Method for predicting growth of corrosion product deposits, storage medium and electronic device

Abstract

The present application relates to the technical field of reactors, and in particular to a method for predicting the growth of corrosion product deposition, a storage medium and an electronic device. The method for predicting the growth of corrosion product deposition comprises: establishing a three-dimensional model, performing mesh division on the three-dimensional model to obtain a mesh model; obtaining a thickness increment of the corrosion product; using a node movement algorithm to convert the thickness increment of the corrosion product on the mesh element surface into a node movement amount, and driving the mesh nodes to displace according to the calculated node movement amount within each time step based on dynamic mesh technology; obtaining an updated mesh model; solving the deposition rate and the erosion rate of the corrosion product on the updated mesh model to obtain an updated thickness increment of the corrosion product. The application can improve the safety of the reactor, and has important value for protecting the integrity of the fuel rod, preventing the leakage of radioactive substances and ensuring the safe operation of the reactor.

Description

Technical Field

[0001] This invention belongs to the field of reactor technology, and more specifically, relates to a method for predicting the deposition and growth of corrosion products, a storage medium, and an electronic device. Background Technology

[0002] In the primary coolant environment of a pressurized water reactor (PWR), CRUD (Chalk River Unidentified Deposit) deposition on the surface of the fuel rod cladding is one of the core issues threatening the safe operation of the reactor. For example, thickening of the CRUD layer significantly increases the thermal resistance between the fuel cladding and the coolant, leading to a localized increase in cladding temperature, which may trigger CRUD-induced localized corrosion (CILC) and power offset (CIPS). Simultaneously, the porous structure of the CRUD adsorbs boric acid, causing localized distortion of the distribution of soluble boron in the coolant, further exacerbating abnormal nuclear fuel power distribution.

[0003] In related technologies, CRUD growth prediction mainly relies on empirical models of uniform deposition rates fitted based on experimental data, which are difficult to reflect non-uniform deposition under complex flow fields. At the same time, existing corrosion product growth prediction models usually use fixed grid models, simplifying the deposition layer to a certain fixed geometry, which cannot describe the dynamic encroachment effect of deposition layer growth on the fluid domain, seriously affecting the calculation accuracy of near-wall flow field, heat flux and material diffusion, resulting in distorted calculation results. Summary of the Invention

[0004] The purpose of this invention is to provide a method, storage medium, and electronic device for predicting the deposition and growth of corrosion products. This method can not only reflect the dynamic changes of CRUD in pressurized water reactor operation in real time, but also provide important technical support for further optimization of numerical prediction methods for nuclear reactors.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows:

[0006] According to one aspect of this application, a method for predicting the deposition and growth of corrosion products is provided, the method comprising:

[0007] A three-dimensional model of the target research object is established, and the three-dimensional model is meshed to obtain a mesh model, which includes multiple mesh elements.

[0008] Set the input conditions for the three-dimensional model and calculate the deposition rate and erosion rate of the corrosion products. Based on the deposition rate and erosion rate of the corrosion products, obtain the thickness increment of the corrosion products.

[0009] The thickness increment of corrosion products on the mesh cell surface is converted into node movement amount using a node movement algorithm, and based on dynamic mesh technology, the mesh nodes are driven to move according to the calculated node movement amount in each time step.

[0010] Determine whether the mesh re-partitioning conditions are met. If they are met, use the mesh re-partitioning algorithm to dynamically adjust the mesh in other regions and obtain the updated mesh model.

[0011] The deposition rate and erosion rate of corrosion products are solved on the updated mesh model to obtain the thickness increment of the updated corrosion products. After the target requirements are met, the three-dimensional morphology and related characteristic parameters of the corrosion products are output.

[0012] In some optional implementations, establishing a three-dimensional model of the target research object includes:

[0013] Taking pressurized water reactor fuel rods with corrosion products on their surface as the target research object, a three-dimensional model is established. The three-dimensional model includes a sub-channel fluid domain and an initial corrosion product solid domain. The initial corrosion product solid domain includes a corrosion product solid seed layer of a predetermined thickness on the surface of the fuel rod cladding.

[0014] The meshing of the three-dimensional model includes meshing the sub-channel fluid domain and the initial corrosion product solid domain.

[0015] In some optional implementations, the flow field and temperature field are solved according to the input conditions, and the deposition rate and erosion rate of corrosion products are calculated based on the obtained flow field and temperature field.

[0016] The input conditions include physical property parameters and boundary conditions. The physical property parameters include the density, thermal conductivity, specific heat capacity, dynamic viscosity, latent heat of phase change, and density of corrosion products of the coolant fluid. The boundary conditions include the velocity, temperature, and cavitation fraction of the coolant fluid inlet, the boundary type of the coolant channel, and the pressure of the coolant fluid outlet.

[0017] In some optional implementations, the mesh model includes an initial base of corrosion products, and the mesh nodes of the initial base of corrosion products include a first type of mesh node, a second type of mesh node, and a third type of mesh node. The first type of mesh node includes corner nodes, the second type of mesh node includes edge nodes, and the third type of mesh node includes center nodes. The calculation method for the node movement of each type of mesh node is different.

[0018] In some alternative implementations, the node movement of the first type of grid node is calculated based on the surface deposition thickness increment of its respective grid cell;

[0019] The node movement of the second type of grid node is calculated based on the arithmetic mean of the deposition thickness increments of two adjacent grid cell surfaces;

[0020] The node movement of the third type of grid node is calculated based on the arithmetic mean of the deposition thickness increments of four adjacent grid cell surfaces.

[0021] In some optional implementations, during the process of re-meshing using the mesh re-meshing algorithm, the relationship between the expected total moving height of the mesh node to be moved and the inter-layer mesh height is monitored each time a mesh node is moved; based on this relationship, the number of mesh layers to move the mesh node is determined.

[0022] In some optional implementations, in the step of obtaining the updated thickness increment of the corrosion products, the updated thickness increment of the corrosion products is obtained, and it is determined whether the simulation has reached a preset time or thickness target.

[0023] If the target requirement is not met, repeat the steps to obtain the thickness increment of the updated corrosion products until the preset requirement is met.

[0024] If the target requirements are met, the three-dimensional morphology and related characteristic parameters of the corrosion products will be directly output.

[0025] In some alternative implementations, the relevant characteristic parameters include the fuel rod cladding temperature distribution and sub-channel flow field parameters.

[0026] According to another aspect of this application, a storage medium is provided that stores computer instructions, which, when invoked by a processor, execute the method for predicting the deposition and growth of corrosion products.

[0027] According to another aspect of this application, an electronic device is provided, comprising:

[0028] processor;

[0029] Memory, which stores computer instructions;

[0030] The processor is used to invoke the computer instructions to execute the method for predicting the deposition and growth of corrosion products.

[0031] The technical solution of this application has at least the following beneficial effects:

[0032] In this application, the proposed method for predicting the deposition and growth of corrosion products achieves, for the first time, a dynamic and high-precision simulation of the three-dimensional morphology of CRUD (Crew Residue Uncovered Surface) using dynamic meshing technology and a node-moving algorithm. This method realistically reproduces the encroachment effect on coolant channels, representing a significant breakthrough in CRUD growth prediction. The application's mesh re-partitioning strategy effectively overcomes mesh distortion problems in long-term simulations, ensuring computational stability. Furthermore, this method dynamically adapts to the parameters of key sub-models in the gas-liquid two-phase flow, accurately capturing the boiling phase transition details of the CRUD surface. This allows for comprehensive prediction from morphological evolution to the cladding temperature field within a numerical framework, providing a more reliable solution for reactor safety assessment. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0034] Figure 1 A three-dimensional model schematic diagram of a pressurized water reactor core channel provided in an embodiment of this application;

[0035] Figure 2 A schematic diagram of a dynamic meshing technique for CRUD growth provided in an embodiment of this application;

[0036] Figure 3 This is a schematic diagram of a grid node movement algorithm provided in an embodiment of this application;

[0037] Figure 4 This is a schematic diagram illustrating a method for determining whether the conditions for mesh re-division are met, as provided in an embodiment of this application. Detailed Implementation

[0038] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0040] To address the problem that existing CRUD prediction models for corrosion products mainly rely on empirical models of uniform deposition rates fitted from experimental data, which are difficult to reflect non-uniform deposition under complex flow fields; at the same time, existing corrosion product growth prediction models usually use fixed grid models, which simplify the deposition layer to a certain fixed geometry and cannot describe the dynamic encroachment effect of deposition layer growth on the fluid domain, which seriously affects the calculation accuracy of near-wall flow field, heat flux and material diffusion, resulting in distorted calculation results.

[0041] Therefore, the dynamic mesh method for numerically predicting corrosion product deposition and growth proposed in this invention is a numerical method that can couple the dynamic growth of corrosion products with the interaction of fluid motion. It can predict the three-dimensional morphological evolution of CRUD (Crew Residue Undulated) and its impact on the system's thermal-hydraulic behavior with high fidelity. The novel numerical prediction method for CRUD deposition and growth provided by this invention can not only reflect the dynamic changes of CRUD during pressurized water reactor operation in real time, but also provide important technical support for further optimizing numerical prediction methods for nuclear reactors. Specific technical solutions are described below.

[0042] In some embodiments, a method for predicting the deposition and growth of corrosion products is provided, the method comprising:

[0043] S101. Establish a three-dimensional model of the target research object, and divide the three-dimensional model into a mesh to obtain a mesh model, wherein the mesh model includes multiple mesh elements;

[0044] S102. Set the input conditions for the three-dimensional model and calculate the deposition rate and erosion rate of the corrosion products. Based on the deposition rate and erosion rate of the corrosion products, obtain the thickness increment of the corrosion products.

[0045] S103. The thickness increment of corrosion products on the mesh cell surface is converted into node movement amount using a node movement algorithm, and based on dynamic mesh technology, the mesh nodes are driven to move according to the calculated node movement amount in each time step.

[0046] S104. Determine whether the mesh re-division condition is met. If it is met, use the mesh re-division algorithm to dynamically adjust the mesh in other regions and obtain the updated mesh model.

[0047] S105. Solve for the deposition rate and erosion rate of corrosion products on the updated mesh model, obtain the thickness increment of the updated corrosion products, and output the three-dimensional morphology and related characteristic parameters of the corrosion products after the target requirements are met.

[0048] The corrosion product deposition growth prediction method provided in this application is a dynamic mesh method for numerically predicting corrosion product (CRUD) deposition growth. It can realize dynamic visualization of CRUD growth morphology and lay the foundation for realistic simulation of the two-way coupling effect between near-wall flow field and deposition layer.

[0049] The corrosion product deposition growth prediction method provided in this application can be applied to the prediction of CRUD deposition growth on the surface of pressurized water reactor fuel rod cladding. Furthermore, this method is not only applicable to CRUD prediction of pressurized water reactor fuel cladding, but can also be extended to numerical simulation of dynamic growth processes such as fouling deposition on steam generator heat exchanger tubes and scaling on chemical pipelines. In practical applications, the application of this method will significantly improve reactor safety and is of great value in protecting the integrity of fuel rods, preventing the leakage of radioactive materials, and ensuring the safe operation of the reactor. The following mainly describes this method using the prediction of CRUD deposition growth on the surface of pressurized water reactor fuel rod cladding. Specifically:

[0050] In this embodiment, a three-dimensional subchannel model containing the subchannel fluid domain and the initial solid domain of the CRUD can be established. By combining dynamic mesh technology and mesh node movement algorithm, the three-dimensional morphological evolution during the CRUD thickness growth process can be predicted, the CRUD encroachment effect on the coolant fluid domain can be realistically restored, and the boiling heat transfer of the shell under different CRUD thicknesses can be calculated.

[0051] In existing technologies, the lack of dynamic meshing technology makes it impossible to capture the encroachment effect on the coolant fluid domain during CRUD thickness growth, nor can it capture the flow field near uneven CRUDs. Therefore, compared to existing technologies, the method of this invention achieves a significant breakthrough in CRUD growth prediction. In particular, this invention, through dynamic meshing technology and node movement algorithms, achieves for the first time a dynamic, high-precision simulation of the CRUD's three-dimensional morphology, realistically reproducing its encroachment effect on the coolant flow channels. Furthermore, this invention, combined with a mesh re-partitioning strategy, effectively overcomes the mesh distortion problem in long-term simulations, ensuring computational stability. Simultaneously, this method can dynamically adapt to the parameters of key sub-models of gas-liquid two-phase flow, accurately capturing the boiling phase transition details of the CRUD surface, thus completing a comprehensive prediction from morphological evolution to the cladding temperature field within the numerical framework, providing a more reliable solution for reactor safety assessment.

[0052] It should be understood that the aforementioned ability to dynamically adapt key sub-model parameters for gas-liquid two-phase flow mainly refers to the fact that in current two-fluid models of gas-liquid two-phase flow, such as those used in software like Fluent (e.g., bubble escape diameter, bubble nucleation point density, and bubble escape frequency), these key sub-model parameters are established by researchers based on smooth surface conditions. If the encroachment effect of the CRUD thickening process on the coolant fluid domain is further considered, the roughness changes caused by local CRUD can be used to correct the aforementioned key sub-model parameters. Therefore, the dynamic meshing technology used in this embodiment of the invention provides an effective way to achieve this type of parameter correction.

[0053] Therefore, this invention addresses the shortcomings of existing computational fluid dynamics techniques in simulating CRUD morphological evolution by proposing a numerical prediction method for CRUD growth based on dynamic mesh technology. This method simulates the CRUD growth process by allocating incremental CRUD thickness data to mesh nodes and driving their movement at each time step, thereby constructing a numerical simulation system capable of predicting the three-dimensional morphology of the growing CRUD. This method considers the encroachment effect on the coolant fluid domain during CRUD thickening, avoiding accuracy loss due to oversimplification of the near-wall flow field. Furthermore, this method can dynamically adjust key sub-models in the gas-liquid two-phase flow model (such as bubble departure diameter, bubble nucleation point density, and bubble departure frequency) in real time, thereby more accurately capturing the boiling phase transition details on the uneven surface of the CRUD. Therefore, the method provided by this invention can more meticulously reflect the impact of the dynamic evolution of CRUD on the flow and heat transfer processes during numerical simulation of CRUD growth.

[0054] Specifically, in some embodiments, step S101, establishing a three-dimensional model of the target research object includes: taking a pressurized water reactor fuel rod with corrosion products on its surface as the target research object, establishing a three-dimensional model, the three-dimensional model including a sub-channel fluid domain and an initial corrosion product solid domain, the initial corrosion product solid domain including a corrosion product solid seed layer of a predetermined thickness on the surface of the fuel rod cladding; meshing the three-dimensional model includes: meshing the sub-channel fluid domain and the initial corrosion product solid domain.

[0055] Optionally, the preset thickness of the corrosion product solid seed layer, i.e., the CRUD solid seed layer, can be 5 μm to 15 μm, or 8 μm to 12 μm, for example, around 10 μm. It should be understood that the smaller the preset thickness of the CRUD solid seed layer, the more meshes are generated, and the more accurate the simulation of the actual process of CRUD growth starting from zero thickness; however, the computational burden will also increase accordingly. Therefore, this preset thickness can be selected and set according to the actual situation, without excessive restrictions.

[0056] As an example, Figure 1 A schematic diagram of a 3D model of the core channel of a pressurized water reactor is shown. (Reference) Figure 1 As shown, step S101 mainly involves modeling the fuel rod and subchannel fluid domain containing the CRUD, i.e., establishing a three-dimensional model, and pre-setting a CRUD solid seed layer with a thickness of approximately 10 μm on the outside of the fuel rod cladding. Then, the three-dimensional model is meshed, that is, the computational meshes for the subchannel fluid domain and the CRUD solid domain are divided to obtain a mesh model.

[0057] In this embodiment, a three-dimensional model of the sub-channel is first established, including the sub-channel fluid domain and the initial CRUD solid domain, as shown in Figure 1. Figure 1 Considering the symmetry and geometric consistency of the coolant flow channels formed between every four fuel rods, the most central channel is selected as a sub-channel during modeling. The solid red area represents the coolant fluid computational domain for geometric modeling. During modeling, at least a portion of the fuel rod cladding surface already possesses an initial corrosion product solid domain of approximately 10 μm thickness, i.e., a CRUD solid seed layer of approximately 10 μm, used to simulate the actual thickening process of CRUD (Crew Damage Unsold). The contact surface between the sub-channel fluid domain and the solid domain serves as the boundary for mesh node movement during the CRUD thickness growth process. By moving mesh nodes on this contact surface, the increase in the CRUD solid domain thickness and the corresponding decrease in the fluid domain space can be simulated, thus realistically reproducing the encroachment effect of CRUD on the coolant fluid domain under actual operating conditions.

[0058] Specifically, in some embodiments, in step S102, the input conditions of the three-dimensional model are set, the flow field and temperature field are solved according to the input conditions, the deposition rate and erosion rate of the corrosion products are calculated according to the obtained flow field and temperature field, and the thickness increment of the corrosion products is obtained according to the obtained deposition rate and erosion rate of the corrosion products.

[0059] The input conditions include physical property parameters and boundary conditions. The physical property parameters include the density, thermal conductivity, specific heat capacity, dynamic viscosity, latent heat of phase change, and density of corrosion products of the coolant fluid. The boundary conditions include the velocity, temperature, and cavitation fraction of the coolant fluid inlet, the boundary type of the coolant channel, and the pressure of the coolant fluid outlet.

[0060] In this embodiment, after mesh generation, the input conditions required for calculation can be set and the flow field, temperature field, and CRUD deposition / erosion rate can be solved to obtain the CRUD thickness increment; that is, based on the solved flow field and temperature field, the CRUD deposition rate and erosion rate can be obtained, and then, based on the obtained CRUD deposition rate and erosion rate, the CRUD thickness increment can be obtained.

[0061] The input conditions mentioned above may include physical property parameters, such as the density, thermal conductivity, specific heat capacity, dynamic viscosity, latent heat of phase change, and density and thermal conductivity of corrosion products of the coolant fluid.

[0062] The above input conditions may also include boundary conditions, which include the velocity, temperature and cavitation fraction of the coolant fluid inlet, the boundary type of the coolant passage (such as symmetrical boundary conditions), and the pressure of the coolant fluid outlet.

[0063] The input conditions mentioned above may also include some calculation settings, such as time step setting, CRUD deposition / erosion rate per unit step, turbulence model selection, and numerical solution methods for the flow field and temperature field. The solvability of the flow field and temperature field is implemented using Fluent software.

[0064] It should be noted that the above-mentioned methods for solving the flow field and temperature field can be obtained by using known models in related technologies; the CRUD deposition / erosion rate can also be obtained by using known models in related technologies; the CRUD thickness increment or CRUD thickness change rate has been reflected in the existing calculation model of CRUD deposition / erosion rate, and there are no restrictions on this.

[0065] In some embodiments, in step S103, the thickness increment on the CRUD mesh surface is converted into node movement amount using a node movement algorithm, and based on dynamic mesh technology, the mesh nodes are driven to move according to the calculated movement amount in each time step.

[0066] The time step can be set as needed, for example, by using a fixed value (such as 0.001s) or by dynamically adjusting it based on some mathematical relationship. Generally speaking, the smaller the time step, the higher the calculation accuracy.

[0067] It should be understood that the node movement algorithm can be used to update the dynamic mesh, and its principle is as follows: Figure 3 As shown, dynamic meshes change the geometry of individual mesh cells by moving nodes. However, CRUD deposition / erosion rates are typically given per unit area or per unit volume. When adjacent mesh surfaces have different CRUD deposition / erosion rates, data conflicts may occur in the movement direction of their shared nodes. In this case, the movement amounts of these nodes need to be redistributed according to certain rules. The core function of the node movement algorithm is to convert the rate data on the mesh surface into the movement amount of each node through the set assignment rules, thereby ensuring that the mesh can deform reasonably, accurately, and coordinately during CRUD growth or dissolution.

[0068] In this embodiment, reference Figure 2As shown, the center of the blue cell represents the physical location of a single grid, the grid enclosed by the black grid nodes represents the cladding wall, and the area enclosed by the black grid nodes and the purple fluid-solid contact surface grid nodes is the initial CRUD base. Before deposition begins, the distribution of dissolved and particulate corrosion products within the fluid computational domain is obtained through numerical calculation. During deposition, based on dynamic meshing technology, the total deposition rate data of dissolved and particulate products is mapped to the initial CRUD base layer grid surface (yellow cell) and grid nodes (such as the purple fluid-solid contact surface grid nodes). According to the mapping relationship between deposition rate and deposition thickness, the real-time deposition rate is converted into deposition thickness, and the purple fluid-solid contact surface grid nodes are driven to move along the normal direction. Considering that the deposition rate is calculated based on the grid cells (yellow cells include the center point of the blue cells), and the dynamic meshing execution object is the grid node (purple fluid-solid contact surface grid node), this embodiment proposes a node movement algorithm based on the arithmetic mean of the thickness increments of adjacent cells to achieve a reasonable evolution of the CRUD thickness growth.

[0069] In some embodiments, all CRUD initial base nodes ( Figure 2 The purple flow-solid contact surface mesh nodes can be divided into three categories: the first category includes corner nodes, the second category includes edge nodes, and the third category includes center nodes. The calculation methods for node movement differ for each category of mesh nodes.

[0070] Optionally, in some embodiments, the node movement of the first type of grid node is calculated based on the surface deposition thickness increment of its respective grid cell; the node movement of the second type of grid node is calculated based on the arithmetic mean of the surface deposition thickness increments of two adjacent grid cells; and the node movement of the third type of grid node is calculated based on the arithmetic mean of the surface deposition thickness increments of four adjacent grid cells.

[0071] refer to Figure 3 As shown in the embodiments of this application, based on the distribution characteristics of the grid nodes, they can be divided into three typical cases, such as three types of grid nodes: the first type of grid node is the corner node (CASE1), the second type of grid node is the edge node (CASE2), and the third type of grid node is the center node (CASE3).

[0072] Among them, the movement of the first type of grid node corner node (CASE1) can be directly taken from the surface deposition thickness increment of its grid cell, and calculated according to the following formula (1). The movement of the second type of grid node edge node (CASE2) is taken as the arithmetic mean of the surface deposition thickness increments of two adjacent grid cells, and calculated according to the following formula (2). The movement of the third type of grid node center node (CASE3) is taken as the arithmetic mean of the surface deposition thickness increments of four adjacent grid cells, and calculated according to the following formula (3).

[0073] (1)

[0074] (2)

[0075] (3)

[0076] Where, Δ x Δ represents the increment of deposition thickness at grid nodes. f Represents the increment of deposition thickness on the grid cell surface, subscript i , j These represent horizontal and vertical sequences, respectively.

[0077] In some embodiments, step S104 involves determining whether the mesh re-division condition is met, for example... Figure 4 As shown, let the cumulative deposition thickness at the current moment be Δ. f and will Figure 2 The first layer fluid domain mesh immediately adjacent to the purple fluid-solid interface mesh node ( Figure 2 The height of the light green unit is defined as follows: f j - f j-1 If the judgment condition Δ is met f < f j - f j-1 This indicates that the normal displacement of the interface nodes is still within the thickness range of the current fluid mesh layer. At this point, there is no risk of mesh line intersection or geometric distortion, and the system determines that mesh re-division is not necessary for the time being. Figure 4 As shown in (a); conversely, if the judgment condition Δ is satisfied... f ≥ f j - f j-1 This indicates that the movement distance of the interface node has reached or exceeded the height of the fluid mesh layer, which can easily lead to physical intersections between the lower and upper mesh lines, thereby inducing "negative volume" elements and causing computational divergence. In this case, the system determines that the mesh re-division condition is met, and then, in conjunction with the formulas (1) to (3) established in this invention, drives the purple fluid-solid interface mesh node and its subsequent fluid domain nodes to perform synchronous and coordinated displacement, such as... Figure 4 As shown in (b), the risk of grid line penetration is effectively avoided by reconstructing the local grid topology, thereby ensuring that the grid quality always meets the requirements of high-precision numerical calculation.

[0078] Optionally, during the mesh re-partitioning process using the mesh re-partitioning algorithm, the relationship between the expected total movement height of the mesh node to be moved and the inter-layer mesh height is monitored each time a mesh node is moved; based on this relationship, the number of mesh layers to move the mesh node is determined. For example, the inter-layer mesh height refers to... Figure 2 The height of the first layer of light green fluid element mesh immediately adjacent to the purple fluid-solid interface mesh node, and also includes the height of the first layer and the second layer, the second layer and the third layer, and so on, of the light green fluid elements. Furthermore, the height of any mesh layer must not be less than the movement height.

[0079] As an example, during dynamic mesh updates, the surface nodes of CRUD elements in contact with fluid elements are defined as the node layers to be moved. At the end of each time step, the expected movement height of the node layers to be moved (i.e., the increase in CRUD thickness per unit time) needs to be compared with... Figure 2 The relationship between the heights of the light green fluid element meshes adjacent to the purple fluid-solid interface mesh nodes is determined and processed according to the following rules: (1) If the height to be moved is less than the height of the first layer of the fluid element mesh, then only the layer of the node to be moved is moved. (2) If the height to be moved is greater than or equal to the height of the first layer of the fluid element mesh, then in addition to moving the layer of the node to be moved, the nodes of the 2nd, 3rd, 4th...nth layers are moved layer by layer into the fluid element until the cumulative moving height is less than the position of the nth layer node.

[0080] The key significance of this judgment is that if the subsequent node layers are not moved synchronously according to situation (2), the node layers to be moved will intersect with the mesh behind, resulting in negative volume of the fluid element, which will lead to calculation errors.

[0081] In some embodiments, in step S105, the thickness increment of the updated corrosion products is obtained, and it is determined whether the simulation has reached the preset time or thickness target.

[0082] If the target requirement is not met, repeat the relevant steps to obtain the thickness increment of the updated corrosion product, that is, repeat steps S102 to S105 until the preset requirement is met.

[0083] If the target requirements are met, the three-dimensional morphology and related characteristic parameters of the corrosion products will be directly output.

[0084] In some embodiments, the relevant characteristic parameters include the fuel rod cladding temperature distribution and the sub-channel flow field parameters.

[0085] In this embodiment, in step S105, the flow field, temperature field, and CRUD deposition / erosion rate field are solved on the updated mesh model to obtain a new round of CRUD thickness increment, and it is determined whether the simulation has reached the preset time or thickness target; if the termination condition is not met, steps S102 to S105 are repeated to realize the dynamic simulation of CRUD growth-fluid-thermal coupling; if the termination condition has been met, the CRUD three-dimensional morphology, cladding temperature distribution, and sub-channel flow field parameters are output.

[0086] Therefore, by using the method provided in this embodiment, and through calculation and verification, the numerical calculation results of the above steps successfully predicted the three-dimensional morphology of the CRUD. The error between the results and the international authoritative experimental data is as follows: when the CRUD thickness reaches 60 μm, the temperature deviation of the cladding wall is only 0.18%, and the porosity deviation of the CRUD is only 2.2%. This shows that the method of the present invention can provide a more reliable solution for reactor safety assessment.

[0087] This application also provides a storage medium storing computer instructions that, when invoked by a processor, execute the aforementioned method for predicting the deposition and growth of corrosion products.

[0088] This application also provides an electronic device, which includes a processor and a memory, the memory storing computer instructions; wherein the processor is used to invoke the computer instructions to execute the aforementioned method for predicting the deposition and growth of corrosion products.

[0089] There can be one or more processors. The processor can be a central processing unit (CPU) or other processing unit, as long as it has data processing and / or instruction execution capabilities and can control other components in the electronic device to execute corresponding instructions.

[0090] Memory may include one or more computer program products, which may be volatile memory, non-volatile memory, etc. Volatile memory may include random access memory (RAM), cache memory, etc. Non-volatile memory may include read-only memory (ROM), hard disk, flash memory, etc.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the deposition and growth of corrosion products, characterized in that, The method includes: A three-dimensional model of the target research object is established, and the three-dimensional model is meshed to obtain a mesh model, which includes multiple mesh elements. Set the input conditions for the three-dimensional model and calculate the deposition rate and erosion rate of the corrosion products. Based on the deposition rate and erosion rate of the corrosion products, obtain the thickness increment of the corrosion products. The thickness increment of corrosion products on the mesh cell surface is converted into node movement amount using a node movement algorithm, and based on dynamic mesh technology, the mesh nodes are driven to move according to the calculated node movement amount in each time step. Determine whether the mesh re-partitioning conditions are met. If they are met, use the mesh re-partitioning algorithm to dynamically adjust the mesh in other regions and obtain the updated mesh model. The deposition rate and erosion rate of corrosion products are solved on the updated mesh model to obtain the thickness increment of the updated corrosion products. After the target requirements are met, the three-dimensional morphology and related characteristic parameters of the corrosion products are output.

2. The method for predicting the deposition and growth of corrosion products according to claim 1, characterized in that, The establishment of a three-dimensional model of the target research object includes: Taking pressurized water reactor fuel rods with corrosion products on their surface as the target research object, a three-dimensional model is established. The three-dimensional model includes a sub-channel fluid domain and an initial corrosion product solid domain. The initial corrosion product solid domain includes a corrosion product solid seed layer of a predetermined thickness on the surface of the fuel rod cladding. The meshing of the three-dimensional model includes meshing the sub-channel fluid domain and the initial corrosion product solid domain.

3. The method for predicting the deposition and growth of corrosion products according to claim 1, characterized in that, The flow field and temperature field are solved based on the input conditions, and the deposition rate and erosion rate of corrosion products are calculated based on the obtained flow field and temperature field. The input conditions include physical property parameters and boundary conditions. The physical property parameters include the density, thermal conductivity, specific heat capacity, dynamic viscosity, latent heat of phase change, and density of corrosion products of the coolant fluid. The boundary conditions include the velocity, temperature, and cavitation fraction of the coolant fluid inlet, the boundary type of the coolant channel, and the pressure of the coolant fluid outlet.

4. The method for predicting the deposition and growth of corrosion products according to claim 1, characterized in that, The mesh model includes an initial base of corrosion products. The mesh nodes of the initial base of corrosion products include a first type of mesh node, a second type of mesh node, and a third type of mesh node. The first type of mesh node includes corner nodes, the second type of mesh node includes edge nodes, and the third type of mesh node includes center nodes. The calculation methods for the node movement of each type of mesh node are different.

5. The method for predicting the deposition and growth of corrosion products according to claim 4, characterized in that, The node movement of the first type of grid node is calculated based on the surface deposition thickness increment of its respective grid cell; The node movement of the second type of grid node is calculated based on the arithmetic mean of the deposition thickness increments of two adjacent grid cell surfaces; The node movement of the third type of grid node is calculated based on the arithmetic mean of the deposition thickness increments of four adjacent grid cell surfaces.

6. The method for predicting the deposition and growth of corrosion products according to claim 1, characterized in that, During the mesh re-partitioning process using the mesh re-partitioning algorithm, the relationship between the expected total moving height of the mesh node to be moved and the inter-layer mesh height is monitored each time a mesh node is moved; based on this relationship, the number of mesh layers to move the mesh node is determined.

7. The method for predicting the deposition and growth of corrosion products according to any one of claims 1 to 6, characterized in that, In the step of obtaining the thickness increment of the updated corrosion products, the thickness increment of the updated corrosion products is obtained, and it is determined whether the simulation has reached the preset time or thickness target. If the target requirement is not met, repeat the steps to obtain the thickness increment of the updated corrosion products until the preset requirement is met. If the target requirements are met, the three-dimensional morphology and related characteristic parameters of the corrosion products will be directly output.

8. The method for predicting the deposition and growth of corrosion products according to claim 2, characterized in that, The relevant characteristic parameters include the fuel rod cladding temperature distribution and the sub-channel flow field parameters.

9. A storage medium, characterized in that, The storage medium stores computer instructions that, when invoked by a processor, execute the method for predicting the deposition and growth of corrosion products as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, include: processor; Memory, which stores computer instructions; The processor is used to invoke the computer instructions to execute the method for predicting the deposition and growth of corrosion products as described in any one of claims 1 to 8.

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