JP6983100B2 - Subcriticality evaluation method and subcriticality evaluation device - Google Patents

Description

The present invention relates to a subcriticality evaluation method and a subcriticality evaluation device for evaluating the subcriticality of a fuel controlled by a nuclear facility.

Conventionally, a method for evaluating the subcriticality of spent fuel stored in a spent fuel pit (SFP: Spent Fuel Pit) as a nuclear facility has been known (for example, Non-Patent Document 1). In this evaluation method, the condition (nominal condition) is that the spent fuel is stored in the used fuel pit that has the required specified design, and the evaluation value evaluated based on this condition (for example, effective multiplication factor). ) Is derived as a nominal value. In addition, this evaluation method evaluates the uncertainty caused by the manufacturing tolerance of the used fuel pit, the uncertainty of the nuclear data as an input parameter used for the evaluation of the subcriticality, and the calculation used for the evaluation of the subcriticality. Evaluate the uncertainty of the code. Then, in this evaluation method, the effective multiplication factor, which is the final evaluation value in which the uncertainty due to the production tolerance, the uncertainty of the nuclear data, and the uncertainty of the calculation code are added to the evaluation result, which is the nominal value, is determined in advance. It is judged whether or not the specified evaluation criteria are satisfied. In other words, this evaluation method is a stacking-type evaluation method in which uncertainty is accumulated in the nominal value.

"Explanation on the non-criticality of nuclear fuel materials in fuel handling equipment, new fuel storage equipment and spent fuel storage equipment", [online], NRA, [Search on February 28, 2018], Internet <URL: http://www.nsr.go.jp/data/000186280.pdf>

However, in the evaluation method of Non-Patent Document 1, since the uncertainty is individually accumulated and evaluated, there is room for rationalizing the uncertainty.

Therefore, it is an object of the present invention to provide a subcriticality evaluation method and a subcriticality evaluation device capable of rationalizing the subcriticality of a fuel managed by a nuclear facility.

The subcriticality evaluation method of the present invention is a subcriticality evaluation method executed by a subcriticality evaluation device that evaluates the subcriticality of fuel managed in a nuclear facility, and is input for evaluating the subcriticality. The input parameters to be input include nuclear data which is data about the nuclear species contained in the fuel, co-dispersion data which is data about the uncertainty of the nuclear data, and tolerance parameters regarding the manufacturing tolerance of the nuclear equipment. Combining a nuclear data perturbation step that perturbs nuclear data based on the covariance data, a tolerance parameter perturbation step that perturbs the tolerance parameter within the manufacturing tolerance, and the perturbed nuclear data and the tolerance parameter. Based on the combined nuclear data and the plurality of input parameters including the tolerance parameter, the evaluation value derivation step for deriving a plurality of subcriticality evaluation values and the plurality of the derived evaluation values are statistically processed. It is characterized by comprising an evaluation step for deriving the evaluation value including uncertainty as an evaluation result.

Further, the subcriticality evaluation device of the present invention is a subcriticality evaluation device that evaluates the subcriticality of the fuel managed in the nuclear facility, and the input parameters input for evaluating the subcriticality are The input parameters are acquired by including nuclear data which is data about the nuclear species contained in the fuel, covariance data which is data about the uncertainty of the nuclear data, and tolerance parameters regarding the manufacturing tolerance of the nuclear equipment. A control unit for deriving an evaluation value of subcriticality based on the acquired input parameters is provided, and the control unit perturbs the nuclear data based on the covariance data and within the manufacturing tolerance. The tolerance parameters are perturbed, the perforated nuclear data and the tolerance parameters are combined to generate a plurality of the input parameters, and a plurality of the evaluation values are derived and derived based on the plurality of input parameters. It is characterized in that the evaluation value is statistically processed and the evaluation value including uncertainty is derived as an evaluation result.

According to these configurations, the uncertainty of the fuel managed by the nuclear facility can be determined because the evaluation value including the uncertainty can be derived in consideration of the uncertainty of the nuclear data and the manufacturing tolerance of the nuclear facility. It can be evaluated reasonably.

Further, the evaluation value is an effective multiplication factor, and in the evaluation value derivation step, the effective multiplication factor is derived as the evaluation value, and in the evaluation step, the effective multiplication factor including uncertainty is used as the evaluation result. It is preferable to derive it.

According to this configuration, the subcriticality of the fuel can be appropriately evaluated by using the effective multiplication factor.

Further, the covariance data has a normal distribution, and in the nuclear data perturbation step, the nuclear data is perturbed based on the normal distribution, and in the tolerance parameter perturbation step, the tolerance parameter is used in the nuclear equipment. It is preferable to perturb the above-mentioned production tolerances based on a probability distribution statistically arranged based on the production results.

According to this configuration, the nuclear data can be appropriately perturbed based on the normal distribution, and the tolerance parameters can be appropriately perturbed based on the production results, so that the evaluation value including uncertainty is appropriately perturbed. Can be derived.

Further, in the evaluation step, an average derivation step in which the derived evaluation values are arithmetically averaged to derive an average evaluation value, and a standard deviation of the evaluation values are calculated based on the derived evaluation values. It is preferable to have a standard deviation derivation step for deriving, and an evaluation result derivation step for adding the standard deviation to the average evaluation value and deriving the evaluation value including uncertainty as the evaluation result.

According to this configuration, by using the average evaluation value and the standard deviation, the evaluation value including uncertainty can be appropriately derived as the evaluation result.

Further, it is preferable that the nuclear power equipment includes a used fuel pit having a rack for storing used fuel.

According to this configuration, the subcriticality of the spent fuel stored in the spent fuel pit can be evaluated.

Further, it is preferable to further include an evaluation result display step for displaying the evaluation result derived in the evaluation step on the display unit of the subcriticality evaluation device.

Further, it is preferable to further include a display unit for displaying the evaluation result.

According to this configuration, by displaying the evaluation value including uncertainty on the display unit, the evaluation result can be easily grasped by visually recognizing the display unit.

Further, in the case of deriving the average evaluation value obtained by arithmetically averaging the plurality of the evaluation values and the standard deviation of the plurality of the evaluation values in the evaluation step, in the evaluation result display step, the average evaluation is performed together with the evaluation result. It is preferable to display at least one of the value and the standard deviation.

According to this configuration, in addition to the evaluation value including uncertainty, the average evaluation value and the standard deviation are displayed on the display unit, so that the evaluation value including uncertainty can be grasped in more detail.

FIG. 1 is a diagram showing a used fuel pit to be evaluated by the subcriticality evaluation method of the present embodiment. FIG. 2 is a diagram showing a rack of used fuel pits. FIG. 3 is a schematic configuration diagram schematically showing the subcriticality evaluation device of the present embodiment. FIG. 4 is a flowchart of a control operation for deriving an effective magnification including uncertainty. FIG. 5 is an explanatory diagram relating to the derivation of the effective multiplication factor including uncertainty. FIG. 6 is a diagram showing an evaluation result displayed on the display unit. FIG. 7 is a diagram comparing the effective magnification of the present embodiment with the conventional effective magnification.

Hereinafter, embodiments according to the present invention will be described in detail with reference to the drawings. The present invention is not limited to this embodiment. In addition, the components in the following embodiments include those that can be easily replaced by those skilled in the art, or those that are substantially the same. Further, the components described below can be appropriately combined, and when there are a plurality of embodiments, each embodiment can be combined.

[The present embodiment]
The subcriticality evaluation method and the subcriticality evaluation device 10 of the present embodiment are methods and devices for evaluating the subcriticality of the fuel managed by the nuclear facility. As the fuel, for example, a used fuel assembly is applied. Further, as the nuclear equipment, for example, a spent fuel pit (SFP: Spent Fuel Pit) for storing spent fuel and a fuel assembly are applied. In the following description, the description is applied to a spent fuel pit and a fuel assembly (also simply referred to as fuel), but the facility for managing fuel is not limited to the used fuel pit, for example. It may be applied to equipment for storing new fuel, or it may be applied to fuel pellets as fuel. Prior to the description of the subcriticality evaluation method and the subcriticality evaluation device 10 of the present embodiment, the used fuel pit 1 to be evaluated will be described.

FIG. 1 is a diagram showing a used fuel pit to be evaluated by the subcriticality evaluation method of the present embodiment. FIG. 1 is a plan view of the used fuel pit 1 as viewed from above. The used fuel pit 1 includes a storage pit 5 for storing water as a moderator, and a plurality of racks 6 for accommodating a fuel assembly 8. The storage pit 5 is a rectangular parallelepiped-shaped tank having a storage space inside. A plurality of racks 6 are arranged in a grid pattern in a horizontal plane.

FIG. 2 is a diagram showing a rack of used fuel pits. FIG. 2 is a plan view of the rack 6 as viewed from above. The rack 6 has a quadrangular square cylinder shape, and a storage space capable of accommodating the fuel assembly 8 is formed inside the rack 6. The rack 6 accommodates the fuel assembly 8 in the vertical direction. Here, as shown in FIG. 2, in the rack 6, the thickness between the inner wall surface and the outer wall surface is the rack plate thickness D, and this rack plate thickness D is the tolerance used in the subcritical evaluation described later. It is one of the parameters. Further, in the rack 6, the distance between the facing inner wall surfaces is the glue L in the rack, and the glue L in the rack is also one of the tolerance parameters used in the subcritical evaluation described later.

The fuel assembly 8 is formed in a rectangular cross section, and is composed of, for example, 17 × 17 cells. Control rods and fuel rods are appropriately inserted into the plurality of cells.

Next, with reference to FIG. 3, a subcriticality evaluation device 10 for evaluating the subcriticality of the fuel stored in the used fuel pit 1 to be evaluated will be described. FIG. 3 is a schematic configuration diagram schematically showing the subcriticality evaluation device of the present embodiment. The subcriticality evaluation device 10 uses the nuclear data of the fuel and the data related to the used fuel pit 1 as input parameters, performs the subcriticality evaluation calculation based on the input parameters, and is effective as the evaluation value of the subcriticality of the fuel. It is a device that calculates the magnification.

The subcriticality evaluation device 10 includes a control unit 11 capable of executing various programs to execute various processes, a storage unit 12 for storing various programs and data, and an input unit 13 composed of an input device such as a keyboard. It has a display unit 14 composed of a display device such as a monitor. The subcriticality evaluation device 10 may be configured as a single device, may be configured integrally with other devices, or may be configured as a system in which various devices such as an arithmetic unit and a data server are combined. It may be, and is not particularly limited.

As various programs, the storage unit 12 stores, for example, a subcriticality evaluation program P including a subcriticality evaluation calculation code (for example, SCALE code) used for subcriticality evaluation. Further, as data, the storage unit 12 relates to, for example, nuclear data D1 regarding the nuclear species contained in the fuel, covariance data D4 regarding the uncertainty of the nuclear data, and production tolerances of the used fuel pit 1 and the fuel assembly 8. The tolerance parameter D2 and the probability distribution D3 of the manufacturing tolerance are included and stored. These data D1, D2, D3, D4 are used for the subcriticality evaluation described later.

When the subcriticality evaluation device 10 executes the subcriticality evaluation program P, the control unit 11 acquires the input data input from the input unit 13 and the data stored in the storage unit 12. The control unit 11 uses the acquired nuclear data D1 and various data including the tolerance parameter D2 as input parameters, and calculates the effective multiplication factor by performing a calculation using a subcritical evaluation calculation code based on the input parameters.

Next, with reference to FIGS. 4 and 5, a control operation for deriving an effective magnification including uncertainty will be described. FIG. 4 is a flowchart of a control operation for deriving an effective magnification including uncertainty. FIG. 5 is an explanatory diagram relating to the derivation of the effective multiplication factor including uncertainty.

The subcriticality evaluation device 10 calculates an effective magnification including uncertainty as an evaluation result. Further, the subcriticality evaluation device 10 calculates the effective multiplication factor including the uncertainty in consideration of the uncertainty of the nuclear data and the production record of the production tolerance.

When calculating the effective multiplication factor including uncertainty, first, the control unit 11 acquires the nuclear data D1 and the covariance data D4 stored in the storage unit 12, and the acquired nuclear data D1 is used as the covariance data D4. (Step S11: Nuclear data perturbation step). Here, since the covariance data D4 has a normal distribution, in step S11, the nuclear data is perturbed based on the normal distribution. In step S11, by perturbing the nuclear data D1, a plurality of (N) nuclear data σ1 to σN are generated.

Next, the control unit 11 acquires the tolerance parameter D2 and the probability distribution D3 stored in the storage unit 12, and perturbs the acquired plurality of tolerance parameters D2 based on the probability distribution D3 (step S12: tolerance parameter). Perturbation step). The probability distribution D3 is a distribution in which the production tolerances of nuclear power equipment are statistically arranged based on the production results (frequency of production tolerances). In step S12, a plurality of (N) tolerance parameters G1 to GN are generated by perturbing the tolerance parameters D2.

Subsequently, the control unit 11 sets one set of input parameters including the perturbated nuclear data D1 and the tolerance parameter D2, and generates a plurality of sets of input parameters having different perturbation patterns. Here, the control unit 11 appropriately combines N nuclear data σ1 to σN and N tolerance parameters G1 to GN to generate a plurality of sets of input parameters. Any combination method may be used. For example, N nuclear data σ1 to σN and N tolerance parameters G1 to GN are randomly (randomly) combined to form N input parameters. It may be generated, or N nuclear data σ1 to σN and N tolerance parameters G1 to GN may be combined in all ways to generate N input parameters. Then, the control unit 11 calculates the effective multiplication factor of each set from the input parameters of a plurality of sets including the perturbated nuclear data D1 and the tolerance parameter D2 by using the subcritical evaluation calculation code, and corresponds to the plurality of sets. A plurality of (N) effective multiplication factors k1 to kN are calculated (step S13: evaluation value derivation step).

Next, the control unit 11 derives the effective magnification K including uncertainty as an evaluation result based on the calculated effective multiplication factor (step S14: evaluation step). Specifically, in the evaluation step S14, the control unit 11, the calculated plurality of the effective multiplication factor arithmetic average to calculate the average effective multiplication factor _{k avg} (step S14a). Subsequently, the control unit 11 calculates the standard deviation σk of the effective multiplying factor based on the calculated effective multiplying factor (step S14b). Then, the control unit 11 multiplies the standard deviation σk by the confidence coefficient α, and adds the standard deviation σk obtained by multiplying the confidence coefficient α to the average effective multiplication factor k _avg , whereby the effective multiplication factor K including uncertainty ( = _{k avg} + α × σk) is calculated (step S14c).

Then, the control unit 11, as shown in FIG. 6, the display of the effective multiplication factor K that contains an uncertainty calculated on the display unit 14 as the evaluation result, the average effective multiplication factor k _avg and the standard deviation .sigma.k, display It is displayed on the unit 14 (step S15: evaluation result display step). Here, FIG. 6 is a diagram showing an evaluation result displayed on the display unit. Further, in step S15, the control unit 11 determines the evaluation result (k + ε) based on the conventional method (for example, a stacking-type evaluation method in which uncertainty is accumulated in the nominal value) and the effective multiplication factor K calculated in step S14. The difference ((k + ε) −K) may be displayed on the display unit 14 as a comparison result. After executing step S15, the control unit 11 ends the control operation for calculating the effective magnification K including uncertainty.

FIG. 7 is a diagram comparing the effective magnification of the present embodiment with the conventional effective magnification. As shown in FIG. 7, when the conventional effective magnification (k + ε) and the effective magnification K of the present embodiment are compared, the effective magnification K of the present embodiment is compared with the conventional effective magnification (k + ε). Then, it was confirmed that the reduction was about 0.6%, and it was confirmed that rationalization could be achieved.

As described above, according to the present embodiment, the effective multiplication factor K including the uncertainty is taken into consideration in consideration of the uncertainty of the nuclear data D1 and the manufacturing tolerance of the nuclear equipment including the used fuel pit 1 and the fuel assembly 8. Therefore, the uncertainty of the fuel managed by the nuclear facility can be reasonably evaluated.

Further, according to the present embodiment, since the effective multiplication factor K is used as the evaluation result, the subcriticality of the fuel can be appropriately evaluated.

Further, according to the present embodiment, the nuclear data D1 can be appropriately perturbed based on the normal distribution, and the tolerance parameter D2 can be appropriately perturbed based on the production results, which includes uncertainty. The effective multiplication factor K can be appropriately derived.

Further, according to the present embodiment, by using the average effective _{multiplying factor k avg} and the standard deviation σk, the effective multiplying factor K including uncertainty can be appropriately calculated as an evaluation result.

Further, according to the present embodiment, the subcriticality of the spent fuel stored in the spent fuel pit 1 can be evaluated.

Further, according to the present embodiment, by displaying the effective magnification K including uncertainty on the display unit 14, the evaluation result can be easily grasped by visually recognizing the display unit 14, and the average evaluation is performed. By displaying the value k _avg and the standard deviation σk on the display unit 14, it is possible to grasp the effective multiplication factor K including uncertainty in more detail.

1 Spent fuel pit 5 Storage pit 6 Rack 8 Fuel assembly 10 Subcriticality evaluation device 11 Control unit 12 Storage unit 13 Input unit 14 Display unit P Subcritical evaluation program D1 Nuclear data D2 Tolerance parameter D3 Probability distribution D4 Covariance data

Claims (9)

It is a subcriticality evaluation method performed by a subcriticality evaluation device that evaluates the subcriticality of fuel managed by nuclear equipment.
The input parameters input for evaluating the subcriticality are nuclear data, which is data on the nuclear species contained in the fuel, covariance data, which is data on the uncertainty of the nuclear data, and manufacturing tolerances of the nuclear equipment. Including tolerance parameters and
A nuclear data perturbation step that perturbs the nuclear data based on the covariance data,
A tolerance parameter perturbation step that perturbs the tolerance parameter within the manufacturing tolerance, and
An evaluation value derivation step of combining the perturbated nuclear data and the tolerance parameter and deriving a plurality of subcriticality evaluation values based on the combined nuclear data and the plurality of input parameters including the tolerance parameter.
A subcriticality evaluation method comprising:, an evaluation step of statistically processing a plurality of derived evaluation values and deriving the evaluation value including uncertainty as an evaluation result. The evaluation value is an effective multiplication factor, and is
In the evaluation value derivation step, the effective multiplication factor is derived as the evaluation value.
The subcriticality evaluation method according to claim 1, wherein in the evaluation step, the effective multiplication factor including uncertainty is derived as the evaluation result. The covariance data has a normal distribution.
In the nuclear data perturbation step, the nuclear data is perturbed based on the normal distribution.
The first or second aspect of claim 1 or 2, wherein in the tolerance parameter perturbation step, the tolerance parameter is perturbed based on a probability distribution in which the manufacturing tolerance of the nuclear facility is statistically arranged based on the manufacturing results. Tolerance evaluation method. In the evaluation step,
An average derivation step for deriving an average evaluation value by arithmetically averaging a plurality of derived evaluation values,
A standard deviation derivation step for deriving the standard deviation of the evaluation value based on the plurality of derived evaluation values, and a standard deviation derivation step.
One of claims 1 to 3, further comprising an evaluation result derivation step of adding the standard deviation to the average evaluation value and deriving the evaluation value including uncertainty as the evaluation result. The subcriticality evaluation method described in. The subcriticality evaluation method according to claim 4, wherein the nuclear power equipment includes a spent fuel pit having a rack for storing the spent fuel. The aspect according to any one of claims 1 to 5, further comprising an evaluation result display step for displaying the evaluation result derived in the evaluation step on the display unit of the subcriticality evaluation device. Subcriticality evaluation method. In the evaluation step, when deriving an average evaluation value obtained by arithmetically averaging a plurality of the evaluation values and a standard deviation of the plurality of the evaluation values.
The subcriticality evaluation method according to claim 6, wherein in the evaluation result display step, at least one of the average evaluation value and the standard deviation is displayed together with the evaluation result. A subcriticality evaluation device that evaluates the subcriticality of fuels managed by nuclear equipment.
The input parameters input for evaluating the subcriticality are nuclear data, which is data on the nuclear species contained in the fuel, covariance data, which is data on the uncertainty of the nuclear data, and manufacturing tolerances of the nuclear equipment. Including tolerance parameters and
A control unit that acquires the input parameter and derives an evaluation value of subcriticality based on the acquired input parameter is provided.
The control unit
The nuclear data is perturbed based on the covariance data, and the tolerance parameters are perturbed within the range of the manufacturing tolerance, and the perforated nuclear data and the tolerance parameters are combined to generate a plurality of the input parameters. A subcriticality characterized by deriving a plurality of the evaluation values based on the plurality of input parameters, statistically processing the derived evaluation values, and deriving the evaluation values including uncertainty as an evaluation result. Sex evaluation device. The subcriticality evaluation device according to claim 8, further comprising a display unit for displaying the evaluation result.

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