JP7555318B2 - Moderator temperature coefficient measuring method, moderator temperature coefficient measuring system and program - Google Patents

Description

This disclosure relates to a moderator temperature coefficient measurement method, a moderator temperature coefficient measurement system, and a program.

The moderator temperature coefficient (MTC; hereinafter, the moderator temperature coefficient may be referred to as MTC) is one of the indices that indicate the inherent safety of a nuclear power plant. Conventionally, MTC is obtained by intentionally changing the moderator temperature in a process for measuring MTC during a reactor physical inspection, measuring the reactivity and moderator temperature of the reactor during the change, and determining the MTC based on the measurement results. For example, Patent Document 1 describes a method of creating a graph showing the relationship between the measured reactivity and moderator temperature and calculating the MTC using the gradient method. Patent Document 2 describes a method of calculating the MTC by analyzing time-series data of reactivity and moderator temperature using the noise method.

Special Publication No. 04-058913 Japanese Patent Application Publication No. 03-084496

Conventional MTC measurement methods require cumbersome work on the nuclear power plant's systems to intentionally change the moderator temperature, and also require a certain amount of time to be allocated to the MTC measurement process to collect measurements of reactivity and moderator temperature. There is a demand for an MTC measurement method that can reduce the workload required for MTC measurement and shorten the measurement time.

The present disclosure provides a moderator temperature coefficient measurement method, a moderator temperature coefficient measurement system, and a program that can solve the above problems.

The moderator temperature coefficient measurement method disclosed herein includes the steps of acquiring time series data of moderator temperature and time series data of reactivity, performing frequency analysis on each of the acquired time series data of moderator temperature and time series data of reactivity, calculating a correlation value between the time series data of moderator temperature and the time series data of reactivity at the corresponding frequency by frequency, and identifying the frequency at which the correlation value is equal to or greater than a threshold value, extracting the time series data of moderator temperature and reactivity at the identified frequency, from the time series data of moderator temperature and reactivity, the time series data of moderator temperature and reactivity at which the time difference based on the phase difference between the time series data of moderator temperature and the time series data of reactivity is within a predetermined set range, and calculating the moderator temperature coefficient using the extracted time series data of moderator temperature and reactivity.

The moderator temperature coefficient measuring device disclosed herein includes a means for acquiring time series data of moderator temperature and time series data of reactivity, a means for frequency-analyzing each of the acquired time series data of moderator temperature and time series data of reactivity, a means for calculating a correlation value between the time series data of moderator temperature and the time series data of reactivity at the corresponding frequency by frequency, and a means for identifying a frequency at which the correlation value is equal to or greater than a threshold value, a means for extracting, from the time series data of moderator temperature and reactivity at the identified frequency, the time series data of moderator temperature and reactivity at which the time difference based on the phase difference between the time series data of moderator temperature and the time series data of reactivity falls within a predetermined set range, and a means for calculating a moderator temperature coefficient using the extracted time series data of moderator temperature and reactivity.

The program disclosed herein causes a computer to execute the steps of acquiring time series data of moderator temperature and time series data of reactivity, frequency analyzing each of the acquired time series data of moderator temperature and time series data of reactivity, calculating a correlation value between the time series data of moderator temperature and the time series data of reactivity at corresponding frequencies by frequency, and identifying a frequency at which the correlation value is equal to or greater than a threshold value, extracting, from the time series data of moderator temperature and reactivity at the identified frequency, time series data of moderator temperature and reactivity for which the time difference based on the phase difference between the time series data of moderator temperature and the time series data of reactivity is within a predetermined set range, and calculating a moderator temperature coefficient using the extracted time series data of moderator temperature and reactivity.

The above-mentioned moderator temperature coefficient measurement method, moderator temperature coefficient measurement system, and program make it possible to measure the moderator temperature coefficient from time series data of moderator temperature and reactivity measured in a situation where reactivity changes due to natural temperature fluctuations in the moderator temperature.

FIG. 1 is a block diagram illustrating an example of an MTC measurement system according to an embodiment. FIG. 2 is a diagram showing an example of changes in reactivity and moderator temperature during a reactor physical inspection. FIG. 4 is a diagram showing the relationship between reactivity and moderator temperature according to the embodiment. FIG. 11 is a diagram illustrating a short period according to the embodiment. 11 is a flowchart illustrating an example of an MTC measurement process according to the embodiment. 11A and 11B are diagrams illustrating a threshold setting process according to an embodiment. 1 is a flowchart illustrating an example of a noise method according to an embodiment. FIG. 2 is a diagram illustrating a noise method according to an embodiment. 11A to 11C are diagrams illustrating the effects of various filter processes provided in the noise method according to the embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration of an MTC measurement system according to an embodiment.

<Embodiment>
The MTC (moderator temperature coefficient) measurement process of the present disclosure will now be described with reference to FIGS.
(System Configuration)
FIG. 1 is a block diagram illustrating an example of an MTC measurement system according to an embodiment.
The MTC measurement system 10 measures the MTC based on time-series data of the moderator temperature and reactivity measured in a situation where the reactivity changes due to natural temperature fluctuations of the moderator temperature in a nuclear power plant 20 undergoing a reactor physical inspection. In the MTC measurement of this embodiment, the MTC can be measured without artificially changing the moderator temperature.

The MTC measurement system 10 is communicatively connected to a nuclear power plant 20, acquires data necessary for MTC measurement from the nuclear power plant 20 undergoing a reactor physical inspection, and measures the MTC.
The nuclear plant 20 includes a moderator temperature measurement system 21 and a neutron detector 22 .
The moderator temperature measurement system 21 measures the moderator temperature (hereinafter, may be referred to as Tavg) and outputs Tavg to the MTC measurement system 10. Tavg can be obtained, for example, by measuring the temperature of the primary coolant of the reactor.
The neutron detector 22 measures the neutron flux φ in the reactor and outputs the measured neutron flux φ to the MTC measurement system 10 .

The MTC measurement system 10 comprises a reactivity processing device 11, a timer 12, a data recording and control device 13, a data extraction device 14, an MTC processing device 15, a statistical processing device 16, a display device 17, an operator console 18, and a threshold calculation device 19. Of these, the reactivity processing device 11, the data recording and control device 13, the data extraction device 14, the MTC processing device 15, the statistical processing device 16, the operator console 18, and the threshold calculation device 19 are configured by a computer such as a PC or a server terminal device.

The reactivity processing device 11 obtains the neutron flux φ measured by the neutron detector 22 and calculates the reactivity. The reactivity processing device 11 outputs the reactivity to the data recording and control device 13.
The timer 12 measures time.
The data recording and control device 13 records and stores data acquired from the nuclear plant 20 and data calculated by the MTC measurement system 10 .
The data extraction device 14 extracts time series data suitable for MTC measurement from the time series data of reactivity and the time series data of Tavg measured throughout the entire reactor physics inspection.

The MTC processing device 15 executes MTC processing, which is processing for calculating the MTC. The MTC processing device 15 includes an MTC processing unit 151, a correction unit 152, and a kernel design code 153.
The MTC processing unit 151 performs MTC processing multiple times based on the time series data of reactivity and Tavg extracted by the data extraction device 14, and calculates multiple MTCs. More specifically, a large number of time series data for a predetermined short period (e.g., 10 minutes) are extracted from the time series data extracted by the data extraction device 14, and the MTC is calculated using the time series data for each short period. In other words, the MTC is calculated for the number of extracted short periods.

The correction unit 152 corrects the MTC calculated by the MTC processing unit 151. In general, the MTC is calculated based on the reactivity and Tavg measured at the boric acid concentration of the primary coolant when the control rod is withdrawn from the reactor and the reactor becomes critical. The control rod is in various positions during the reactor physical inspection, and the boric acid concentration fluctuates. The MTC is affected by the boric acid concentration. The correction unit 152 corrects the variation in MTC due to the difference in boric acid concentration, taking into consideration the effect on the MTC of the deviation of the boric acid concentration of the primary coolant when the time-series data extracted by the data extraction device 14 is measured from the boric acid concentration of the primary coolant when the control rod is withdrawn and the reactor becomes critical. The correction unit 152 corrects the MTC calculated by the MTC processing unit 151 to the MTC at the boric acid concentration when the control rod is withdrawn from the reactor and the reactor becomes critical, using the nuclear design code 153.

Nuclear design code 153 is a program and data for performing core analysis. When the boric acid concentration and the position of the control rods are input, nuclear design code 153 calculates the amount of change in reactivity when Tavg changes under those conditions. Using the result, it is possible to calculate the MTC value. Nuclear design code 153 can also calculate the MTC value when the control rods are withdrawn and criticality is reached.

The statistical processing device 16 acquires the multiple MTCs calculated by the MTC processing device 15, performs statistical processing, and determines a final MTC value.
The display device 17 is a display that displays the final MTC value and the like.
The operator console 18 is a terminal device used by an operator to instruct the MTC measurement system 10 to perform MTC measurement.
The threshold calculation device 19 calculates thresholds (a threshold Th and a range from τmin to τmax, which will be described later) used in the MTC processing, and sets them in the MTC processing device 15 .

(Overview of MTC measurement process)
Next, an outline of the MTC measurement process of this embodiment will be described with reference to FIG.
FIG. 2 shows an example of the transition of reactivity and Tavg during a conventional reactor physics inspection.
A reactor physical inspection is performed when a nuclear reactor is started up, as required by law or the like. A graph 200 shows the variation of reactivity during a reactor control physical inspection, and a graph 201 shows the variation of Tavg during a reactor control physical inspection. A typical reactor physical inspection includes steps 1 to 5. Step 4 is a step for measuring MTC. In step 4, Tavg is intentionally changed by about 1° C. by operating the main steam relief valve or the like. As shown in the figure, the reactivity varies due to the variation of Tavg. When Tavg is increased, the reactivity decreases. Conventionally, Tavg and reactivity are measured at a predetermined time interval during step 4, and the MTC is calculated by a noise method or the like using the measured time series data of Tavg and time series data of reactivity. Step 4 requires a certain time (for example, 2 hours), and the work for changing Tavg (operating the main steam relief valve or the like) is cumbersome. In contrast, in the present embodiment, step 4 is omitted, and MTC is calculated using time-series data of Tavg and reactivity measured during a period in which reactivity changes due to natural fluctuations in Tavg during a reactor physical inspection without intentionally changing Tavg, thereby shortening the reactor physical inspection time and reducing the burden on the operator by eliminating the need for complicated operations such as the main steam relief valve.

The period during which the reactivity changes due to the natural temperature fluctuation of Tavg is shown in a frame enclosed by a dashed line in Figure 2. Periods 202 to 211 are periods during which the reactivity changes due to the natural temperature fluctuation of Tavg, and no intentional changes are made to Tavg. In this embodiment, the MTC is calculated using time-series data of Tavg and reactivity measured during these periods. For example, part of the period 202 to 211 is a waiting time for a reactor physical inspection. The MTC can be calculated by the following formula (1).
MTC (pcm/℃) = Δ reactivity ÷ ΔTavg (1)
From formula (1), it can be seen that the measurement of MTC requires the measurement of Tavg and reactivity in a state where the reactivity does not change due to factors other than the temperature change of Tavg. In steps 1 to 3 and part of step 5, the boric acid concentration of the primary coolant fluctuates due to the operation of the control rod, etc., and the fluctuation of the boric acid concentration affects the reactivity. In other words, during the period of steps 1 to 3 and part of step 5, the fluctuation of the boric acid concentration affects the reactivity in addition to Tavg, so it is difficult to obtain the relationship between the change in Tavg and the change in reactivity using Tavg and reactivity measured during this period. In other words, Tavg and reactivity measured during steps 1 to 3, part of step 5, and the period immediately after the influence thereof are not suitable for MTC measurement. Therefore, in this embodiment, MTC is calculated using Tavg and reactivity measured during the period 202 to 211, excluding the period not suitable for MTC measurement from the entire reactor physical inspection.

As shown in FIG. 2, there is no significant change in Tavg and reactivity during the period 202-211. FIG. 3 shows an example (enlarged view) of the transition of reactivity and Tavg during the MTC measurement period of this embodiment. In the above step 4, Tavg is artificially changed by about 1° C., whereas the change in Tavg during the period 202-211 used in this embodiment is, for example, about 0.05° C. to 0.2° C. Noise may be included in the measurement of Tavg and reactivity, but since the change in Tavg and reactivity during the period 202-211 is smaller than that during step 4, the calculation result of MTC using formula (1) is more susceptible to noise than when MTC is calculated from Tavg and reactivity measured in step 4. (A) Therefore, in this embodiment, a large number of consecutive short periods are extracted from each of the periods 202-211, and MTC is calculated using the time series data of Tavg and reactivity measured in each short period. This is shown in FIG. 4. FIG. 4 shows an example of sub-periods 202-1 to 202-6 extracted from the period 202. The length of each of the sub-periods 202-1 to 202-6 is about 10 minutes. For example, the start point of each sub-period is shifted backward one by one in order, such as sub-period 202-1 being 10 minutes from the first observation point of the period 202, sub-period 202-2 being 10 minutes from the second observation point, and so on, and many sub-periods are extracted. In the last sub-period 202-6, the last observation point included in the sub-period coincides with the end of the period 202. Similarly, many sub-periods are extracted for the periods 203 to 211. In this way, many sub-periods are extracted and the MTC is calculated for each sub-period, thereby obtaining many MTCs. Then, the final MTC is determined based on the many calculated MTCs. For example, the average of many MTCs may be used as the final MTC. (B) In addition, in this embodiment, when calculating the MTC for each short period, filtering (filtering based on frequency, filtering based on time difference) is performed to remove the effects of noise from the data for the short period, and data corresponding to the relationship between Tavg and reactivity observed when Tavg is artificially changed by about 1°C is extracted. Then, multiple MTCs are calculated based on the data extracted by filtering after removing noise. By using multiple highly accurate MTCs, the effects of noise during measurement are reduced and accuracy is improved. Next, the MTC measurement process of this embodiment will be described in more detail with reference to FIG. 5.

(Operation)
FIG. 5 is a flowchart illustrating an example of an MTC measurement process according to the embodiment.
<Preliminary threshold setting process>
Prior to the MTC measurement process, it is determined whether or not the threshold value used in the filtering process has not been set (step S1). Specifically, if both the threshold value used in the filtering process based on the frequency and the threshold value used in the filtering process based on the time difference have already been set (step S1; No), the process proceeds to step S5. If any threshold value has not been set (step S1; Yes), an operation is performed for a predetermined time period (step S2) to artificially vary Tavg by about 1°C for the reactor to be inspected physically. This operation is the same as the operation in the case of performing MTC measurement by the conventional method in step 4 of FIG. 2. The data recording and control device 13 acquires time series data of Tavg from the moderator temperature measurement system 21 and acquires time series data of reactivity from the reactivity processing device 11 (step S3). The data recording and control device 13 stores Tavg obtained from the moderator temperature measurement system 21 in association with the time when the timer 12 is measuring (time series data of Tavg), and also stores the reactivity obtained from the reactivity processing device 11 in association with the time when the timer 12 is measuring (time series data of reactivity). The threshold calculation device 19 obtains the time series data of Tavg and the time series data of reactivity from the data recording and control device 13 and calculates a threshold (step S4). A method of calculating the threshold will now be described with reference to FIG. 6.

FIG. 6 is a diagram for explaining the threshold setting process according to the embodiment. As shown in the graph in frame 61, the threshold calculation device 19 cuts out the time series data of Tavg and the time series data of reactivity acquired from the data recording and control device 13 for each of the predetermined time windows W1 to W3, performs a Fourier transform on each of them, and decomposes them by frequency. For example, the threshold calculation device 19 calculates a cross power spectrum density indicating the correlation strength between Tavg and reactivity at the same frequency using the time series data of Tavg and reactivity for each frequency calculated for the time window W1. The threshold calculation device 19 also calculates cross power spectrum densities for other time windows such as W2, and averages these. The graph in frame 62 shows the relationship between the calculated frequency and the maximum value of the cross power spectrum density (CP). In addition, the threshold calculation device 19 calculates the coherence between Tavg and reactivity at the same frequency using the time series data of Tavg and reactivity for each frequency calculated for the time windows W1 to W3, and calculates the average value of these. Then, the threshold calculation device 19 calculates the cross power spectral density value corresponding to the range where the calculated average value of coherence exceeds a predetermined threshold, and sets the lower limit value as the threshold Th of the cross power spectral density (coherence and cross power spectral density can be converted into each other by calculation, and the lower limit value is set as the threshold Th in order to extract the range of cross power spectral density values corresponding to the range where coherence is greater than the threshold as positive data). Furthermore, the threshold calculation device 19 compares the time series data of Tavg and reactivity for each frequency calculated for each of the time windows W1 to W3 in the same time frame and at the same frequency, and calculates, from the phase difference between the two, the time difference between Tavg and reactivity corresponding to the phase difference (the time corresponding to "Tavg - time difference ΔT of reactivity" shown in the figure). Then, based on the calculated time difference, the threshold calculation device 19 creates a histogram of time differences by frequency, as exemplified by the graph in frame 63, calculates the time difference corresponding to the frequency range in which the previously calculated average coherence value exceeds a predetermined threshold, and sets the calculated time difference range (upper limit τmax to lower limit τmin) as the time difference threshold. The threshold calculation device 19 records the calculated cross power spectral density threshold Th and the time difference range in the MTC processing device 15. Once the threshold Th and range have been set, the MTC measurement process from step S5 onwards is ready to be executed.

<MTC measurement process>
First, the operator operates the operator console 18 to instruct the start of the MTC measurement process. Then, during the execution of the reactor physical inspection, the data recording and control device 13 acquires time series data of Tavg from the moderator temperature measurement system 21 and acquires time series data of reactivity from the reactivity processing device 11 (step S5). Specifically, the data recording and control device 13 stores the Tavg acquired from the moderator temperature measurement system 21 in association with the time measured by the timer 12 at that time (time series data of Tavg), and also stores the reactivity acquired from the reactivity processing device 11 in association with the time measured by the timer 12 at that time (time series data of reactivity). The data recording and control device 13 stores these time series data. In addition, the data recording and control device 13 acquires information on the position of the control rod and the boric acid concentration of the primary coolant material from the nuclear plant 20 at a predetermined time interval, and stores the information in association with the time measured by the timer 12 at that time (time series data of the control rod and the boric acid concentration). The data recording and control device 13 acquires the time-series data of Tavg, reactivity, and control rod and boric acid concentrations throughout the entire reactor physical inspection. Note that the reactor physical inspection does not include step 4 in FIG.

Next, the data extraction device 14 extracts data on changes in reactivity due to natural temperature fluctuations of Tavg (step S6). Based on the time series data of Tavg, the time series data of reactivity, and the time series data of control rods and boric acid concentrations stored in the data recording and control device 13, the data extraction device 14 extracts the time series data of Tavg and the time series data of reactivity for a period that satisfies the following two conditions:
(Condition 1) This is a period during which no control rod operation or addition of reactivity by diluting or concentrating boric acid is taking place.
(Condition 2) A certain amount of time must have elapsed after the control rod was operated and reactivity was added by diluting and concentrating boric acid (60 seconds after the control rod was operated and 30 minutes after the boric acid was diluted and concentrated).
The period satisfying the condition 1 and the condition 2 is, for example, the period 202 to 211 in Fig. 2. The data extracting device 14 outputs the extracted time series data of Tavg and time series data of reactivity to the MTC processing device 15.

Next, the MTC processing unit 151 of the MTC processing device 15 executes MTC processing (step S7). The MTC processing is a process for calculating the MTC. For example, the noise method is used for the MTC processing. The MTC processing unit 151 executes the noise method multiple times (as many times as the number of short periods) to calculate a large number of MTCs. Although the noise method itself is well known, in this embodiment, the above-mentioned (A) and (B) are implemented to improve the measurement accuracy. (B) is realized by incorporating a filter process into a general noise method. Below, the process for calculating the MTC using the noise method will be described using the periods 202 to 211 in FIG. 2 and the short periods 202-1 to 202-6 in FIG. 4 as examples.

Figures 7 and 8 show the procedure of the noise method according to this embodiment. First, the MTC processing unit 151 creates multiple sets of time series data of Tavg and time series data of reactivity (step S11). The MTC processing unit 151 extracts short periods from each of the periods 202 to 211. The time windows W1' to W3' in the graph in frame 81 of Figure 8 are examples of short periods. For example, one short period is 10 minutes, and 296 observation points are included in the 10 minutes. The MTC processing unit 151 extracts 256 observation points from the first observation point out of the 296 observation points. Next, the MTC processing unit 151 extracts 256 observation points from the second observation point out of the 296 observation points. Thereafter, the MTC processing unit 151 repeats the process of extracting 256 observation points while shifting the starting observation point one by one until the 256th observation point coincides with the last observation point of the 296 observation points. The MTC processing unit 151 performs this process on each of the Tavg time series data and the reactivity time series data. This allows 40 Tavg time series data and 40 reactivity time series data to be extracted from one short period. The MTC processing unit 151 performs the same process on all the short periods extracted from the periods 202 to 211. Next, the MTC processing unit 151 performs frequency analysis on each of the time series data (step S12). The MTC processing unit 151 decomposes all the Tavg time series data and the reactivity time series data by frequency using a Fourier transform. The MTC processing unit 151 averages the Tavg time series data by frequency for the number of data (40) for one short period. For one short period, the MTC processing unit 151 averages the frequency-specific reactivity time series data for the number of data (40 pieces) by frequency.

Next, the MTC processing unit 151 calculates the cross power spectral density, coherence, time difference (phase difference), and MTC for each frequency using the Tavg time series data and reactivity time series data for each frequency (step S13). If the frequency is ω1, Tavg is T, and reactivity is ρ, then MTC (ω1) = T (ω1) ÷ ρ (ω1). The MTC processing unit 151 calculates the MTC (ωX) for each frequency and each subperiod (X = 1 to the number divided by frequency). In addition, the MTC processing unit 151 calculates the power spectral density of Tavg, the power spectral density of reactivity, and the cross power spectral density of Tavg and reactivity for each frequency using 40 pieces of Tavg and reactivity time series data for each subperiod (frame 82 in Figure 8), and calculates the coherence for each subperiod using these. In addition, the 40 cross power spectral densities in one subperiod are averaged to calculate the cross power spectral density for each subperiod. The calculation method of these values is known. In addition, the MTC processing unit 151 compares the time series data of Tavg for each frequency with the time series data of reactivity to calculate the phase difference between the two, and calculates the time difference from when Tavg changes to when the reactivity changes for each small period based on the phase difference. The cross power spectrum density and coherence of Tavg and reactivity are both indicators that indicate the strength of the correlation between Tavg and reactivity. As shown in the graph in box 61 of FIG. 6 and the graph in box 81 of FIG. 8, when Tavg fluctuates, whether the fluctuation is artificial or natural, the reactivity fluctuates with a slight delay due to the influence. Since MTC is a value that represents this relationship, in order to calculate an accurate MTC, it is necessary to extract a combination of the fluctuation of Tavg and the fluctuation of reactivity due to the influence of that fluctuation, that is, data of Tavg and reactivity that are highly correlated. For this purpose, cross power spectrum density and coherence are used. Although coherence is also used in conventional noise methods, in this embodiment, cross power spectral density is used in addition to coherence. Both coherence and cross power spectral density have large values (indicating strong correlation) when the phase difference and amplitude ratio of two signals are stable and consistent. Specifically, for one short period, if the phase difference and amplitude ratio of 40 Tavgs by frequency and the corresponding 40 reactivity are constant throughout the 40, both coherence and cross power spectral density will have values that indicate a strong correlation.

In the case of cross power spectral density, unless the signal strength is relatively large (if the amplitude is not large), it does not show a value indicating a strong correlation, but if the Tavg or reactivity signal becomes large due to the influence of electrical noise from sensors, etc., regardless of the reaction in the reactor, it may show a large value even if the correlation between the two is not very strong. Conversely, in the case of coherence, if the correlation between Tavg and the amplitude or phase difference of the reactivity is strong, even a signal with low strength (small amplitude) will show a value indicating a strong correlation. A signal with low strength is data of a frequency that is completely unrelated to the calculation of MTC or simply noise, and it is possible that these are the ones that showed a strong correlation. In summary, if only data with a strong correlation between the two can be extracted based on the cross power spectral density of Tavg and reactivity, signals unrelated to MTC processing and noise with low signal strength can be removed, and if data with a strong correlation between the two can be extracted based on the coherence of Tavg and reactivity, noise with large signal strength unrelated to MTC processing can be removed. In other words, by combining cross power spectral density and coherence, it is possible to remove data that shows a strong correlation but is actually considered noise from the time series data of frequency-specific Tavg and reactivity, leaving only the data truly necessary for MTC calculation.

Furthermore, even if data is extracted by performing filtering from the standpoint of both cross power spectral density and coherence, there is a possibility that data unrelated to MTC will remain. This is because, whether it is cross power spectral density or coherence, as long as the time difference (phase difference) between Tavg and reactivity is stable and constant, the value indicates a strong correlation even if the time difference deviates from the true time from when Tavg changes until its effect appears in the reactivity. Therefore, in this embodiment, for data with a strong correlation between Tavg and reactivity, it is confirmed whether the time from when Tavg changes until when the reactivity changes is correct, and only data with a correct time difference is extracted.

Next, the MTC processing unit 151 extracts frequencies where the cross power spectral density is equal to or greater than the threshold value Th (step S14). For example, the MTC processing unit 151 calculates the average value of the cross power spectral density over the subperiods 202-1 to 202-6, compares the average value of the cross power spectral density for each frequency with the threshold value Th, and identifies and extracts frequencies where the cross power spectral density is equal to or greater than the threshold value Th (box 83 in FIG. 8). In the subsequent processing, only the time series data of Tavg and reactivity for the extracted frequencies are used, and time series data for frequencies below the threshold value are not used (frequency-based filter).

Next, the MTC processing unit 151 extracts data within a range in which the time difference between Tavg and reactivity is set as a threshold value from the time series data of Tavg and reactivity of the frequency extracted in step S14 (step S15). The MTC processing unit 151 calculates whether the time difference corresponding to the phase difference between the time series data of Tavg and the time series data of reactivity is included in the range from the lower limit value τmin to the upper limit value τmax, and extracts only the time series data of the frequency included in this range (box 84 in FIG. 8). The process of step S15 is a filter process based on the time difference.
By steps S14 and S15, data with strong correlation and appropriate time difference is extracted as data with high reliability for MTC processing.

Next, the MTC processing unit 151 extracts data whose coherence value is equal to or greater than a threshold value (step S16). From the time series data of Tavg and reactivity by frequency extracted in steps S14 and S15, the MTC processing unit 151 extracts time series data of frequencies whose coherence is equal to or greater than a predetermined threshold value (coherence cutoff in box 85 in FIG. 8). Data extraction based on coherence has been performed conventionally, but in this embodiment, it has the effect of removing noise that cannot be removed by a filter in terms of the above-mentioned cross power spectral density. Note that the order of the coherence cutoff in step S16 and the filter processing based on the time difference in step S15 may be reversed.

Next, the MTC processing unit 151 selects an MTC based on the extracted time series data (step S17). The MTC processing unit 151 selects the MTC for each frequency and short period that corresponds to the time series data extracted in steps S14 to S16 from the MTCs for each frequency and short period calculated in step S11 (box 86 in FIG. 8).

As described above, the processing in step S7 calculates MTC based on highly reliable time series data of Tavg and reactivity for the number of extracted sub-periods. From the viewpoint of accuracy of MTC measurement, the more sub-periods that are extracted, the better.

Return to FIG. 5. Next, the correction unit 152 corrects the MTC variation due to differences in boron concentration using the nuclear design code 153 (step S8). The boric acid concentration of the primary coolant significantly affects the MTC value. It is stipulated that the MTC is calculated from Tavg and reactivity at the boric acid concentration when the control rods are fully withdrawn and the reactor becomes critical. In contrast, the Tavg and reactivity measured during the reactor physical inspection are not necessarily measured under the conditions of the boric acid concentration when the control rods are fully withdrawn and the reactor becomes critical. Therefore, in step S8, the MTC value for each short period calculated by the MTC processing unit 151 is corrected to the MTC value under the above conditions. Specifically, the correction unit 152 inputs the position of the control rod and the boric acid concentration of the primary coolant in the short period corresponding to the calculated MTC into the nuclear design code 153, and causes the nuclear design code 153 to calculate the MTC value at that boric acid concentration. The calculated value of MTC at this time is set to α1. Next, the correction unit 152 fully withdraws the control rod, and causes the nuclear design code 153 to calculate the MTC value at the boric acid concentration when the reactor becomes critical. The calculated value of MTC at this time is set to α2. The correction unit 152 obtains the calculated values α1 and α2 from the nuclear design code 153, and calculates the difference between the calculated values α1 and α2. The correction unit 152 adds the difference between the calculated values α1 and α2 to the MTC calculated by the MTC processing unit 151. As a result, the value of MTC calculated by the MTC processing unit 151 is corrected to the value of MTC under the above conditions. The correction unit 152 performs this correction process for all of the MTCs calculated for each short period. The MTC processing device 15 outputs all of the corrected MTCs to the data recording and control device 13. The data recording and control device 13 stores all of the corrected MTCs.

Next, the statistical processing device 16 obtains the corrected MTC from the data recording and control device 13, creates a histogram of the MTC, and determines the final MTC evaluation value (step S9). An image of the histogram created by the statistical processing device 16 is shown in box 87 of FIG. 8. The vertical axis of the histogram in box 87 indicates the number (frequency) of calculated MTC processing results, and the horizontal axis indicates the MTC processing result (pcm/°C). The histogram in box 87 indicates the distribution of the corrected MTC. For example, the statistical processing device 16 determines the average or median of the range of the most frequently occurring MTC values obtained from the histogram as the final MTC evaluation value. This makes it possible to calculate a final MTC with high accuracy using a large number of MTCs calculated for each short period extracted from the long-term time-series data collected throughout the entire reactor physical inspection.

In the flowchart of FIG. 5, a histogram is created and the average value of the histogram is determined as the final MTC, but the statistical processing device 16 may simply calculate the average value of the MTCs calculated in step S8 and determine this average value as the final MTC. Alternatively, the created histogram may be fitted with a normal distribution, and the average value of the normal distribution obtained by the fitting may be determined as the final MTC.

Figure 9 shows the effect of the processing of steps S14 to S16. Graphs 91 to 94 in Figure 9 are histograms of MTC, and the vertical axis of each graph indicates the number (frequency) of MTC processing results, and the horizontal axis indicates the MTC processing result (pcm/°C). Graph 91 is an example of a histogram created using all the MTCs calculated in step S13 before the processing of steps S14 to S16 is performed. Graph 92 is an example of a histogram created using the MTCs extracted by the processing of step S14. Graph 93 is an example of a histogram created using the MTCs extracted by the processing of steps S14 and S15. Graph 94 is an example of a histogram created using the MTCs extracted by the processing of steps S14 to S16. As shown in the figure, each time filtering is performed, noise component data that is not suitable for use in calculating MTC is removed, and only suitable data can be extracted. In addition, when the final MTC evaluation value calculated based on the histogram of graph 94 was compared with the MTC evaluation value obtained by the conventional MTC measurement method in which Tavg is artificially varied to measure MTC in step 4 of FIG. 2, it was confirmed that the MTC measurement method according to this embodiment enables highly accurate measurement of MTC.

In general, MTC measurement is often performed during a process for MTC measurement in a reactor physical inspection. This process takes several hours, and in order to collect data for MTC measurement, cumbersome work is required for the nuclear power plant. In contrast, according to this embodiment, data in which reactivity fluctuates only due to natural fluctuations of Tavg is extracted as MTC measurement data from the time series data of Tavg and reactivity collected during the reactor physical inspection (step S6), and then, from each of the multiple MTC measurement data that are distributed throughout the entire range of the reactor physical inspection, multiple short-term time series data containing the amount of data required for MTC measurement are extracted, and the MTC is calculated for each short period to obtain multiple MTCs. At this time, a filter based on frequency and a filter based on time difference are applied to remove noise components from the data used to calculate the MTC, thereby calculating multiple MTCs with high accuracy (steps S14 to S16). Then, the final MTC is determined based on the multiple MTCs. This makes it possible to measure the MTC without providing a process for MTC measurement in the reactor physical inspection. Since there is no need to set up a process for measuring MTC, the cumbersome work of intentionally changing Tavg can be omitted. In addition, the time required for reactor physical inspection can be shortened.

10 is a diagram showing an example of a hardware configuration of the MTC measurement system according to the embodiment. A computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage device 903, an input/output interface 904, and a communication interface 905.
The above-mentioned reactivity processing device 11, data recording and control device 13, data extraction device 14, MTC processing device 15, statistical processing device 16, operator console 18, and threshold calculation device 19 are implemented in a computer 900. The above-mentioned functions are stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads out the program from the auxiliary storage device 903, expands it in the main storage device 902, and executes the above-mentioned processing according to the program. The CPU 901 also secures a storage area in the main storage device 902 according to the program. The CPU 901 also secures a storage area in the auxiliary storage device 903 for storing data being processed according to the program.

In addition, a program for realizing all or part of the functions of the reactivity processing device 11, the data recording and control device 13, the data extraction device 14, the MTC processing device 15, the statistical processing device 16, the operator console 18, and the threshold calculation device 19 may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed to perform processing by each functional unit. The "computer system" here includes hardware such as an OS and peripheral devices. In addition, if a WWW system is used, the "computer system" also includes a homepage providing environment (or display environment). In addition, the "computer-readable recording medium" refers to a portable medium such as a CD, DVD, or USB, and a storage device such as a hard disk built into a computer system. In addition, if this program is distributed to a computer 900 via a communication line, the computer 900 that receives the program may expand the program in the main storage device 902 and execute the above processing. In addition, the above program may be for realizing part of the above-mentioned functions, and may further be capable of realizing the above-mentioned functions in combination with a program already recorded in the computer system.

As described above, several embodiments of the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and gist of the invention.

<Additional Notes>
The moderator temperature coefficient measuring method, the moderator temperature coefficient measuring system, and the program described in each embodiment can be understood, for example, as follows.

(1) The moderator temperature coefficient measurement method according to the first aspect includes the steps of acquiring time series data of moderator temperature and time series data of reactivity, performing frequency analysis on each of the acquired time series data of moderator temperature and time series data of reactivity, calculating a correlation value between the time series data of moderator temperature and the time series data of reactivity at the corresponding frequency by frequency, and identifying the frequency at which the correlation value is equal to or greater than a threshold value (S14), extracting the time series data of moderator temperature and reactivity at the identified frequency, which is a time difference based on the phase difference between the time series data of moderator temperature and the time series data of reactivity, within a predetermined set range (S15), and calculating the moderator temperature coefficient using the extracted time series data of moderator temperature and reactivity.

This makes it possible to remove noise components from the time series data of moderator temperature and reactivity, extract the data necessary for MTC measurement, and calculate MTC with high accuracy.

(2) A moderator temperature coefficient measurement method according to a second aspect is a moderator temperature coefficient measurement method of (1), further comprising a step (S16) of extracting time series data of the moderator temperature and the reactivity at the identified frequency, or time series data of the moderator temperature and the reactivity extracted based on the time difference, the data having coherence equal to or greater than a threshold value.
This makes it possible to further remove noise components from the time series data of the moderator temperature and the time series data of the reactivity.

(3) A moderator temperature coefficient measurement method according to a third aspect is a moderator temperature coefficient measurement method according to (1) to (2), wherein in the step of identifying the frequency, a frequency at which the cross power spectral density of the moderator temperature and the reactivity is equal to or greater than a threshold value is identified from the time series data of the moderator temperature and the reactivity by frequency.
This makes it possible to identify the frequency at which the correlation value between the time series data of the moderator temperature and the time series data of the reactivity is equal to or greater than the threshold value.

(4) A moderator temperature coefficient measuring method according to a fourth aspect is a moderator temperature coefficient measuring method according to any one of (1) to (3), further comprising the steps of: acquiring time series data of the moderator temperature and time series data of reactivity when the moderator temperature is artificially varied (S3); and performing frequency analysis on the time series data of the moderator temperature and the reactivity to calculate the correlation value of the time series data of the moderator temperature and the reactivity for each frequency; and setting a threshold value for the calculated correlation value (S4).
This makes it possible to determine a threshold value for the correlation value when identifying a frequency at which the correlation value between the time-series data of the moderator temperature and the time-series data of the reactivity is equal to or greater than the threshold value.

(5) A moderator temperature coefficient measuring method according to a fifth aspect is a moderator temperature coefficient measuring method according to any one of (1) to (4), further comprising the steps of: acquiring time series data of moderator temperature and time series data of reactivity when the moderator temperature is artificially varied (S3); performing frequency analysis on the time series data of the moderator temperature and the reactivity to calculate a phase difference between the moderator temperature and the reactivity for each frequency; and setting the setting range for the time difference based on the phase difference (S4).
This makes it possible to determine the range of time difference when extracting data with a time difference within a set value from the time series data of moderator temperature and the time series data of reactivity.

(6) A moderator temperature coefficient measuring method according to a sixth aspect is a moderator temperature coefficient measuring method according to any one of (1) to (5), further comprising a step (S6) of extracting, based on predetermined extraction conditions, time series data of the moderator temperature and time series data of the reactivity, in which the reactivity changes due to natural temperature fluctuations of the moderator temperature, from the time series data of the moderator temperature and the time series data of the reactivity acquired in the acquiring step, and in the step of calculating the moderator temperature coefficient, a process of extracting the time series data of a plurality of short periods from the time series data extracted in the extracting step, and performing frequency analysis on each of the time series data of the moderator temperature and the time series data of the reactivity included in the extracted time series data to calculate the moderator temperature coefficient by a noise method is performed for each of the short periods (S7).
From the time series data of the moderator temperature and the time series data of the reactivity, the time series data in which the reactivity changes due to the natural temperature fluctuation of the moderator temperature required for the MTC measurement is extracted, and the MTC can be calculated. This eliminates the need to provide a process for MTC measurement in the reactor physical inspection, shortening the time and saving the labor required for the reactor physical inspection. By performing the MTC process multiple times, multiple MTCs can be calculated, and the final MTC can be determined based on the multiple MTCs. This compensates for the decrease in accuracy caused by using the time series data in which the reactivity changes due to the natural temperature fluctuation of the moderator temperature, and allows the MTC to be measured with high accuracy.

(7) A seventh aspect of the moderator temperature coefficient measuring method is the moderator temperature coefficient measuring method of (6), in which, in the extracting step, the time series data that satisfies the two extraction conditions, that is, (a) the time series data is for a period during which no control rod operation and no addition of reactivity by diluting and concentrating boric acid is performed, and (b) the time series data is for a certain period of time after the control rod operation and the addition of reactivity by diluting and concentrating boric acid are performed.
This makes it possible to extract time series data showing changes in reactivity due to natural temperature fluctuations in the moderator temperature. The MTC measurement can be performed using time series data of Tavg and reactivity collected during a reactor physical inspection that does not include a process for MTC measurement. In other words, the process for MTC measurement can be omitted from the reactor physical inspection.

(8) A moderator temperature coefficient measuring method according to an eighth aspect is a moderator temperature coefficient measuring method according to any one of (6) to (7), further comprising a step (S9) of performing statistical processing (creating a histogram and calculating the average and median) of the multiple moderator temperature coefficients calculated by performing the process of calculating the moderator temperature coefficient for each short period to determine a final moderator temperature coefficient.
A large number of MTCs can be calculated from multiple short periods of time-series data extracted. Based on the distribution of the multiple MTCs, an appropriate value can be determined as the final MTC, and an accurate MTC can be obtained.

(9) The moderator temperature coefficient measurement system (MTC measurement system 10) according to the ninth aspect includes a means for acquiring time series data of moderator temperature and time series data of reactivity, a means for frequency-analyzing each of the acquired time series data of moderator temperature and time series data of reactivity, a means for calculating a correlation value between the time series data of moderator temperature and the time series data of reactivity at corresponding frequencies by frequency, and a means for identifying a frequency at which the correlation value is equal to or greater than a threshold value, a means for extracting, from the time series data of moderator temperature and reactivity at the identified frequency, the time series data of moderator temperature and reactivity at which the time difference based on the phase difference between the time series data of moderator temperature and the time series data of reactivity falls within a predetermined set range, and a means for calculating a moderator temperature coefficient using the extracted time series data of moderator temperature and reactivity.

(10) The program according to the tenth aspect causes the computer 900 to execute the steps of acquiring time series data of moderator temperature and time series data of reactivity, performing frequency analysis on each of the acquired time series data of moderator temperature and time series data of reactivity, calculating a correlation value between the time series data of moderator temperature and the time series data of reactivity at corresponding frequencies by frequency, and identifying a frequency at which the correlation value is equal to or greater than a threshold value, extracting, from the time series data of moderator temperature and reactivity at the identified frequency, time series data of moderator temperature and reactivity for which the time difference based on the phase difference between the time series data of moderator temperature and the time series data of reactivity is within a predetermined set range, and calculating a moderator temperature coefficient using the extracted time series data of moderator temperature and reactivity.

10: MTC measurement system 11: Reactivity processing device 12: Timer 13: Data recording and control device 14: Data extraction device 15: MTC processing device 151: MTC processing unit 152: Correction unit 153: Nuclear design code 16: Statistical processing device 17: Display device 18: Operator console 19: Threshold calculation device 20: Nuclear plant 21: Moderator temperature measurement system 22: Neutron detector 900: Computer 901: CPU
902: Main memory device 903: Auxiliary memory device 904: Input/output interface 905: Communication interface

Claims (10)

acquiring time series data of moderator temperature and time series data of reactivity;
performing frequency analysis on each of the acquired time series data of the moderator temperature and the time series data of the reactivity, calculating a correlation value between the time series data of the moderator temperature and the time series data of the reactivity at corresponding frequencies for each frequency, and identifying a frequency at which the correlation value is equal to or greater than a threshold value;
extracting time series data of the moderator temperature and the reactivity, from the time series data of the moderator temperature and the reactivity of the identified frequency, such that a time difference based on a phase difference between the time series data of the moderator temperature and the reactivity falls within a predetermined set range;
Calculating a moderator temperature coefficient using the extracted time series data of the moderator temperature and the reactivity;
A method for measuring a moderator temperature coefficient. extracting time series data of the moderator temperature and the reactivity at the specified frequency or the time series data of the moderator temperature and the reactivity extracted based on the time difference, the data having a coherence equal to or greater than a threshold value;
The method of claim 1 further comprising: In the step of identifying the frequency,
identifying a frequency at which the cross power spectral density of the moderator temperature and the reactivity is equal to or greater than a threshold value from the time series data of the moderator temperature and the reactivity by frequency;
The method for measuring a moderator temperature coefficient according to claim 1 or 2. acquiring time series data of the moderator temperature and time series data of reactivity when the moderator temperature is artificially varied;
performing a frequency analysis on the time series data of the moderator temperature and the reactivity, calculating the correlation value of the time series data of the moderator temperature and the reactivity for each frequency, and setting a threshold value for the calculated correlation value;
The method for measuring a moderator temperature coefficient according to any one of claims 1 to 3, further comprising: acquiring time series data of the moderator temperature and time series data of reactivity when the moderator temperature is artificially varied;
performing frequency analysis on time series data of the moderator temperature and the reactivity, calculating a phase difference between the moderator temperature and the reactivity for each frequency, and setting the setting range for the time difference based on the phase difference;
The method for measuring a moderator temperature coefficient according to any one of claims 1 to 4, further comprising: The method further includes a step of extracting, based on a predetermined extraction condition, the time series data of the moderator temperature and the time series data of the reactivity, in which the reactivity changes due to natural temperature fluctuations of the moderator temperature, from the time series data of the moderator temperature and the time series data of the reactivity acquired in the acquiring step,
In the step of calculating the moderator temperature coefficient, time series data of the moderator temperature and the reactivity for a plurality of short periods are extracted from the time series data extracted in the extracting step, and a process of calculating the moderator temperature coefficient by a noise method is performed for each of the short periods by performing a frequency analysis on each of the time series data of the moderator temperature and the time series data of the reactivity included in the extracted time series data.
The method for measuring a moderator temperature coefficient according to any one of claims 1 to 5. In the extracting step, the time series data of the moderator temperature and the reactivity are extracted, which satisfy the two extraction conditions: (1) the time series data is from a period when no control rod operation and no addition of reactivity by diluting and concentrating boric acid are performed, and (2) the time series data is from a certain time after the control rod operation and the addition of reactivity by diluting and concentrating boric acid are performed.
The method for measuring a moderator temperature coefficient according to claim 6. a step of performing a process of calculating the moderator temperature coefficient for each of the short periods, and statistically processing the plurality of moderator temperature coefficients calculated to determine a final moderator temperature coefficient;
The method for measuring a moderator temperature coefficient according to claim 6 or 7, further comprising: A means for acquiring time series data of moderator temperature and time series data of reactivity;
a means for performing frequency analysis on each of the acquired time series data of the moderator temperature and the time series data of the reactivity, calculating a correlation value between the time series data of the moderator temperature and the time series data of the reactivity at corresponding frequencies for each frequency, and identifying a frequency at which the correlation value is equal to or greater than a threshold value;
means for extracting, from the time series data of the moderator temperature and the reactivity of the identified frequency, time series data of the moderator temperature and the reactivity, the time difference based on the phase difference of the time series data of the moderator temperature and the reactivity being within a predetermined set range;
A means for calculating a moderator temperature coefficient using the extracted time series data of the moderator temperature and the reactivity;
A moderator temperature coefficient measurement system having a On the computer,
obtaining time series data of moderator temperature and time series data of reactivity;
performing frequency analysis on each of the acquired time series data of the moderator temperature and the time series data of the reactivity, calculating a correlation value between the time series data of the moderator temperature and the time series data of the reactivity at corresponding frequencies for each frequency, and identifying a frequency at which the correlation value is equal to or greater than a threshold value;
extracting time series data of the moderator temperature and the reactivity, from the time series data of the moderator temperature and the reactivity of the identified frequency, such that a time difference based on a phase difference between the time series data of the moderator temperature and the reactivity falls within a predetermined set range;
Calculating a moderator temperature coefficient using the extracted time series data of the moderator temperature and the reactivity;
A program that executes the following.

Images