JP2019169104A - Analysis condition setting device, method, and program, and analysis device - Google Patents

Abstract

To provide an analysis condition setting technique capable of reducing an analysis load.SOLUTION: An analysis condition setting device 10 at least includes: a register unit 14 for registering a previously set first reference value ΔW; a data acquisition unit 12 for acquiring a time series data group 11 of analytic values or the integrated quantity of them; a time section setting unit 15 for setting a time section having a time width on the temporal axis of the time series data group 11, on the basis of the differential value of the integrated quantity and the first reference value ΔW; and a plume division unit 16 for dividing the time series data group 11 into plumes on the basis of the set time section.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to a technique for setting analysis conditions for a time-series data group.

  Probabilistic risk assessment (PRA) for all accidents that occur in nuclear facilities, etc., by quantitatively evaluating the frequency of occurrence and the impact at the time of occurrence, and expressing the degree of safety based on how small the product risk is Probabilistic Risk Assessment) is being used. PRA in an accident at a nuclear power plant is divided into level 1 for analyzing the core damage probability, level 2 for analyzing the containment vessel failure probability and the out-of-facility release probability of radioactive materials, and level 3 for analyzing the health effect probability.

  This uncertainty analysis in Level 3 PRA evaluates the public risk from accidents at nuclear power plants, and hundreds of cases of source terms (radioactive material release conditions) are set for one accident scenario. Yes. In this uncertainty analysis, for example, when an hourly case is considered in order to cope with a change in annual weather conditions, calculation is performed on a weather sequence of 8760 cases. For this reason, the number of analysis cases increases from hundreds of thousands to millions of cases.

  Furthermore, in the analysis code used in Level 3 PRA, the source term is divided into a plurality of plumes (clouds containing radioactive materials), and the migration / diffusion in the atmosphere is calculated for each plume. Since source terms vary depending on the accident scenario, division into plumes is either done manually while looking at the trends of the source terms, or simply divided at regular intervals in time. .

NUREG / CR-7155 “State-of-the-Art Reactor Consequence Analyzes Project, Uncertainty Analysis of the Unmitigated Long-Term Station Blackout of the Peach Bottom Atomic Power Station,” 2016

  The source terms analyzed at Level 2PRA and delivered to Level 3PRA are classified into several groups based on physical and chemical behavior of radioactive substances, and the time series showing the release amount of each group into the environment at each time step. Generally composed of data groups.

  In level 3PRA, a time series data group representing such a series of radioactive substance release is divided into a plurality of plumes, and the migration / diffusion in the atmosphere is calculated for each plume. As described above, in the level 3 PRA uncertainty analysis, analysis is performed for several hundred cases of source terms for one accident scenario. Therefore, it is necessary to perform a plume division operation on several hundred source terms, and it is not realistic to manually determine the division position while observing the tendency of the source terms.

  As a method of automatically dividing the plume, there is a method of dividing the plume at regular time intervals. However, in this method, if the interval of the plume division is set to, for example, 1 hour which is a unit time of weather data, the number of plumes increases in an accident scenario in which the radioactive material release period is long. Then, the analysis load, that is, the time required for the analysis and the necessary memory capacity are increased by the increase in the number of plumes.

  On the other hand, if the plume division is set roughly at such regular intervals, the number of plumes can be reduced and the analysis load can be reduced. However, the accuracy of the analysis results is reduced and the public risk is correctly grasped. It becomes difficult. Furthermore, when there is a release of radioactive material from a plurality of release positions in one accident scenario, a plume is set for each release position, and the number of plumes increases accordingly.

  The embodiment of the present invention has been made in view of such circumstances, and an object thereof is to provide an analysis condition setting technique capable of reducing an analysis load.

  In the analysis condition setting device according to the embodiment, a registration unit for registering a preset first reference value, a data acquisition unit for acquiring a time-series data group of analysis values or integrated values thereof, and the time-series data group A time interval setting unit for setting a time interval having a time width on the time axis based on the difference value of the integrated amount and the first reference value, and the time series data group based on the set time interval And a plume dividing unit that divides the plume.

  The analysis apparatus according to the embodiment includes: the analysis condition setting device; an analysis unit that performs analysis with a predetermined analysis code in units of the plurality of plumes; and an output unit that outputs the analysis result. Features.

  According to the embodiment of the present invention, an analysis condition setting technique capable of reducing an analysis load is provided.

The block diagram of the analysis condition setting apparatus and analysis apparatus which concern on each embodiment of this invention. The graph which shows the detail of the rising part of the time series data group processed with the analysis condition setting apparatus which concerns on 1st Embodiment. The graph which shows the whole time series data group processed with the analysis condition setting apparatus which concerns on 1st Embodiment. The flowchart explaining the analysis condition setting method or analysis condition setting program which concerns on 1st Embodiment. The graph which shows the detail of the rising part of the time series data group processed with the analysis condition setting apparatus which concerns on 2nd Embodiment. The graph which shows the whole time series data group processed with the analysis condition setting apparatus which concerns on 2nd Embodiment. The flowchart explaining the analysis condition setting method or analysis condition setting program which concerns on 2nd Embodiment. The graph which shows the several time series data group processed with the analysis condition setting apparatus which concerns on 3rd Embodiment. The flowchart explaining the analysis condition setting method or analysis condition setting program which concerns on 3rd Embodiment. The graph which shows the several time series data group processed with the analysis condition setting apparatus which concerns on 4th Embodiment.

  Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. FIG. 1 is a block diagram of an analysis condition setting device and an analysis device according to each embodiment of the present invention.

(First embodiment)
FIG. 2 is a graph showing details of a rising portion of a time-series data group processed by the analysis condition setting device according to the first embodiment. FIG. 3 is a graph showing the entire time-series data group processed by the analysis condition setting device according to the first embodiment.

As shown in FIG. 1, the analysis condition setting device 10 according to the first embodiment includes a registration unit 14 that registers a first reference value ΔW that is set in advance, and an analysis value or an integrated value W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )} (see FIGS. 2 and 3), a data acquisition unit 12 that acquires the time series data group 11, and the time axis of the time series data group 11 ( 2, at least one, integrated quantity of the difference value of the time interval Z _g in Figure 3 a plurality of time width in the reference) is adjusted time interval _{Z g (Z 1, Z 2} , ... Z G) (W ( t _{m + n} ) −W (t _m )) (see FIG. 4) and the time interval setting unit 15 set based on the first reference value ΔW, and the set time intervals Z _g (Z ₁ , Z ₂ ,... Based on Z _G ), the time series data group 11 is divided into a plurality of plumes P _g {W (t _m ),... W (t _{m + n−1} )} (g = _{1 to} G) (see FIG. 4). The plume division 16 I have at least.

The first reference value ΔW registered in the registration unit 14 may be set to a value calculated by the calculation unit 13 other than the case where the value itself is set in advance. The calculation unit 13 divides the maximum value W (t _R ) (see FIG. 3) of the integrated amount in the time series data group 11 in which the time range is t ₀ to t _R by the specified number of divisions and refers to the first. The value ΔW is calculated. Thus, regardless of the maximum value W (t _R ) of the integrated amount, the time-series data group 11 has a number of plumes P _g {W (t _m ),... W (t _{m +} ) corresponding to the number of divisions. _n-1 )} (g = _{1 to} G) (see FIG. 4).

As described above, the time interval setting unit 15 has a plurality of time intervals Z _g (Z ₁ , Z ₂ ,... Z _G ) whose time widths are adjusted on the time axis (see FIGS. 2 and 3) of the time series data group 11. ) Is _calculated based on the difference value (W (t _{m + n} ) −W (t _m ) etc.) (see FIG. 4) and the first reference value ΔW, that is, the integration of the time interval Z _g . It is set with reference to the amount difference value and the first reference value ΔW.

In general, the analysis value or its integrated value W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )} is often obtained as temporal discrete data, and will be described later. In addition, the time segment setting unit 15 sequentially calculates the difference value of the integrated amount while increasing n by 1, and the difference value (W (t _{m + n} ) −W (t _m )) is the first reference value ΔW. If the interval exceeds W (t _m ) and W (t _{m + n-1} ), the plume P _g {W (t _m ), ... W (t _{m + n-1} )} of the time interval Z _g (G = 1 to G) is set. In other words, in this case, the time segment setting unit 15 has at least one time interval Z _g , and the difference value (W (t _{m + n−1} ) −W (t _m )) of the integration amount is equal to or less than the first reference value ΔW The maximum value is set.

Furthermore, the analysis value or its integrated value W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )} is, for example, when the amount of temporal discrete data is sufficiently large. The first reference value ΔW may have a predetermined constant width.

In the example shown in FIG. 2, the analysis value obtained as temporally discrete data or its integrated value W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )} is included. By inserting or extrapolating, a recent curve (function) as continuous data is determined, and the time segment setting unit 15 determines the time interval Z _g based on the approximate curve that is the continuous data and the integrated amount in each time interval Z _g . The difference value (W (t _{m + n} ) −W (t _m )) (see FIG. 4) is set to a first reference value ΔW that is a predetermined constant value.

  In Level 3PRA, there are many items depending on the purpose, such as early exposure dose at each distance from the release point, long-term exposure dose, dose for each exposure route (cloud shine, ground shine, inhalation, ingestion, inhalation of resuspended substances). Evaluation is possible. In addition, taking into account fluctuations in weather conditions, statistical values such as an average value, 50% value, 95% value, and maximum value with respect to annual weather changes are output. Since the relationship between the specified value of the number of divisions, the analysis accuracy, and the total number of plumes differs depending on the source term conditions, evaluation target items, weather statistics, etc. When the is selected, a database that outputs the number of divisions, the effect on analysis accuracy, and the number of single plumes is provided to support the user. The database is created based on the result of performing the plume division on the representative source terms by the method of the present invention and performing the level 3 PRA in advance. For analysis accuracy, for example, when the duration of a single plume is equally divided in time by 1 hour which is the unit time of weather data, the base case is used, and the value of the number of divisions is changed and divided by the method of the present invention. There is a method of storing an error between the level 3PRA evaluation result of the base case and the base case as a database. Thereby, the user can designate the number of divisions for performing appropriate plume division from the relationship between the analysis accuracy and the analysis load according to the purpose of analysis.

  The time series data group 11 acquired by the data acquisition unit 12 is a source term delivered from the PRA level 2. This source term is generally classified into several groups of radioactive substances according to physical and chemical properties, etc., and the release rate to the environment or the initial built-in amount for each group is time-series data. This is output as the group 11. In the first embodiment, the case where there is one group of radioactive substances is taken up.

This time-series data group 11 is an analysis value indicating the amount of change per unit time, or an integrated amount W (t _r ) {W (t ₀ ), W (t ₁ ) obtained by integrating the analysis value with time. , ... W (t _R ). When the time series data group 11 has such an analysis value, an integration process is performed in a timely manner after the data acquisition unit 12.

The time series data group 11 is divided based on each of the plurality of time intervals Z _g (Z ₁ , Z ₂ ,... Z _G ) set by the time interval setting unit 15. A plurality of plumes P _g {W (t _m ),... W (t _{m + n-1} )} (g = _{1 to} G) obtained by dividing the time series data group 11 are accumulated in the accumulation unit 17. Is done. Note the display section 18 can display a graph of time series data group 11 is divided into a plurality of plumes P _g.

Further, the analysis device 20 outputs the analysis result setting device 10, the analysis unit 21 that performs analysis with a predetermined analysis code in units of a plurality of plumes P _g (g = 1 to G), and the result of the analysis. And at least an output unit 23. Here, the analysis unit 21 performs, for example, a diffusion simulation of a radioactive substance in which a change in the accumulated release amount is given as the time-series data group 11 based on weather information provided every other hour. .

FIG. 4 is a flowchart for explaining the analysis condition setting method or the analysis condition setting program according to the first embodiment. The data processing in FIGS. 2 and 3 will be described with reference to FIG.
First, a preset first reference value ΔW is registered (S11). Next, the time series data group 11 of the integrated amount W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )} is acquired (S12). The acquired time series data group 11 exemplifies the integrated amount W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )}. It may be an analysis value corresponding to the value.

Next, referring to FIG. 2, assuming that m = 0 (S13) and n = 1 (S14), the difference between the first reference value ΔW and the integrated amount (W (t _{m + n} ) −W (t _m )) (S18 No). Subsequently, as m = 0 and n = 2 (S22), the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount (S18 Yes). As a result, the set time interval Z ₁ of [t _0, t _1], is allocated to this time interval _{Z 1 {W (t 0)} , W (t 1)} is split as plumes P ₁ (S23).

Subsequently, as m = 2 (S24 No, S25) and n = 1 (S14), the first reference value ΔW and the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount are compared. (S18 No). Further, as m = 2 and n = 2 (S22), the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount (Yes in S18). As a result, the set time interval Z ₂ of [t _2, t _3], are assigned to the time interval _{Z 2 {W (t 2)} , W (t 3)} is split as plumes P ₂ (S23).

Subsequently, assuming that m = 4 (S24 No, S25) and n = 1, 2, the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount. It performs (S18 No). Further, as m = 4 and n = 3 (S22), the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount (Yes in S18). As a result, a time interval Z _{3 of} [t ₄ , t ₆ ] is set, and {W (t ₄ ), W (t ₅ ), W (t ₆ )} assigned to this time interval Z ₃ is a plume. It is split as P ₃ (S23).

Subsequently, assuming that m = 7 (S24 No, S25) and n = 1 to 12, the comparison between the first reference value ΔW and the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount is performed. It performs (S18 No). Further, as m = 7 and n = 13 (S22), the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount (S18 Yes). As a result, [t _7, t _19] time interval Z ₄ of the set, are assigned to the time interval _{Z 4 {W (t 7)} , ..., W (t 19)} is split as plumes P ₄ (S23).

The above-described flow is repeated until the last integration amount W (t _R ), and the time-series data group 11 is divided into a plurality of plumes P _g (g = 1 to G). Thereby, an appropriate relationship between the analysis accuracy and the analysis load can be maintained without setting the plume division coarsely.

(Second Embodiment)
Next, a second embodiment of the present invention will be described with reference to FIG. 1, FIG. 5, FIG. 6, and FIG. In the analysis condition setting device 10 of the second embodiment shown in FIG. 1, the description of the same configuration as that of the first embodiment is omitted.

In addition to the first reference value ΔW, a preset second reference value Δt _min and third reference value Δt _max are further registered in the registration unit 14 of the analysis condition setting device 10 of the second embodiment. Then, the time interval Z _g (Z ₁ , Z ₂ ,... Z _G ) is divided so as not to be smaller than the second reference value Δt _min , and further divided so as not to be larger than the third reference value Δt _max. . Here, the setting of the second reference value Δt _min is based on an update interval (for example, about one hour) of data such as weather information used by the analysis unit 21.

  FIG. 5 is a graph showing details of a rising portion of a time-series data group processed by the analysis condition setting device according to the second embodiment. FIG. 6 is a graph showing the entire time-series data group processed by the analysis condition setting device according to the second embodiment. FIG. 7 is a flowchart for explaining an analysis condition setting method or an analysis condition setting program according to the second embodiment. The data processing in FIGS. 5 and 6 will be described based on FIG.

First, a preset first reference value ΔW, second reference value Δt _min and third reference value Δt _max are registered (S11). Next, the time series data group 11 of the integrated amount W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )} is acquired (S12). The acquired time series data group 11 exemplifies the integrated amount W (t _r ) {W (t ₀ ), W (t ₁ ),... W (t _R )}. It may be an analysis value corresponding to the value.

Next, referring to FIG. 5, assuming that m = 0 (S13) and n = 1, 2 (S14, S16), the comparison between the second reference value Δt _min and the time width (t _{m + n} −t _m ) is performed. It performs (S15 No). Then, as m = 0 (S13) and n = 3 (S16), the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15 Yes).

Subsequently, the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount while maintaining m = 0 and n = 3 (S18 Yes). As a result, a time interval Z _{1 of} [t ₀ , t ₂ ] is set, and {W (t ₀ ), W (t ₁ ), W (t ₂ )} assigned to this time interval Z ₁ is a plume. Divided as P ₁ (S23).

Subsequently, as m = 3 (S24 No, S25) and n = 1, 2 (S14, S16), the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15). No). Then, with m = 3, n = 3 (S16), and the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15 Yes).

Subsequently, the first reference value ΔW is compared with the integrated value difference value (W (t _{m + n} ) −W (t _m )) while maintaining m = 3 and n = 3 (Yes in S18). As a result, a time interval Z _{2 of} [t ₃ , t ₅ ] is set, and {W (t ₃ ), W (t ₄ ), W (t ₅ )} assigned to this time interval Z ₂ is a plume. Divided as P ₂ (S23).

Subsequently, as m = 6 (S24 No, S25) and n = 1 (S14), the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15 No). Then, with m = 6, n = 2 (S16), and the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15 Yes).

Subsequently, the first reference value ΔW and the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount are compared with m = 6 and n = 2 (No in S18). Further, while updating m = 6, it is updated to n = 3 (S21 No, S22), and the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount. Perform (S18 Yes). As a result, a time interval Z _{3 of} [t ₆ , t ₈ ] is set, and {W (t ₆ ), W (t ₇ ), W (t ₈ )} assigned to this time interval Z ₃ is a plume. It is split as P ₃ (S23).

Subsequently, as m = 9 (S24 No, S25) and n = 1, 2 (S14, S16), the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15). No). Then, with m = 9, n = 3 (S16), and the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15 Yes).

Taking m = 9 into succession, n = 4 to 6 (S22), and the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount ( S18 No). Furthermore, m = 9 is updated to n = 7 (S21 No, S22), and the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount. Perform (S18 Yes). As a result, [t _9, t _15] time interval Z ₄ of the set, are assigned to the time interval _{Z 4 {W (t 9)} , ..., W (t 15)} divided as plumes P ₄ (S23).

Subsequently, as m = 16 (S24 No, S25) and n = 1, 2 (S14, S16), the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15). No). Then, with m = 16, n = 3 (S16), and the second reference value Δt _min is compared with the time width (t _{m + n} −t _m ) (S15 Yes).

Taking m = 16 into succession, n = 4 to 9 (S22), and the first reference value ΔW is compared with the difference value (W (t _{m + n} ) −W (t _m )) of the integrated amount ( S18 No, S21 No). Furthermore, when updating to n = 10 with m = 16, the third reference value Δt _max is compared with the time width (t _{m + n} −t _m ) (S21 Yes). As a result, [t _16, t _25] time interval Z ₅ are set, are assigned to the time interval _{Z 5 {W (t 16)} , ..., W (t 25)} is split as plumes P ₅ (S23).

The above-described flow is repeated until the last integration amount W (t _R ), and the time-series data group 11 is divided into a plurality of plumes P _g (g = 1 to G). As a result, it is possible to maintain an appropriate relationship between the analysis accuracy and the analysis load without setting the plume division coarsely and without having too many plumes.

(Third embodiment)
Next, a third embodiment of the present invention will be described with reference to FIG. 1, FIG. 8, and FIG. In the analysis condition setting device 10 of the third embodiment shown in FIG. 1, the description of the same configuration as that of the first embodiment and the second embodiment is omitted.

In the analysis condition setting device 10 (FIG. 1) according to the third embodiment, the data acquisition unit 12 includes a plurality of time series data groups W ₁ (t _r ), W ₂ (t _r ),..., W _Q (t _r ) (See FIG. 8). Then, these time-series data group _{W 1 (t r), W} 2 (t r), ..., W Q (t r) , each set being a plurality of time intervals Z _g of _{(Z 1, Z 2, ...} Z _G ) is set so that the time widths of the corresponding ones are equal. Further, the plume dividing unit 16 divides a plurality of time series data groups W ₁ (t _r ), W ₂ (t _r ),..., W _Q (t _r ) into mutually corresponding time intervals Z _g (Z ₁ , Z _2, ... for each Z _G), the plume _{P g (P 1, P 2} , divides ... to P _G).

FIG. 8 is a graph showing a plurality of time-series data groups processed by the analysis condition setting device according to the third embodiment. FIG. 9 is a flowchart illustrating an analysis condition setting method or an analysis condition setting program according to the third embodiment. 9 is based on the flow of (S17), (S19), (S20) added from FIG. 8, and a plurality of different time series data groups W ₁ (t _r ), W ₂ (t _r ),. , W _Q (t _r ) coincide with each other corresponding time intervals Z _g (Z ₁ , Z ₂ ,... Z _G ), and are divided into common plumes P _g (P ₁ , P ₂ ,... P _G ). can do.

Simulate the case where radioactive substances are classified into several groups by using multiple time series data groups W ₁ (t _r ), W ₂ (t _r ), ..., W _Q (t _r ) Can do. The groups of radioactive materials that are important for radiation dose assessment by radioactive materials released to the environment are the group that represents rare gas, the group that represents iodine, and the group that represents cesium.

  Since noble gases contribute greatly to external exposure from radioactive clouds and are not easily removed by filters, they are dominant in the exposure dose during filter venting. Iodine contributes greatly to the internal exposure dose when inhaled. Cesium is the dominant nuclide for long-term external exposure doses from contaminated ground due to surface deposition. By performing plume division focusing on these three groups, it is possible to perform division that appropriately evaluates the dose of main exposure routes.

(Fourth embodiment)
FIG. 10 is a graph showing a plurality of time-series data groups processed by the analysis condition setting device according to the fourth embodiment. The analysis device 20 according to the fourth embodiment includes a first analysis result analyzed based on the time series data group W (t _r ) derived from the first position and a time series data group V (t _r derived from the second position. ) Based on the second analysis result is stored. Further, an adder 24 that adds and outputs the first analysis result and the second analysis result is further provided.

Since the time series data group W (t _r ) derived from the first position and the time series data group V (t _r ) derived from the second position have different start times, the time interval Z set in each system is set. Plume is divided based on ₁ , Z ₂ ,... Z ₄ and time intervals X ₁ , X ₂ , X ₃ . However, the first analysis result and the second analysis result derived from the respective time series data groups W (t _r ) and V (t _r ) can be evaluated by adding each other along the time axis.

  In a typical scenario of radioactive material release at the time of an accident, there is a case where the radioactive material leaks from the building first and then the filter vent is performed from the vent stack as the accident progresses. In this case, the radioactive material is released at two locations, the building and the vent stack, and the radioactive material emission height and the initial diffusion width are different from each other. In Level 3PRA, these are handled as separate plumes. Moreover, although the case where there is one group in each of the first position and the second position is taken up, the case where there are a plurality of groups can also be applied.

  Further, in the fourth embodiment, when the calculation unit 13 calculates the first reference value ΔW, it is possible to specify different division numbers in each of the first position and the second position. In a typical scenario of radioactive material release at the time of an accident, there is a case where the radioactive material leaks from the building first and then the filter vent is performed from the vent stack as the accident progresses. Since the value of the appropriate number of divisions differs depending on the emission form, it is possible to perform more optimal plume division by designating this number of divisions for each emission position.

  According to the analysis condition setting device of at least one embodiment described above, the relationship between the analysis accuracy and the analysis load is traded off by plume dividing the time series data group so that the difference value of the integrated amount becomes the reference value. Thus, it is possible to reduce the analysis load and evaluate in a realistic calculation time.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, changes, and combinations can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof. The components of the analysis condition setting device can be realized by a processor of a computer and can be operated by an analysis condition setting program.

  The analysis condition setting device described above includes a control device in which a processor such as a dedicated chip, FPGA (Field Programmable Gate Array), GPU (Graphics Processing Unit), or CPU (Central Processing Unit) is highly integrated, and ROM ( Storage devices such as Read Only Memory (RAM) and Random Access Memory (RAM), external storage devices such as HDD (Hard Disk Drive) and SSD (Solid State Drive), display devices such as a display, and inputs such as a mouse and a keyboard The apparatus and the communication I / F are provided, and can be realized by a hardware configuration using a normal computer.

  A program executed by the analysis condition setting device is provided by being incorporated in advance in a ROM or the like. Alternatively, this program is stored in a computer-readable storage medium such as a CD-ROM, CD-R, memory card, DVD, or flexible disk (FD) as an installable or executable file. You may make it do.

  The program executed by the analysis condition setting apparatus according to the present embodiment may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. In addition, the apparatus 10 can be configured by combining separate modules that perform each function of the component elements independently by a network or a dedicated line.

DESCRIPTION OF SYMBOLS 10 ... Analysis condition setting apparatus, 11 ... Time series data group, 12 ... Data acquisition part, 13 ... Operation part, 14 ... Registration part, 15 ... Time section setting part, 16 ... Plume division part, 17 ... Accumulation part, 18 ... Display unit, 20 ... analyzing device, 21 ... analyzing unit, 22 ... accumulating unit, 23 ... output unit, 24 ... adding unit, W (t _r ) {W (t ₀ ), W (t ₁ ), ... W (t _R )} ... integrated amount, Z _g (Z ₁ , Z ₂ , ... Z _G ) ... time interval, W (t _{m + n} ) -W (t _m ) ... difference value, ΔW ... first reference value, P _g {W (t _m ), ... W (t _{m + n-1} )} ... Plum.

Claims (7)

A registration unit for registering a preset first reference value;
A data acquisition unit for acquiring a time-series data group of analysis values or integrated values thereof;
A time interval setting unit that sets a time interval having a time width on the time axis of the time-series data group based on the difference value of the integrated amount and the first reference value;
An analysis condition setting device comprising: a plume dividing unit that divides the time-series data group into plumes based on the set time interval. In the analysis condition setting device according to claim 1,
A second reference value set in advance is further registered in the registration unit,
The analysis condition setting device, wherein the time interval is divided so as not to be smaller than the second reference value. In the analysis condition setting device according to claim 1 or claim 2,
A third reference value set in advance is further registered in the registration unit,
The analysis condition setting device, wherein the time interval is divided so as not to be larger than the third reference value. In the analysis condition setting device according to any one of claims 1 to 3,
The data acquisition unit acquires a plurality of the time series data groups,
The plurality of time intervals set in each of the time series data groups are set so that the time widths of the corresponding ones are equal to each other,
The plume dividing unit divides the plurality of time-series data groups into the plumes for each time interval corresponding to each other. The analysis condition setting device according to any one of claims 1 to 4,
An analysis unit that performs analysis with a predetermined analysis code in units of a plurality of the plumes;
And an output unit for outputting the result of the analysis. A registration step for registering a preset first reference value;
A data acquisition step for acquiring a time-series data group of the analysis value or its integrated amount;
A time interval setting step for setting a time interval having a time width on the time axis of the time series data group based on the difference value of the integrated amount and the first reference value;
A dividing step of dividing the time-series data group into plumes based on the set time interval. On the computer,
A registration step for registering a preset first reference value;
A data acquisition step for acquiring a time-series data group of the analysis value or the accumulated amount thereof,
A time interval setting step for setting a time interval having a time width on the time axis of the time series data group based on the difference value of the integrated amount and the first reference value;
An analysis condition setting program for executing a dividing step of dividing the time series data group into a plurality of plumes based on the set time interval.

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