Power tilt prediction method, apparatus, device, storage medium and program product

Abstract

This application relates to a power tilt prediction method, apparatus, device, storage medium, and program product. The method includes: first, acquiring the measured activity at any burnup level during core operation; then, iteratively solving for the optimal water gap parameters based on the measured activity and a pre-established objective function; and finally, predicting the power tilt based on the optimal water gap parameters to obtain the target tilt factor. This method can obtain the trend of quadrant power tilt with burnup and power level, thereby quantitatively predicting the core quadrant power tilt factor. It offers good prediction results, avoids the arbitrariness of manual adjustments, has high reliability, and allows for early intervention in the unit's operating plan, improving the safety of unit operation.

Description

Technical Field

[0001] This application relates to the field of pressurized water reactor core operation and safety technology, and in particular to a power tilt prediction method, apparatus, equipment, storage medium and program product. Background Technology

[0002] In the daily operation of pressurized water reactor nuclear power plants, the fuel assembly arrangement of the reactor core is usually 1 / 4 rotationally symmetrical. However, the actual measured power distribution of the core is not 1 / 4 symmetrical. When the asymmetry of the actual power distribution of the core exceeds a certain limit, the local hot spots of the core will exceed the design limit, causing potential safety risks.

[0003] Currently, the water gap parameters are typically adjusted manually, and power tilt is then predicted based on these manually adjusted parameters. However, manual adjustment is arbitrary, resulting in poor prediction performance and low reliability. Summary of the Invention

[0004] Therefore, it is necessary to provide a power tilt prediction method, apparatus, device, storage medium, and program product to address the aforementioned technical problems.

[0005] Firstly, this application provides a power tilt prediction method. The method includes:

[0006] To obtain the measured activity at any burnup level during core operation;

[0007] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0008] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0009] In one embodiment, the above-mentioned iterative solution based on activity and a pre-established objective function to obtain the optimal water gap parameters includes:

[0010] The initial water gap parameters are input into the objective function as the water gap parameters to be optimized, and the maximum difference between the theoretical power distribution and the measured power distribution is obtained.

[0011] If the maximum difference meets the preset constraints, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

[0012] In one embodiment, the method further includes:

[0013] If the maximum difference does not meet the preset constraints, the parameters of the water gap to be optimized will be adjusted to update the parameters of the water gap to be optimized.

[0014] The updated water gap parameters to be optimized are input into the objective function for iterative calculation.

[0015] In one embodiment, the method further includes:

[0016] Obtain the reaction cross-section parameters under any fuel consumption condition; wherein, the reaction cross-section parameters are taken as the state variables of the water gap parameters to be optimized.

[0017] The theoretical power distribution is obtained by solving the neutron diffusion equation based on the reaction cross-section parameters.

[0018] In one embodiment, the measured power distribution is determined based on the theoretical power distribution and the measured activity, including:

[0019] The correction factor is determined using the previously obtained theoretical activity and measured activity;

[0020] The theoretical power distribution is corrected using a correction factor to obtain the measured power distribution.

[0021] In one embodiment, the above-mentioned power tilt prediction based on optimal water gap parameters to obtain the target tilt factor includes:

[0022] Based on the optimal water gap parameters, the next step is to calculate the power distribution of burnup to obtain the average component power of the core and the average component power of each quadrant of the core.

[0023] The target tilt factor for each quadrant of the reactor core is determined based on the average component power in each quadrant of the reactor core.

[0024] Secondly, this application also provides a power tilt prediction device. The device includes:

[0025] The activity acquisition module is used to acquire the measured activity at any burnup level during core operation;

[0026] The calculation module is used to iteratively solve for the optimal water gap parameters based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0027] The prediction module is used to predict power tilt based on the optimal water gap parameters and obtain the target tilt factor.

[0028] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0029] To obtain the measured activity at any burnup level during core operation;

[0030] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0031] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0032] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0033] To obtain the measured activity at any burnup level during core operation;

[0034] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0035] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0036] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0037] To obtain the measured activity at any burnup level during core operation;

[0038] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0039] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0040] The aforementioned power tilt prediction method, apparatus, equipment, storage medium, and program products first acquire the measured activity at any burnup level during core operation. Then, they iteratively solve for the optimal water gap parameters based on the measured activity and a pre-established objective function. Finally, they predict the power tilt based on the optimal water gap parameters to obtain the target tilt factor. Through the embodiments of this application, the optimal water gap parameters can be obtained through iterative solving based on the measured activity and a pre-established objective function, avoiding the arbitrariness of manual adjustments and ensuring high reliability. Furthermore, predicting the power tilt based on the optimal water gap parameters to obtain the target tilt factor yields better prediction results. Since the power tilt limit varies with the power level, changing the power level can alter the power tilt margin, thereby allowing for early intervention in the unit's operation plan and improving the safety of unit operation. Attached Figure Description

[0041] Figure 1 Here is a flowchart of a power tilt prediction method in one embodiment;

[0042] Figure 2 This is one of the flowcharts for determining the optimal water gap parameters in one embodiment;

[0043] Figure 3 This is a second flowchart of the steps for determining the optimal water gap parameters in one embodiment;

[0044] Figure 4 This is a flowchart for obtaining the theoretical power distribution in one embodiment;

[0045] Figure 5 This is a schematic diagram of the water gap parameters between fuel assemblies in one embodiment;

[0046] Figure 6 This is a flowchart of obtaining the target tilt factor in one embodiment;

[0047] Figure 7 This is a schematic diagram of the core partitioning in one embodiment;

[0048] Figure 8 A flowchart of a power tilt prediction method in another embodiment;

[0049] Figure 9 This is one of the structural diagrams of the power tilt prediction device in one embodiment;

[0050] Figure 10 This is a structural diagram of the computing module in one embodiment;

[0051] Figure 11 This is a second structural diagram of the power tilt prediction device in one embodiment;

[0052] Figure 12Structural diagram of the prediction module in an embodiment

[0053] Figure 13 Internal structural diagram of a computer device in an embodiment Detailed implementation manners

[0054] To make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application

[0055] In one embodiment, a power tilt prediction method is provided. As Figure 1 shown, taking the application of this method to a computer device as an example, it can be understood that the computer device can be a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The embodiments of the present application include the following steps

[0056] Step 101: Obtain the measured activity at any burnup during the operation of the reactor core

[0057] Among them, the reactor core refers to the area composed of nuclear fuel elements where the chain fission reaction takes place, and is composed of nuclear fuel assemblies, control rod assemblies, and water that serves as both a neutron moderator and a coolant; burnup refers to the degree of consumption of nuclear fuel during the operation of the reactor; the measured activity characterizes the measure of the nuclear reaction between the detector and the reactor core neutrons, indicating the level of local power

[0058] The detector measures the measured activity of the nuclear fuel at any burnup during the operation of the reactor core, and then the computer device obtains this measured activity. The embodiments of the present application do not limit the type of the detector

[0059] For example, the measured activity A M,n (k) of the nuclear fuel at any burnup during the operation of the reactor core is measured by using a miniature fission chamber detector, where k = 1, 2,.., K (K < NFA), and then the computer device obtains this measured activity A M,n (k), where n is the burnup point identifier, k is the assembly identifier, K is the number of fuel assemblies capable of activity measurement, and NFA is the total number of fuel assemblies in the reactor core

[0060] Step 102: Perform iterative solution according to the measured activity and a pre-established objective function to obtain the optimal water gap parameter

[0061] The objective function aims to maximize the difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized. The theoretical power distribution refers to the power distribution theoretically calculated for the fuel assembly under any burnup condition; the measured power distribution refers to the power distribution actually measured for the fuel assembly under any burnup condition.

[0062] The computer equipment first calculates the objective function based on the measured activity and the objective function with the maximum difference between the theoretical power distribution and the measured power distribution as the objective function. The objective function is constrained by the condition that it is less than or equal to the set accuracy value. The objective function is then iteratively solved to finally obtain the optimal water gap parameters.

[0063] For example, the activity is measured as A M,n (k), the theoretical power distribution is P C,n (i,j), the measured power distribution is P M,n (i,j), the objective function is E=max(P C,n (i,j)-P M,n (i,j)), with the objective function E=max(P C,n (i,j)-P M,n The constraint condition is that the accuracy value ε is less than or equal to the set accuracy value. The objective function is then iteratively solved. Since the theoretical power distribution is related to the water gap parameter, and the measured power distribution is determined based on the theoretical power distribution and the measured activity, both the theoretical and measured power distributions can be converted into expressions including the water gap parameter. Furthermore, the objective function can also be converted into an expression including the water gap parameter. Thus, after iteratively solving the objective function, the optimal water gap parameter h can be obtained. n (i,j), where i=1,2,...I, j=1,2,...,J, and I and J are the number of rows and columns of the core rectangle arrangement.

[0064] Step 103: Based on the optimal water gap parameters, perform power tilt prediction to obtain the target tilt factor.

[0065] Among them, power tilt refers to the uneven power distribution of fuel assemblies in the four quadrants, and the tilt factor can characterize the uneven power distribution.

[0066] The computer equipment obtains the average component power of the reactor core and the average component power of the largest quadrant in the reactor core based on the optimal water gap parameters. It then uses the ratio of the average component power of the largest quadrant in the reactor core to the average component power of the reactor core to calculate the target tilt factor as the predicted value of power tilt.

[0067] For example, based on the optimal water gap parameter h n (i,j), obtain the average component power of the stack core. and the average component power in the largest quadrant of the core Utilizing the average component power of the largest quadrant in the reactor core With the average component power of the core ratio The target tilt factor is calculated as a predicted value for power tilt. Understandably, the target tilt factor is determined by the average component power in the largest quadrant of the reactor core. With the average component power of the core ratio The calculated average component power when the largest quadrant is changed. or average component power of the core Then, the target tilt factor can be changed.

[0068] In the aforementioned power tilt prediction method, the measured activity under any burnup during core operation is first obtained. Then, the optimal water gap parameters are obtained by iteratively solving based on the measured activity and a pre-established objective function. Finally, power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor. Through the embodiments of this application, relatively accurate water gap parameters can be obtained through iterative solving. Since the water gap parameters to be optimized can determine the theoretical power distribution, and the measured power distribution is obtained from the activity and the theoretical power distribution, the optimal water gap parameters can be obtained. Then, power tilt prediction is performed using the solved optimal water gap parameters, which can achieve better prediction results, avoid the arbitrariness of manual adjustment, and has high reliability. Since the quadrant power tilt changes with the power level, the quadrant power tilt can be changed by changing the power level, thereby intervening in the unit's operation plan in advance and improving the safety of unit operation.

[0069] In one embodiment, such as Figure 2 As shown, the process of iteratively solving for the optimal water gap parameters based on activity and a pre-established objective function can include the following steps:

[0070] Step 201: Input the initial water gap parameters as the water gap parameters to be optimized into the objective function to obtain the maximum difference between the theoretical power distribution and the measured power distribution.

[0071] The computer equipment inputs the initial water gap parameters as the parameters to be optimized into the objective function. That is, the initial values ​​of the water gap parameters are input into the objective function, and the maximum value of the difference between the theoretical power distribution and the measured power distribution is obtained based on the difference in the objective function. This maximum value of the difference is the maximum difference between the theoretical power distribution and the measured power distribution.

[0072] For example, the initial water gap parameter d(i,j)=d0 is input as the water gap parameter to be optimized into the objective function E=max(P C,n (i,j)-P M，nIn (i,j)), according to the objective function, the theoretical power distribution P C,n (i,j) and the measured power distribution P M,n The difference (P) between (i,j) C,n (i,j)-P M，n (i,j)), find the difference (P) C,n (i,j)-P M，n The maximum value of (i,j)).

[0073] Step 202: If the maximum difference meets the preset constraint conditions, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

[0074] Among them, the constraint condition refers to the objective function being less than or equal to the set precision value.

[0075] After the computer equipment inputs the water gap parameters to be optimized into the objective function, if the maximum difference between the obtained theoretical power distribution and the measured power distribution meets the preset constraint conditions, the iterative calculation can be terminated, and the water gap parameters to be optimized at this time can be determined as the optimal water gap parameters.

[0076] For example, if the maximum difference between the theoretical power distribution and the measured power distribution obtained after inputting the initial water gap parameters as the water gap parameters to be optimized into the objective function is less than or equal to the set accuracy value, then the iterative calculation can be ended, and the water gap parameters to be optimized at this time can be determined as the optimal water gap parameters, that is, the initial water gap parameters can be determined as the optimal water gap parameters.

[0077] In the above embodiments, the initial water gap parameters are first input into the objective function as the water gap parameters to be optimized, and the maximum difference between the theoretical power distribution and the measured power distribution is obtained. Then, it is determined whether the maximum difference meets the preset constraints. If the maximum difference meets the preset constraints, the iterative calculation ends, and the water gap parameters to be optimized at the end of the iterative calculation are determined as the optimal water gap parameters. Through the embodiments of this application, by establishing the objective function and constraints, the water gap parameters to be optimized can be optimized, and the optimal water gap parameters can be obtained. This narrows the gap between the theoretical power distribution and the measured power distribution, thereby achieving the optimization purpose and improving the reliability of predicting the core quadrant power tilt factor.

[0078] In one embodiment, such as Figure 3 As shown, embodiments of this application may further include the following steps:

[0079] Step 203: If the maximum difference does not meet the preset constraints, the water gap parameters to be optimized are adjusted to update the water gap parameters to be optimized.

[0080] When the computer determines that the maximum difference between the theoretical power distribution and the measured power distribution is greater than the set accuracy value, it will adjust the water gap parameters to be optimized in order to update the water gap parameters to be optimized.

[0081] Step 204: Input the updated water gap parameters to be optimized into the objective function for iterative calculation.

[0082] If the maximum difference does not meet the preset constraints, the computer equipment adjusts the parameters of the water gap to be optimized to update the parameters of the water gap to be optimized, and inputs the updated parameters of the water gap to be optimized into the objective function for iterative calculation until the maximum difference between the theoretical power distribution and the measured power distribution meets the preset constraints. The iterative calculation ends, and the parameters of the water gap to be optimized at the end of the iterative calculation are determined as the optimal parameters of the water gap.

[0083] For example, the initial water gap parameter is input into the objective function as the water gap parameter to be optimized, d0. If the maximum difference between the theoretical power distribution and the measured power distribution does not meet the preset constraint, the water gap parameter to be optimized, d0, is adjusted to obtain an updated water gap parameter to be optimized, d1. The updated water gap parameter to be optimized, d1, is then input into the objective function. If the maximum difference between the theoretical power distribution and the measured power distribution at this point meets the preset constraint, the iterative calculation ends, and the optimized water gap parameter d1 at this point is determined to be the optimal water gap parameter. If the maximum difference between the theoretical power distribution and the measured power distribution at this point does not meet the preset constraint, the water gap parameter to be optimized, d1, is adjusted to obtain an updated water gap parameter to be optimized, d2. The updated water gap parameter to be optimized, d2, is then input into the objective function. The above steps are repeated until the maximum difference between the theoretical power distribution and the measured power distribution meets the preset constraint, at which point the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined to be the optimal water gap parameter.

[0084] In the above embodiments, it is first determined that the maximum difference does not meet the preset constraints, and the initial water gap parameters are adjusted; then the adjusted water gap parameters are input into the objective function to continue iterative calculation. Through the embodiments of this application, by establishing the objective function and constraints, the water gap parameters can be optimized, and the optimal water gap parameters can be obtained, thus narrowing the gap between the theoretical power distribution and the measured power distribution, thereby achieving the optimization purpose and improving the reliability of predicting the core quadrant power tilt factor.

[0085] In the process of iterative calculation by inputting the water gap parameters to be optimized into the objective function, the water gap parameters input in each subsequent iteration are obtained by adjusting the water gap parameters input in the previous iteration. Therefore, in multiple iterations, the input water gap parameters are different each time. Since the theoretical power distribution is determined based on the water gap parameters to be optimized, it can be understood that in multiple iterations, the theoretical power distribution corresponds to the water gap parameters input in the current iteration.

[0086] Taking the acquisition of the theoretical power distribution corresponding to the water gap parameters to be optimized during a certain iteration as an example, the following explanation is provided. Specifically, in one embodiment, such as... Figure 4 As shown, embodiments of this application may further include the following steps:

[0087] Step 301: Obtain the reaction cross-section parameters under any fuel consumption condition.

[0088] Among them, the reaction cross section parameter is a parameter that measures the magnitude of the nuclear reaction between neutrons and matter, and the reaction cross section parameter uses the water gap parameter to be optimized as the state variable.

[0089] The computer equipment uses a standard width as a reference. When the water gap parameter to be optimized is the standard width, the obtained reaction section parameter is used as the reference section. When the water gap parameter to be optimized is higher or lower than the standard width, the reaction section parameter is obtained by superimposing a water gap-related correction amount onto the reference section. The correction amount can be positive or negative.

[0090] For example, such as Figure 5 As shown, with the standard width as the reference, when the water gap parameter to be optimized is the standard width, the reaction section parameter Σ is the reference section Σ0 determined by parameters such as fuel temperature and water density. When the water gap parameter to be optimized is higher or lower than the standard width, the reaction section parameter Σ is obtained by superimposing a water gap-related correction amount ΔΣ(d) on the reference section parameter Σ0. The correction amount can be positive or negative.

[0091] Step 302: Solve the neutron diffusion equation based on the reaction cross-section parameters to obtain the theoretical power distribution.

[0092] The neutron diffusion equation is a second-order partial differential equation that describes the nuclear reaction of neutrons in a medium.

[0093] The reaction cross-section parameters are taken as the water gap parameters to be optimized as state variables. The computer equipment determines the reaction cross-section parameters based on the water gap parameters to be optimized, and then obtains the theoretical power distribution by numerically solving the neutron diffusion equation through the reaction cross-section parameters.

[0094] In the above embodiments, the reaction cross-section parameters under any burnup condition are first obtained; wherein, the reaction cross-section parameters are taken as the state variables of the water gap parameters to be optimized; then, the neutron diffusion equation is solved based on the reaction cross-section parameters to obtain the theoretical power distribution. Through the embodiments of this application, the relationship between the water gap parameters to be optimized and the theoretical power distribution can be determined, establishing the necessary conditions for obtaining the measured power distribution and the optimal water gap parameters in the later stages.

[0095] In one embodiment, the step of determining the measured power distribution based on the theoretical power distribution and the measured activity may include: determining a correction factor using the pre-acquired theoretical activity and measured activity; and correcting the theoretical power distribution using the correction factor to obtain the measured power distribution.

[0096] After the computer equipment obtains the theoretical activity and the measured activity and determines the correction factor, it uses the correction factor to correct the theoretical power distribution and obtain the measured power distribution.

[0097] For example, obtaining the measured activity A M,n (k) and theoretical activity A C,n After (k), determine the ratio A of the measured activity to the theoretical activity. M,n (k) / A C,n (k) is the correction factor, and then the correction factor A is used. M,n (k) / A C,n (k) and theoretical power distribution P C,n The product of (i,j) yields the measured power distribution P. M,n (i,j)=(A M,n (k) / A C,n (k))*P C,n (i,j).

[0098] In the above embodiments, after calculating the theoretical activity and measured activity to determine the correction factor, the theoretical power distribution is then corrected using the correction factor to obtain the measured power distribution. Through the embodiments of this application, a more accurate measured power distribution can be obtained based on the correction factor to the theoretical power distribution, thus laying the groundwork for establishing the objective function and facilitating the subsequent acquisition of the optimal water gap parameters.

[0099] In one embodiment, such as Figure 6 As shown, the process of predicting power tilt based on optimal water gap parameters and obtaining the target tilt factor can include the following steps:

[0100] Step 401: Based on the optimal water gap parameters, perform the next step of power distribution calculation for burnup to obtain the average component power of the reactor core and the average component power of each quadrant of the reactor core.

[0101] The computer equipment, based on the optimal water gap parameters, performs the next burnup calculation to obtain the corresponding power distribution, and then obtains the average assembly power of the reactor core and the average assembly power of each quadrant of the reactor core. The next burnup calculation refers to the process of calculating the assembly burnup distribution for the next step based on the power distribution obtained from the optimal water gap parameters and the fuel assembly burnup distribution under any given burnup. After obtaining the assembly burnup distribution for the next step, the power distribution can be updated based on the assembly burnup distribution on both sides of the equation and the assembly burnup distribution for the next step.

[0102] For example, based on the optimal water gap parameter h n The power distribution P corresponding to (i,j) n (i,j) are used to perform the next fuel consumption calculation, and the corresponding power distribution P is obtained. n+1 (i,j), then obtain the average component power of the core. and the average component power in each quadrant of the core The next step, fuel consumption calculation, refers to the power distribution P obtained based on the optimal water gap parameters. n (i,j) and the fuel consumption distribution of the fuel assembly under any fuel consumption condition. n (i,j), obtain the component fuel consumption distribution bu for the next step. n+1 The calculation process of (i,j), where bu n+1 (i,j)=bu n (i,j)+t / m×(P n (i,j)+P n+1 (i,j)) / 2. This yields the component fuel consumption distribution bu for the next step. n+1 After (i,j), the power distribution P can be updated by solving the neutron diffusion equation. n+1 (i,j). Where t is the equivalent full-power operating time, m is the uranium loading, and n is the burnup point indicator.

[0103] Step 402: Determine the target tilt factor for each quadrant of the core based on the average component power in each quadrant of the core and the average component power in the core.

[0104] The computer equipment obtains the target tilt factor for each quadrant of the reactor core based on the ratio of the average component power in each quadrant to the average component power in the reactor core.

[0105] For example, using the average component power of the largest quadrant in the core. With the average component power of the core The target tilt factor is obtained by the ratio of the two values, where the denominator of the target tilt factor is the average component power of the reactor core. Average component power of molecules in the largest quadrant Among them, the average component power of the core It is the power distribution P of each componentn+1 The result is obtained by adding (i,j) together and then dividing by the total number of components. like Figure 7 As shown, when i = 1, 2, ... 4, it indicates that the core is divided in a "+" shape; when i = 1 = 5, 6, ... 8, it indicates that the core is divided in a "×" shape.

[0106] In the above embodiments, the average component power of the reactor core and the average component power of each quadrant of the reactor core are first obtained. Then, based on the ratio of the average component power of each quadrant of the reactor core to the average component power of the reactor core, the target tilt factor of each quadrant of the reactor core is obtained. Through the embodiments of this application, the trend of quadrant power tilt with power level can be obtained. Since quadrant power tilt changes with power level, the quadrant power tilt can be changed by changing the power level, thereby intervening in the unit's operation plan in advance and improving the safety of unit operation.

[0107] In one embodiment, for example Figure 8 As shown, a power tilt prediction method is provided. Embodiments of this application may include the following steps:

[0108] Step 501: Obtain the measured activity under any burnup condition during core operation.

[0109] Step 502: Obtain the reaction cross-section parameters under any burnup condition; solve the neutron diffusion equation based on the reaction cross-section parameters to obtain the theoretical power distribution.

[0110] Among them, the reaction section parameters are taken as the water gap parameters to be optimized as state variables.

[0111] Step 503: Determine the correction factor using the pre-acquired theoretical activity and measured activity; correct the theoretical power distribution using the correction factor to obtain the measured power distribution.

[0112] Step 504: Input the initial water gap parameters as the water gap parameters to be optimized into the objective function to obtain the maximum difference between the theoretical power distribution and the measured power distribution.

[0113] Step 505: If the maximum difference does not meet the preset constraint conditions, the water gap parameters to be optimized are adjusted to update the water gap parameters to be optimized, and the updated water gap parameters to be optimized are input into the objective function for iterative calculation.

[0114] Step 506: If the maximum difference meets the preset constraint conditions, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

[0115] Step 507: Based on the optimal water gap parameters, perform the next step of power distribution calculation for burnup to obtain the average component power of the reactor core and the average component power of each quadrant of the reactor core.

[0116] Step 508: Determine the target tilt factor for each quadrant of the core based on the average component power in each quadrant of the core and the average component power in the core.

[0117] In the above embodiments, the measured activity under any burnup during core operation is first obtained to determine the correspondence between the water gap parameter to be optimized and the theoretical power distribution. A correction factor is determined using the pre-obtained theoretical and measured activities, and then the theoretical power distribution is corrected using the correction factor to obtain the measured power distribution. Next, the initial water gap parameter is input into the objective function as the water gap parameter to be optimized, and the maximum difference between the theoretical and measured power distribution is obtained. The optimal water gap parameter is obtained when the maximum difference meets the constraint conditions. Based on the optimal water gap parameter, the power distribution calculation for the next burnup stage is performed to obtain the average component power of the core and the average component power of each quadrant of the core. Finally, the target tilt factor for each quadrant of the core is determined based on the average component power of each quadrant and the average component power of the core. A suitable water gap parameter can be obtained through iterative calculation. Therefore, using the optimal water gap parameter for power tilt prediction can achieve better prediction results. Furthermore, since the tilt factor can characterize the trend of quadrant power tilt with power level, the problem of quadrant power tilt can be changed according to the tilt factor, thereby interfering with the unit's operation plan in advance and improving the safety of unit operation.

[0118] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0119] Based on the same inventive concept, this application also provides a power tilt prediction device for implementing the power tilt prediction method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more power tilt prediction device embodiments provided below can be found in the limitations of the power tilt prediction method described above, and will not be repeated here.

[0120] In one embodiment, such as Figure 9As shown, a power tilt prediction device is provided, comprising: an activity acquisition module, a calculation module, and a prediction module, wherein:

[0121] The activity acquisition module 601 is used to acquire the measured activity at any burnup level during core operation;

[0122] The calculation module 602 is used to iteratively solve for the optimal water gap parameters based on the measured activity and the pre-established objective function. The objective function is to maximize the difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity. The theoretical power distribution is determined based on the water gap parameters to be optimized.

[0123] The prediction module 603 is used to predict the power tilt based on the optimal water gap parameters and obtain the target tilt factor.

[0124] In one embodiment, such as Figure 10 As shown, the calculation module 602 includes:

[0125] The maximum difference acquisition submodule 6021 is used to input the initial water gap parameters as the water gap parameters to be optimized into the objective function to obtain the maximum difference between the theoretical power distribution and the measured power distribution;

[0126] The optimal parameter acquisition submodule 6022 is used to end the iterative calculation if the maximum difference meets the preset constraint conditions, and to determine the water gap parameter to be optimized at the end of the iterative calculation as the optimal water gap parameter.

[0127] In one embodiment, it further includes:

[0128] The iterative calculation submodule 6023 is used to adjust the water gap parameters to be optimized if the maximum difference does not meet the preset constraints, so as to update the water gap parameters to be optimized; and input the updated water gap parameters to be optimized into the objective function for iterative calculation.

[0129] In one embodiment, such as Figure 11 As shown, the device also includes:

[0130] The reaction section parameter acquisition module 604 is used to acquire the reaction section parameters under any fuel consumption; wherein, the reaction section parameters take the water gap parameter to be optimized as the state variable.

[0131] The theoretical power acquisition module 605 is used to solve the neutron diffusion equation based on the reaction cross-section parameters to obtain the theoretical power distribution.

[0132] In one embodiment, the calculation module 602 is specifically used to determine a correction factor using the pre-acquired theoretical activity and measured activity, and to correct the theoretical power distribution using the correction factor to obtain the measured power distribution.

[0133] In one embodiment, such as Figure 12 As shown, the prediction module 603 includes:

[0134] The power acquisition submodule 6031 is used to perform the next step of power distribution calculation of burnup based on the optimal water gap parameters, and to obtain the average component power of the core and the average component power of each quadrant of the core.

[0135] The factor determination submodule 6032 is used to determine the target tilt factor for each quadrant of the reactor core based on the average component power and the average component power of the reactor core.

[0136] Each module in the aforementioned power tilt prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0137] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores optimal water gap parameters, power distribution, fuel assembly burnup distribution data under any burnup, average assembly power in each quadrant of the reactor core, and average assembly power data of the reactor core. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a power tilt prediction method.

[0138] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0139] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0140] To obtain the measured activity at any burnup level during core operation;

[0141] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0142] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0143] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0144] The initial water gap parameters are input into the objective function as the water gap parameters to be optimized, and the maximum difference between the theoretical power distribution and the measured power distribution is obtained.

[0145] If the maximum difference meets the preset constraints, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

[0146] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0147] If the maximum difference does not meet the preset constraints, the parameters of the water gap to be optimized will be adjusted to update the parameters of the water gap to be optimized.

[0148] The updated water gap parameters to be optimized are input into the objective function for iterative calculation.

[0149] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0150] Obtain the reaction cross-section parameters under any fuel consumption condition; wherein, the reaction cross-section parameters are taken as the state variables of the water gap parameters to be optimized.

[0151] The theoretical power distribution is obtained by solving the neutron diffusion equation based on the reaction cross-section parameters.

[0152] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0153] The correction factor is determined using the previously obtained theoretical activity and measured activity;

[0154] The theoretical power distribution is corrected using a correction factor to obtain the measured power distribution.

[0155] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0156] Based on the optimal water gap parameters, the next step is to calculate the power distribution of burnup to obtain the average component power of the core and the average component power of each quadrant of the core.

[0157] The target tilt factor for each quadrant of the reactor core is determined based on the average component power in each quadrant of the reactor core.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0159] To obtain the measured activity at any burnup level during core operation;

[0160] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0161] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0162] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0163] The initial water gap parameters are input into the objective function as the water gap parameters to be optimized, and the maximum difference between the theoretical power distribution and the measured power distribution is obtained.

[0164] If the maximum difference meets the preset constraints, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

[0165] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0166] If the maximum difference does not meet the preset constraints, the parameters of the water gap to be optimized will be adjusted to update the parameters of the water gap to be optimized.

[0167] The updated water gap parameters to be optimized are input into the objective function for iterative calculation.

[0168] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0169] Obtain the reaction cross-section parameters under any fuel consumption condition; wherein, the reaction cross-section parameters are taken as the state variables of the water gap parameters to be optimized.

[0170] The theoretical power distribution is obtained by solving the neutron diffusion equation based on the reaction cross-section parameters.

[0171] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0172] The correction factor is determined using the previously obtained theoretical activity and measured activity;

[0173] The theoretical power distribution is corrected using a correction factor to obtain the measured power distribution.

[0174] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0175] Based on the optimal water gap parameters, the next step is to calculate the power distribution of burnup to obtain the average component power of the core and the average component power of each quadrant of the core.

[0176] The target tilt factor for each quadrant of the reactor core is determined based on the average component power in each quadrant of the reactor core.

[0177] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0178] To obtain the measured activity at any burnup level during core operation;

[0179] The optimal water gap parameters are obtained by iteratively solving based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity, while the theoretical power distribution is determined based on the water gap parameters to be optimized.

[0180] Power tilt prediction is performed based on the optimal water gap parameters to obtain the target tilt factor.

[0181] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0182] The initial water gap parameters are input into the objective function as the water gap parameters to be optimized, and the maximum difference between the theoretical power distribution and the measured power distribution is obtained.

[0183] If the maximum difference meets the preset constraints, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

[0184] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0185] If the maximum difference does not meet the preset constraints, the parameters of the water gap to be optimized will be adjusted to update the parameters of the water gap to be optimized.

[0186] The updated water gap parameters to be optimized are input into the objective function for iterative calculation.

[0187] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0188] Obtain the reaction cross-section parameters under any neutron burnup; wherein, the reaction cross-section parameters are taken as the water gap parameters to be optimized as state variables;

[0189] The theoretical power distribution is obtained by solving the neutron diffusion equation based on the reaction cross-section parameters.

[0190] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0191] The correction factor is determined using the previously obtained theoretical activity and measured activity;

[0192] The theoretical power distribution is corrected using a correction factor to obtain the measured power distribution.

[0193] In one embodiment, when a computer program is executed by a processor, it performs the following steps:

[0194] Based on the optimal water gap parameters, the next step is to calculate the power distribution of burnup to obtain the average component power of the core and the average component power of each quadrant of the core.

[0195] The target tilt factor for each quadrant of the reactor core is determined based on the average component power in each quadrant of the reactor core.

[0196] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0197] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0198] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power tilt prediction method, characterized in that, The method includes: To obtain the measured activity at any burnup level during core operation; The optimal water gap parameters are obtained by iteratively solving the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity. The theoretical power distribution is determined based on the water gap parameters to be optimized. Based on the optimal water gap parameters, the next step is to calculate the power distribution of burnup to obtain the average component power of the core and the average component power of each quadrant of the core; based on the average component power of each quadrant of the core and the average component power of the core, the target tilt factor of each quadrant of the core is determined.

2. The method according to claim 1, characterized in that, The step of iteratively solving for the optimal water gap parameters based on the activity and a pre-established objective function includes: The initial water gap parameters are input into the objective function as the water gap parameters to be optimized, and the maximum difference between the theoretical power distribution and the measured power distribution is obtained. If the maximum difference meets the preset constraint conditions, the iterative calculation ends, and the water gap parameter to be optimized at the end of the iterative calculation is determined as the optimal water gap parameter.

3. The method according to claim 2, characterized in that, The method further includes: If the maximum difference does not meet the preset constraint conditions, the water gap parameter to be optimized is adjusted to update the water gap parameter to be optimized. The updated water gap parameters to be optimized are input into the objective function for iterative calculation.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Obtain the reaction cross-section parameters under any of the stated fuel consumption conditions; wherein, the reaction cross-section parameters are state variables with the water gap parameters to be optimized. The theoretical power distribution is obtained by solving the neutron diffusion equation based on the reaction cross-section parameters.

5. The method according to claim 1, characterized in that, The measured power distribution is determined based on the theoretical power distribution and the measured activity, including: The correction factor is determined using the pre-obtained theoretical activity and the measured activity; The theoretical power distribution is corrected using the correction factor to obtain the measured power distribution.

6. The method according to claim 1, characterized in that, The step of determining the target tilt factor for each quadrant of the reactor core based on the average component power in each quadrant of the reactor core includes: The target tilt factor for each quadrant of the reactor core is obtained based on the ratio of the average component power in each quadrant of the reactor core to the average component power in the reactor core.

7. A power tilt prediction device, characterized in that, The device includes: The activity acquisition module is used to acquire the measured activity at any burnup level during core operation; The calculation module is used to iteratively solve for the optimal water gap parameters based on the measured activity and the pre-established objective function. The objective function aims at the maximum difference between the theoretical power distribution and the measured power distribution. The measured power distribution is determined based on the theoretical power distribution and the measured activity. The theoretical power distribution is determined based on the water gap parameters to be optimized. The power acquisition submodule is used to perform the next step of power distribution calculation of burnup based on the optimal water gap parameters, and to obtain the average component power of the core and the average component power of each quadrant of the core. The factor determination submodule is used to determine the target tilt factor for each quadrant of the core based on the average component power of each quadrant of the core and the average component power of the core.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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