KR20220115336A - System for modeling failure prediction of vanadium detector in CANDU and method therefor - Google Patents

Abstract

The present invention relates to a system for modeling failure prediction of a vanadium detector in CANDU for accurately evaluating failure prediction and accordingly establishing a replacement plan. According to the present invention, the present invention can prevent unnecessary investment by determining a replacement time point by accurately calculating a lifetime of a vanadium detector, take a measure to ensure that there is no problem in inspecting operation limit conditions such as reactor control, channels, bundle power, and the like by stably maintaining an FLX program, and have an effect of accurately evaluating failure prevention and accordingly establishing a replacement plan.

Description

System for modeling failure prediction of vanadium detector in CANDU and method therefor

The present invention relates to a heavy water vanadium detector failure prediction modeling system, and more particularly, to a heavy water vanadium detector failure prediction modeling system capable of accurately evaluating failure prediction and establishing a replacement plan accordingly.

In heavy water reactors, vanadium detectors are used in Flux mapping (FLX program). The FLX program calculates the output of 14 areas and calibrates the output of the platinum detector measurement area (Pic: Calibrated Flux Power). In addition, using 15 mode shapes and 102 detector outputs in the FLX program, it produces channel, bundle output, and channel output peak coefficients in on-line (DCC) and off-line (core code) and checks whether operation limit conditions are exceeded. do.

Although the vanadium detector lifespan was predicted to be about 10 years after use, as the detector failure occurred too soon (5-7 years after operation), internal confusion occurred, such as taking issue with the operation of the core for the failure. When the number of vanadium detector failures exceeds 20, the control computer freezes the function of the FLX program, making it impossible to calculate the area output, channel, multiple output, and channel output peak coefficient. Therefore, it is necessary to accurately evaluate the failure prediction and establish a replacement plan accordingly.

Patent Publication No. 10-2018-0079649 (2018.07.11)

The present invention was devised to solve the above-mentioned problem, and by accurately calculating the vanadium detector lifetime, determining the replacement time, preventing unnecessary investment, and stably maintaining the FLX program to check the operation limiting conditions such as reactor control, channel, and bundle output An object of the present invention is to provide a heavy water vanadium detector failure prediction modeling system that can take measures so that there is no problem.

A heavy water vanadium detector failure prediction modeling system according to an aspect of the present invention includes: an operating data input unit for inputting operating data to obtain a post simulation result; a mapping unit receiving outputs of a plurality of vanadium detectors and mapping the fluxes; a selection input unit for selecting a fuel pipe to be replaced, and inputting the characteristics of the selected fuel pipe into a core code diffusion equation provided by a designer to obtain a pre-simulation result; and a failure prediction unit that calculates the results of the operation data input unit, the mapping unit, and the selection input unit, counts the number of vanadium detector failures, and calculates the channel prediction output, the multiple prediction output, and the predicted peak coefficient within a set value; The prediction unit predicts and evaluates the vanadium detector failure by using the standard normal "Z distribution" of the probability from the data collected based on the results of the operation data input unit, the mapping unit, and the selection input unit.

Preferably, the failure prediction unit performs failure prediction of the vanadium detector from the date of manufacture, uses an average of 13.2 years for the failure of the vanadium detector, and evaluates the standard deviation as 2 years.

Preferably, the failure prediction unit includes a failure prediction condition module for predicting the first year of failure while performing failure prediction by identifying the manufacturing date of the vanadium detector.

Preferably, the failure prediction unit includes a failure accumulation count module for evaluating the accumulated number of detector failures over time.

Preferably, the failure prediction unit evaluates the reliability of the failure prediction modeling by comparing the vanadium detector failure life evaluation table data DB with the result of predicting and evaluating the actual failure of the vanadium detector.

On the other hand, the heavy water vanadium detector failure prediction modeling method comprises the steps of (a) inputting operating data to obtain a post-simulation result; (b) receiving the outputs of the plurality of vanadium detectors and mapping the fluxes; (c) obtaining a pre-simulation result by selecting a fuel pipe to be replaced, and inputting the characteristics of the selected fuel pipe into a core code diffusion equation provided by a designer; (d) calculating the result of the steps (a) to (c) and counting the number of vanadium detector failures to calculate the channel prediction output, the cluster prediction output, and the prediction peak coefficient within a set value; Step (d) predicts and evaluates the vanadium detector failure using the standard normal “Z distribution” of probability from the data collected based on the results of steps (a) to (c).

According to the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention, the lifespan of the vanadium detector is accurately calculated, the replacement time is determined, unnecessary investment is prevented, and the FLX program is stably maintained to control the reactor and control the channel, bundle output, etc. It is possible to take measures so that there is no problem in checking the operation limit condition, and it has the effect of accurately evaluating the failure prediction and establishing a replacement plan accordingly.

1 is a diagram illustrating a method for predicting a core output of a heavy water reactor.
2 is a diagram illustrating a failure prediction modeling system for a heavy water vanadium detector according to an embodiment of the present invention.
3 is a view showing the prediction results of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention.
4 is a view showing a vanadium detector installation position of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention, and FIG. 5 is a reactor area control of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention. It is a diagram showing (Calibrated Flux Power).
6 is a flowchart illustrating a method for predicting failure of a heavy water vanadium detector according to an embodiment of the present invention.

The specific structural or functional descriptions presented in the embodiments of the present invention are merely exemplified for the purpose of describing the embodiments according to the concept of the present invention, and the embodiments according to the concept of the present invention may be implemented in various forms. In addition, it should not be construed as being limited to the embodiments described herein, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention.

Meanwhile, in the present invention, terms such as first and/or second may be used to describe various components, but the components are not limited to the above terms. The above terms are used only for the purpose of distinguishing one component from other components, for example, within the scope of not departing from the scope of the rights according to the concept of the present invention, the first component may be named as the second component, Similarly, the second component may also be referred to as the first component.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the description of the embodiment of the present invention, if it is determined that the description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the description thereof is omitted.

1 is a diagram illustrating a method for predicting a core output of a heavy water reactor.

First, a method for predicting the core output of the heavy water reactor shown in FIG. 1 will be described. A step of obtaining a simulation result by inputting operating data (S11), a step of selecting a fuel pipe to be replaced (S12), and a step of inputting the characteristics of the selected fuel pipe into the core code diffusion equation provided by the designer (S13) And, as the analysis result of the core code diffusion equation, calculate the peak coefficient channel and bundle output, check whether the calculation is completed for all fuel pipes, and if not completed, return to step S12 (S14), and judgment of step S14 As a result, if the peak coefficient channel output and multiple output for all fuel pipes have been calculated, the step of determining the reactor output based on this (S15), the step of adjusting the power plant output according to the predicted reactor output (S16), and replacing the fuel It is configured to include a step (S17).

To explain in more detail the method of predicting the core output of the heavy water reactor, in step S11, operating data such as fuel replacement history and liquid level control system are input into the RFSP 2GROUP diffusion equation to perform a post-simulation (POST-SIMULATION). carry out

Next, in step S12, a fuel pipe to be replaced is selected, and the output is predicted by inputting the results of the post simulation and the characteristics of the selected fuel pipe into the core code diffusion equation provided by the designer in step S13. In this case, the core code diffusion equation provided by the designer is an equation that meets the conditions for designing each heavy water reactor, and can calculate the output of the corresponding heavy water reactor by inputting information about a specific fuel pipe.

Next, in step S14, the output values of the peak coefficient channel output and the bundle output for the specific fuel pipe are obtained, and it is checked whether the output values of the peak coefficient channel output and the bundle output are obtained for all the fuel pipes to be replaced. As a result of this check, if there are more fuel pipes to be analyzed, the process returns to step S12 to input the characteristics of another fuel pipe to be replaced.

Next, in step S15, the output of heavy water is predicted and determined based on the peak coefficient channel output and bundle output for each fuel pipe obtained through the above process, and after adjusting the output of the power plant as in step S16, As in step S17, the fuel replacement operation is performed.

As described above, after the fuel is replaced, the heavy water core output increases due to the input of new fuel. Since the output exceeding the trip set value related to the stop of operation may occur, the heavy water reactor core output is predicted, and then the fuel is replaced in a state in which the output of the power plant is lowered according to the predicted value.

Based on this prediction method, the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention reads the output of the vanadium detector and counts the number of vanadium detector failures in the result of mapping the flux, and predicts the heavy water core output more accurately within the set value By doing so, it is possible to appropriately set the amount of reduction that lowers the power plant output when the fuel tube is replaced, and thus, there is an effect that does not cause operation interruption when the fuel tube is replaced and the amount of reduction in the amount of power generation can be minimized. That is, by counting the number of vanadium detector failures and predicting the failure within the set value, when the number of vanadium detector failures exceeds the set value by 20, the control computer freezes the FLX program function so that the area output, channel, multiple output, and channel output peak coefficients are calculated. It has the effect of solving the impossible point, thus accurately evaluating the failure prediction and establishing a replacement plan accordingly.

2 is a view showing a heavy water vanadium detector failure prediction modeling system 10 according to an embodiment of the present invention. As shown in FIG. 2 , it includes a driving data input unit 110 , a mapping unit 120 , a selection input unit 130 , and a failure prediction unit 140 .

The driving data input unit 110 obtains a post simulation result by inputting the driving data. This operating data input unit obtains post simulation by inputting operating data into the core code diffusion equation. The operating data is the fuel change history and the level of the liquid area control system. At this time, the post simulation can check the channel output, multiple output, and peak coefficient of the current state.

The mapping unit 120 receives the outputs of the plurality of vanadium detectors and maps the fluxes. The mapping unit receives the outputs of 102 vanadium detectors separately from the operation data input unit and maps the flux. Flux mapping is also called POWERMAP, and it is possible to obtain an accurate output value reflecting the aging deterioration of the current heavy water reactor. The vanadium detector is located in the nuclear reactor assembly to detect neutron flux, and maps the detected neutron flux using a predetermined flux shape. At this time, channel output, bundle output, and peak coefficient can be obtained by neutron flux mapping.

The selection input unit 130 selects a fuel pipe to be replaced, and inputs the characteristics of the selected fuel pipe into a core code diffusion equation provided by a designer to obtain a pre-simulation result. The selection input unit selects a fuel pipe to be replaced and inputs it into the core code diffusion equation provided by the designer to obtain a pre-simulation. In this case, the pre-simulation predicts the output value after a certain time has elapsed after replacing the fuel tube. When a new fuel tube is used for the first time, the core output rapidly rises and is stabilized to the original output value 3 days after the passage of a certain time. This pre-simulation includes channel output, multiple output, and peak coefficient information after a certain time has elapsed.

The failure prediction unit 140 calculates the results of the operation data input unit, the mapping unit, and the selection input unit, counts the number of vanadium detector failures, and calculates a channel prediction output, a multiple prediction output, and a prediction peak coefficient within a set value. The failure prediction unit predicts the channel output, the multiple output and the peak coefficient by using the pre-simulation, post-simulation and flux mapping results.

The calculation of the predicted channel output is shown in Equation 1 below.

[Formula 1]

Prediction channel output = Channel output of flux mapping + Channel output of pre-simulation - Channel output of post-simulation

As described above, the output predicted value is obtained using the diffusion equation presented by the designer, the difference from the current output value obtained using the diffusion equation is obtained, and then this is added to the currently detected neutron flux mapping result to obtain the predicted value, which is The same can be applied not only to the channel output but also to the prediction of the multiple output and the peak coefficient.

Equation 2 below is an equation for calculating the predicted multiple output, and Equation 3 is an equation for calculating the predicted peak coefficient.

[Formula 2]

Predicted bunch output = bunch output of flux mapping + bunch power of pre-simulation - bunch output of post simulation

[Formula 3]

Predicted peak coefficient = peak coefficient of flux mapping + peak coefficient of pre-simulation - peak coefficient of post-simulation

Through this process, it is possible to accurately calculate the channel output, bundle output, and peak coefficient considering aging.

The failure prediction unit according to this embodiment counts the number of vanadium detector failures and calculates the channel prediction output, multiple prediction output, and predicted peak coefficient within the set value. It solves the problem that it becomes impossible to calculate the area output, channel, multiple output, and channel output peak coefficient, accurately evaluates the failure prediction, and establishes a replacement plan accordingly.

The failure prediction unit 140 of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention includes a regular analysis module 141, a failure prediction condition module 142, a failure accumulation count module 143, a comparative evaluation module ( 144 ), a regression module 145 , and a control module 146 .

The normal analysis module 141 predicts and evaluates the detector failure by using the standard normal "Z distribution" of the probability in the data collected based on the performance results of the operation data input unit, the mapping unit, and the selection input unit. At this time, the average of 13.2 years for detector failure is used and the standard deviation is evaluated as 2 years.

[Formula 4]

과 표준편차

에 대해 모양이 결정되고, 이때의 분포를

로 표기한다. 이러한 바나듐 검출기 고장예측 모델링에 의한 확률밀도는 다음 수식과 같다.This standard normal distribution is used to approximate the distribution of the collected data.

and standard deviation

The shape is determined for , and the distribution at this time is

marked with The probability density by this vanadium detector failure prediction modeling is as follows.

[Formula 5]

3 is a view showing prediction results of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention.

As shown in FIG. 3 , the failure prediction condition module 142 performs failure prediction by identifying the manufacturing date of the vanadium detector, and displays the actual failure vanadium detector in a square form. By grasping the detector failure average (13.2 years), the vanadium detector failure is predicted and evaluated from the time of manufacture, not the age of use, as shown in FIG. At this time, the first year of detector failure is predicted.

The accumulated failure count module 143 evaluates the accumulated number of detector failures over time.

FIG. 3 is a view showing the evaluation result of vanadium detector failure prediction from the time of manufacture of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention.

The failure prediction unit 140 compares the vanadium detector actual failure stored in the past vanadium detector failure life evaluation table data DB of Table 1 below with the result of the prediction evaluation before and reflects it in FIG. 3 to evaluate the adequacy of the failure prediction modeling. and a comparative evaluation module 144 that does.

The comparative evaluation module 144 may evaluate the appropriateness of modeling by comparing the actual failure and the predicted failure.

[Table 1]

4 is a view showing a vanadium detector installation position of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention, and FIG. 5 is a reactor area control of the heavy water vanadium detector failure prediction modeling system according to an embodiment of the present invention. It is a diagram showing (Calibrated Flux Power).

The regression module 145 of the failure prediction unit according to the present embodiment checks whether prediction is completed for all fuel pipes to be replaced. The regression module 145 checks whether the prediction is completed, and if not, returns to select a fuel pipe to be replaced, thereby obtaining the predicted values of the channel output, the multiple output, and the peak coefficient for all the fuel pipes to be replaced.

When the prediction of all fuel pipes is completed as a result of the determination of the regression module 145 , the control module 146 determines the reactor output by using the predicted channel output, the predicted bundle output, and the predicted peak coefficient. At this time, the reactor output can be predicted more accurately than the conventional method of predicting only the diffusion equation provided by the designer as the output of the heavy water reactor core expected when the fuel pipe is replaced. Then, the control module reduces the power plant output in consideration of the reactor output predicted value and the currently set trip setpoint. In this case, the amount of power reduction can be accurately calculated because the predicted value of the reactor power is more accurately predicted. That is, by setting the amount of output reduction to the minimum value within a range that does not exceed the trip set value, the reduction in the amount of power generation can be minimized. After this process, replace the fuel line.

6 is a flowchart illustrating a method for predicting failure of a heavy water vanadium detector according to an embodiment of the present invention. 5, the heavy water vanadium detector failure prediction modeling method according to an embodiment of the present invention includes the steps of: (a) inputting operating data to obtain a post-simulation result; (b) receiving the outputs of the plurality of vanadium detectors and mapping the fluxes; (c) obtaining a pre-simulation result by selecting a fuel pipe to be replaced, and inputting the characteristics of the selected fuel pipe into a core code diffusion equation provided by a designer; (d) calculating the result of the steps (a) to (c) and counting the number of vanadium detector failures to calculate the channel prediction output, the cluster prediction output, and the prediction peak coefficient within a set value; Step d) predicts and evaluates the vanadium detector failure using the standard normal “Z distribution” of probability from the data collected based on the results of steps (a) to (c).

In step (d), the failure prediction of the vanadium detector is performed from the date of manufacture, and the average of 13.2 years is used for the failure of the vanadium detector and the standard deviation is evaluated as 2 years. In addition, failure prediction is performed by identifying the manufacturing date of the vanadium detector, but the first year of failure is predicted. It also evaluates the accumulated number of detector failures over time. In addition, the predictive value of the failure prediction modeling is evaluated by comparing the vanadium detector failure life evaluation table data DB with the result of predicting and evaluating the actual failure of the vanadium detector.

After step (d), the regression module 145 of the failure prediction unit checks whether prediction is completed for all fuel pipes to be replaced. The regression module 145 checks whether the prediction is completed, and if not, returns to select a fuel pipe to be replaced, so that the predicted values of the channel output, the multiple output, and the peak coefficient can be obtained for all the fuel pipes to be replaced.

Next, when the prediction of all fuel pipes is completed as a result of the determination of the regression module 145 , the control module 146 determines the reactor output using the predicted channel output, the predicted multiple output, and the predicted peak coefficient. At this time, the reactor output can be predicted more accurately than the conventional method of predicting only the diffusion equation provided by the designer as the output of the heavy water reactor core expected when the fuel pipe is replaced. Then, the control module reduces the power plant output in consideration of the reactor output predicted value and the currently set trip setpoint. At this time, the amount of power reduction can be accurately calculated because the predicted value of the reactor power is more accurately predicted. That is, by setting the amount of output reduction to the minimum value within a range that does not exceed the trip set value, the reduction in the amount of power generation can be minimized. After this process, replace the fuel line.

According to the heavy water vanadium detector failure prediction modeling system and the method according to an embodiment of the present invention, by evaluating the failure prediction of the vanadium detector from the date of manufacture, it can be determined that there is no correlation with the use period of the detector. In addition, it is possible to present a clear methodology for vanadium detector failure evaluation, compare it with actual failures and predictions of the vanadium detector, and reduce unnecessary replacement costs by approximately KRW 1.7 billion/unit by establishing a detector replacement strategy (no need for early replacement).

The present invention described above is not limited by the above-described embodiments and the accompanying drawings, and it will be apparent to those skilled in the art that various substitutions, modifications and changes are possible without departing from the technical spirit of the present invention.

110: driving data input unit
120: mapping unit
130: selection input unit
140: failure prediction unit
141: regular analysis module
142: failure prediction condition module
143: fault accumulation count module
144: comparative evaluation module
145: regression module
146: control module

Claims (7)

a driving data input unit for obtaining a post simulation result by inputting operating data;
a mapping unit receiving outputs of a plurality of vanadium detectors and mapping the fluxes;
a selection input unit for selecting a fuel pipe to be replaced, and inputting the characteristics of the selected fuel pipe into a core code diffusion equation provided by a designer to obtain a pre-simulation result; and
A failure prediction unit that calculates the results of the operation data input unit, the mapping unit, and the selection input unit, counts the number of vanadium detector failures, and calculates the channel prediction output, the multiple prediction output, and the predicted peak coefficient within the set value;
The failure prediction unit predicts and evaluates the failure of the vanadium detector using the standard normal "Z distribution" of probability from data collected based on the results of the operation data input unit, the mapping unit, and the selection input unit. According to claim 1,
The failure prediction unit is a heavy water vanadium detector failure prediction modeling system for performing failure prediction of the vanadium detector from the date of manufacture. According to claim 1,
The failure prediction unit uses an average of 13.2 years for the failure of the vanadium detector and evaluates the standard deviation as 2 years for a heavy water vanadium detector failure prediction modeling system. According to claim 1,
The failure prediction unit performs failure prediction by grasping the manufacturing date of the vanadium detector, but the heavy water vanadium detector failure prediction modeling system including a failure prediction condition module for predicting the first year of failure. According to claim 1,
The failure prediction unit is a heavy water vanadium detector failure prediction modeling system comprising a failure accumulation count module for evaluating the accumulated number of detector failures over time. According to claim 1,
The failure prediction unit is a heavy water vanadium detector failure prediction modeling system for evaluating the snowiness of the failure prediction modeling by comparing with the result of predicting and evaluating the actual failure of the vanadium detector stored in the vanadium detector failure life evaluation table data DB. (a) obtaining a post simulation result by inputting operating data;
(b) receiving the outputs of the plurality of vanadium detectors and mapping the fluxes;
(c) obtaining a pre-simulation result by selecting a fuel pipe to be replaced, and inputting the characteristics of the selected fuel pipe into a core code diffusion equation provided by a designer; and
(d) calculating the result of the steps (a) to (c), counting the number of vanadium detector failures, and calculating the channel prediction output, the cluster prediction output, and the prediction peak coefficient within a set value;
The step (d) is a heavy water vanadium detector failure prediction modeling method in which the vanadium detector failure is predicted and evaluated using the standard normal “Z distribution” of the probability from the data collected based on the results of the steps (a) to (c).

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