Reactor core power distribution reconstruction method for processing detector movement

Abstract

According to the reactor core power distribution reconstruction method for processing detector movement, effective domain division is carried out by taking the position of a detector as a core, observation values of the detector are distributed to the space belonging to the same region, and then an observation field with the same resolution as a high-resolution physical field is constructed. Based on a large number of samples of an observation field and a high-dimensional observation field, a convolutional neural network is adopted for training, and finally a neural network of a physical field is obtained through observation. Based on the neural network, the physical field reconstruction problem under the condition of small-amount movement of the position of the detector can be solved, so that a reactor core power distribution reconstruction system for processing the movement of the detector can be constructed. Based on 2D IAEA testing, it is found that the moving position of a detector is limited within the range of 5 cm, the mean square error of flux and power distribution of a reconstructed fast heat group is within 5%, and the engineering precision requirement is met.

Description

Technical Field

[0001] The present invention relates to the field of nuclear reactor core operation and safety technology, and in particular to an online reconstruction method for core power distribution based on a deep machine learning method. Background Art

[0002] Online monitoring of nuclear reactor core power distribution is of great significance to ensure reactor core safety and improve the economic benefits of nuclear power plants. However, core power distribution is not a direct monitoring quantity, but is indirectly measured by measuring neutron flux density. In order to measure the core neutron flux density in real time, neutron detectors are installed inside or outside the reactor, but these neutron detectors are only arranged in specific locations and the number is limited. Therefore, if we want to achieve real-time monitoring of spatially continuous power distribution, it is necessary to study the online reconstruction method of core power distribution.

[0003] One of the core technologies of the core monitoring system is the core physical field reconstruction algorithm, that is, how to reconstruct the power distribution of the entire reactor through limited readings of internal and external detectors. Domestic and foreign scholars have conducted a lot of research on this issue, and one important branch is the reconstruction method based on the background physical field distribution.

[0004] This type of physical field reconstruction method, based on a given background physical field distribution, corrects or fits the background through the measured values ​​of the detectors inside and outside the reactor to obtain the measured core power distribution. The background physical field is usually distributed by theoretical calculations or the measured distribution at the previous moment, and there is a certain deviation from the current real power distribution. The main methods of this type of physical field reconstruction include interpolation fitting method, coupling coefficient method, data assimilation method, etc.

[0005] 1) Interpolation fitting method: The BEACON system developed by Westinghouse uses spline function interpolation to reconstruct the physical field based on the deviation between the measured and theoretically calculated physical fields at the measured points. In addition, there are also the weight coefficient method and the ordinary Kriging method developed earlier.

[0006] 2) Coupling coefficient method: The coupling coefficient method (CECOR) developed by Combustion Engineering has a simple calculation process and is widely used in engineering. Its core idea is to assume that the theoretical calculation distribution is not much different from the actual distribution, which can basically describe the characteristics of the physical field. The small deviation between the theory and the actual physical field is corrected by actual measurement combined with the coupling coefficient. Webb improved the coupling coefficient method and developed the Lagrange multiplier method, whose calculation idea and calculation accuracy are not much different from those of CECOR.

[0007] 3) Data assimilation: Data assimilation is a method to integrate new observation data during the dynamic operation of the numerical model based on the temporal and spatial distribution of data and the errors of the observation field and background field. It is a method to automatically adjust the model trajectory by continuously integrating direct or indirect observation information of different sources and different resolutions with discrete distribution in time and space through the data assimilation algorithm within the dynamic framework of the process model to improve the estimation accuracy of the dynamic model state and enhance the model prediction ability. Data assimilation is widely used in meteorology, weather forecasting, ocean, environment and other fields to reconstruct physical fields by combining limited measured values ​​and theoretical values. Bryson first tried to apply the Kalman filter method to the reconstruction of core power distribution; in recent years, research teams represented by Electricité de France (EDF) have conducted extensive research on the application of different data assimilation methods to core flux and power reconstruction.

[0008] According to data released by the International Atomic Energy Agency, more than 60% of the world's nuclear power plants have been in operation for more than 30 years, and the aging of nuclear power plants has followed. The aging of nuclear power plant equipment components causes vibrations inside the reactor core, which also causes vibrations in the neutron detectors installed inside the core. However, almost all of the methods mentioned above do not have the ability to handle physical field reconstruction when the detector moves or vibrates. Summary of the invention

[0009] In order to solve the problem of inaccurate physical field reconstruction caused by the vibration of the core neutron detector due to the vibration of the internal components of the reactor core, the present invention proposes a core physical field reconstruction method for processing the displacement of the detector relative to the original position. The method can reconstruct the core physical field distribution when the detector has a certain displacement, meeting the engineering accuracy requirements. The method provided by the present invention can be integrated into the existing nuclear power plant core online monitoring system to expand the functions of the core online monitoring system, especially to expand the ability to process the physical field reconstruction under the condition of detector vibration, and provide guarantee for the safe and economical operation of nuclear reactors. The method described in the present invention can be implemented on existing pressurized water reactors, and can also be used to expand the application to any other form of reactor with an in-core measurement system.

[0010] The present invention is achieved through the following technical solutions:

[0011] A method for reconstructing core power distribution by processing detector movement is characterized in that it comprises the following steps:

[0012] S1. Regularize the core geometry and expand it into a two-dimensional or three-dimensional rectangle Ω. The grid points are grouped into n x ×n y The total number of grid points is N, N = n x ×n y, the discrete representation of the physical field f(r) is

[0013] S2. Determine the distribution of core detector positions:

[0014] Assume that there are M detectors in the core, with positions r s =(r1,r2,…,r M ) T , for a given physical field f, at each detector location r i The observed value y i (f), the total observation vector is y(f) = (y1(f), y2(f), …, y M (f)) T , the superscript “T” indicates transpose;

[0015] S3. Determine the disturbance interval δ of the core detector at the nominal position, that is, the detector can be i For any activity in a square area with a length and width of 2δ as the center, the signal detected by the detector is the signal of the physical field at the current position of the square area;

[0016] S4. Divide the effective range of detector positions:

[0017] Calculate the relationship between all grid points in the two-dimensional or three-dimensional rectangular Ω area and each detector position. k , calculated with r s The distance between the points in the i If point r is closest, k Classify into the i-th group and obtain a segmentation g1,g2,…,g in Ω M , regarded as detector r s The segmentation of each region g i Included in r i The i-th detector at ;

[0018] S5. Construct an observation field consistent with the structure of the physical field F

[0019] For each point Y(r k ), if r k In the segmentation i , then take the detector y i (f) The value of, that is, Y contains the detection values ​​and detector position information of M detectors;

[0020] S6. Based on S1-S5, a set of representations of the detector and the physical field in the same network structure is obtained, namely (Y, F);

[0021] S7. Determine the reactor operating parameter domain And in the Run Parameters field Randomly sample the heap parameters in the formation of N sample The physical field F set of samples;

[0022] S8. Based on the detector vibration interval 2δ determined in S3., perform joint random sampling on specific positions within the vibration interval of the M detectors, and obtain a detector random position sample Y for a given physical field F;

[0023] S9. According to S7 and S8, obtain samples of detectors and physical fields under the same network structure

[0024] S10. Using deep convolutional neural network (CNN), Train to get the mapping relationship from Y to F, that is, the neural network model

[0025] S11. Physical field reconstruction:

[0026] After getting the trained neural network For the core detector configuration at any time, obtain the measured value, repeat S5 according to the preparations of S1.-S4., and obtain the current observation field Y according to the detector's observation value y, and input it into the trained neural network model The reconstructed physical field distribution F is obtained.

[0027] Further, the step S7 determines the reactor operation parameter domain And in the Run Parameters field Randomly sample the heap parameters in the formation of N sample The physical field F set of samples includes:

[0028] S7.1 Assume that the nuclear reactor state parameter μ that affects the distribution of the physical field F is μ = (μ1, μ2, …, μ p ) T , μ i The operating parameters of the nuclear reactor are reactor power level, control rod position, burnup level, boron concentration and coolant inlet temperature.

[0029] S7.2 is the state parameter μ for each dimension (μ i ,i=1,2,…,p) determine a local interval [μ i -Δμ i ,μ i +Δμ i ], where Δμ i μ iThe domain formed by the offset values ​​of all p dimensions of the state parameter μ is called the operating parameter domain of the state parameter μ.

[0030] S7.3 in the operating parameters area The parameters in the middle pile are randomly sampled according to the sampled parameters μ sample , perform core physics calculations, and obtain the corresponding physical field F, including power distribution P(r), fast group flux distribution φ1(r) and hot group flux distribution φ2(r), forming N sample A collection of physical fields F of samples.

[0031] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0032] 1. The present invention realizes the reconstruction of the multi-physical fields of the reactor core, especially the rapid reconstruction of power distribution, fast group flux and thermal group flux.

[0033] 2. Compared with other conventional physical field reconstruction methods, the present invention has the ability to handle physical field reconstruction when the detector moves, which has never been achieved in previous technical solutions.

[0034] 3. The physical field reconstruction method provided by the present invention has both fast speed and, after the CNN is trained, the time required for physical field reconstruction for any new observation parameters is less than 0.1 seconds, which provides convenience for online power reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the regularized completion of the core geometry.

[0036] Figure 2 Schematic diagram of the detector vibrating near the nominal position

[0037] Figure 3 This is a schematic diagram of reconstructing the fast group flux, hot group flux and power distribution based on the hot group flux training.

[0038] Figure 4 This is the core layout of the 2D IAEA benchmark example.

[0039] Figure 5 is the 2D IAEA fast group flux distribution

[0040] Figure 6 is the reconstructed 2D IAEA fast group flux distribution, with detector activity within 1 cm

[0041] Figure 7 is the 2D IAEA heat cluster flux distribution

[0042] Figure 8is the reconstructed 2D IAEA thermal cluster flux distribution, with detector activity within 1 cm

[0043] Fig. 9 is the 2D IAEA power distribution

[0044] Fig.10 is the reconstructed 2D IAEA power distribution, with the detector active within 1 cm DETAILED DESCRIPTION

[0045] The technical solution of the present invention is further described below in conjunction with embodiments and drawings, but this should not limit the protection scope of the present invention.

[0046] A method for reconstructing core power distribution by processing detector movement comprises the following steps:

[0047] (1) Determine the canonical geometric representation of the core. To facilitate subsequent implementation, the given reactor core is expanded into a normal two-dimensional rectangle or three-dimensional cuboid Ω. For traditional pressurized water reactors, especially Figure 1 The core shown is filled into a normal cuboid (three-dimensional core) or rectangle (two-dimensional core). For ease of description, the following is explained in two dimensions.

[0048] (2) Divide the reactor core grid point set into n x ×n y The total number of points is recorded as N = n x ×n y , the corresponding physical field f(r) (such as power distribution P(r), fast group flux distribution φ1(r) and hot group flux distribution φ2(r)) is expressed as For areas where the core is blank, the relevant value can be set to 0.

[0049] (3) Determine the nominal position distribution of the core detectors. Assume that there are M detectors in the core, with positions r s =(r1,r2,…,r M ) T , for a given physical field f, at each detector location r i The observed value is y i (f), the total observation vector is recorded as y(f) = (y1(f), y2(f), …, y M (f)) T . The superscript “T” indicates transpose.

[0050] (4) Determine the disturbance interval of the core detector at the nominal position. Let the disturbance interval of the detector at the nominal position be δ, which means that the detector can be iFor any activity in a square area with a length and width of 2δ as the center, the signal detected by the detector is also the signal of the physical field at the current position of the square area.

[0051] (5) Divide the effective range of the detector position. s , calculate the total area Ω in N = n x ×n y Position point to r s The relationship between the points, for the midpoint r of Ω k , calculated with r s The distance between the points in the i Click the nearest one, and r k Classify into the i-th group and obtain a segmentation g1,g2,…,g in Ω M , regarded as detector r s Each region g i Included in r i The i-th detector at .

[0052] (6) Constructing an observation field consistent with the structure of the physical field F The construction method is as follows: For each point Y(r k ), if r k In the segmentation i , then take the detector y i (f) Thus, Y contains the detection values ​​of M detectors and also contains the segmentation information determined by the location of the detector, that is, it contains the location information of the detector.

[0053] Based on steps (1) to (6), a set of representations of the detector and the physical field under the same network structure is obtained, namely (Y, F).

[0054] (7) Determine the reactor operating parameter domain. Assume that the nuclear reactor input parameters that affect the distribution of the physical field F are μ = (μ1, μ2, …, μ p ) T , which can be reactor power level, control rod position, burnup level, boron concentration, coolant inlet temperature, etc. Its operating parameter domain is expressed as In one embodiment, each dimension (μ i , i=1,2,…,p) determine a local interval, for example [μ i -Δμ i ,μ i +Δμ i ], where Δμ i It can be called μ i , then the domain formed by the offset values ​​of all p dimensions of the state parameter μ is called the parameter domain of the state parameter μ.

[0055] (8) In the operating parameter field The parameters in the middle pile are randomly sampled according to the sampled parameters μ sample Based on the core physics calculation software (such as CORCA-3D), the core physics calculation is performed to obtain the corresponding physical fields (such as power distribution P(r), fast group flux distribution φ1(r) and hot group flux distribution φ2(r)), forming a N sample A collection of physical fields F of samples.

[0056] (9) Based on the detector vibration range 2δ determined by (4) and the physical field sample generated by (8), the specific positions within the vibration range of the M detectors are jointly randomly sampled. For a given physical field F, a detector random position sample Y can be obtained.

[0057] (10) Generate samples: According to the operations of (8) and (9), a large number of samples corresponding to the detector and the physical field can be obtained.

[0058] (11) Training: Using deep convolutional neural network (CNN) Train and obtain the mapping relationship from Y to F, that is The CNN training network proposed in this invention has specific training parameters according to the sample It is confirmed that the adopted method is consistent with the commonly used CNN training method.

[0059] (12) During training, the observation field Y is measurable, specifically the thermal group flux in the core; the physical field F can be the thermal group flux, the fast group flux or the power distribution. Different CNN networks can be trained according to different pairings. See Figure 3 .

[0060] (13) Physical field reconstruction: After obtaining the trained neural network For the core detector configuration at any time, obtain the relevant measurement values, repeat step (6) according to the preparations (1) to (5), and obtain the current observation field Y based on the detector observation value y. After inputting into the trained network, the reconstructed physical field distribution F can be obtained.

[0061] Example:

[0062] Taking the 2D IAEA benchmark example certified by the International Atomic Energy Agency as an example, the physical field reconstruction process and effect provided by the present invention are demonstrated, referring to the aforementioned steps (1) to (13).

[0063] First, the core geometry is regularized and supplemented into a rectangular shape.

[0064] Determine the detector location distribution. In this verification, a detector is installed at the center of each component.

[0065] Based on the core physics calculation software (such as CORCA-3D), the two groups of neutron diffusion equations are calculated to obtain the flux and power distribution of the core.

[0066] In order to obtain a large number of training samples, the present invention randomly samples within the range of plus or minus 10% according to the given parameters of the 2D IAEA benchmark example, performs core physics calculations, and obtains N sample A collection of physical fields F of samples.

[0067] The detector is sampled for observation values ​​in the vibration areas of 1cm, 3cm, and 5cm to construct the observation field Y corresponding to the physical field F.

[0068] Based on the convolutional neural network training, the model is obtained

[0069] Get the model After that, the discrete representation F of the corresponding physical field distribution f can be reconstructed according to any given observation value y.

[0070] Figure 5 The 2D IAEA fast group flux distribution is shown. Figure 6 Shown is the reconstructed 2D IAEA fast group flux distribution with detector activity over a 1 cm range. Figure 7 The 2D IAEA heat cluster flux distribution is shown. Figure 8 Shown is the reconstructed 2D IAEA thermal cluster flux distribution with detector activity at 1 cm. Fig. 9 The 2D IAEA power distribution is shown, Fig.10 Shown is the reconstructed 2D IAEA power distribution with detector activity over a 1 cm range.

[0071] It can be found that the present invention can reconstruct the core physical field distribution with high accuracy when the detector moves. After a large number of verifications, it was found that the present invention can reconstruct the physical field within 5 cm of the detector moving position, and the mean square error is within 5%, which meets the engineering accuracy requirements.

Claims

1. A method for reconstructing core power distribution by processing detector movement, characterized in that: The steps include: S1. Regularize the core geometry and expand it into a two-dimensional or three-dimensional rectangle Ω. The grid points are grouped into n x ×n y The total number of grid points is N, N = n x ×n y , the discrete representation of the physical field f(r) is S2. Determine the distribution of core detector positions: Assume that there are M detectors in the core, with positions r s =(r1, r2, ..., r M ) T , for a given physical field f, at each detector location r i The observed value y i (f), the total observation vector is y(f) = (y1(f), y2(f), ..., y M (f)) T , the superscript "T" indicates transposition; S3. Determine the disturbance interval δ of the core detector at the nominal position, that is, the detector can be i For any activity in a square area with a length and width of 2δ as the center, the signal detected by the detector is the signal of the physical field at the current position of the square area; S4. Divide the effective range of detector positions: Calculate the relationship between all grid points in the two-dimensional or three-dimensional rectangular Ω area and each detector position. k , calculated with r s The distance between the points in the i If point r is closest, k Assign to the i-th group and obtain a segmentation g1, g2, ..., g in Ω M , regarded as detector r s The segmentation of each region g i Included in r i The i-th detector at ; S5. Construct an observation field consistent with the structure of the physical field F For each point Y(r k ), if r k In the segmentation i Then take the detector y i (f) The value of, that is, Y contains the detection values ​​and detector position information of M detectors; S6. Based on S1-S5, a set of representations of a detector and a physical field under the same network structure is obtained, namely (Y, F); S7. Determine the reactor operating parameter domain And in the Run Parameters field Randomly sample the heap parameters in the formation of N sample The physical field F set of samples; S8. Based on the detector vibration interval 2δ determined in S3., perform joint random sampling on specific positions within the vibration interval of the M detectors, and obtain a detector random position sample Y for a given physical field F; S9. According to S7 and S8, obtain samples of detectors and physical fields under the same network structure S10. Using deep convolutional neural network (CNN), Train to get the mapping relationship from Y to F, that is, the neural network model S11. Physical field reconstruction: After getting the trained neural network For the core detector configuration at any time, obtain the measured value, repeat S5 according to the preparations of S1.-S4., and obtain the current observation field Y according to the detector's observation value y, and input it into the trained neural network model The reconstructed physical field distribution F is obtained.

2. The method for reconstructing the core power distribution by processing the detector movement according to claim 1, characterized in that: The step S7 determines the reactor operating parameter domain And in the Run Parameters field Randomly sample the heap parameters in the formation of N sample The physical field F set of samples includes: S7.1 Assume that the nuclear reactor state parameter μ that affects the distribution of the physical field F is μ = (μ1, μ2, ..., μ p ) T , μ i The operating parameters of the nuclear reactor are reactor power level, control rod position, burnup level, boron concentration and coolant inlet temperature. S7.2 is the state parameter μ for each dimension (μ i , i = 1, 2, ..., p) determine a local interval [μ i -Δμ i , μ i +Δμ i ], where Δμ i μ i The domain formed by the offset values ​​of all p dimensions of the state parameter μ is called the operating parameter domain of the state parameter μ. S7.3 in the operating parameters area The parameters in the middle pile are randomly sampled according to the sampled parameters μ sample , perform core physics calculations, and obtain the corresponding physical field F, including power distribution P(r), fast group flux distribution φ1(r) and hot group flux distribution φ2(r), forming N sample A collection of physical fields F of samples.

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