Method and apparatus for determining critical heat flux of a reactor

Abstract

Embodiments of the present application provide a method and device for determining the critical heat flux density of a reactor. The method comprises: obtaining a first critical heat flux density of a rod bundle channel when the reactor is in a stationary state, and a second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state; calculating the penalty coefficient in each motion state according to the first critical heat flux density and the second critical heat flux density, wherein the motion state includes a tilt state, a swing state or a heave state; for each motion state, constructing a penalty relationship of the motion parameter in the motion state according to the penalty coefficient in the motion state and the motion parameter in the motion state; and determining the target critical heat flux density of the reactor according to the penalty relationship in each motion state and the first critical heat flux density. Embodiments of the present application can solve the problem of poor safety of the existing CHF prediction technology applied to the reactor in a motion environment, and improve the reliability of the reactor CHF prediction in a motion environment.

Description

Technical Field

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

[0002] Critical Heat Flux (CHF) is a key safety limit parameter for reactor thermal-hydraulic design, directly determining the reactor core's power ceiling and operational safety. In traditional pressurized water reactor engineering practice, dedicated CHF formulas are typically developed based on CHF test data for specific fuel assemblies to assess reactor operating status in real time. However, for reactors in certain specific application scenarios, they inevitably operate under complex motion environments such as tilting, swaying, and undulation for extended periods. These motion factors significantly alter the flow and heat transfer characteristics of the coolant within the core, thus having a non-negligible impact on CHF. This makes it impossible to directly apply mature experience from traditional static operating conditions to reactor devices operating in motion environments.

[0003] Existing CHF prediction technologies are primarily developed for pressurized water reactors in a static state, and their formulas do not consider the impact of motion environments such as tilting and swaying on CHF characteristics. Because tilting, swaying, and undulating motions caused by these environments disrupt the symmetry of the internal flow field, they alter the CHF mechanism and exert a non-negligible penalty effect on CHF values. Therefore, directly applying existing CHF formulas for static pressurized water reactors to reactors in motion environments leads to significant deviations in prediction results, failing to accurately reflect the true thermal-hydraulic safety margin and posing substantial safety hazards. Summary of the Invention

[0004] The main objective of this application is to propose a method and apparatus for determining the critical heat flux density of a reactor, aiming to solve the problem of poor safety when existing CHF prediction technologies are applied to reactors in motion environments, and to improve the reliability of CHF prediction for reactors in motion environments.

[0005] To achieve the above objectives, a first aspect of this application provides a method for determining the critical heat flux density of a reactor, the method comprising: The first critical heat flux density of the rod bundle channel when the reactor is in a static state and the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state, including tilting, rocking or undulating states. The penalty coefficient for each of the motion states is calculated based on the first critical heat flux density and the second critical heat flux density. For each of the aforementioned motion states, a penalty relationship is constructed for the motion parameters under the aforementioned motion state based on the penalty coefficient and the motion parameters under the aforementioned motion state. The penalty relationship is the correspondence between the parameter value of the motion parameter and the penalty coefficient under the aforementioned motion parameter. The target critical heat flux density of the reactor is determined based on the penalty relationship for each of the aforementioned motion states and the first critical heat flux density.

[0006] In some embodiments, determining the target critical heat flux density of the reactor based on the penalty relationship for each of the motion states and the first critical heat flux density includes: Based on the penalty relationship of motion parameters under N motion states, a coupled penalty relationship is constructed for N motion states. The coupled penalty relationship is the correspondence between the comprehensive penalty coefficient under the combined action of motion parameters corresponding to N motion states and the penalty coefficient under each motion state, where N is an integer greater than or equal to 2. The target critical heat flux density of the reactor is determined based on the coupling penalty relationship under the N motion states and the first critical heat flux density.

[0007] In some embodiments, obtaining the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state includes: For each of the aforementioned motion parameters, the critical heat flux density corresponding to the motion parameter is obtained from a single motion parameter database, which is used to record the critical heat flux density corresponding to the single motion parameter under the condition that only the parameter value of the single motion parameter is changed and the parameter values ​​of the other motion parameters are all zero. For each of the aforementioned motion states, the critical heat flux density corresponding to the motion state is obtained from the single motion condition database. The single motion condition database is used to record the critical heat flux density corresponding to the single motion state under the condition that the parameter values ​​of multiple motion parameters of a single motion state are changed only, and the parameter values ​​of motion parameters of other motion states remain non-zero fixed values. The critical heat flux density corresponding to each of the aforementioned motion parameters and the critical heat flux density corresponding to each of the aforementioned motion states are used as the second critical heat flux density.

[0008] In some embodiments, the calculation of the penalty coefficient for each of the motion states based on the first critical heat flux density and the second critical heat flux density includes: For each of the motion states, the second critical heat flux density in the motion state is divided by the first critical heat flux density in the motion state to obtain the penalty coefficient for the motion state.

[0009] In some embodiments, constructing a penalty relationship between the motion parameters in the motion state based on the penalty coefficient in the motion state and the motion parameters in the motion state includes: For each motion parameter in the motion state, obtain multiple penalty coefficients corresponding to different parameter values ​​of the motion parameter; and, The penalty relationship of the motion parameters is obtained by fitting the relationship between different parameter values ​​of the motion parameters and multiple penalty coefficients.

[0010] In some embodiments, the step of fitting the relationship between different values ​​of the motion parameters and multiple penalty coefficients to obtain the penalty relationship of the motion parameters includes: Multiple fitting methods are used to fit different parameter values ​​of the motion parameters to the corresponding penalty coefficients to obtain candidate penalty relationships for each fitting method; For each of the motion parameters, the different parameter values ​​are substituted into the candidate penalty relationship corresponding to each of the fitting methods for calculation, to obtain the predicted value of the penalty coefficient corresponding to different parameter values ​​under each candidate penalty relationship; and, For each parameter value under each candidate penalty relationship, the ratio of the predicted value of the penalty coefficient corresponding to the parameter value to the measured value of the penalty coefficient corresponding to the parameter value is taken as the first predicted-to-measured ratio of the penalty coefficient corresponding to the parameter value. For each of the motion parameters, the mean and standard deviation of the multiple first predicted measured ratios corresponding to each candidate penalty relationship are calculated, and the candidate penalty relationship with the mean within a first preset range and the smallest standard deviation is selected as the penalty relationship for the motion parameter.

[0011] In some embodiments, the step of fitting the relationship between different values ​​of the motion parameters and multiple penalty coefficients to obtain the penalty relationship of the motion parameters includes: If the mean of all candidate penalty relationships is outside the first preset range, then the candidate penalty relationship with the smallest standard deviation is selected as the penalty relationship for the motion parameters, and the value of the constant term in the candidate penalty relationship is adjusted so that the adjusted mean falls within the first preset range.

[0012] In some embodiments, constructing the coupling penalty relationship for the N motion states based on the penalty relationship of the motion parameters under the N motion states includes: Multiple motion parameters are combined according to different parameter values ​​to form multiple sets of motion conditions; Using each set of motion conditions as input and the measured value of the penalty coefficient under each set of motion conditions as output, a function relationship is fitted to obtain the initial coupling function relationship; For each set of motion conditions, the penalty coefficient corresponding to each motion parameter in the motion condition is substituted into the initial coupling function relationship for calculation to obtain the predicted value of the comprehensive penalty coefficient corresponding to the motion condition; and, The ratio of the predicted value of the comprehensive penalty coefficient corresponding to the motion condition to the measured value of the comprehensive penalty coefficient corresponding to the motion condition is used as the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to the motion condition. Based on the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to each of the motion conditions, the initial coupling function relationship is adjusted to obtain the coupling penalty relationship under N motion conditions.

[0013] In some embodiments, the motion parameters include: tilt angle in the tilt state, sway angle and sway period in the swing state, and undulation acceleration and undulation period in the undulation state. The process of fitting a function relationship using each set of motion conditions as input and the measured value of the penalty coefficient under each set of motion conditions as output to obtain an initial coupling function relationship includes: Obtain the penalty relationship for tilt angle in tilt state, penalty relationship for sway angle and penalty relationship for sway period in sway state, and penalty relationship for undulation acceleration and penalty relationship for undulation period in undulation state for each set of motion conditions. Construct a swing state coupling penalty relationship, which is a linear combination of the penalty coefficient of the swing angle, the penalty coefficient of the swing period, and the product of the swing angle and the swing period; Construct a fluctuation state coupling penalty relationship, which is a linear combination of the penalty coefficient of the fluctuation acceleration, the penalty coefficient of the fluctuation period, and the product of the penalty coefficient of the fluctuation acceleration and the penalty coefficient of the fluctuation period. The initial coupling function relationship is constructed by linearly combining the penalty coefficient of the tilt angle, the coupling penalty relationship of the swaying state, the coupling penalty relationship of the fluctuation state, the product of the penalty coefficient of the tilt angle and the coupling penalty relationship of the swaying state, the product of the penalty coefficient of the tilt angle and the coupling penalty relationship of the fluctuation state, the product of the coupling penalty relationship of the swaying state and the coupling penalty relationship of the fluctuation state, and a constant term. The coefficients in the linear combination are determined by the least squares method.

[0014] In some embodiments, adjusting the initial coupling function relationship based on the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to each of the motion conditions to obtain the coupling penalty relationship under N motion states includes: The mean of the second predicted-measured ratio of the comprehensive penalty coefficient corresponding to each of the aforementioned motion conditions is calculated. If the mean is less than the lower limit of the second preset range, the value of the constant term in the initial coupling function relationship is increased until the mean is within the second preset range, thereby obtaining the coupling penalty relationship. If the mean is greater than the upper limit of the second preset range, then the value of the constant term in the initial coupling function relationship is reduced until the mean is within the second preset range, thus obtaining the coupling penalty relationship.

[0015] In some embodiments, determining the target critical heat flux density of the reactor based on the coupling penalty relationships under N motion states and the first critical heat flux density includes: For each of the motion states, each motion parameter in the motion state is substituted into the penalty relationship corresponding to each motion parameter to obtain the penalty coefficient for each motion parameter; and, Substituting the penalty coefficient for each of the aforementioned motion parameters into the coupling penalty relationship, a comprehensive penalty coefficient for N motion states is obtained; and, The product of the first critical heat flux density and the comprehensive penalty coefficient is taken as the target critical heat flux density.

[0016] To achieve the above objectives, a second aspect of this application provides a device for determining the critical heat flux density of a reactor, the device comprising: The acquisition module is used to acquire the first critical heat flux density of the rod bundle channel when the reactor is in a static state, and the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state, including tilting, swaying or undulating states. The calculation module is used to calculate the penalty coefficient for each of the motion states based on the first critical heat flux density and the second critical heat flux density. A construction module is used to construct a penalty relationship between the motion parameters in each motion state, based on the penalty coefficient in the motion state and the motion parameters in the motion state. The penalty relationship is the correspondence between the parameter value of the motion parameter and the penalty coefficient under the motion parameter. The determination module is used to determine the target critical heat flux density of the reactor based on the penalty relationship in each of the said motion states and the first critical heat flux density.

[0017] The method and apparatus for determining the critical heat flux density of a reactor proposed in this application first obtain the first critical heat flux density of the reactor in a static state, and the second critical heat flux density in at least one motion state such as tilting, rocking, or undulating. By calculating the ratio of the first to the second critical heat flux density, a penalty coefficient corresponding to each motion state is obtained. Then, for each motion state, a correspondence between the motion parameters and the penalty coefficient is established, i.e., a penalty relationship. Finally, based on the constructed penalty relationship and the first critical heat flux density in the static state, the target critical heat flux density of the reactor in the motion state is determined. By introducing a penalty coefficient and a penalty relationship, this application can quantitatively characterize the influence of motion parameters on the critical heat flux density, thereby achieving accurate prediction of the reactor's critical heat flux density under motion conditions, effectively improving the reliability and accuracy of the prediction results. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the method for determining the critical heat flux density of a reactor provided in the embodiments of this application; Figure 2 This is a flowchart of the critical heat flux density prediction method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the reactor critical heat flux density determination device provided in the embodiments of this application. Detailed Implementation

[0019] 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.

[0020] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Critical Heat Flux (CHF) is a key safety limit parameter for reactor thermal-hydraulic design, directly determining the reactor core's power ceiling and operational safety. In traditional pressurized water reactor engineering practice, dedicated CHF formulas are typically developed based on CHF test data for specific fuel assemblies to assess reactor operating status in real time. However, for reactors in certain specific application scenarios, they inevitably operate under complex motion environments such as tilting, swaying, and undulation for extended periods. These motion factors significantly alter the flow and heat transfer characteristics of the coolant within the core, thus having a non-negligible impact on CHF. This makes it impossible to directly apply mature experience from traditional static operating conditions to reactor devices operating in motion environments.

[0023] Existing CHF prediction technologies are primarily developed for pressurized water reactors in a static state, and their formulas do not consider the impact of motion environments such as tilting and swaying on CHF characteristics. Because tilting, swaying, and undulating motions caused by these environments disrupt the symmetry of the internal flow field, they alter the CHF mechanism and exert a non-negligible penalty effect on CHF values. Therefore, directly applying existing CHF formulas for static pressurized water reactors to reactors in motion environments leads to significant deviations in prediction results, failing to accurately reflect the true thermal-hydraulic safety margin and posing substantial safety hazards.

[0024] Based on this, embodiments of this application provide a method and apparatus for determining the critical heat flux density of a reactor, aiming to propose a method for accurately predicting the CHF of a reactor under motion environments such as rocking and tilting, so as to improve the reliability of CHF prediction in complex motion states.

[0025] The method and apparatus for determining the critical heat flux density of a reactor provided in this application are specifically described through the following embodiments. First, the method for determining the critical heat flux density of a reactor in this application is described.

[0026] The method for determining the critical heat flux density of a reactor provided in this application relates to the field of nuclear power technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the method for determining the critical heat flux density of a reactor, etc., but is not limited to the above forms.

[0027] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0028] Figure 1 This is a flowchart of the method for determining the critical heat flux density of a reactor provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S100 to S400.

[0029] Step S100: Obtain the first critical heat flux density of the rod bundle channel when the reactor is in a static state, and the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state, including tilting, swaying, or undulating states.

[0030] In this embodiment, the motion state includes tilting, swaying, or undulating states. The first critical heat flux density is the critical heat flux density value of the rod bundle channel in a static environment. The first critical heat flux density can be obtained through three methods: first, by conducting heating tests on the rod bundle channel under static conditions for direct measurement; second, by using a simulation program to establish a rod bundle channel model and applying the same boundary conditions as the static condition for calculation; and third, by directly obtaining it through a lookup table method when historical data exists.

[0031] In this embodiment, the second critical heat flux density is the critical heat flux density value of the rod bundle channel of the reactor under specific motion states, which needs to be obtained separately for tilting, rocking, and undulating states. Taking the tilting state as an example, the corresponding motion parameter is the tilt angle. By setting multiple different tilt angle values ​​(such as five degrees, ten degrees, fifteen degrees, etc.), and under the premise that other boundary conditions remain consistent with the static condition, the critical heat flux density at these different tilt angles is obtained through experiments or simulations, thus obtaining the second critical heat flux density. Similarly, for the rocking state, two motion parameters need to be considered: the rocking angle and the rocking period; for the undulating state, two motion parameters need to be considered: the undulating acceleration and the undulating period.

[0032] In one specific implementation, three motion modes—tilting, swaying, and undulating—are analyzed. A complete database is fundamental to ensuring the accuracy of CHF prediction for the rod bundle. The following parameters should be considered for the three motion modes: Inclined working condition: Inclined angle θ Q ; Swinging condition: Swing angle θ Y Swing period T Y ; Fluctuating operating conditions: Fluctuating acceleration a F Fluctuation period T F .

[0033] In CHF data, the above five parameters should be considered in two levels of databases: a single motion parameter database and a single motion condition database. These are described below: Single motion parameter database: Only the change of a single motion parameter is considered, while the other four parameters are kept at 0. Table 1 is the single motion parameter data matrix. As shown in Table 1, M can represent any kind of motion parameter, and A represents the static condition (i.e., the CHF test condition corresponding to the condition that does not contain any motion conditions).

[0034] Table 1

[0035] Single motion condition database: Only the variation of a single motion condition is considered, while the parameters in other motion conditions remain unchanged (but not zero). Table 2 shows the single motion condition data matrix. As shown in Table 2, Table 2(a) is the data matrix for the tilting condition, Table 2(b) is the data matrix for the swaying condition, and Table 2(c) is the data matrix for the undulating condition. The tilting condition contains only one variable, therefore its database is a first-order matrix. The other four parameters remain unchanged, as shown in Table 2(a). The values ​​of the other four parameters should be indicated in the table header. Furthermore, both the swaying and undulating conditions involve two variables; therefore, the database is a second-order matrix. and As shown in Tables 2(b) and 2(c), the values ​​of the other three parameters need to be indicated in the table header.

[0036] Table 2(a)

[0037] Table 2(b)

[0038] Table 2(c)

[0039] In the database mentioned above, each motion parameter requires a separate single motion parameter database.

[0040] The corresponding parameters in Tables 1 and 2 were obtained through experiments and entered into the tables to ensure the data in the tables is complete.

[0041] The CHF of the rod bundle under static conditions is the benchmark value for determining the CHF penalty of the rod bundle under ocean conditions, and can be obtained using any of the following methods: The test was conducted using experimental methods. It is necessary to ensure that the test static conditions are consistent with the conditions A in the headers of Tables 1 and 2, and completely cover all static conditions Ai. The CHF value corresponding to each static condition Ai was recorded. The CHF value was obtained by simulation program calculation: a rod bundle model was established in the simulation program, the same boundary conditions as the static condition Ai were set, and the CHF value corresponding to each static condition Ai was calculated. The CHF value corresponding to each static condition Ai is obtained by looking up a table: if an existing test data table exists, the CHF value is obtained.

[0042] Step S200: Calculate the penalty coefficient for each of the motion states based on the first critical heat flux density and the second critical heat flux density.

[0043] In this embodiment, after obtaining the first critical heat flux density at rest and the second critical heat flux density under the same reference operating conditions, the penalty coefficient is obtained by calculating the ratio of the second critical heat flux density to the first critical heat flux density. Since motion typically deteriorates the coolant flow field, leading to a decrease in the critical heat flux density, this penalty coefficient is usually a value less than one. This value directly reflects the degree to which a specific motion parameter weakens the reactor's thermal safety margin.

[0044] In one specific implementation, the CHF penalty coefficient for ocean conditions is calculated as follows: .

[0045] The calculation results for single motion parameters and single motion conditions are filled into Tables 3 and 4, respectively. Table 3 is the penalty coefficient matrix for single motion parameters, and Table 4 is the penalty coefficient matrix for single motion conditions. Table 4(a) is the penalty coefficient matrix for tilting conditions, Table 4(b) is the penalty coefficient matrix for swaying conditions, and Table 4(c) is the penalty coefficient matrix for undulating conditions.

[0046] Table 3

[0047] Table 4(a)

[0048] Table 4(b)

[0049] Table 4(c)

[0050] Taking the swing condition penalty coefficient matrix in Table 4(b) as an example, its specific calculation formula is as follows: .

[0051] Step S300: For each motion state, construct a penalty relationship between the motion parameters in the motion state based on the penalty coefficient in the motion state and the motion parameters in the motion state. The penalty relationship is the correspondence between the parameter value of the motion parameter and the penalty coefficient under the motion parameter.

[0052] In this embodiment, the penalty relationship is the correspondence between the parameter values ​​of motion parameters and the corresponding penalty coefficients. The construction process includes: fitting the correspondence between the penalty coefficient and the corresponding motion parameter for each motion state using various function forms such as quadratic polynomial, exponential, and power functions to obtain multiple candidate penalty relationships; substituting each motion parameter into each candidate penalty relationship to calculate the predicted value of the penalty coefficient; dividing each predicted value by the corresponding measured penalty coefficient to obtain the predicted-to-measured ratio; calculating the mean and dispersion of all predicted-to-measured ratios; and selecting the optimal penalty relationship from the multiple candidate penalty relationships based on the mean and dispersion. The selection principle for the optimal penalty relationship is: the mean is within a first preset range and the dispersion is minimal; if the mean of all candidate penalty relationships is not within the first preset range, the candidate penalty relationship with the minimal dispersion is selected as the penalty relationship to be adjusted, and the constant compensation term in the penalty relationship to be adjusted is adjusted so that the adjusted mean is within the first preset range; finally, the selected optimal penalty relationship or the adjusted penalty relationship to be adjusted is used as the penalty relationship for the motion parameter in the corresponding motion state.

[0053] In one specific implementation, Table 3 is used to fit the relationship between each motion parameter and the penalty coefficient individually. For example, when fitting motion parameter M (M can be any type of motion parameter), the possible fitting relationship forms that usually need to be considered are as follows: Quadratic polynomial: ; Exponential form: ; Power function form: .

[0054] After the fitting is completed, each motion parameter M in Table 3 is... n Substituting into the fitted relation, the penalty coefficient R is calculated. cal (M n And calculate the prediction / measured ratio: .

[0055] Statistics of all mean with standard deviation Comparing the fitting results of the three relationships mentioned above, the relationship with the mean between [0.98, 1.02] and the smallest standard deviation is selected as the fitting relationship for the penalty factor of the single motion condition M.

[0056] If the fitting results of the above three relationships are such that the mean If the mean is not within the range of [0.98, 1.02], then first select the formula with the smallest standard deviation, and then adjust the value of c so that its mean is within the range of [0.98, 1.02]. The adjustment method is as follows: If the mean If it is less than 0.98, then ; If the mean If it is greater than 1.02, then .

[0057] It should be noted that the relational expression is not limited to the three forms mentioned above, but generally speaking, using these three forms can achieve good fitting results. More complex forms can also be used.

[0058] The final result will be a relational form: Where M is the tilt angle θ Q , swing angle θ Y Swing period T Y , undulating acceleration a F With fluctuation period T F There are any one of them, therefore there are a total of five relations.

[0059] Step S400: Determine the target critical heat flux density of the reactor based on the penalty relationship for each of the motion states and the first critical heat flux density.

[0060] In this embodiment, in practical applications, when it is necessary to determine the safety limits of a reactor under a certain unknown or real-time complex motion state, the specific values ​​of each motion parameter in the current actual environment are first obtained, such as the current tilt angle and swing period. These actual values ​​are then substituted into the pre-constructed penalty relationships to quickly calculate the real-time penalty coefficient for the current state. Finally, this real-time penalty coefficient is used to correct the obtained first critical heat flux density, thereby accurately determining the target critical heat flux density of the reactor under this complex motion state.

[0061] In one specific implementation, the data in Table 4 are used for fitting to obtain the final penalty coefficient relationship. The least squares method is used for fitting, and the form of the penalty coefficient relationship is as follows: ; ; .

[0062] After completing the fitting, substitute the motion conditions corresponding to each penalty coefficient in Table 4 into the fitting formula to calculate the penalty coefficient value. And calculate the prediction / measured ratio. .

[0063] Statistics of all mean If the mean is between [1, 1.02], the fitting ends; otherwise, the following steps are taken: If the mean If less than 1, then ; If the mean If it is greater than 1.02, then .

[0064] After completing the above steps, we obtain the CHF prediction method for marine conditional rod bundles based on motion factor penalty, with the following relational form: .

[0065] Among them, the penalty coefficient It is obtained through the penalty coefficient relationship. It is then calculated based on the existing static condition CHF relation.

[0066] Figure 2 This is a flowchart of the critical heat flux density prediction method in this embodiment. Figure 2 As shown, two types of data collection tables were first designed. Table 1 (single motion parameter database) records the CHF values ​​when only one motion parameter is changed (e.g., only the tilt angle is changed, while all other sway and undulation parameters are 0). Table 2 (single motion condition database) records the CHF values ​​when only one motion condition is changed (e.g., only swaying motion is performed, while the tilt and undulation parameters are kept at a fixed non-zero value). Based on these two databases, CHF data of the rod bundle channel under different combinations of motion parameters / conditions were obtained through experiments or simulations, forming a CHF dataset under marine conditions. CHF data of the rod bundle channel when the reactor is in a static state (no motion conditions) was obtained as the benchmark value for subsequent calculation of the penalty coefficient. Using the static condition CHF as the benchmark, the penalty coefficient corresponding to each motion parameter / condition was calculated by dividing the marine condition CHF by the static condition CHF. This coefficient quantifies the degree to which motion factors weaken CHF. For each individual motion parameter (such as tilt angle) in the single motion parameter database, its value and corresponding penalty coefficient are used as samples. A correspondence between the motion parameter and the penalty coefficient is established through mathematical fitting (such as quadratic polynomials, exponential functions, etc.), yielding the penalty relationship for each single parameter. Using the penalty coefficients of each motion condition (such as swaying condition) in the single motion condition database as input, mathematical methods such as least squares are used for fitting to establish a relationship between the coupling of multiple motion parameters and the comprehensive penalty coefficient, integrating the cross-influence of different motion factors. Finally, combining the single-parameter penalty relationship and the coupled penalty relationship, a mathematical relationship is formed that can be used to predict CHF in rod bundles under marine conditions, achieving accurate CHF prediction based on motion factor penalties.

[0067] This embodiment constructs a penalty relationship system from a single motion parameter to multiple motion states coupling in a hierarchical and progressive manner, realizing reliable prediction from the critical heat flux density under static conditions to the critical heat flux density under motion conditions, effectively improving the accuracy of thermal safety assessment of reactors under complex motion environments.

[0068] In some embodiments, step S400 may include, but is not limited to, steps S410 to S420: Step S410: Based on the penalty relationship of motion parameters under N motion states, construct a coupling penalty relationship under N motion states. The coupling penalty relationship is the correspondence between the comprehensive penalty coefficient under the combined action of motion parameters corresponding to N motion states and the penalty coefficient under each motion state. N is an integer greater than or equal to 2. Step S420: Determine the target critical heat flux density of the reactor based on the coupling penalty relationship under the N motion states and the first critical heat flux density.

[0069] In this embodiment, when the reactor is simultaneously in two or more motion states (i.e., N≥2, for example, simultaneously in a tilting and rocking state, or simultaneously in a tilting, rocking, and undulating state), complex nonlinear interactions occur between these motion states, and a simple linear superposition cannot accurately describe the comprehensive penalty effect. Therefore, this embodiment introduces the concept of a coupling penalty relationship. The coupling penalty relationship is the correspondence between the comprehensive penalty coefficient under the combined action of motion parameters corresponding to multiple motion states and the penalty coefficient under each motion state. The comprehensive penalty coefficient includes the penalty component when each motion state acts alone and the interactive penalty component generated by the mutual influence between the motion states.

[0070] Specifically, for N motion states, various combinations of motion parameters are set (e.g., tilt angles of 5°, 10°, and 15°; swing angles of 10°, 15°, and 20°; swing periods of 5s, 10s, and 15s, etc.). Critical heat flux density data under the corresponding combinations are obtained through experiments or simulations, and the comprehensive penalty coefficient under each combination is calculated, which is the critical heat flux density under the motion state divided by the critical heat flux density under the stationary state.

[0071] Using the penalty coefficient for each motion state as the input variable and the comprehensive penalty coefficient as the output variable, a mathematical fitting method is employed to fit the data, yielding a mathematical expression for the coupled penalty relationship. During the fitting process, the function form can be selected according to actual needs to accurately capture the interaction between different motion parameters.

[0072] After obtaining the coupling penalty relationship of multiple motion states, the comprehensive penalty coefficient can be calculated based on the actual motion state of the current reactor (i.e., the measured value of the motion parameters), and then the target critical heat flux density can be determined.

[0073] Specifically, firstly, the actual values ​​of each motion parameter of the reactor under the current operating environment are obtained. Each actual motion parameter is then substituted into the corresponding single motion state penalty relationship (such as the penalty relationship for tilt angle, the penalty relationship for sway angle, etc.) to calculate the penalty coefficient for each motion parameter. The penalty coefficients of each motion parameter are then substituted into the coupling penalty relationship to calculate the comprehensive penalty coefficient under the current combination of motion states. Finally, the first critical heat flux density (critical heat flux density in the static state) is multiplied by the comprehensive penalty coefficient to obtain the target critical heat flux density of the reactor under the coupling effect of the current multiple motion states.

[0074] This embodiment, by constructing coupling penalty relationships for multiple motion states, can accurately capture the cross-coupling effect between different motion parameters, solving the problem that a single motion state penalty relationship cannot reflect complex coupled operating conditions; the target critical heat flux density calculated based on the coupling penalty relationship can more realistically reflect the thermal safety margin of the reactor under the coupling effect of multiple motion states, significantly improving the prediction accuracy.

[0075] In some embodiments, step S100 may include, but is not limited to, steps S110 to S130: Step S110: For each of the motion parameters, obtain the critical heat flux density corresponding to the motion parameter from the single motion parameter database. The single motion parameter database is used to record the critical heat flux density corresponding to the single motion parameter under the condition that only the parameter value of the single motion parameter is changed and the parameter values ​​of the other motion parameters are all zero. Step S120: For each of the motion states, obtain the critical heat flux density corresponding to the motion state from the single motion condition database. The single motion condition database is used to record the critical heat flux density corresponding to the single motion state under the condition that the parameter values ​​of multiple motion parameters of a single motion state are changed only, and the parameter values ​​of motion parameters of other motion states are kept at non-zero fixed values. Step S130: The critical heat flux density corresponding to each of the motion parameters and the critical heat flux density corresponding to each of the motion states are used as the second critical heat flux density.

[0076] In this embodiment, the single motion parameter database is used to record the critical heat flux density corresponding to a single motion parameter when only the value of that single motion parameter is changed and all other motion parameters are zero. For example, when studying the effect of tilting on the critical heat flux density, only the tilt amplitude is changed, while the swaying amplitude, swaying period, undulation acceleration, and undulation period are all set to zero, and the critical heat flux density values ​​corresponding to different tilt amplitudes are recorded. Similarly, when studying the effect of swaying amplitude on the critical heat flux density, only the swaying amplitude is changed, while all other motion parameters are set to zero, and the critical heat flux density values ​​corresponding to different swaying amplitudes are recorded.

[0077] In this embodiment, the single motion condition database is used to record the critical heat flux density corresponding to a single motion state under the condition that the parameter values ​​of multiple motion parameters for only a single motion state are changed, while the parameter values ​​of motion parameters for other motion states remain at non-zero fixed values. For example, when studying the effect of a rocking state on the critical heat flux density, the values ​​of the rocking amplitude and rocking period are changed simultaneously, while the tilt amplitude, undulation acceleration, and undulation period remain at fixed non-zero values, and the critical heat flux density values ​​corresponding to different combinations of rocking amplitude and rocking period are recorded. Similarly, when studying the effect of a fluctuating state on the critical heat flux density, the values ​​of the undulation acceleration and undulation period are changed simultaneously, while the motion parameters for other motion states remain at fixed non-zero values, and the critical heat flux density values ​​corresponding to different combinations of undulation acceleration and undulation period are recorded.

[0078] The difference between a single motion condition database and a single motion parameter database lies in the following: In a single motion parameter database, all other motion parameters are zero, used to obtain the independent effect of a single motion parameter; while in a single motion condition database, the motion parameters of other motion states are kept at non-zero fixed values, used to simulate real-world scenarios where multiple motion states coexist but only the target motion state changes. For example, in actual sea conditions, a reactor may simultaneously be in tilting, rocking, and undulating states; a single motion condition database can reflect the critical heat flux density response characteristics when a certain motion state parameter changes under this complex background.

[0079] In this embodiment, after acquiring the critical heat flux density data from the single motion parameter database and the single motion condition database respectively, the two types of data are unified as the second critical heat flux density, which is used for subsequent calculation of penalty coefficients and construction of penalty relationships. Specifically, the data from the single motion parameter database is used to establish independent penalty relationships for each motion parameter, while the data from the single motion condition database is used to verify and correct the prediction model under multi-parameter coupling conditions.

[0080] This embodiment constructs a single motion parameter database, enabling the clear separation and quantification of the independent effects of each motion parameter, avoiding the confusion caused by mutual interference when multiple motion parameters change simultaneously. It also constructs a single motion condition database, allowing the accurate acquisition of the comprehensive effects of a single motion state under complex backgrounds, avoiding prediction bias caused by studying a single motion parameter in isolation while ignoring the actual multi-state coexistence background. Based on the control condition of keeping the remaining motion state parameters at non-zero fixed values, it ensures that the acquired critical heat flux density data is closer to actual operating conditions, providing reliable data support for subsequently establishing coupling penalty relationships with engineering practical value. By integrating the critical heat flux density data from the two types of databases, it achieves complete data coverage from the independent effects of a single parameter to the comprehensive effects of a single state, ensuring both the accuracy of constructing penalty relationships for each motion parameter and the reliability of coupling penalty relationships under actual complex operating conditions. This significantly improves the comprehensiveness and accuracy of critical heat flux density prediction for reactors under the combined effects of multiple motion states.

[0081] In some embodiments, step S200 may include, but is not limited to, step S210: Step S210: For each of the motion states, divide the second critical heat flux density of the motion state by the first critical heat flux density of the motion state to obtain the penalty coefficient of the motion state.

[0082] In this embodiment, to quantify the impact of motion state on the reactor's critical heat flux density, a ratio calculation is used to calculate the penalty coefficient. After the ratio calculation, the resulting ratio is directly used as the penalty coefficient for that motion state. The numerical meaning of the penalty coefficient is as follows: when the penalty coefficient is less than one, it indicates that the motion state leads to a decrease in critical heat flux density, that is, the motion state has a negative impact on the reactor's thermal safety performance; when the penalty coefficient is equal to one, it indicates that the motion state has no impact on critical heat flux density; when the penalty coefficient is greater than one, it indicates that the motion state leads to an increase in critical heat flux density, that is, the motion state has a positive impact on the reactor's thermal safety performance.

[0083] Specifically, the tilt penalty coefficient is the ratio of the second critical heat flux density to the corresponding first critical heat flux density under tilted conditions, used to quantify the influence of tilt amplitude on critical heat flux density; the sway penalty coefficient is the ratio of the second critical heat flux density to the corresponding first critical heat flux density under swayed conditions, used to quantify the influence of the combined effect of sway amplitude and sway period on critical heat flux density; and the fluctuation penalty coefficient is the ratio of the second critical heat flux density to the corresponding first critical heat flux density under fluctuating conditions, used to quantify the influence of the combined effect of fluctuation acceleration and fluctuation period on critical heat flux density.

[0084] In this embodiment, the system calculates the ratio between the critical heat flux density in motion and the critical heat flux density in rest, and converts the original second critical heat flux density data into dimensionless penalty coefficient data. Compared with directly using the absolute value of the critical heat flux density, the penalty coefficient eliminates the differences in dimensions and absolute values ​​caused by different benchmark conditions, so that the motion effects under different conditions can be uniformly compared and fitted, which greatly simplifies the complexity of subsequent modeling.

[0085] In some embodiments, step S300 may include, but is not limited to, steps S310 to S320: Step S310: For each motion parameter in the motion state, obtain multiple penalty coefficients corresponding to different parameter values ​​of the motion parameter; Step S320: Fit the relationship between the different values ​​of the motion parameters and multiple penalty coefficients to obtain the penalty relationship of the motion parameters.

[0086] Step S320 may include, but is not limited to, steps S321 to S325: Step S321: Use multiple fitting methods to fit different parameter values ​​of the motion parameters with the corresponding penalty coefficients to obtain the candidate penalty relationship corresponding to each fitting method; Step S322: For each of the motion parameters, the different parameter values ​​of the motion parameters are substituted into the candidate penalty relationship corresponding to each of the fitting methods for calculation, so as to obtain the predicted value of the penalty coefficient corresponding to different parameter values ​​under each candidate penalty relationship; Step S323: For each parameter value under each candidate penalty relationship, the ratio of the predicted value of the penalty coefficient corresponding to the parameter value to the measured value of the penalty coefficient corresponding to the parameter value is used as the first predicted-measured ratio of the penalty coefficient corresponding to the parameter value. Step S324: For each of the motion parameters, calculate the mean and standard deviation of the multiple first predicted measured ratios corresponding to each candidate penalty relationship, and select the candidate penalty relationship with the mean within a first preset range and the smallest standard deviation as the penalty relationship for the motion parameter. Step S325: If the mean of all candidate penalty relationships is outside the first preset range, then select the candidate penalty relationship with the smallest standard deviation as the penalty relationship of the motion parameter, and adjust the value of the constant term in the candidate penalty relationship so that the adjusted mean falls into the first preset range.

[0087] In this embodiment, to establish a quantitative correspondence between motion parameters and penalty coefficients, it is first necessary to acquire multiple sets of discrete data points covering the range of motion parameter values. Specifically, for the tilt amplitude in the tilt state, the penalty coefficients corresponding to different tilt amplitude values ​​are acquired to form a set of discrete correspondence data between tilt amplitude and penalty coefficient; for the sway amplitude in the swing state, the penalty coefficients corresponding to different sway amplitude values ​​are acquired to form a set of discrete correspondence data between sway amplitude and penalty coefficient; for the sway period in the swing state, the penalty coefficients corresponding to different sway period values ​​are acquired to form a set of discrete correspondence data between sway period and penalty coefficient; similarly, for the undulation acceleration and undulation period in the fluctuation state, multiple penalty coefficients corresponding to different parameter values ​​are acquired respectively.

[0088] It should be noted that the selection of different parameter values ​​should cover the complete range of values ​​that the motion parameter may exhibit in actual operation, and the interval between adjacent parameter values ​​should be reasonably set to ensure that the penalty relationship obtained by subsequent fitting has sufficient accuracy and reliability throughout the entire range of values. For example, the range of tilt amplitude can be set from zero degrees to the maximum design tilt angle, with multiple test points selected at equal intervals or non-equal intervals according to physical characteristics; the swing amplitude, swing period, undulation acceleration, and undulation period are also selected according to this principle.

[0089] In this embodiment, after obtaining different values ​​of the motion parameters and their corresponding multiple penalty coefficients, a mathematical fitting method is used to establish a continuous functional correspondence between the motion parameter values ​​and the penalty coefficients, i.e., the penalty relationship of the motion parameters. To comprehensively explore the possible functional relationships between the motion parameters and the penalty coefficients, multiple fitting methods are used to fit the same set of discrete data. Specifically, these fitting methods include, but are not limited to, quadratic polynomial, exponential, and power function forms. The quadratic polynomial form assumes a quadratic curve relationship between the penalty coefficient and the motion parameter, suitable for parameter influence patterns with extreme points or bending characteristics; the exponential form assumes that the penalty coefficient decreases or increases exponentially with the motion parameter, suitable for scenarios where the degree of influence changes rapidly with increasing parameters; and the power function form assumes that the penalty coefficient is proportional to a certain power of the motion parameter, suitable for scenarios where the degree of influence follows a power-law distribution with parameter changes.

[0090] Specifically, multiple functional forms are used to fit the discrete data to obtain multiple candidate penalty relationships; each actual experimental parameter value of the motion parameter is substituted into each candidate penalty relationship to calculate the predicted penalty coefficient value of the candidate penalty relationship under that parameter value.

[0091] In this embodiment, to quantify the prediction deviation of each candidate penalty relationship under each parameter value, the ratio of the predicted value to the measured value is used as an evaluation index. Specifically, for a certain candidate penalty relationship under a certain parameter value, the predicted penalty coefficient calculated by substituting the parameter value into the candidate penalty relationship is divided by the measured penalty coefficient obtained through experimental testing corresponding to that parameter value to obtain the first prediction-to-measurement ratio under that parameter value.

[0092] In this embodiment, to select the optimal penalty relationship from multiple candidate penalty relationships, a statistical analysis method is used to comprehensively evaluate the first predicted-to-measured ratio. Specifically, for each candidate penalty relationship, the arithmetic mean of the first predicted-to-measured ratios corresponding to all its parameter values ​​is calculated. This mean reflects the degree of systematic deviation of the candidate relationship. Simultaneously, the standard deviation of all first predicted-to-measured ratios is calculated, reflecting the degree of random fluctuation of the candidate relationship. The first preset range is a preset allowable deviation interval, for example, 0.98 to 1.02. The selection principle for the optimal penalty relationship is: the mean is within the first preset range and the standard deviation is minimized.

[0093] In this embodiment, considering the inherent characteristics of actual data that may cause the mean of all candidate penalty relationships to fail to fall naturally into the first preset range, a correction mechanism is designed. When the mean of all candidate penalty relationships deviates from the first preset range, the candidate penalty relationship with the smallest standard deviation is first selected; then, the system deviation is corrected by adjusting the value of the constant term in this candidate penalty relationship. Specifically, if the mean is less than the lower limit of the first preset range, the value of the constant term is increased by the difference between the lower limit and the mean, so that the adjusted mean is exactly equal to the lower limit; if the mean is greater than the upper limit of the first preset range, the value of the constant term is decreased by the difference between the mean and the upper limit, so that the adjusted mean is exactly equal to the upper limit. Through this adjustment, the mean that originally deviated from the preset range is forcibly pulled back to the allowable range, while keeping the standard deviation unchanged, that is, without changing the predictive stability of the model.

[0094] This embodiment achieves quantitative evaluation and adaptive optimization of the penalty relationship by employing multiple fitting methods, calculating the predicted-to-measured ratio, statistically analyzing the mean and standard deviation, and introducing a constant term adjustment mechanism. This significantly improves the fitting accuracy and generalization ability of the penalty relationship, ensuring its applicability and reliability under different motion parameters and reactor types.

[0095] In some embodiments, step S410 may include, but is not limited to, steps S411 to S415: Step S411: Combine the various motion parameters according to different parameter values ​​to form multiple sets of motion conditions; Step S412: Using each set of motion conditions as input and the measured value of the penalty coefficient under each set of motion conditions as output, perform function fitting to obtain the initial coupling function relationship; Step S413: For each set of motion conditions, substitute the penalty coefficient corresponding to each motion parameter in the motion condition into the initial coupling function relationship for calculation to obtain the predicted value of the comprehensive penalty coefficient corresponding to the motion condition. Step S414: The ratio of the predicted value of the comprehensive penalty coefficient corresponding to the motion condition to the measured value of the comprehensive penalty coefficient corresponding to the motion condition is used as the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to the motion condition. Step S415: Adjust the initial coupling function relationship according to the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to each of the motion conditions to obtain the coupling penalty relationship under N motion conditions.

[0096] In this embodiment, when constructing the coupling penalty relationship, the motion parameters corresponding to N motion states (N≥2) are first combined with different values ​​to form multiple sets of motion conditions. The motion parameters include, but are not limited to, tilt angle, sway angle, sway period, undulation acceleration, undulation period, etc., and each set of motion conditions corresponds to a specific combination of motion parameter values.

[0097] Specifically, various motion parameters, such as tilt amplitude, sway amplitude, sway period, undulation acceleration, and undulation period, are selected according to their effective ranges. Several representative parameter values ​​are then chosen, and these values ​​are either fully combined or partially combined according to the experimental design requirements to form multiple sets of motion conditions. For example, if the tilt amplitude is taken as three horizontal values, the sway amplitude as three horizontal values, and the sway period as two horizontal values, then the three can be combined to form eighteen different sway-tilt coupling conditions. By adding the combination of undulation acceleration and undulation period, it can be further expanded into more complex multi-factor coupling conditions.

[0098] It should be noted that the selection of parameter values ​​should follow the principles of experimental design, ensuring that the level settings of each parameter can cover typical operating conditions in actual operation, and that the total number of combined operating conditions is within the range of experimental feasibility. For cases where the parameter space is too large, making the entire combination infeasible, orthogonal experimental design, uniform design, or Latin hypercube sampling can be used to reduce the number of experiments while ensuring the representativeness of the parameter combinations.

[0099] In this embodiment, after constructing multiple sets of motion conditions and completing corresponding experimental measurements, a mathematical fitting method is used to establish a quantitative functional expression between the comprehensive penalty coefficient and the penalty components of each motion parameter. Specifically, the specific values ​​of each motion parameter included in each set of motion conditions are used as input variables, and the comprehensive penalty coefficient obtained through experimental measurement under that set of motion conditions is used as the output response. Based on a preset functional form, fitting processing is performed to determine the values ​​of each coefficient in the function, thus obtaining the initial coupling functional relationship.

[0100] In this embodiment, the preset function form should fully reflect the independent and interactive effects of multiple motion states acting together. It typically includes penalty components for the individual effects of each motion state, penalty components for pairwise interactions between different motion states, and a constant compensation term. For example, for the three motion states of tilting, swaying, and undulating, the function form may include independent terms for the tilt component, independent terms for the swaying composite component, independent terms for the undulating composite component, interaction terms for tilting and swaying, interaction terms for tilting and undulating, interaction terms for swaying and undulating, and a constant term. The fitting process can employ optimization algorithms such as the least squares method to minimize the sum of squared errors between the predicted and measured values.

[0101] In this embodiment, after obtaining the initial coupling function relationship, its predictive performance needs to be verified and evaluated. Specifically, for each set of motion conditions, firstly, based on the specific values ​​of each motion parameter, the penalty coefficient corresponding to each motion parameter is retrieved or calculated from the established single motion parameter penalty relationship; then, these penalty coefficients are used as input and substituted into the initial coupling function relationship for calculation to obtain the predicted value of the comprehensive penalty coefficient under that motion condition. For example, for a set of motion conditions that simultaneously includes a specific tilt amplitude, sway amplitude, sway period, undulation acceleration, and undulation period, the penalty coefficients for tilt amplitude, sway amplitude, sway period, undulation acceleration, and undulation period are calculated or retrieved respectively, and then these penalty coefficients are substituted into the initial coupling function relationship to calculate the predicted value of the comprehensive penalty coefficient under that condition.

[0102] In this embodiment, the initial coupling function relationship is adjusted based on the second predicted-to-measured ratio corresponding to each set of motion conditions to optimize prediction accuracy. The adjustment method can be coefficient optimization (such as adjusting the function coefficients using the least squares method to make the predicted value closer to the measured value) or constant term adjustment.

[0103] In this embodiment, to quantify the degree of prediction deviation of the initial coupling function relationship under various motion conditions, the ratio of the predicted value to the measured value is used as an evaluation index. Specifically, for each set of motion conditions, the predicted value of the comprehensive penalty coefficient calculated from the initial coupling function relationship under that condition is divided by the measured value of the comprehensive penalty coefficient obtained through experimental measurement under that condition to obtain the second prediction-to-measurement ratio for that motion condition.

[0104] This embodiment combines various motion parameters with different values ​​to form multiple motion conditions. It fits the initial coupling function relationship with the measured comprehensive penalty coefficient as the target, and then calculates the mean of the second predicted measured ratio and adjusts the constant term to achieve accurate construction and optimization of the coupling penalty relationship. It fully considers the nonlinear coupling effect between tilt, sway, and undulation, and ensures the balance between prediction accuracy, safety and economy in the practical application of the coupling penalty relationship.

[0105] In some embodiments, step S415 may include, but is not limited to, the following steps: The mean of the second predicted-measured ratio of the comprehensive penalty coefficient corresponding to each of the aforementioned motion conditions is calculated. If the mean is less than the lower limit of the second preset range, the value of the constant term in the initial coupling function relationship is increased until the mean is within the second preset range, thereby obtaining the coupling penalty relationship. If the mean is greater than the upper limit of the second preset range, then the value of the constant term in the initial coupling function relationship is reduced until the mean is within the second preset range, thus obtaining the coupling penalty relationship.

[0106] In this embodiment, after obtaining the second predicted-to-measured ratios for each motion condition, the initial coupling function relationship needs to be corrected and optimized based on the statistical characteristics of these ratios to ensure that its prediction accuracy meets engineering requirements. Specifically, the mean of the second predicted-to-measured ratios for all motion conditions is calculated, reflecting the degree of systematic deviation in the initial coupling function relationship. It is then determined whether the mean is within a second preset range. If the mean is less than the lower limit of the second preset range, the value of the constant term in the initial coupling function relationship is increased until the mean is within the second preset range. If the mean is greater than the upper limit of the second preset range, the value of the constant term in the initial coupling function relationship is decreased until the mean is within the second preset range. Through this adjustment process, the systematic deviation of the initial coupling function relationship is eliminated, ultimately yielding the coupling penalty relationship under various motion states.

[0107] This embodiment uses the statistical characteristics of the second predicted-measured ratio to adjust the initial coupling function relationship, making the prediction results of the coupling penalty relationship systematically biasable and ensuring its reliability in engineering applications. Based on the targeted adjustment of the constant term, the overall offset can be quickly eliminated without changing the overall structure of the model and the relative weights of each interaction term. This ensures that the corrected coupling penalty relationship retains the physical laws captured by the original fitting and meets the preset accuracy threshold, significantly improving the engineering practicality and prediction credibility of the model.

[0108] In some embodiments, the motion parameters include: tilt angle in the tilt state, sway angle and sway period in the swing state, and undulation acceleration and undulation period in the undulating state. Step S412 may include, but is not limited to, the following steps: Obtain the penalty relationship for tilt angle in tilt state, penalty relationship for sway angle and penalty relationship for sway period in sway state, and penalty relationship for undulation acceleration and penalty relationship for undulation period in undulation state for each set of motion conditions. Construct a swing state coupling penalty relationship, which is a linear combination of the penalty coefficient of the swing angle, the penalty coefficient of the swing period, and the product of the swing angle and the swing period; Construct a fluctuation state coupling penalty relationship, which is a linear combination of the penalty coefficient of the fluctuation acceleration, the penalty coefficient of the fluctuation period, and the product of the penalty coefficient of the fluctuation acceleration and the penalty coefficient of the fluctuation period. The initial coupling function relationship is constructed by linearly combining the penalty coefficient of the tilt angle, the coupling penalty relationship of the swaying state, the coupling penalty relationship of the fluctuation state, the product of the penalty coefficient of the tilt angle and the coupling penalty relationship of the swaying state, the product of the penalty coefficient of the tilt angle and the coupling penalty relationship of the fluctuation state, the product of the coupling penalty relationship of the swaying state and the coupling penalty relationship of the fluctuation state, and a constant term. The coefficients in the linear combination are determined by the least squares method.

[0109] In this embodiment, to construct an initial coupling function relationship that reflects the coupling effects of multiple motion states, it is first necessary to establish a basic correspondence between each individual motion parameter and the penalty coefficient. For each set of motion conditions, the penalty relationships corresponding to all motion parameters involved in that condition are obtained from the constructed penalty relationship library. The penalty relationship is a continuous functional correspondence between the parameter value of each motion parameter and the corresponding penalty coefficient, obtained through the aforementioned single motion parameter penalty coefficient fitting step.

[0110] In this embodiment, to describe the combined effect of two motion parameters acting together under oscillating conditions, a oscillating state coupling penalty relationship is introduced. Specifically, the oscillating state coupling penalty relationship linearly combines the penalty coefficients of the oscillation amplitude and oscillation period, as well as their product. The coefficients in this linear combination are determined using the least squares method. The structure of this relationship demonstrates that the oscillation amplitude and oscillation period not only independently affect the critical heat flux density, but also have a mutually coupled interactive effect, which is quantified by the product of their penalty coefficients.

[0111] In this embodiment, to describe the combined effect of two motion parameters acting together under fluctuating conditions, a fluctuating state coupling penalty relationship is introduced. Specifically, the fluctuating state coupling penalty relationship linearly combines the penalty coefficients of fluctuating acceleration and fluctuating period, as well as their product. The coefficients in this linear combination are determined using the least squares method. The structure of this relationship demonstrates that fluctuating acceleration and fluctuating period not only independently affect the critical heat flux density, but also have a mutually coupled interaction effect, which is quantified by the product of their penalty coefficients.

[0112] In this embodiment, after obtaining the penalty relationships for each individual motion parameter and the coupling penalty relationships within each motion state, an initial coupling function relationship describing the coupling effect between multiple motion states is further constructed. Specifically, the initial coupling function relationship includes the following seven components: the penalty coefficient of the tilt amplitude as the independent effect term of the tilt state; the coupling penalty relationship of the swaying state as the comprehensive effect term of the swaying state; the coupling penalty relationship of the fluctuation state as the comprehensive effect term of the fluctuation state; the product of the coupling penalty relationship of the tilt amplitude and the swaying state as the interaction effect term of the tilt and swaying states; the product of the coupling penalty relationship of the tilt amplitude and the fluctuation state as the interaction effect term of the tilt and fluctuation states; the product of the coupling penalty relationship of the swaying state and the coupling penalty relationship of the fluctuation state as the interaction effect term of the sway and fluctuation states; and a constant term as the overall deviation compensation term.

[0113] In this embodiment, the coefficients in the linear combination are determined using the least squares method. That is, each set of motion conditions is taken as input, and the measured value of the comprehensive penalty coefficient under that condition is used as output. An optimization algorithm is then used to find the combination of coefficients that minimizes the sum of squared errors between the predicted and measured values. This fitting process ensures that the initial coupling function relationship has optimal overall predictive performance within the global motion condition space.

[0114] This embodiment fully considers the nonlinear coupling effects between different parameters under the same motion state and between different motion states by clearly defining the specific types of motion parameters and constructing the coupling penalty relationship for the swaying state, the coupling penalty relationship for the undulating state, and the total coupling function relationship containing the product terms of each term.

[0115] In some embodiments, step S420 may include, but is not limited to, steps S421 to S423: Step S421: For each of the motion states, substitute each of the motion parameters in the motion state into the penalty relationship corresponding to each of the motion parameters to obtain the penalty coefficient for each of the motion parameters; and, Step S422: Substitute the penalty coefficient of each motion parameter into the coupling penalty relationship for calculation to obtain the comprehensive penalty coefficient for N motion states; and, Step S423: The product of the first critical heat flux density and the comprehensive penalty coefficient is taken as the target critical heat flux density.

[0116] In this embodiment, when it is necessary to predict the critical heat flux density of the reactor under the combined effect of a specific multi-motion state, it is first necessary to obtain the actual motion parameter values ​​of the reactor at its current state. For each motion parameter, the system has established the corresponding penalty relationship using the method described in the preceding claims. Therefore, the system substitutes the value of each motion parameter currently measured or monitored into its corresponding penalty relationship to calculate the penalty coefficient for each motion parameter in its current state.

[0117] It should be noted that the calculation process relies on the penalty relationships of each motion parameter that have been constructed and verified in the preceding steps. The penalty relationship is in the form of a continuous function. Therefore, for any given parameter value (as long as it is within the valid range), the corresponding penalty coefficient can be calculated through the function without relying on discrete test data points. This characteristic enables this method to predict any combination of working conditions, and is not limited to specific working conditions that have been tested.

[0118] In this embodiment, after obtaining the penalty coefficients for each motion parameter, the system needs to combine these single-parameter penalty coefficients to reflect the total penalty effect when tilting, swaying, and undulating motion states coexist. The penalty coefficients for each motion parameter obtained above are substituted into the coupling penalty relationship to calculate the comprehensive penalty coefficient under the combined action of N motion states. The coupling penalty relationship is a mathematical expression pre-fitted using experimental data; its input is the penalty coefficient of each motion parameter, and its output is a comprehensive penalty coefficient. This expression typically includes a linear term, a product term, and a constant term for each penalty coefficient to capture the nonlinear coupling effect between different motion states.

[0119] In this embodiment, after obtaining the comprehensive penalty coefficient, the first critical heat flux density is multiplied by the comprehensive penalty coefficient. The resulting product is the target critical heat flux density of the reactor under the combined action of the current N motion states. The target critical heat flux density refers to the maximum heat flux density value that the reactor rod bundle channels can withstand under the motion conditions composed of a specific tilt amplitude, sway amplitude, sway period, fluctuation acceleration, and fluctuation period. This value is an important basis for determining the reactor's thermal safety design and operating limits. When the actual operating heat flux density approaches or exceeds the target critical heat flux density, it indicates that the reactor is in a dangerous state and corresponding safety protection measures need to be taken.

[0120] This embodiment calculates the combined effect by substituting the penalty coefficients of each individual motion parameter into the coupling penalty relationship. This fully incorporates the independent effects, intra-state interaction effects, and inter-state interaction effects when multiple motion states act together, avoiding the prediction distortion caused by simply superimposing the independent effects while ignoring the complex coupling relationship. Based on the verified coupling penalty relationship structure, it ensures that the comprehensive penalty coefficient can accurately reflect the real physical laws when multiple factors act together, providing a reliable penalty coefficient basis for determining the final critical heat flux density, and ensuring the prediction accuracy and reliability of the final target critical heat flux density.

[0121] This application embodiment obtains the first critical heat flux density (CHF) of the reactor in a static state and the second critical heat flux density in at least one motion state such as tilting, rocking, and undulating, and calculates the penalty coefficient corresponding to each motion state. Then, it establishes a penalty relationship between the parameter value and the penalty coefficient for each motion parameter. When multiple motion states act together, a coupled penalty relationship is further constructed, that is, a comprehensive penalty coefficient is obtained by using the penalty coefficients of each motion parameter through a linear combination including product terms. Finally, the first critical heat flux density in the static state is multiplied by this comprehensive penalty coefficient to obtain the target critical heat flux density of the reactor under complex motion environments. This application embodiment, by systematically obtaining critical heat flux density data under motion states, constructing a penalty relationship between a single motion parameter and a single motion condition, and then establishing a coupled penalty relationship for multiple motion states, effectively quantifies the weakening effect of different motion parameters and their cross-coupling on CHF. It solves the problem of large CHF prediction deviation in traditional methods under complex motion conditions, significantly improves prediction accuracy, provides a reliable thermal safety limit assessment basis for the safe operation of the reactor under complex motion environments such as tilting, rocking, and undulating, and enhances the applicability of engineering applications.

[0122] Please see Figure 3 This application also provides a reactor critical heat flux density determination device 500, which can implement the above-described reactor critical heat flux density determination method. The device includes: The acquisition module 10 is used to acquire the first critical heat flux density of the rod bundle channel when the reactor is in a static state, and the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state, including tilting, swaying or undulating states. The calculation module 20 is used to calculate the penalty coefficient for each of the motion states based on the first critical heat flux density and the second critical heat flux density. The construction module 30 is used to construct a penalty relationship between the motion parameters in the motion state and the motion parameters in the motion state for each motion state, wherein the penalty relationship is the correspondence between the parameter value of the motion parameter and the penalty coefficient under the motion parameter; The determination module 40 is used to determine the target critical heat flux density of the reactor based on the penalty relationship in each of the said motion states and the first critical heat flux density.

[0123] The specific implementation of the device for determining the critical heat flux density of the reactor is basically the same as the specific implementation of the method for determining the critical heat flux density of the reactor described above, and will not be repeated here.

[0124] The method and apparatus for determining the critical heat flux density of a reactor provided in this application first obtain the first critical heat flux density of the reactor in a static state, and the second critical heat flux density in at least one motion state such as tilting, swaying, or undulating. By calculating the ratio of the first to the second critical heat flux density, a penalty coefficient corresponding to each motion state is obtained. Then, for each motion state, a correspondence between the motion parameters and the penalty coefficient is established, i.e., a penalty relationship. Finally, based on the constructed penalty relationship and the first critical heat flux density in the static state, the target critical heat flux density of the reactor in the motion state is determined. By introducing a penalty coefficient and a penalty relationship, this application can quantitatively characterize the influence of motion parameters on the critical heat flux density, thereby achieving accurate prediction of the reactor's critical heat flux density under motion conditions, effectively improving the reliability and accuracy of the prediction results.

[0125] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0126] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0129] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0130] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0132] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for determining the critical heat flux density of a reactor, characterized in that, The method includes: The first critical heat flux density of the rod bundle channel when the reactor is in a static state and the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state, including tilting, rocking or undulating states. The penalty coefficient for each of the motion states is calculated based on the first critical heat flux density and the second critical heat flux density. For each of the aforementioned motion states, a penalty relationship is constructed for the motion parameters under the aforementioned motion state based on the penalty coefficient and the motion parameters under the aforementioned motion state. The penalty relationship is the correspondence between the parameter value of the motion parameter and the penalty coefficient under the aforementioned motion parameter. The target critical heat flux density of the reactor is determined based on the penalty relationship for each of the aforementioned motion states and the first critical heat flux density.

2. The method according to claim 1, characterized in that, Determining the target critical heat flux density of the reactor based on the penalty relationship under each of the aforementioned motion states and the first critical heat flux density includes: Based on the penalty relationship of motion parameters under N motion states, a coupled penalty relationship is constructed for N motion states. The coupled penalty relationship is the correspondence between the comprehensive penalty coefficient under the combined action of motion parameters corresponding to N motion states and the penalty coefficient under each motion state, where N is an integer greater than or equal to 2. The target critical heat flux density of the reactor is determined based on the coupling penalty relationship under the N motion states and the first critical heat flux density.

3. The method according to claim 1, characterized in that, Obtaining the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state includes: For each of the aforementioned motion parameters, the critical heat flux density corresponding to the motion parameter is obtained from a single motion parameter database, which is used to record the critical heat flux density corresponding to the single motion parameter under the condition that only the parameter value of the single motion parameter is changed and the parameter values ​​of the other motion parameters are all zero. For each of the aforementioned motion states, the critical heat flux density corresponding to the motion state is obtained from the single motion condition database. The single motion condition database is used to record the critical heat flux density corresponding to the single motion state under the condition that the parameter values ​​of multiple motion parameters of a single motion state are changed only, and the parameter values ​​of motion parameters of other motion states remain non-zero fixed values. The critical heat flux density corresponding to each of the aforementioned motion parameters and the critical heat flux density corresponding to each of the aforementioned motion states are used as the second critical heat flux density.

4. The method according to claim 1, characterized in that, The calculation of the penalty coefficient for each motion state based on the first critical heat flux density and the second critical heat flux density includes: For each of the motion states, the second critical heat flux density in the motion state is divided by the first critical heat flux density in the motion state to obtain the penalty coefficient for the motion state.

5. The method according to claim 1, characterized in that, The step of constructing a penalty relationship between the motion parameters in the motion state and the penalty coefficient in the motion state includes: For each motion parameter in the motion state, obtain multiple penalty coefficients corresponding to different parameter values ​​of the motion parameter; and, The penalty relationship of the motion parameters is obtained by fitting the relationship between different parameter values ​​of the motion parameters and multiple penalty coefficients.

6. The method according to claim 5, characterized in that, The step of fitting the relationship between different values ​​of the motion parameters and multiple penalty coefficients to obtain the penalty relationship of the motion parameters includes: Multiple fitting methods are used to fit different parameter values ​​of the motion parameters to the corresponding penalty coefficients to obtain candidate penalty relationships for each fitting method; For each of the motion parameters, the different parameter values ​​are substituted into the candidate penalty relationship corresponding to each of the fitting methods for calculation, to obtain the predicted value of the penalty coefficient corresponding to different parameter values ​​under each candidate penalty relationship; and, For each parameter value under each candidate penalty relationship, the ratio of the predicted value of the penalty coefficient corresponding to the parameter value to the measured value of the penalty coefficient corresponding to the parameter value is taken as the first predicted-to-measured ratio of the penalty coefficient corresponding to the parameter value. For each of the motion parameters, the mean and standard deviation of the multiple first predicted measured ratios corresponding to each candidate penalty relationship are calculated, and the candidate penalty relationship with the mean within a first preset range and the smallest standard deviation is selected as the penalty relationship for the motion parameter.

7. The method according to claim 6, characterized in that, The step of fitting the relationship between different values ​​of the motion parameters and multiple penalty coefficients to obtain the penalty relationship of the motion parameters includes: If the mean of all candidate penalty relationships is outside the first preset range, then the candidate penalty relationship with the smallest standard deviation is selected as the penalty relationship for the motion parameters, and the value of the constant term in the candidate penalty relationship is adjusted so that the adjusted mean falls within the first preset range.

8. The method according to claim 2, characterized in that, The step of constructing a coupling penalty relationship for N motion states based on the penalty relationship of motion parameters under N motion states includes: Multiple motion parameters are combined according to different parameter values ​​to form multiple sets of motion conditions; Using each set of motion conditions as input and the measured value of the penalty coefficient under each set of motion conditions as output, a function relationship is fitted to obtain the initial coupling function relationship; For each set of motion conditions, the penalty coefficient corresponding to each motion parameter in the motion condition is substituted into the initial coupling function relationship for calculation to obtain the predicted value of the comprehensive penalty coefficient corresponding to the motion condition; and, The ratio of the predicted value of the comprehensive penalty coefficient corresponding to the motion condition to the measured value of the comprehensive penalty coefficient corresponding to the motion condition is used as the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to the motion condition. Based on the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to each of the motion conditions, the initial coupling function relationship is adjusted to obtain the coupling penalty relationship under N motion conditions.

9. The method according to claim 8, characterized in that, The motion parameters include: tilt angle in tilt state, sway angle and sway period in swing state, and undulation acceleration and undulation period in undulation state. The process of fitting a function relationship using each set of motion conditions as input and the measured value of the penalty coefficient under each set of motion conditions as output to obtain an initial coupling function relationship includes: Obtain the penalty relationship for tilt angle in tilt state, penalty relationship for sway angle and penalty relationship for sway period in sway state, and penalty relationship for undulation acceleration and penalty relationship for undulation period in undulation state for each set of motion conditions. Construct a swing state coupling penalty relationship, which is a linear combination of the penalty coefficient of the swing angle, the penalty coefficient of the swing period, and the product of the swing angle and the swing period; Construct a fluctuation state coupling penalty relationship, which is a linear combination of the penalty coefficient of the fluctuation acceleration, the penalty coefficient of the fluctuation period, and the product of the penalty coefficient of the fluctuation acceleration and the penalty coefficient of the fluctuation period. The initial coupling function relationship is constructed by linearly combining the penalty coefficient of the tilt angle, the coupling penalty relationship of the swaying state, the coupling penalty relationship of the fluctuation state, the product of the penalty coefficient of the tilt angle and the coupling penalty relationship of the swaying state, the product of the penalty coefficient of the tilt angle and the coupling penalty relationship of the fluctuation state, the product of the coupling penalty relationship of the swaying state and the coupling penalty relationship of the fluctuation state, and a constant term. The coefficients in the linear combination are determined by the least squares method.

10. The method according to claim 8, characterized in that, The initial coupling function relationship is adjusted based on the second predicted-to-measured ratio of the comprehensive penalty coefficient corresponding to each of the motion conditions to obtain the coupling penalty relationship under N motion states, including: The mean of the second predicted-measured ratio of the comprehensive penalty coefficient corresponding to each of the aforementioned motion conditions is calculated. If the mean is less than the lower limit of the second preset range, the value of the constant term in the initial coupling function relationship is increased until the mean is within the second preset range, thereby obtaining the coupling penalty relationship. If the mean is greater than the upper limit of the second preset range, then the value of the constant term in the initial coupling function relationship is reduced until the mean is within the second preset range, thus obtaining the coupling penalty relationship.

11. The method according to claim 2, characterized in that, The step of determining the target critical heat flux density of the reactor based on the coupling penalty relationship under N motion states and the first critical heat flux density includes: For each of the motion states, each motion parameter in the motion state is substituted into the penalty relationship corresponding to each motion parameter to obtain the penalty coefficient for each motion parameter; and, Substituting the penalty coefficient for each of the aforementioned motion parameters into the coupling penalty relationship, a comprehensive penalty coefficient for N motion states is obtained; and, The product of the first critical heat flux density and the comprehensive penalty coefficient is taken as the target critical heat flux density.

12. A device for determining the critical heat flux density of a reactor, characterized in that, The device includes: The acquisition module is used to acquire the first critical heat flux density of the rod bundle channel when the reactor is in a static state, and the second critical heat flux density of the rod bundle channel when the reactor is in at least one motion state, including tilting, swaying or undulating states. The calculation module is used to calculate the penalty coefficient for each of the motion states based on the first critical heat flux density and the second critical heat flux density. A construction module is used to construct a penalty relationship between the motion parameters in each motion state, based on the penalty coefficient in the motion state and the motion parameters in the motion state. The penalty relationship is the correspondence between the parameter value of the motion parameter and the penalty coefficient under the motion parameter. The determination module is used to determine the target critical heat flux density of the reactor based on the penalty relationship in each of the said motion states and the first critical heat flux density.

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