Reactor control rod life prediction method and device, medium and exchange method

Abstract

The invention provides a life prediction method and device of a reactor control rod, a medium and an exchange method, and relates to the field of life prediction of reactor control rods. The method for predicting the service life of the reactor control rod comprises the following steps: acquiring nondestructive testing data of each control rod in a current preset working period, wherein the nondestructive testing data comprises real wear data corresponding to each cladding wear position of each control rod and corresponding real wear increase data in the period; constructing at least one wear increase distribution model through the real wear increase data of all the control rods; for each wear increase distribution model, selecting one piece of simulated wear increase data as predicted wear increase data of the control rod corresponding to the wear increase distribution model in the next period according to the data information; and for each cladding wear position of each control rod, adding the real wear data in the current period and the corresponding predicted wear increase data in the next period to obtain predicted wear data in the next preset work period.

Description

TECHNICAL FIELD

[0001] The present application mainly relates to the field of reactor control rod life prediction, and particularly relates to a reactor control rod life prediction method, device and medium and a replacement method. BACKGROUND

[0002] The reactor includes a plurality of parts such as an in-core component, a control rod assembly, a fuel assembly and a steam generator heat transfer tube. Among them, the control rod assembly (Rod Cluster Control Assembly, RCCA) undertakes the functions such as reactivity regulation and emergency shutdown in the nuclear power plant, which are related to the essential safety of the nuclear power plant. The control rod assembly further includes a guide cylinder and a control rod located in the guide cylinder. In the long-term operation of the reactor, the micro-motion of the control rod is caused by the fluid excitation force due to the action of fluid flow, and then the micro-motion wear between the control rod and the guide cylinder is caused. In some cases, the micro-motion wear will cause the leakage of the neutron absorbing material in the control rod cladding, the jamming of the control rod during the emergency shutdown, and the breakage of the control rod due to serious wear, and the like, which will pose a serious threat to the safety of the nuclear power plant. However, the existing technology cannot monitor the wear condition of the control rod in real time, and can only obtain the wear condition of the control rod after one operation cycle, and cannot guarantee that the control rod does not fail due to breakage during the operation cycle. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a reactor control rod life prediction method, device and medium and a replacement method, to predict the wear data of the control rod in the next cycle according to the real wear data of the control rod in each cycle, so as to ensure that the control rod does not fail due to breakage during the cycle.

[0004] To solve the above technical problems, the application provides a reactor control rod life prediction method, which is suitable for predicting the life prediction data of each control rod in the next preset working period in each preset working period. The reactor includes a plurality of guide tubes at different positions, and each guide tube includes a plurality of control rods at different positions. The life prediction method includes: obtaining non-destructive testing data of each control rod in the current preset working period, the non-destructive testing data including real wear data corresponding to each cladding wear position of each control rod and real wear growth data in the current period; constructing at least one wear growth distribution model through the real wear growth data of all control rods, each wear growth distribution model corresponding to at least part of the control rods, and the wear growth distribution model including data information associated with a plurality of different simulated wear growth data; for each wear growth distribution model, selecting a simulated wear growth data as the predicted wear growth data of the corresponding control rod in the next period according to the data information; for each cladding wear position of each control rod, adding the real wear data in the current period to the corresponding predicted wear growth data in the next period to obtain the predicted wear data in the next preset working period, wherein the life prediction data of each control rod includes the predicted wear data of each corresponding cladding wear position.

[0005] Optionally, the step of constructing at least one wear growth distribution model through the real wear growth data of all control rods further includes: constructing a measurement error model satisfying a normal distribution; constructing at least one basic model satisfying an exponential normal distribution, each basic model corresponding to at least part of the control rods, and the mean value of the exponential normal distribution corresponding to the basic model being the logarithm of the mean value of the real wear growth data of all control rods corresponding to the basic model; for each basic model, setting a plurality of candidate standard deviations, and selecting an optimal standard deviation from the plurality of candidate standard deviations as the optimal standard deviation according to the measurement error model and the real wear growth data of all control rods corresponding to the basic model; for each basic model, taking the corresponding optimal standard deviation as the standard deviation of the basic model to obtain the wear growth distribution model.

[0006] Optionally, the step of setting a plurality of candidate standard deviations for each basic model and selecting an optimal standard deviation from the plurality of candidate standard deviations as the optimal standard deviation according to the measurement error model and the real wear growth data of all control rods corresponding to the basic model further includes: generating a plurality of detection error data through the measurement error model; setting a plurality of candidate standard deviations; for each candidate standard deviation, taking the candidate standard deviation as the standard deviation of the basic model to make the basic model generate a plurality of first candidate wear growth data corresponding to the candidate standard deviation; selecting an optimal standard deviation from the plurality of candidate standard deviations as the optimal standard deviation according to the fitting degree of the plurality of first candidate wear growth data combined with the plurality of detection error data and the real wear growth data of all control rods corresponding to the basic model.

[0007] Optionally, the step of selecting one of the plurality of candidate standard deviations as the optimal standard deviation according to the fitting degree of the real wear growth data of all the control rods corresponding to the base model after the plurality of first candidate wear growth data and the plurality of detection error data are combined further comprises: for each candidate standard deviation, adding the plurality of first candidate wear growth data corresponding to the candidate standard deviation and the plurality of detection error data corresponding to the candidate standard deviation to obtain a plurality of second candidate wear growth data corresponding to the candidate standard deviation; for each candidate standard deviation, calculating the fitting degree of the candidate cumulative distribution curve and the real cumulative distribution curve, the candidate cumulative distribution curve containing each different second candidate wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction, and the real cumulative distribution curve containing each different real wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction; and selecting the candidate standard deviation corresponding to the highest fitting degree as the optimal standard deviation.

[0008] Optionally, the step of selecting one of the plurality of candidate standard deviations as the optimal standard deviation according to the fitting degree of the real wear growth data of all the control rods corresponding to the base model after the plurality of first candidate wear growth data and the plurality of detection error data are combined further comprises: for each candidate standard deviation, adding the plurality of first candidate wear growth data corresponding to the candidate standard deviation and the plurality of detection error data corresponding to the candidate standard deviation to obtain a plurality of second candidate wear growth data corresponding to the candidate standard deviation; for each candidate standard deviation, calculating the fitting degree of the candidate cumulative distribution curve and the real cumulative distribution curve, the candidate cumulative distribution curve containing each different second candidate wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction, and the real cumulative distribution curve containing each different real wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction; and selecting the candidate standard deviation corresponding to the highest fitting degree as the optimal standard deviation.

[0009] Optionally, the real wear data is the cladding wear thickness of the corresponding control rod at the cladding wear position.

[0010] Optionally, the real wear growth data of the current period at the cladding wear position is the difference between the real wear data of the current preset work period and the real wear data of the last preset work period corresponding to the cladding wear position.

[0011] Optionally, the step of calculating the real wear growth data of the current period at the cladding wear position further comprises: calculating the real wear growth data of the current period at the cladding wear position by subtracting the maximum value of the real wear data of the last preset work period corresponding to the cladding wear position from the maximum value of the real wear data of the current preset work period in a preset area centered on the cladding wear position.

[0012] Optionally, the method further comprises: for each control rod, calculating the corresponding predicted residual cladding thickness according to the predicted wear data of each cladding wear position; and for each control rod, determining whether the corresponding predicted residual cladding thickness is greater than a preset thickness, and if the determination result is negative, replacing the control rod with a new control rod in the next preset work period.

[0013] Optionally, before the step of constructing the at least one wear growth distribution model by using the real wear growth data of all the control rods, the method further comprises: dividing all the control rods into at least one group according to the positions of the control rods in the guide tubes and the positions of the guide tubes in the reactor, and each wear growth distribution model corresponds to a same group to which all the control rods belong.

[0014] To solve the above technical problem, the application provides a method for exchanging control rods of a reactor, which is suitable for exchanging the positions of the control rods in each preset working period, the reactor comprises a plurality of guide tubes in different positions, and each guide tube comprises a plurality of control rods in different positions, the method comprising: step S21, determining a guide tube exchange scheme, the guide tube exchange scheme comprising each guide tube that needs to be exchanged and a corresponding new position; step S22, obtaining predicted wear data corresponding to each guide tube that needs to be exchanged in the new position by using the above-mentioned life prediction method for control rods of a reactor; step S23, judging whether each predicted wear data corresponding to each control rod of each guide tube is less than a preset wear threshold, and if the result of the judgment is no, adjusting the guide tube exchange scheme and continuing to perform step S22.

[0015] Optionally, the original position and the new position of the guide tube that needs to be exchanged belong to different areas, and the areas are divided according to the positions of all the guide tubes.

[0016] To solve the above technical problem, the application provides a device for predicting the life of control rods of a reactor, comprising: a memory for storing instructions executable by a processor; and the processor for executing the instructions to realize the above-mentioned life prediction method for control rods of a reactor.

[0017] To solve the above technical problem, the application provides a computer readable medium storing computer program codes, which realize the above-mentioned life prediction method for control rods of a reactor when executed by a processor.

[0018] Compared with the prior art, the application has the following advantages: a wear growth distribution model capable of reflecting the wear growth of the control rod is constructed according to the non-destructive testing data, the predicted wear growth data of the corresponding control rod in the next period is obtained from the wear growth distribution model, and then the predicted wear growth data is added to the real wear data in the current period, so as to predict the predicted wear data of the control rod at each cladding wear position in the next period, thereby reflecting the life of the control rod through each predicted wear data of the control rod. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and together with the description serve to explain the principle of the present application. In the drawings:

[0020] Figure 1 is a flowchart of the life prediction method of the reactor control rod according to an embodiment of the present application;

[0021] Figure 2 is Figure 1 is a top view schematic diagram of the position distribution of the guide tube in the reactor in the life prediction method of the reactor control rod;

[0022] Figure 3 is Figure 2 is a top view schematic diagram of the position distribution of the control rod in the guide tube;

[0023] Figure 4 is Figure 1 is a flowchart of the sub-steps of step S12;

[0024] Figure 5 is Figure 3 is a schematic diagram of the control rod corresponding control rod group and the probability density distribution of the real wear data of the control rod in each control rod group;

[0025] Figure 6 is Figure 4 is a flowchart of the step S123 of obtaining the optimal standard deviation of one base model;

[0026] Figure 7 is Figure 6 is a flowchart of the sub-steps of step S34;

[0027] Figure 8 is Figure 7 is a schematic diagram of the simulation of the cumulative distribution curve and the real cumulative distribution curve in step S342;

[0028] Figure 9 is Figure 1 is a flowchart of the sub-steps of step S13;

[0029] Figure 10 is Figure 1 is a flowchart of the life prediction method of the reactor control rod for judging whether to replace the control rod;

[0030] Figure 11 is a flowchart of the replacement method of the reactor control rod according to an embodiment of the present application; and

[0031] Figure 12is a block diagram of a reactor control rod life prediction device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can also be applied to other similar scenarios without creative labor on the basis of the drawings. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structures or operations.

[0033] As shown in the present application and claims, unless the context clearly indicates otherwise, the words "one", "an", "a", and / or "the" do not mean to specify a single number, but can also include a plurality. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0034] Unless otherwise specifically indicated, the relative arrangement of the components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship for the convenience of description. The technology, methods and devices known to those skilled in the relevant art can not be discussed in detail, but under appropriate circumstances, the technology, methods and devices should be considered as part of the authorized description. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, and not as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0035] In the description of the present application, it should be understood that the orientation words such as "front, back, up, down, left, right", "horizontal, vertical, perpendicular, horizontal" and "top, bottom" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and in the absence of contrary indications, these orientation words do not indicate and imply that the indicated device or element must have a particular orientation or be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the scope of protection of the present application; the orientation words "inner, outer" refer to the inner and outer relative to the contour of the parts themselves.

[0036] Furthermore, although the terms used in the present application are selected from publicly-known terms, some of the terms mentioned in the specification of the present application can be selected by the applicant(s) in his or her discretion, and the detailed meanings thereof are explained in the relevant portions of the description herein. In addition, the present application is to be understood not only by the actual terms used, but also by the meanings implied by each term.

[0037] Figure 1 is a flowchart of a reactor control rod life prediction method according to an embodiment of the present application. As shown in Figure 1 , the reactor control rod life prediction method 100 is adapted to predict life prediction data of each control rod in the next preset working period in each preset working period. It should be noted that the preset working period is, for example, a running period, a reactor overhaul period, etc., a period in which the control rod can be extracted from the reactor to obtain the wear detection result of the control rod. Continuing to refer to Figure 1 , the reactor control rod life prediction method 100 comprises the following steps.

[0038] First, step S11 is to obtain the non-destructive testing data of each control rod at the current preset working period. The non-destructive testing data includes the actual wear data corresponding to each cladding wear position of each control rod and the actual wear growth data in the current period. It can be understood that the non-destructive testing data is the wear detection result mentioned above. In the present embodiment, the actual wear data is the cladding wear thickness of the corresponding control rod at the cladding wear position. For example, in the present embodiment, the cladding thickness of the control rod when it is not worn is 0.47 mm, and when the non-destructive testing obtains the cladding thickness of a cladding wear position as 0.37 mm, the cladding wear thickness of the cladding wear position is 0.1 mm. In the present embodiment, the actual wear growth data of the cladding wear position in the current period is the difference between the actual wear data of the cladding wear position in the current preset working period and the actual wear data of the cladding wear position in the last preset working period. It should be noted that when the control rod only experiences one cycle of operation in the current preset working period in the reactor, the actual wear data of the control rod corresponding to the last preset working period is 0. Further, the maximum value of all the actual wear data of the current preset working period in the preset region centered on the cladding wear position and the maximum value of all the actual wear data of the last preset working period are subtracted to obtain the actual wear growth data in the current period. In the present embodiment, the preset region is a region formed by expanding ±100 mm along the axial direction of the control rod and expanding ±10 degrees along the circumferential direction of the control rod. By setting the above-mentioned preset region, the error that may exist in the non-destructive testing can be reduced, and the accuracy of the actual wear growth data is further improved.

[0039] Continuing to refer to Figure 1, step S12 is constructing at least one wear growth distribution model by all the real wear growth data of the control rods. Wherein, each wear growth distribution model corresponds to at least part of the control rods. In this embodiment, all the control rods are divided into at least one group according to the positions of the control rods in the guide tubes and the positions of the guide tubes in the reactor, and all the control rods corresponding to each wear growth distribution model belong to the same group. For the specific principle and examples of the above control rod grouping, see the following description. Exemplarily, Figure 2 is a top view schematic diagram of the position distribution of the guide tubes in the reactor. As shown in Figure 2 , each solid rectangle represents a guide tube, and it can be seen that the reactor contains multiple guide tubes and each guide tube is at a different position. Figure 3 is a top view schematic diagram of the position distribution of the control rods in the guide tubes. As shown in Figure 3 , each guide tube contains 24 control rods, and the position coordinates of each control rod are divided into (A, 3), (B, 2), (C, 1), (C, 3), (D, 1), (D, 3), (E, 1), (E, 3), (F, 2), (G, 3), (E, 4), (G, 4), (G, 5), (E, 5), (F, 6), (E, 7), (D, 5), (D, 7), (C, 7), (C, 5), (B, 6), (A, 5), (A, 4) and (C, 4). It should be noted that, Figure 2 and Figure 3 only exemplarily show the number and position of the guide tubes and the control rods, and the present application does not limit the arrangement and number of the guide tubes in the reactor and the arrangement and number of the control rods in the guide tubes. In addition, Figure 3 , 0°, 90°, 180° and 270° in Figure 2 and Figure 3 are used to show the direction information of the control rods in the reactor, which can be used in combination with the position coordinates. Continuing to refer to Figure 2 and Figure 3 , it can be understood that the position of the guide tube where the control rod is located in the reactor and the position of the control rod in the guide tube affect the fluid excitation force received by the control rod, and thus different control rods can receive different fluid excitation forces, thereby forming different micro-wear, i.e. different real wear data. Therefore, by grouping the control rods by position, control rods with the same or similar micro-wear are divided into a group, so that the wear growth distribution model corresponding to each group can accurately fit the micro-wear characteristics of the control rods in the group. For the grouping method, exemplarily, refer to Figure 2 , all the guide tubes close to the core are taken as a guide tube group by the dashed box, and all the guide tubes far from the core outside the dashed box are taken as a guide tube group, i.e. all the guide tubes are divided into 2 groups. Further refer to Figure 3The control rods close to the center of the guide cylinder, i.e., the control rods with coordinates (C, 3), (D, 3), (E, 3), (E, 4), (E, 5), (D, 5), (C, 5) and (C, 4), are taken as one control rod group, and the remaining control rods are taken as another control rod group, i.e., the control rods in the guide cylinder are divided into two groups. Then all the control rods in the reactor are divided into 2*2=4 groups according to the guide cylinder in which the control rods are located and the positions of the control rods, and then four wear growth distribution models are constructed according to the four groups.

[0040] The step S12 has been briefly described above, and further description is made below with reference to Figures 4-7 The specific details of the step S12 are described. As shown in Figure 4 The step S12 includes the following sub-steps. The step S121 is to construct a measurement error model satisfying a normal distribution. In the embodiment, the mean value of the measurement error model satisfying the normal distribution is 0. In the embodiment, the measurement error model is constructed to reflect the influence of the measurement error of the nondestructive testing on the real wear growth data, which will be further described below. The step S122 is to construct at least one basic model satisfying an exponential normal distribution. The basic model corresponds to at least part of the control rods, and the mean value of the exponential normal distribution corresponding to the basic model is the logarithm of the mean value of the real wear growth data of all the control rods corresponding to the basic model. It can be understood that in the example above, four wear growth distribution models are needed for the four control rod groups, and therefore four basic models are needed to be constructed in the step S122. It should be noted that, with reference to Figure 5 , the control rods in the guide cylinder are divided into four control rod groups, and are marked with pentagonal marks, circular marks, triangular marks and rectangular marks in the upper right corner of the guide cylinder position diagram, and a curve graph with the abscissa being the real wear data and the ordinate being the probability density function is constructed according to the grouping. It can be seen that the curves corresponding to the control rod groups present a normal distribution, and therefore the basic model constructed in the embodiment is also set to satisfy the normal distribution.

[0041] Continuing to refer to Figure 4 , the step S123 is to set a plurality of selected standard deviations for each basic model, and select one selected standard deviation as an optimal standard deviation from the plurality of selected standard deviations according to the measurement error model and the real wear growth data of all the control rods corresponding to the basic model. Further reference is made to Figure 6, the specific steps of obtaining the optimal standard deviation of the base model in step S123 are as follows. Step S31 is to generate a plurality of detection error data by measuring the error model. Step S32 is to set a plurality of candidate standard deviations. In this embodiment, a plurality of candidate standard deviations are selected between [0.65, 0.9]. Step S33 is to, for each candidate standard deviation, take the candidate standard deviation as the standard deviation of the base model, and make the base model generate a plurality of first candidate wear growth data corresponding to the candidate standard deviation. Step S34 is to select a candidate standard deviation from the plurality of candidate standard deviations as the optimal standard deviation according to the fitting degree of the plurality of first candidate wear growth data combined with the plurality of detection error data to the real wear growth data of all control rods corresponding to the base model. Further referring to Figure 7 , step S34 includes the following sub-steps. Step S341 is to, for each candidate standard deviation, add the plurality of first candidate wear growth data corresponding thereto to the plurality of detection error data corresponding thereto to obtain a plurality of second candidate wear growth data corresponding thereto. It can be understood that each detection error data reflects the influence of non-destructive testing error on real wear data in non-destructive testing. In addition, since all first candidate wear growth data are derived from the base model based on normal distribution, each candidate wear growth data represents the accurate wear growth data of the control rod without the influence of non-destructive testing error. Therefore, the sum of the one-to-one addition of the first candidate wear growth data and the detection error data, i.e. the second candidate wear growth data, can fit the real wear growth data affected by the non-destructive testing error. Step S342 is to, for each candidate standard deviation, calculate the fitting degree of the candidate cumulative distribution curve and the real cumulative distribution curve, simulate the cumulative distribution curve containing each different second candidate wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction, and simulate the real cumulative distribution curve containing each different real wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction. For example, referring to Figure 8 , the dashed line is the NDE simulation growth rate, i.e. the simulation cumulative distribution curve, and the solid line is the NDE measurement growth rate, i.e. the real cumulative distribution curve. The more the simulation cumulative distribution curve fits the real cumulative distribution curve, the higher the fitting degree of the two. Specifically, in this embodiment, the Mean Absolute Percentage Error (MAPE) is used to reflect the fitting degree of the two in numerical form. Step S343 is to take the candidate standard deviation corresponding to the highest fitting degree as the optimal standard deviation.

[0042] Continuing to refer to Figure 4, step S124 is to each base model, the corresponding optimal standard deviation as the standard deviation of the base model, the wear growth distribution model is obtained. Wherein, the wear growth distribution model contains data information associated with a plurality of different simulated wear growth data. In the process of performing step S11~step S12 in this embodiment, the real wear growth data reduced by the influence of nondestructive testing error is obtained through the preset area, and then the real wear growth data is used to construct the wear growth distribution model combined with the measurement error model, so that the wear growth distribution model can more accurately and objectively reflect the wear growth of the control rod. Compared with the method of directly using the real wear data which may contain the influence of nondestructive testing error to model and predict the wear of the control rod, the prediction result is more stable and accurate.

[0043] Continue to refer to Figure 1 , step S13 is to each wear growth distribution model, according to the data information, select a simulated wear growth data as the predicted wear growth data of the control rod in the next cycle corresponding to the wear growth distribution model. Further refer to Figure 9 , step S13 includes the following sub steps. Step S131 is to each wear growth distribution model, according to the data information, determine a plurality of simulated wear growth data. Step S132 is to each wear growth distribution model, generate a cumulative distribution curve corresponding to the plurality of simulated wear growth data, and the cumulative distribution curve contains each different simulated wear growth data and the corresponding cumulative score. Step S133 is to each wear growth distribution model, determine a preset score and take the simulated wear growth data corresponding to the cumulative score of the preset score in the corresponding cumulative distribution curve as the predicted wear growth data of the control rod in the next cycle corresponding to the wear growth distribution model. In this embodiment, the preset score is 0.95, under the premise of covering most of the wear growth situation of the control rod, filtering a small number of situations with large wear growth due to nondestructive testing error, so as to give reasonable predicted wear growth data.

[0044] Continue to refer to Figure 1 , step S14 is to each cladding wear position of each control rod, add the real wear data in the current cycle to the predicted wear growth data in the corresponding next cycle to obtain the predicted wear data of the next preset working cycle. Wherein, the life prediction data of each control rod contains the predicted wear data of each corresponding cladding wear position. Further refer to Figure 10The reactor control rod lifetime prediction method 100 of this embodiment further includes the following steps. Step S15: For each control rod, calculate the corresponding predicted remaining cladding thickness based on the predicted wear data at each cladding wear location. Specifically, referring to the example above where the unworn thickness is 0.47mm, when the predicted wear data is 0.27mm, the corresponding predicted remaining cladding thickness is 0.47mm - 0.27mm = 0.2mm. Step S16: For each control rod, determine whether the corresponding predicted remaining cladding thickness is greater than a preset thickness. If the determination result is yes, otherwise, replace the control rod with a new control rod in the next preset working cycle, that is, the lifespan of the control rod ends and it is no longer used. For example, when the unworn thickness is 0.47mm, the corresponding preset thickness is 0.1mm.

[0045] This application also proposes a method for replacing reactor control rods. For example... Figure 11 As shown, the reactor control rod replacement method 200 includes the following steps. Step S21 is to determine the guide tube replacement scheme, which includes each guide tube to be replaced and its corresponding new position. In this embodiment, the original position and the new position of the guide tube to be replaced belong to different regions, which are divided according to the positions of all guide tubes. Specifically, the regions are determined with reference to the grouping of guide tubes when grouping control rods above, that is, each guide tube group corresponds to one region. Since the position of the control rod inside the guide tube is not adjusted when the guide tube is replaced in this embodiment, when the guide tube is replaced in the same region, the fluid excitation force on the control rod inside the guide tube before and after replacement is similar, and therefore the wear is similar. Therefore, it is impossible to achieve the effect of uniform wear at each wear position of the control rod or delay the wear growth at severely worn positions by replacing the guide tube. In contrast, by replacing the guide tube in different regions, the fluid excitation force characteristics of different regions can be utilized to delay the wear growth at severely worn positions in the next preset working cycle, thereby extending the life of the control rod inside the guide tube.

[0046] In order to achieve the effect of prolonging the service life of the control rod, the embodiment further groups each wear position according to the contact position of the guide cylinder and the control rod in the axial direction of the control rod. For example, the positions in the guide cylinder where fretting wear occurs with the control rod include the first upper guide plate, the second upper guide plate, the third upper guide plate, the fourth upper guide plate, the flange area, the first lower guide plate, the second lower guide plate, the third lower guide plate, the fourth lower guide plate, the fifth lower guide plate, the upper edge of the continuous guide section, the middle of the continuous guide section, the lower edge of the continuous guide section, and the inside of the guide tube, a total of 14 wear-prone areas. The entire guide cylinder is then divided into 14 longitudinal area groups in sequence according to the wear areas described above, and each wear position can correspond to a unique longitudinal area group. Then, based on the guide cylinder groups and the control rod groups described above, the wear degree of the wear positions in different combinations is analyzed in combination with the longitudinal area groups, and a guide cylinder replacement scheme that meets the requirement of prolonging the service life of the control rod is developed. It should be noted that the present application does not limit the way the longitudinal area groups are divided. In other embodiments, the entire guide cylinder can be divided into less than 14 longitudinal area groups or more than 14 longitudinal area groups.

[0047] With reference to the foregoing Figure 11 , step S22 is to obtain the predicted wear data corresponding to the new position of each guide cylinder that needs to be replaced by using the service life prediction method 100 of the reactor control rod of the embodiment. It can be understood that in step S22, for the guide cylinder that needs to be replaced, the actual wear data of each cladding wear position of the control rod in the guide cylinder is added to the predicted wear growth data in the next cycle generated by the wear growth distribution model corresponding to the new position, so as to obtain the predicted wear data of each control rod in the new position of the guide cylinder that needs to be replaced. Step S23 is to determine whether each predicted wear data corresponding to each control rod of each guide cylinder is less than a preset wear threshold. If the result of the determination is no, the guide cylinder replacement scheme is adjusted and step S22 is continued. In the embodiment, when the un-worn thickness is 0.47 mm, the corresponding preset wear threshold is 0.37 mm, but the present application does not limit the value of the preset wear threshold. The replacement method 200 of the reactor control rod of the embodiment can generate the service life prediction data of the control rod by using the service life prediction method 100 of the reactor control rod after generating the guide cylinder replacement scheme, so as to evaluate the feasibility of the guide cylinder replacement scheme according to the service life prediction data, and adjust the guide cylinder replacement scheme that is not feasible. Therefore, the replacement method 200 of the reactor control rod can generate an effective and feasible guide cylinder replacement scheme to prolong the service life of the control rod.

[0048] An embodiment of the present application also provides a service life prediction device 300 of a reactor control rod as shown in Figure 12 . According to Figure 12The reactor control rod life prediction apparatus 300 can include a memory 31 and a processor 32. The memory 31 is configured to store instructions executable by the processor 32. The processor 32 is configured to execute the instructions to implement the reactor control rod life prediction method described above.

[0049] In addition, another aspect of the present application provides a computer readable medium storing computer program codes which, when executed by a processor, implement the reactor control rod life prediction method described above.

[0050] Flowcharts have been used in the present application to illustrate the operations performed by the system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in sequence. Instead, various steps can be processed in reverse order or simultaneously. Meanwhile, other operations can be added to these processes or some or several operations can be removed from these processes.

[0051] The above has described the basic concepts, and it is obvious that the above application disclosure is only as an example and does not constitute a limitation on the present application for those skilled in the art. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present application. Such modifications, improvements and corrections are suggested in the present application, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present application.

[0052] At the same time, specific words are used in the present application to describe the embodiments of the present application. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "an alternative embodiment" mentioned in different positions in the specification twice or more does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be properly combined.

[0053] Aspects of the application can be implemented in, completely, in hardware, completely in software (including firmware, resident software, micro-code, etc.), or combinations thereof. The foregoing hardware or software can be referred to as a "data block", "module", "engine", "unit", "component", or "system". The processor can be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, or combinations thereof. Furthermore, aspects of the application can be presented in a computer program product, which can include a computer-readable medium having stored computer program codes. For example, the computer-readable medium can include, but is not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips...), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)...), smart cards, and flash memory devices (e.g., card, stick, key drive...).

[0054] The computer readable medium can include a propagated data signal with computer program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. Computer readable medium can be any medium that can be read by a computer, including, but not limited to, storage devices, memory devices, and communication devices. The computer program code can be transmitted in any suitable format, including, but not limited to, radio frequency, light, electrical, or any suitable combination thereof.

[0055] Similarly, it is to be noticed that the term "comprising", used in the description, is not intended to exclude other features but to comprise them. Other expressions, such as "containing" or "including", shall be construed in a like manner. Furthermore, the term "comprising" is used in the broadest sense as including equivalents, similar structures, and / or functions of a structure.

[0056] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0057] Although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of this application. Therefore, any changes or modifications to the above embodiments within the essential spirit of this application will fall within the scope of the claims of this application.

Claims

1. A method of predicting the lifetime of a reactor control rod, characterized by, The application relates to a life prediction method for a nuclear reactor, the nuclear reactor comprising a plurality of guide tubes arranged at different positions, each of the guide tubes comprising a plurality of control rods arranged at different positions, the life prediction method comprising: obtaining non-destructive testing data of each of the control rods in a current preset working period, the non-destructive testing data comprising actual wear data corresponding to each cladding wear position of each of the control rods and actual wear growth data in the current period; constructing at least one wear growth distribution model through the actual wear growth data of all the control rods, each of the wear growth distribution models corresponding to at least part of the control rods, the wear growth distribution model comprising data information associated with a plurality of different simulated wear growth data; for each of the wear growth distribution models, selecting one of the simulated wear growth data as predicted wear growth data in a next period of each of the control rods corresponding to the wear growth distribution model according to the data information; for each of the cladding wear positions of each of the control rods, adding the actual wear data in the current period to the predicted wear growth data in the next period corresponding to the cladding wear position to obtain predicted wear data in the next preset working period, wherein the life prediction data of each of the control rods comprises the predicted wear data of each of the cladding wear positions corresponding to the control rod.

2. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 1, characterized in that, The step of constructing at least one wear growth distribution model through the actual wear growth data of all the control rods further comprises: constructing a measurement error model satisfying a normal distribution; constructing at least one basic model satisfying an exponential normal distribution, each of the basic models corresponding to at least part of the control rods, a mean value of the exponential normal distribution corresponding to the basic model being a logarithm of a mean value of the actual wear growth data of all the control rods corresponding to the basic model; for each of the basic models, setting a plurality of candidate standard deviations, and selecting one of the candidate standard deviations as an optimal standard deviation according to the measurement error model and the actual wear growth data of all the control rods corresponding to the basic model; for each of the basic models, taking the optimal standard deviation corresponding to the basic model as a standard deviation of the basic model to obtain the wear growth distribution model.

3. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 2, characterized in that, The step of setting a plurality of candidate standard deviations for each of the basic models and selecting one of the candidate standard deviations as an optimal standard deviation according to the measurement error model and the actual wear growth data of all the control rods corresponding to the basic model further comprises: generating a plurality of detection error data through the measurement error model; setting a plurality of the candidate standard deviations; for each of the candidate standard deviations, taking the candidate standard deviation as a standard deviation of the basic model, so that the basic model generates a plurality of first candidate wear growth data corresponding to the candidate standard deviation; According to the fitting degree of the real wear growth data of all the control rods corresponding to the basic model after the combination of the plurality of first candidate wear growth data and the plurality of detection error data, one of the plurality of candidate standard deviations is selected as the optimal standard deviation from the plurality of candidate standard deviations.

4. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 3, wherein, According to the fitting degree of the real wear growth data of all the control rods corresponding to the basic model after the combination of the plurality of first candidate wear growth data and the plurality of detection error data, one of the plurality of candidate standard deviations is selected as the optimal standard deviation from the plurality of candidate standard deviations. The step further comprises: For each of the candidate standard deviations, the corresponding plurality of first candidate wear growth data and the plurality of detection error data are added to obtain the corresponding plurality of second candidate wear growth data. For each of the candidate standard deviations, the fitting degree of the simulated cumulative distribution curve and the real cumulative distribution curve is calculated, the simulated cumulative distribution curve contains each different second candidate wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction, and the real cumulative distribution curve contains each different real wear growth data corresponding to the candidate standard deviation and the corresponding cumulative fraction. The candidate standard deviation corresponding to the highest fitting degree is selected as the optimal standard deviation.

5. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 1, wherein, For each of the wear growth distribution models, according to the data information, one of the simulated wear growth data is selected as the predicted wear growth data of the control rod in the next cycle corresponding to the wear growth distribution model. For each of the wear growth distribution models, a plurality of simulated wear growth data are determined according to the data information. For each of the wear growth distribution models, a plurality of simulated wear growth data corresponding to the cumulative distribution curve are generated, and the cumulative distribution curve contains each different simulated wear growth data and the corresponding cumulative fraction. For each of the wear growth distribution models, a preset fraction is determined, and the simulated wear growth data corresponding to the cumulative fraction in the corresponding cumulative distribution curve is selected as the predicted wear growth data of the control rod in the next cycle corresponding to the wear growth distribution model.

6. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 1, wherein, The real wear data is the cladding wear thickness of the corresponding control rod at the cladding wear position.

7. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 6, characterized in that, The real wear growth data in the current cycle corresponding to the cladding wear position is the difference between the real wear data of the current preset working cycle corresponding to the cladding wear position and the real wear data of the last preset working cycle corresponding to the cladding wear position.

8. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 7, characterized in that, Further comprising: the difference between the maximum value of all the real wear data of the current preset working cycle corresponding to the cladding wear position and the maximum value of all the real wear data of the last preset working cycle corresponding to the cladding wear position is obtained as the real wear growth data in the current cycle.

9. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 6, wherein, Further comprising: For each of the control rods, the predicted residual cladding thickness corresponding to each of the cladding wear positions is calculated based on the predicted wear data of each of the cladding wear positions. For each control rod, it is determined whether the corresponding predicted residual cladding thickness is greater than a preset thickness. If the determination result is no, a new control rod is used to replace the control rod in the next preset operation cycle.

10. The method of predicting the life of a control rod of a nuclear reactor as claimed in claim 1, wherein, Before the step of constructing at least one wear growth distribution model through the real wear growth data of all the control rods, the method further comprises: According to the positions of the control rods in the guide tubes and the positions of the guide tubes in the reactor, all the control rods are divided into at least one group, All the control rods corresponding to each wear growth distribution model belong to the same group.

11. A method of exchanging a control rod of a nuclear reactor, characterized in that, The method is suitable for position adjustment of control rods in each preset operation cycle. The reactor comprises multiple guide tubes in different positions, and each guide tube comprises multiple control rods in different positions. The method comprises: Step S21, determining a guide tube adjustment scheme, wherein the guide tube adjustment scheme comprises each guide tube that needs to be adjusted and a corresponding new position; Step S22, acquiring predicted wear data corresponding to each guide tube that needs to be adjusted in the new position by using the reactor control rod life prediction method according to any one of claims 1-10; Step S23, determining whether the predicted wear data corresponding to each control rod of each guide tube is less than a preset wear threshold. If the determination result is no, the guide tube adjustment scheme is adjusted, and the step S22 is continued.

12. The method of exchanging control rods of a nuclear reactor as set forth in claim 11, wherein, The original position and the new position of the guide tube that needs to be adjusted belong to different regions, and the regions are divided according to the positions of all the guide tubes.

13. A device for predicting the lifetime of a control rod of a nuclear reactor, characterized in that it comprises: Comprise: A memory for storing instructions executable by a processor; A processor for executing the instructions to implement the reactor control rod life prediction method according to any one of claims 1-10.

14. A computer readable medium having stored computer program code that, when executed by a processor, implements the reactor control rod life prediction method according to any one of claims 1-10.

14. A computer readable medium having stored computer program code that, when executed by a processor, implements the reactor control rod life prediction method according to any one of claims 1-10.

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