Method for reconstructing microstructure of corrosion product deposit layer of pressurized water reactor

Abstract

The application discloses a method for reconstructing microstructure of a corrosion product deposition layer of a pressurized water reactor, which comprises the following steps: pre-processing an image of the corrosion product deposition layer; obtaining a fractal dimension and porosity of the deposition layer by using a box counting method; and finally, fitting a corresponding function relationship between the fractal dimension and the porosity according to the fractal dimension and the porosity of all the images of the corrosion product deposition layer. The application can accurately evaluate the representation between the deposition layer obtained by reproduction and the deposition layer in the real reactor by using a unified correlation method for the microstructure of the corrosion product deposition layer of the pressurized water reactor based on the fractal theory. The application can inversely deduce the fractal dimension or the equivalent porosity based on the fractal dimension extracted from the image, realize the parameterized reconstruction of the morphology of the deposition layer, and thus be used for generating a deposition layer model with specific transport characteristics, and support the geometric input modeling of heat transfer and boron migration behavior in numerical simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reactors, and particularly relates to a method for reconstructing microstructure of corrosion product deposition layer of pressurized water reactor. BACKGROUND

[0002] The corrosion product deposition layer of pressurized water reactor has complex porous micro-morphology, and changes in local conditions of the reactor core, such as thermal parameters, water chemistry and surface parameters, will have a great influence on the microstructure of the deposition layer. SUMMARY

[0003] In view of the above problems in the prior art, the present application provides a method for reconstructing microstructure of corrosion product deposition layer of pressurized water reactor, which can accurately evaluate the representation between the deposition layer obtained by reproduction and the deposition layer in the real reactor core through a unified correlation method for microstructure of corrosion product deposition layer of pressurized water reactor based on fractal theory, and can back-calculate the fractal dimension or equivalent porosity based on fractal dimension extracted from an image, realize parameterized reconstruction of the morphology of the deposition layer, and thus generate a deposition layer model with specific transport characteristics, thereby supporting geometric input modeling of heat transfer and boron migration behavior in numerical simulation.

[0004] The present application is implemented through the following technical scheme:

[0005] The present application relates to a method for reconstructing microstructure of corrosion product deposition layer of pressurized water reactor, which comprises the following steps: after pre-processing the image of the corrosion product deposition layer, obtaining the fractal dimension and porosity of the deposition layer through a box counting method, and finally fitting the corresponding function relationship between the fractal dimension and the porosity according to the fractal dimension and the porosity of all the reproduced images of the corrosion product deposition layer.

[0006] The present application relates to a system for reconstructing microstructure of corrosion product deposition layer of pressurized water reactor, which realizes the above method and comprises an image processing unit, a feature extraction unit, a parameter fitting unit and a structure mapping unit, wherein: the image processing unit performs gray scale normalization, edge detection and noise filtering processing on the collected image of the corrosion product deposition layer to obtain a standardized image that can be used for fractal analysis; the feature extraction unit performs fractal analysis on the deposition area in the standardized image by using a box counting method, calculates and outputs corresponding fractal dimension and porosity data; the parameter fitting unit constructs a function relationship between the fractal dimension and the porosity sample points by using a fitting algorithm, and outputs a function expression or an interpolation data table; and the structure mapping unit maps and back-calculates according to the target fractal dimension or porosity parameter set by the user by using the function expression or the interpolation data table, generates corresponding microstructure information of the deposition layer, and is used for subsequent modeling or simulation input.

[0007] TECHNICAL EFFECT

[0008] The present application accurately depicts and reconstructs the microstructure of the corrosion product deposition layer of the pressurized water reactor by constructing the fractal dimension and the porosity as a function relationship, realizes the bidirectional mapping from image information to structure parameters, extracts the fractal dimension of the corrosion product image by the box counting method, and realizes the digital feature expression of the real deposition layer morphology complexity by combining the equivalent porosity calculation after image processing. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 The image processing flowchart of the corrosion product deposition layer is shown in the figure.

[0010] In the figure, from left to right are the original image, the preliminary binary processing result, and the result after morphological processing.

[0011] Figure 2 The iteration diagram of the box counting method is shown in the figure.

[0012] Figure 3 The statistical process diagram of the box counting method is shown in the figure.

[0013] Figure 4 The function relationship diagram of the fractal dimension and the porosity is shown in the figure.

[0014] Figure 5 The effect diagram of the embodiment is shown in the figure.

[0015] In the figure, red is the reproduced deposition layer, yellow is the true reactor deposition layer, and green is the error. DETAILED DESCRIPTION

[0016] As shown in the figure, a pressurized water reactor corrosion product deposition layer microstructure reconstruction method is provided, which comprises: Figure 1

[0017] Step one, as shown in the figure, the image processing of the corrosion product deposition layer specifically comprises: Figure 1

[0018] 1.1 The surface SEM scanning electron microscope image and the cross-sectional SEM scanning electron microscope image of the reproduced oxidation corrosion product deposition layer sample are subjected to binary processing.

[0019] 1.2 In order to avoid that the local dark area is mistaken for a pore in the corrosion product deposition layer, the adaptive image contrast threshold is applied to the local area with different contrast in the corrected original image by the Otsu algorithm.

[0020] ​​1.3 Remove single-pixel artifacts caused by digital noise or small contrast changes by using small connected regions to avoid mistaking single pixels for small holes. Specifically, use the area screening method to remove independent connected regions with an area of ​​less than 5 pixels in the image (such as 1×1 or 1×2 black pixel blocks, which are mostly noise in actual images and have no physical meaning).

[0021] Step 2: Obtain the fractal dimension and porosity parameters of the sedimentary layer, specifically including:

[0022] 2.1 The fractal size is measured by calculating the number of boxes containing pixels corresponding to pores in the image processed in step one using the box-counting method, such as... Figure 2 As shown. By changing the size of the box, a series of corresponding box numbers containing holes are obtained.

[0023] 2.2 A logarithmic fitting curve of fractal dimension versus porosity is generated using the linear best-fit line. For example... Figure 3 As shown, the negative value of the slope of the logarithmic fitting curve is the fractal size of the corrosion product deposition layer image.

[0024] 2.3 By counting the number of 0 and 1 pixels in the binarized image of the corrosion product deposition layer, where the porosity value is converted to 0 and the solid skeleton value of the deposition layer is converted to 1, the corrosion product deposition layer is statistically calculated. ,in: For porosity statistical synthesis, Statistical synthesis of the solid skeleton of the sedimentary layer.

[0025] The fractal dimension measured from the replicated corrosion product deposit samples is related to the replication conditions (thermal and hydrochemical conditions). The r² values ​​in the box-counting fitting process are all above 0.99, indicating a strong correlation between the fractal dimension of the replicated corrosion product deposits and porosity. This strong correlation also demonstrates that the replicated corrosion product deposit samples possess strong fractal characteristics.

[0026] Step 3: Construct the corresponding functional relationship between fractal dimension and porosity. Specifically, repeat steps 1 and 2 to obtain the fractal dimension and porosity of all reproduced corrosion product deposition layer images, and fit the fractal dimension and porosity to construct the corresponding functional relationship between fractal dimension and porosity. ,in This is the fractal dimension, typically taking values ​​in the range [1, 2]. Porosity is typically taken as [40, 80], such as... Figure 4 As shown.

[0027] Step 4: Based on the functional relationship obtained in Step 3, the fractal dimension is derived from the porosity or the equivalent porosity is derived from the fractal dimension extracted from the image. This enables the parameterized reconstruction of the sedimentary layer morphology, which is then used to generate a sedimentary layer model with specific transport characteristics. This allows for accurate simulation of heat transfer and boron migration behavior in numerical simulations, and ultimately optimizes the reactor core structural parameters.

[0028] Due to the different thermal and hydrochemical conditions in the process of reproducing corrosion product deposits, the microstructures of the reproduced corrosion product deposits also differ. However, the r2 value of the fitting curve is higher than 0.99, indicating that these reproduced corrosion product deposit samples have a strong similarity. The fractal dimension and porosity used to describe the microstructure of the deposits can be well matched with this functional relationship.

[0029] Based on specific practical experiments, we selected corrosion product deposits from four real reactors—Seabrook, Callaway, Vogtle, and VCSummer—from the publicly available reports of the Electric Power Research Institute (EPRI) as examples, as shown in Table 1.

[0030] Table 1

[0031]

[0032] like Figure 5 As shown, real corrosion product deposit layer samples from different reactors can well satisfy the fitted function curves, with a maximum relative error of less than 3%, indicating that the unified correlation method for the microstructure of corrosion product deposit layers constructed in this invention can be used to evaluate the representativeness of the reproduced corrosion product deposit layers.

[0033] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A method for reconstructing the microstructure of a pressurized water reactor corrosion product deposition layer, characterized in that, After preprocessing the images of the corrosion product deposit layer, the fractal dimension and porosity of the deposit layer are obtained by box counting. Finally, the fractal dimension and porosity of all reproduced corrosion product deposit layer images are fitted to obtain the corresponding functional relationship between fractal dimension and porosity. The acquisition of the fractal dimension and porosity of the sedimentary layer specifically includes: i) Measure the fractal size by counting the number of boxes containing the corresponding pixels of the pores in the image after processing in step one using the box counting method. By changing the size of the boxes, obtain a series of corresponding box numbers containing pores. ii) Generate a logarithmic fitting curve of fractal dimension as a function of porosity by using the linear best-fit line. The negative value of the slope of the logarithmic fitting curve is the fractal size of the corrosion product deposition layer image. iii) Calculate the number of 0 and 1 pixels in the binarized image of the corrosion product deposit layer, where pore values ​​are converted to 0 and solid skeleton values ​​are converted to 1. ,in: For porosity statistical synthesis, Statistical synthesis of the solid skeleton of the sedimentary layer.

2. The method for reconstructing the microstructure of pressurized water reactor corrosion product deposits according to claim 1, characterized in that, The preprocessing includes: 2.1 The surface and cross-sectional SEM images of the samples reproducing the oxidative corrosion product deposition layer were binary processed; 2.2 To avoid local dark areas being mistaken for pores within the corrosion product deposition layer, an adaptive image contrast threshold is applied using the Otsu algorithm to correct local areas with different contrasts in the original image. 2.3 Remove single-pixel artifacts caused by digital noise or small contrast changes by using small connected domains to avoid mistaking single pixels for small apertures.

3. The method for reconstructing the microstructure of pressurized water reactor corrosion product deposits according to claim 1, characterized in that, The aforementioned functional relationship refers to: after obtaining the fractal dimension and porosity of all reproduced corrosion product deposition layer images, fitting the fractal dimension and porosity to construct the corresponding functional relationship between the fractal dimension and porosity.

4. The method for reconstructing the microstructure of pressurized water reactor corrosion product deposits according to claim 1, characterized in that, Based on functional relationships, the fractal dimension is derived from the porosity or the equivalent porosity is derived from the fractal dimension extracted from the image, enabling parameterized reconstruction of the sedimentary layer morphology. This allows for the generation of sedimentary layer models with specific transport characteristics, enabling accurate simulation of heat transfer and boron migration behavior in numerical simulations, and further optimizing reactor core structural parameters.

5. A system for reconstructing the microstructure of pressurized water reactor corrosion product deposits using the method described in any one of claims 1-4, characterized in that, include: The system comprises an image processing unit, a feature extraction unit, a parameter fitting unit, and a structure mapping unit. Specifically: the image processing unit performs grayscale normalization, edge detection, and noise filtering on the acquired corrosion product deposition layer images to obtain a standardized image suitable for fractal analysis; the feature extraction unit uses box counting to perform fractal analysis on the deposition regions in the standardized image, calculating and outputting the corresponding fractal dimension and porosity data; the parameter fitting unit uses a fitting algorithm to construct a functional relationship between the fractal dimension and porosity sample points, outputting a function expression or interpolation data table; and the structure mapping unit, based on the user-defined target fractal dimension or porosity parameters, uses the function expression or interpolation data table to perform mapping and inverse calculation, generating corresponding microstructural information of the deposition layer for subsequent modeling or simulation input.

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