variable; evaluating the integrity of the critical heat flux density data according to the type of variable statistical table to obtain an integrity evaluation result.

2. The method of claim 1, wherein, Further comprising: determining a type of variable required for evaluating the critical heat flux density data, wherein the priority of the type of variable is higher than that of the type of variable; determining a preset type of the type of variable; generating a type of variable statistical table according to the data points, the type of variable and the preset type of the type of variable; evaluating the integrity of the critical heat flux density data according to the type of variable statistical table and the type of variable statistical table to obtain an integrity evaluation result.

3. The method of claim 1, wherein, The generating of the type of variable statistical table according to the data points, the type of variable and the preset continuous range of the type of variable comprises: dividing each preset continuous range into at least one continuous interval; determining a type of area of the type of variable statistical table according to the continuous interval of the type of variable; selecting a key area from the type of area according to the working condition of the nuclear reactor; determining the proportion of the number of data points in each key area; labeling the type of area according to the data points; labeling the key area according to the proportion of the number of data points in the key area to obtain a type of variable statistical table.

4. The method of claim 3, wherein, The type of variable comprises outlet pressure, inlet mass flow and local equilibrium quality of the nuclear reactor, and the determining of the type of area of the type of variable statistical table according to the continuous interval of the type of variable comprises: corresponding any continuous interval of outlet pressure and any continuous interval of inlet mass flow to obtain each corresponding interval, and dividing the corresponding interval according to all continuous intervals of local equilibrium quality to obtain a plurality of type of areas.

5. The method of claim 3, wherein, The selecting of the key area from the type of area according to the working condition of the nuclear reactor comprises: determining that the type of area includes the thermal hydraulic state in which the nuclear reactor is located and the minimum deviation of nucleate boiling ratio corresponding to each thermal hydraulic state; obtaining the nuclear fuel position corresponding to the minimum deviation of nucleate boiling ratio, and recording the data at the nuclear fuel position; determining the type of area in which the data at the recorded nuclear fuel position is located as the key area.

6. The method of claim 3, wherein, The labeling of the type of area according to the data points comprises: performing first labeling on the type of area when the type of area has corresponding data points; performing second labeling on the type of area when the type of area does not have corresponding data points, wherein the second labeling is different from the first labeling.

7. The method of claim 3, wherein, The labeling of the key area according to the proportion of the number of data points in the key area comprises: performing third labeling on the key area when the proportion of the number of data points in the key area is greater than a preset proportion; performing fourth labeling on the key area when the proportion of the number of data points in the key area is less than or equal to the preset proportion, wherein the fourth labeling is different from the third labeling.

8. The method according to any one of claims 3 to 7, characterized in that, The integrity of the critical heat flux density data is evaluated according to the first-type variable statistical table to obtain an integrity evaluation result, including: determining the number of first-type regions, the first-type region marking condition, the number of key regions, and the key region marking condition in the first-type variable statistical table; calculating a first integrity index according to the number of first-type regions, the first-type region marking condition, the number of key regions, and the key region marking condition; evaluating the integrity of the critical heat flux density data according to the first integrity index to obtain an integrity evaluation result.

9. The method of claim 2, wherein, The second-type variable statistical table is generated according to the data points, the second-type variables, and preset types of the second-type variables, including: determining the number of each preset type; determining the second-type regions of the second-type variable statistical table according to the number of each second-type variable preset type, wherein the number of second-type regions of each second-type variable statistical table is the same as the number of the corresponding second-type variable preset type; marking each second-type region according to the data points to obtain a second-type variable statistical table.

10. The method of claim 9, wherein, The second-type region is marked according to the data points, including: performing first marking on the second-type region when the second-type region has a corresponding data point; performing second marking on the second-type region when the second-type region does not have a corresponding data point, wherein the second marking is different from the first marking.

11. The method according to claim 9 or 10, characterized in that, The integrity of the critical heat flux density data is evaluated according to the first-type variable statistical table and the second-type variable statistical table to obtain an integrity evaluation result, including: determining the number of first-type regions, the first-type region marking condition, the number of key regions, and the key region marking condition in the first-type variable statistical table; determining the number of second-type variable statistical tables, the number of second-type regions, and the second-type region marking condition in each second-type variable statistical table; calculating a second integrity index according to the number of first-type regions, the first-type region marking condition, the number of key regions, the key region marking condition, the number of second-type variable statistical tables, the number of second-type regions, and the second-type region marking condition; evaluating the integrity of the critical heat flux density data according to the second integrity index to obtain an integrity evaluation result.

12. The method of claim 11, wherein, The integrity of the critical heat flux density data is evaluated according to the second integrity index, including: determining that the critical heat flux density data has sufficient integrity when the second integrity index is greater than or equal to a first integrity preset value; determining that the critical heat flux density data has basic integrity when the second integrity index is less than the first integrity preset value and greater than or equal to a second integrity preset value, wherein the second integrity preset value is less than the first integrity preset value; determining that the critical heat flux density data does not have integrity when the second integrity index is less than the second integrity preset value.

13. The method of claim 2 or 9, wherein, The second-type variables include grid cell types and heating power distributions.

14. An apparatus for evaluating nuclear reactor critical heat flux data, characterized by, The device includes: a first determination module configured to determine data points of critical heat flux density data and a first-type variable required for evaluating the critical heat flux density data; a second determining module, configured to determine a preset continuous range of the one type of variables; a first generating module, configured to generate a one-type-of-variable statistical table according to the data points, the one type of variables and the preset continuous range of the one type of variables; a first evaluating module, configured to evaluate completeness of the critical heat flux density data according to the one-type-of-variable statistical table to obtain a completeness evaluation result.

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