Pressurized water reactor core axial power offset phenomenon prediction method and electronic equipment

Abstract

The invention provides a pressurized water reactor core axial power offset phenomenon prediction method and electronic equipment. The prediction method comprises the following steps: firstly, constructing a prediction model of the axial power offset phenomenon of the reactor core; wherein the prediction model is used for representing a model that the axial power offset phenomenon of the reactor core changes along with oxidation corrosion sediment CRUD and fuel burn-up on the surface of a cladding, and the prediction model comprises a neutron physical module, a thermal hydraulic module and a CRUD material module which have a coupling relationship; and then, carrying out coupling calculation on the prediction model within a preset duration, and generating prediction information of the axial power offset phenomenon of the reactor core. According to the prediction mode, the reactor core axial power offset phenomenon changing along with fuel burnup and CRUD deposition in the operation process of the pressurized water reactor is described, and therefore the prediction precision of the reactor core axial power offset phenomenon is improved.

Description

Technical Field

[0001] The present invention relates to the field of reactor technology, in particular to a method for predicting axial power deviation phenomenon in a pressurized water reactor core and electronic equipment. Background Art

[0002] During pressurized water reactor (PWR) operation, metal ions and corrosion products in the primary circuit deposit on the surface of the fuel cladding above the core, forming a thin fouling layer. This Chalk River Unidentified Deposit (CRUD) on the fuel cladding surface has a loose structure that intensifies boiling within the cladding, causing boron to precipitate from the coolant. This CRUD is then adsorbed by the porous morphology of the deposit, resulting in an uneven distribution of boron along the axial surface of the fuel rods. Boron's strong neutron absorption can trigger CRUD-induced power shift (CIPS), posing a safety risk to the reactor. Therefore, predicting corrosion product deposition and CIPS during burnup is crucial for the safety and economics of reactor operation.

[0003] In related technical solutions, predictions of corrosion product deposition and core axial power excursion are mostly conducted using fixed heat flux or initial reactor life conditions. These predictions are evaluated at steady-state conditions at specific time points under these corresponding operating conditions. This makes it impossible to assess the axial power excursion during PWR fuel burnup. Furthermore, changes in power distribution caused by PWR fuel burnup can also affect the axial power excursion, resulting in low accuracy in the existing predictions of corrosion product deposition and core axial power excursion during PWR fuel burnup. Summary of the Invention

[0004] In view of this, an object of the present invention is to provide a method and electronic equipment for predicting the axial power deviation phenomenon of a pressurized water reactor core, so as to alleviate the above-mentioned problem.

[0005] In a first aspect, an embodiment of the present invention provides a method for predicting the axial power excursion phenomenon in a pressurized water reactor core. The method includes: constructing a prediction model for the axial power excursion phenomenon in the core; wherein the prediction model is used to characterize how the axial power excursion phenomenon in the core changes with the oxidative corrosion deposits (CRUD) on the cladding surface and fuel burnup, and the prediction model includes a neutron physics module, a thermal-hydraulic module, and a CRUD material module with a coupling relationship. The neutron physics module is used to simulate the relationship between the thickness of the CRUD, the axial power distribution information in the core, and the fuel burnup; the thermal-hydraulic module is used to simulate the boiling heat transfer process of the fuel rods and the coolant through the porous structure of the CRUD; and the CRUD material module is used to simulate the growth process of the CRUD as well as the heat transfer, capillary flow, and chemical equilibrium inside the CRUD; and coupling calculations are performed on the prediction model within a preset time period to generate prediction information of the axial power excursion phenomenon in the core.

[0006] Preferably, the preset time length includes multiple calculation moments from the initial moment to the final moment, and the time difference between any two adjacent calculation moments is equal to the calculation step length; the step of performing coupling calculation on the prediction model within the preset time length includes: obtaining calculation parameters; wherein the calculation parameters include: fuel power, coolant pressure, inlet temperature at the coolant channel inlet, inlet flow rate and inlet corrosion product concentration, as well as the inlet lithium concentration and inlet boron concentration at the initial moment; the inlet corrosion product concentration includes at least one of the following: soluble iron corrosion product concentration at the inlet, soluble nickel corrosion product concentration at the inlet and insoluble iron-nickel particle concentration at the inlet; the prediction model is coupled calculated based on the calculation step length and the calculation parameters to obtain the core axial power distribution information corresponding to each calculation moment.

[0007] Preferably, the step of performing coupled calculation on the prediction model based on the calculation step size and the calculation parameters to obtain the core axial power distribution information corresponding to each calculation moment includes: obtaining the current calculation moment t i The calculation results include: core axial power distribution information, critical boron concentration, fuel temperature, cladding temperature, coolant temperature, and cladding heat flux density; the critical boron concentration is used to characterize the boron concentration in the CRUD external coolant when the proliferation factor reaches a critical state; the value range of i is 0~M, where M represents the total number of calculation moments within a preset time period; in the first time period (t i , t i +t half_step ], according to the calculation parameters and the current calculation time t i The calculation results are coupled with the prediction model until the convergence condition is reached and t i +t half_step The calculation result at the moment; where t half_step Indicates half a calculation step; in the second time period (t i +thalf_step , t i+1 ], according to the calculation parameters and t i +t half_step The calculation results of the time are coupled with the prediction model until the convergence condition is reached, and the next calculation time t is obtained. i+1 The calculation results of .

[0008] Preferably, according to the calculation parameters and the current calculation time t i The step of coupling calculation of the prediction model with the calculation results includes: in the first time period (t i , t i +t half_step ], firstly, the fuel power is input to the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the first time period; then, the current calculation time t i The cladding temperature, coolant temperature, and cladding heat flux density are input into the CRUD material module, so that the CRUD material module outputs the CRUD information within the first time period; wherein the CRUD information includes: CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information; finally, the fuel burnup, critical boron concentration, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information within the first time period, as well as the current calculation time t i The fuel temperature, cladding temperature, and coolant temperature are input to the neutron physics module so that the neutron physics module outputs t i +t half_step The core axial power distribution information and critical boron concentration at the moment; the coolant pressure, inlet temperature, inlet flow rate, t i +t half_step The core axial power distribution information at time t and the CRUD effective thermal conductivity in the first time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs t i +t half_step The fuel temperature, cladding temperature, coolant temperature and cladding heat flux at time t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached, and t i +t half_step The calculation result at the moment.

[0009] Preferably, according to the calculation parameters and t i +t half_step The step of coupling calculation of the prediction model based on the calculation result at the moment includes: in the second time period (t i +thalf_step , t i+1 ], firstly, the fuel power is input to the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the second time period; then, t i +t half_step The cladding temperature, coolant temperature, and cladding heat flux density are input to the CRUD material module so that the CRUD material module outputs the CRUD information in the second time period; finally, the fuel burnup, critical boron concentration, CRUD internal boron concentration distribution information, CRUD internal lithium concentration distribution information, and t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input to the neutron physics module so that the neutron physics module outputs t i+1 The core axial power distribution information and critical boron concentration at the moment; the coolant pressure, inlet temperature, inlet flow rate, t i+1 The core axial power distribution information at the moment and the CRUD effective thermal conductivity in the second time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs t i+1 The fuel temperature, cladding temperature, coolant temperature and cladding heat flux at time t i+1 The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached, and t i+1 The calculation result at the moment.

[0010] Preferably, the step of repeating the calculation until the convergence condition is reached comprises: in a first time period (t i , t i +t half_step ] and the second time period (t i +t half_step , t i+1 ], respectively calculate the power difference between the axial power distribution information of two adjacent cores, the temperature field difference between the internal temperature fields of two adjacent CRUDs, and the boron concentration difference between the internal boron concentration distribution information of two adjacent CRUDs; if the power difference, temperature field difference and boron concentration difference are all less than the preset error sum, it is determined that the convergence condition is met.

[0011] Preferably, the preset error is less than 10 -5 .

[0012] Preferably, the step of generating prediction information of the core axial power offset phenomenon includes: generating prediction information of the core axial power offset phenomenon according to core axial power distribution information corresponding to each calculation moment.

[0013] In a second aspect, an embodiment of the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the computer program.

[0014] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method of the first aspect are executed.

[0015] The embodiments of the present invention bring the following beneficial effects: Embodiments of the present invention provide a method and electronic device for predicting the axial power excursion phenomenon in a pressurized water reactor (PWR). First, a prediction model for the axial power excursion phenomenon is constructed. The prediction model is used to characterize how the axial power excursion phenomenon changes with oxidative corrosion deposits (CRUD) on the cladding surface and fuel burnup. The prediction model includes a coupled neutron physics module, a thermal-hydraulic module, and a CRUD material module. The neutron physics module simulates the relationship between CRUD thickness, core axial power distribution information, and fuel burnup. The thermal-hydraulic module simulates the boiling heat transfer of fuel rods and coolant through the porous CRUD structure. The CRUD material module simulates the growth process of the CRUD and the heat transfer, capillary flow, and chemical equilibrium within the CRUD. The prediction model is then coupled and calculated over a preset time period to generate prediction information for the axial power excursion phenomenon. This prediction method describes the axial power excursion phenomenon as it changes with fuel burnup and CRUD deposition during PWR operation, thereby improving the prediction accuracy of the axial power excursion phenomenon.

[0016] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1A flow chart of a method for predicting axial power deviation in a pressurized water reactor core provided by an embodiment of the present invention; Figure 2 A schematic diagram of the positions of fuel rods and coolant provided in an embodiment of the present invention; Figure 3 A schematic diagram of the positions of another fuel rod and coolant provided in an embodiment of the present invention; Figure 4 A schematic structural diagram of a fuel rod provided in an embodiment of the present invention; Figure 5 A schematic diagram of a CRUD deposition layer provided in an embodiment of the present invention; Figure 6 A schematic diagram of a coolant channel provided by an embodiment of the present invention; Figure 7 A schematic diagram of parameter transfer in a coupled calculation of a prediction model provided by an embodiment of the present invention; Figure 8 A flowchart of another method for predicting axial power deviation in a pressurized water reactor core provided by an embodiment of the present invention; Figure 9 A schematic diagram of prediction results of a core axial power offset phenomenon provided by an embodiment of the present invention; Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0022] The embodiment of the present invention provides a method for predicting the axial power deviation phenomenon of a pressurized water reactor core. Figure 1 As shown, the method includes the following steps: Step S102: constructing a prediction model for the core axial power offset phenomenon.

[0023] Among them, a prediction model is used to characterize the core axial power excursion phenomenon as it changes with oxidative corrosion deposits (CRUD) on the cladding surface and fuel burnup. This prediction model includes a coupled neutron physics module, a thermal-hydraulic module, and a CRUD material module. The neutron physics module is used to simulate the relationship between CRUD thickness, core axial power distribution information, and fuel burnup. The thermal-hydraulic module is used to simulate the boiling heat transfer process of fuel rods and coolant through the porous structure of CRUD. The CRUD material module is used to simulate the growth process of CRUD as well as the heat transfer, capillary flow, and chemical equilibrium within the CRUD. Therefore, through the coupled neutron physics module, the core axial power excursion phenomenon of the pressurized water reactor as it changes with fuel burnup and CRUD deposition during operation is described, thereby improving the prediction accuracy of the core axial power excursion phenomenon.

[0024] Step S104 , performing coupling calculation on the prediction model within a preset time period to generate prediction information of the core axial power offset phenomenon.

[0025] The method for predicting the axial power excursion phenomenon in the core of a pressurized water reactor provided in an embodiment of the present invention describes the axial power excursion phenomenon in the core as it changes with fuel burnup and CRUD deposition during the operation of the pressurized water reactor through a prediction model, thereby improving the prediction accuracy of the axial power excursion phenomenon in the core.

[0026] To facilitate understanding, we first explain in detail the neutron physics module, thermal hydraulic module, and CRUD material module, which are coupled in the prediction model. The details are as follows: (1) Neutron physics module; Specifically, the open source Monte Carlo software OpenMC is used to model and solve the neutron physics module; OpenMC uses the superposition of the front and back sides of the half-space to construct solid geometry, which can accurately model arbitrarily complex geometric objects without the errors caused by mesh discretization. In addition, the counter system built into OpenMC allows each filter to perform statistics based on the properties of the particles, thereby obtaining interesting physical results such as neutron flux, fission reaction rate, neutron production rate, or local fission energy deposition. Therefore, the burnup function of OpenMC has accurate results, and the simplified burnup chain provides improved performance while ensuring accuracy.

[0027] For pressurized water reactors, the boron concentration of the CRUD external coolant and the deposition of corrosion products in chemical compensation control need to be updated in real time according to the operating conditions. The restart function in the burnup function of OpenMC can realize the update of the geometric model in OpenMC. Among them, the process of OpenMC modeling the neutron physics module is as follows: Figure 2As shown, it mainly includes a fuel rod and the coolant 21 around it, and both the X plane and the Y plane are set to full reflection conditions. Here, the full reflection condition can be understood as a type of boundary condition for neutron transport in neutron physics, that is, the reflectivity can reach 100% after the neutron reaches the boundary condition. Figure 3 As shown, the Z planes at the top and bottom of the fuel rods are set to vacuum. Here, vacuum refers to the boundary condition type of the Z plane, meaning that neutrons are emitted after reaching the boundary and do not return. The specific total reflection condition and vacuum both refer to boundary condition types. For details, please refer to the prior art and will not be detailed in this embodiment of the present invention.

[0028] In addition, if Figure 2 and Figure 4 As shown, the fuel rods are composed of fuel 22, air gap 23 and cladding 24 from the inside out. The fuel rod unit spacing is 12.60 mm. The fuel 22 is set to uranium dioxide UO2 with an enrichment of 3.3 wt.%, and its outer diameter is 8.19 mm; the air gap 23 is set to helium He, and the cladding 24 is set to zirconium alloy Zr-4, with an inner diameter of 8.36 mm and an outer diameter of 9.50 mm.

[0029] In addition, if Figure 4 As shown, CRUD 25 is deposited on the outer surface of the cladding 24. In the neutron physics module, the geometric structure of CRUD 25 is set to a grid scale of 1 μm and is divided into multiple cylindrical grids surrounding the fuel rod. The thickness of CRUD 25 changes with fuel burnup and the axial height of the fuel rod. Since the probability of CRUD 25 at different heights of the fuel rod varies, its thickness also varies at different heights. Therefore, in actual calculations, the CRUD thickness needs to be updated in real time with fuel burnup. The remaining area is coolant 21. In actual calculations, the boron concentration in coolant 21 is also updated in real time with fuel burnup. Here, the boron concentration in coolant 21 is the critical boron concentration determined by the proliferation factor.

[0030] And, when building the neutron physics module, the boron distribution is also refined, such as Figure 5 As shown, the boron distribution is achieved by using a micron-scale grid in the CRUD deposition layer, and a gradient color is used to express the refined distribution grid. This method, similar to local encryption, can further refine the impact of the boron distribution within the CRUD, greatly improving the simulation accuracy of the impact of the boron concentration within the CRUD on the neutron physics module. In contrast, most related technical solutions shuffle the entire CRUD. That is, there is no grid division in the CRUD structure, and the entire area adopts a uniform distribution. This results in a uniform boron concentration distribution throughout the CRUD, resulting in a loss of boron information and reduced simulation accuracy.

[0031] Therefore, the neutron physics module established based on OpenMC is mainly used to simulate the relationship between the thickness of the CRUD, the boron concentration information inside the CRUD, the core axial power distribution information and the fuel burnup; at the same time, by refining the boron distribution, the simulation accuracy of the neutron physics module is further improved, thereby improving the prediction accuracy of the core axial power offset phenomenon.

[0032] It should be noted that the fuel burnup can be achieved by solving the nuclide information of the burnup by using the Boltzmann neutron transport equation. The calculation of the specific nuclide information can refer to the existing technology and will not be described in detail in the embodiment of the present invention.

[0033] (2) Thermal hydraulic module; Specifically, the thermal hydraulic module is solved using ANASY Fluent. Since ANASY Fluent can simulate the conjugate heat transfer from the fuel rod to the coolant, it can simulate the boiling heat transfer process of the fuel rod and coolant through the porous structure of the CRUD.

[0034] CRUD-related heat transfer is input and modeled using UDFs (User-defined functions). The convective heat transfer model between the CRUD surface and the coolant is implemented using the modified Euler RPI (Rensselaer Polytechnic Institute) model. The modified Euler PRI model includes, but is not limited to, the Cole model (Formula 1) for solving the bubble detachment frequency, the Kocamustafaogullari-Ishii model (Formula 2) for solving the bubble detachment diameter, the Lemmert-Chawla model for surface nucleation sites in the absence of CRUD (i.e., when there are no CRUDs on the cladding surface, the smooth surface boiling heat transfer bubble nucleation site model (Formula 3) is used), and the CRUD Nucleation Site Density (CNSD) model for surface nucleation sites in the presence of CRUD (i.e., when there are CRUDs on the cladding surface, the porous surface boiling heat transfer bubble nucleation site model (Formula 4) is used, which is suitable for the CRUD structural characteristics). The expressions for each model are as follows: (1) (2) (3) (4) in, f represents the bubble detachment frequency, g represents the acceleration due to gravity, Indicates the density of the coolant in liquid state, represents the density of the coolant in gaseous state, Indicates the bubble detachment diameter, represents the contact angle of the CRUD porous surface, represents the surface tension, Indicates the surface nucleation point without CRUD working condition, C represents the model empirical constant, T w represents the cladding surface temperature, T sat Indicates the coolant saturation temperature, n represents the empirical power of the model, Indicates the presence of surface nucleation points for CRUD conditions. N s represents the cavity density of the CRUD porous surface, represents the probability function of the cone angle of the CRUD porous surface cavity, represents the cone angle of the CRUD porous surface cavity, represents the probability function of the cavity radius of the CRUD porous surface, r represents the cavity radius of the CRUD porous surface, r min and r max represent the minimum and maximum values ​​of the cavity radius of the CRUD porous surface, respectively.

[0035] In the improved Euler PRI model, the bubble detachment frequency, bubble detachment diameter, and surface nucleation sites in both the no-CRUD and CRUD-existing conditions are important simulation parameters for boiling two-phase heat transfer, collectively characterizing the characteristics of boiling two-phase heat transfer. Therefore, the improved Euler PRI model and ANASY Fluent allow for the evaluation of the impact of CRUD on flow heat transfer. Specifically, the thermal-hydraulic module simulates the boiling heat transfer of fuel rods and coolant through the porous CRUD structure.

[0036] In practical applications, the ANASY Fluent solution domain includes the fuel cladding, CRUD, and coolant surrounding the fuel rods. The thermal hydraulic conditions are set to be consistent with the typical pressurized water reactor operating conditions, including a coolant pressure of 15.50 MPa and a fuel power of 107.17 kW. Figure 6 As shown, the inlet temperature at the coolant channel inlet is 556.76K and the inlet flow rate is 5.278 m / s. Furthermore, related art solutions do not consider the effect of the porous CRUD structure on heat transfer in the thermal-hydraulic module. Therefore, the embodiments of the present invention further improve the prediction accuracy of the core axial power excursion phenomenon by considering the effect of the porous CRUD structure on the boiling heat transfer between the fuel rods and the coolant in the thermal-hydraulic module.

[0037] It should be noted that in some scenarios, ANASY Fluent can also be used to solve the coolant flow field. In flow field simulation, the Reynolds-Averaged Navier-Stokes (RANS) equations can be used in conjunction with the k-ε turbulence model and the enhanced near-wall model. In practical applications, the RANS equations effectively balance simulation accuracy and simulation time, and are suitable for complex flows such as high Reynolds number conditions for pressurized water reactor coolant flow, vortices within rod bundle channels, and wall-separated flow. The specific coolant flow field solution process can be referenced to existing technologies and will not be described in detail in the present embodiments.

[0038] (3) CRUD material module: In practical applications, self-compiled code is used to solve CRUD-related multi-physics phenomena, mainly including ① the time-related CRUD growth process and ② the heat transfer, capillary flow and chemical equilibrium inside the CRUD.

[0039] Specifically, the CRUD material module builds a porous model with a characteristic CRUD chimney structure. For a specific CRUD model at a specific fuel rod axial height, its thickness and composition dynamically change with fuel burnup. The CRUD material module primarily considers solutes including soluble iron ions, soluble nickel ions, sparingly soluble iron-nickel particles, boric acid, lithium hydroxide, and their reactions with water and hydrogen. The concentrations of boric acid and lithium hydroxide, the primary coolant components, decrease with increasing fuel burnup. The concentrations of soluble iron ions, soluble nickel ions, and sparingly soluble iron-nickel particles are set to 4.94 ppb, 20.54 ppb, and 2.6 ppb, respectively. The CRUD material module does not consider the variation in corrosion product concentration with fuel burnup. The CRUD region is divided into 1µm cells in the fuel rod radial direction, consistent with OpenMC. Input conditions are set as an initial porosity of 60%, a boiling chimney radius of 2.5µm, and a boiling chimney density of 3000# / mm. 2 It should be noted that in some scenarios, the input conditions can be adaptively adjusted according to actual conditions.

[0040] In summary, for the neutron physics module, thermal-hydraulic module, and CRUD material module, which are coupled in the prediction model, during the calculation process, the neutron physics module calculates neutron physics and core axial power distribution information; the thermal-hydraulic module calculates coolant flow and heat transfer, including but not limited to fuel temperature, cladding temperature, coolant temperature, and cladding heat flux density; and the CRUD material module calculates CRUD information, which is divided into CRUD deposition, internal chimney boiling, and boron adsorption phenomena, including but not limited to: CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal micron-scale boron concentration distribution information, and CRUD internal lithium concentration distribution information.

[0041] Since the core axial power distribution information is not only affected by the neutron flux distribution in neutron physics, but also by temperature, material, etc., the calculation of the core axial power distribution information is mainly completed by OpenMC, and multi-physics coupling is required to obtain more accurate core axial power distribution information. Among them, in multi-physics coupling, parameter transfer is as follows Figure 7 As shown, the neutron physics module provides the thermal-hydraulic module with information on the core axial power distribution. The thermal-hydraulic module, in turn, provides the neutron physics module with information on the fuel, cladding, and coolant temperatures. The thermal-hydraulic module provides the cladding, coolant, and cladding heat flux densities to the CRUD materials module. The CRUD materials module provides the thermal-hydraulic module with the CRUD effective thermal conductivity, while also accounting for the effects of the CRUD deposition surface on convective heat transfer and subcooled boiling. Furthermore, the CRUD materials module provides the neutron physics module with a detailed boron concentration distribution (micrometer-scale) within the CRUD. The neutron physics module updates the critical boron concentration for the CRUD materials module.

[0042] Therefore, in the prediction model, the coupling of nuclear, thermal and material aspects of the PWR is realized through the coupled calculation of the neutron physics module, the thermal-hydraulic module and the CRUD material module, thereby describing the core axial power excursion phenomenon that changes with fuel burnup and CRUD deposition during the operation of the PWR, thereby improving the prediction accuracy of the core axial power excursion phenomenon.

[0043] In one embodiment, the preset time length includes multiple calculation moments from the initial moment to the final moment, and the time difference between any two adjacent calculation moments is equal to the calculation step length; the step of performing coupled calculation on the prediction model within the preset time length includes: obtaining calculation parameters; wherein the calculation parameters include: fuel power, coolant pressure, inlet temperature at the coolant channel inlet, inlet flow rate and inlet corrosion product concentration, as well as the inlet lithium concentration and inlet boron concentration at the initial moment; the inlet corrosion product concentration includes at least one of the following: soluble iron corrosion product concentration at the inlet, soluble nickel corrosion product concentration at the inlet and insoluble iron-nickel particle concentration at the inlet; the prediction model is coupled calculated based on the calculation step length and the calculation parameters to obtain the core axial power distribution information corresponding to each calculation moment.

[0044] Specifically, the above calculation parameters can be determined according to the operating conditions of the pressurized water reactor. For example, the fuel power can be determined according to the pressurized water reactor power, the coolant pressure is approximately 15.5 MPa, the inlet temperature is in the range of 550K to 570K, the inlet flow rate is in the range of 1 m / s to 6 m / s, the inlet corrosion product concentration is in the range of 0.1 ppb to 1 ppm, the inlet boron concentration at the initial moment is in the range of 1800 ppm to 2500 ppm, the inlet lithium concentration is determined according to the inlet boron concentration, and the inlet lithium concentration and the inlet boron concentration can ensure that the pH is between 6.9 and 7.4.

[0045] In addition, the calculation step between any two adjacent calculation moments mentioned above must ensure accuracy and calculation efficiency, and is generally taken as 60 days or less. When the prediction model is coupled according to the calculation step and calculation parameters, half a calculation step is used for coupling calculation, that is, one and a half calculation steps are used as the intermediate calculation moment for coupling calculation between two adjacent calculation moments, thereby alleviating the lag of coupling parameter transmission and further improving the prediction accuracy of the core axial power offset phenomenon.

[0046] In one embodiment, for ease of understanding, two adjacent calculation moments are used: the current calculation moment t i and the next calculation time t i+1 As an example, the process of coupling calculation is explained; among them, the current calculation time t i The calculation of the core axial power distribution information has been completed, and according to the calculation step size and the current calculation time t i The next calculation time t of the coupled calculation i+1 The core axial power distribution information.

[0047] Specifically, the process of coupling the prediction model based on the calculation step size and calculation parameters to obtain the core axial power distribution information corresponding to each calculation time includes: obtaining the current calculation time t iThe calculation results include: core axial power distribution information, critical boron concentration, fuel temperature, cladding temperature, coolant temperature, and cladding heat flux density; the critical boron concentration is used to characterize the boron concentration in the CRUD external coolant when the proliferation factor reaches a critical state; the value range of i is 0~M, where M represents the total number of calculation moments within a preset time period; in the first time period (t i , t i +t half_step ], according to the calculation parameters and the current calculation time t i The calculation results are coupled with the prediction model until the convergence condition is reached and t i +t half_step The calculation result at the moment; where t half_step Indicates half a calculation step; in the second time period (t i +t half_step , t i+1 ], according to the calculation parameters and t i +t half_step The calculation results of the time are coupled with the prediction model until the convergence condition is reached, and the next calculation time t is obtained. i+1 It should be noted that for the first time period (t i , t i +t half_step ], indicating that the first time period does not include the moment t on the left i , but includes the time t on the right i +t half_step Similarly, for the second time period (t i +t half_step , t i+1 ], the second time period does not include the left moment t i +t half_step , but includes the time t on the right i+1 .

[0048] Among them, in the first time period (t i , t i +t half_step ], firstly, the fuel power is input to the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the first time period; then, the current calculation time t iThe cladding temperature, coolant temperature, and cladding heat flux density are input into the CRUD material module, so that the CRUD material module outputs the CRUD information within the first time period; wherein the CRUD information includes: CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information; finally, the fuel burnup, critical boron concentration, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information within the first time period, as well as the current calculation time t i The fuel temperature, cladding temperature, and coolant temperature are input to the neutron physics module so that the neutron physics module outputs t i +t half_step The core axial power distribution information and critical boron concentration at the moment; the coolant pressure, inlet temperature, inlet flow rate, t i +t half_step The core axial power distribution information at time t and the CRUD effective thermal conductivity in the first time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs t i +t half_step The fuel temperature, cladding temperature, coolant temperature and cladding heat flux at time t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached, and t i +t half_step The calculation result at the moment.

[0049] Similarly, in the second time period (t i +t half_step , t i+1 ], firstly, the fuel power is input to the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the second time period; then, t i +t half_step The cladding temperature, coolant temperature, and cladding heat flux density are input to the CRUD material module so that the CRUD material module outputs the CRUD information in the second time period; finally, the fuel burnup, critical boron concentration, CRUD internal boron concentration distribution information, CRUD internal lithium concentration distribution information, and t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input to the neutron physics module so that the neutron physics module outputs t i+1 The core axial power distribution information and critical boron concentration at the moment; the coolant pressure, inlet temperature, inlet flow rate, t i+1The core axial power distribution information at the moment and the CRUD effective thermal conductivity in the second time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs t i+1 The fuel temperature, cladding temperature, coolant temperature and cladding heat flux at time t i+1 The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached, and t i+1 The calculation result at the moment.

[0050] Therefore, in the first time period, the fuel consumption and CRUD information calculation within half a calculation step is first performed, and then the coupled calculation is repeated to obtain t when convergence is reached. i +t half_step Then enter the second time period and calculate the fuel consumption and CRUD information within half the calculation step. Then, through coupling and repeated calculation, when convergence is reached, t i+1 The calculation results of the moment are used until the preset time length is reached, thereby using half a calculation step, which not only alleviates the lag of coupling parameter transmission, but also facilitates timely updating of CRUD information, further improving the prediction accuracy of the core axial power offset phenomenon.

[0051] In one embodiment, the step of repeating the calculation until the convergence condition is reached includes: in a first time period (t i , t i +t half_step ] and the second time period (t i +t half_step , t i+1 ], respectively calculate the power difference between the axial power distribution information of two adjacent cores, the temperature field difference between the internal temperature fields of two adjacent CRUDs, and the boron concentration difference between the internal boron concentration distribution information of two adjacent CRUDs; if the power difference, temperature field difference and boron concentration difference are all less than the preset error sum, it is determined that the convergence condition is met.

[0052] Among them, the temperature field inside the CRUD includes the temperature values ​​corresponding to multiple grids. The temperature field difference mentioned above refers to the temperature difference of the same grid in the temperature field inside the CRUD, that is, the difference between two adjacent temperature values ​​at the same position inside the CRUD. Therefore, when the power difference, temperature field difference and boron concentration difference are all less than the preset error sum, it means that the coupled calculation has reached the convergence condition. Here the preset error is less than 10 -5 , the specific value can be set according to the actual situation.

[0053] In particular, the above-mentioned power difference refers to the absolute value of the difference between the axial power distribution information of two adjacent cores, the temperature field difference refers to the absolute value of the difference between the internal temperature fields of two adjacent CRUDs, and the boron concentration difference refers to the absolute value of the difference between the internal boron concentration distribution information of two adjacent CRUDs.

[0054] In one embodiment, the step of generating prediction information of the core axial power excursion phenomenon includes: generating prediction information of the core axial power excursion phenomenon based on the core axial power distribution information corresponding to each calculation time. The calculation formula of the core axial power excursion phenomenon CIPS is as follows: (5) Among them, 0 represents the coordinate of the middle position of the fuel rod, represents the coordinates of the highest position of the fuel rod, represents the coordinates of the lowest position of the fuel rod, It represents the core axial power distribution function, that is, the core axial power distribution information. z Indicates the axial height of the fuel rod, and its value range is .

[0055] Based on the above method embodiment, the embodiment of the present invention provides another method for predicting the axial power deviation phenomenon of the pressurized water reactor core, such as Figure 8 As shown, the method includes the following steps: Step S802: construct a prediction model.

[0056] This step, also known as the preprocessing phase, is used to construct a predictive model. This includes establishing models for the coupled neutron physics module, thermal-hydraulic module, and CRUD material module. Furthermore, this step prepares the cross-sectional data required for neutron physics module calculations, including but not limited to scattering cross sections, absorption cross sections, capture cross sections, and fission cross sections. The path for acquiring cross-sectional data during OpenMC calculations is also specified. Furthermore, based on the geometry of the pressurized water reactor, models for the neutron physics module, thermal-hydraulic module, and CRUD material module are established, and meshing is performed. For details, please refer to the aforementioned embodiments, and this embodiment of the present invention will not be described in detail here.

[0057] Step S804: Initialize the prediction model.

[0058] During the initialization process, the operator also needs to set calculation parameters. These parameters include, but are not limited to, fuel power, coolant pressure, inlet temperature at the coolant channel inlet, inlet flow rate, and inlet corrosion product concentration, as well as the initial inlet lithium concentration and inlet boron concentration. The inlet corrosion product concentration includes at least one of the following: inlet soluble iron corrosion product concentration, inlet soluble nickel corrosion product concentration, and inlet insoluble iron-nickel particle concentration. Furthermore, a preset duration, number of calculation moments, and calculation step size are also set.

[0059] Step S806, calculate t i The calculation results of .

[0060] Specifically, the fuel power and the boron concentration at the inlet at the initial moment are first obtained and input into the neutron physics module, thereby calculating the core axial power distribution information and the critical boron concentration; then, the core axial power distribution information is input into the thermal hydraulic module, and the coolant pressure, inlet temperature and inlet flow rate are input into the thermal hydraulic module, thereby calculating the fuel temperature, cladding temperature, coolant temperature and cladding heat flux density; finally, the fuel temperature, cladding temperature and coolant temperature are input into the neutron physics module; the calculation and parameter transfer process are repeated until the convergence condition is reached, and t i =0, including the calculation results of the core axial power distribution information, critical boron concentration, fuel temperature, cladding temperature, coolant temperature, cladding heat flux density, etc.

[0061] It should be noted that here t i =0 indicates the first calculation moment within the preset time length. For the remaining calculation moments, you can refer to the subsequent t i+ Calculation process at 1 moment.

[0062] Step S808: Calculate the first time period (t i , t i +t half_step ] fuel consumption and CRUD information within.

[0063] Specifically, in the first time period (t i , t i +t half_step ], firstly, the fuel power is input to the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the first time period; then, the current calculation time t iThe cladding temperature, coolant temperature, and cladding heat flux are input into the CRUD material module, so that the CRUD material module outputs CRUD information within a first time period; wherein the CRUD information includes: CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information; thereby obtaining neutron physics information and CRUD information within the first time period, including fuel burnup, critical boron concentration, CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information.

[0064] Step S810, calculate t i +t half_step The calculation result at the moment.

[0065] Specifically, first, the fuel consumption, critical boron concentration, CRUD internal boron concentration distribution information and CRUD internal lithium concentration distribution information in the first time period, as well as the current calculation time t i The fuel temperature, cladding temperature, and coolant temperature are input to the neutron physics module so that the neutron physics module outputs t i +t half_step The core axial power distribution information and critical boron concentration at time t are then converted into the coolant pressure, inlet temperature, inlet flow rate, i +t half_step The core axial power distribution information at time t and the CRUD effective thermal conductivity in the first time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs t i +t half_step The fuel temperature, cladding temperature, coolant temperature and cladding heat flux density at the moment t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached, and t i +t half_step The calculation result at the moment.

[0066] Simultaneously, the CRUD geometry and composition information are updated in the neutron physics, thermal-hydraulic, and CRUD material modules. This update refers to updating the spatial structure of the CRUD and coolant in these modules after half a computational step to accommodate the CRUD growth process. Because the CRUD and coolant together occupy all space within the modules (including the neutron physics, thermal-hydraulic, and CRUD material modules), excluding the fuel rods (outside the fuel and cladding), the space occupied by the CRUD increases as the CRUD grows. Therefore, updating the CRUD geometry in these modules further improves the prediction accuracy of the core axial power excursion phenomenon.

[0067] Step S812, calculate the second time period (t i +t half_step , t i+1 ] fuel consumption and CRUD information within.

[0068] Specifically, after the coupling calculation of the first time period is completed, the second time period (t i +t half_step , t i+1 ], in the second time period (t i +t half_step , t i+1 ], firstly, the fuel power is input to the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the second time period; then, t i +t half_step The cladding temperature, coolant temperature, and cladding heat flux are input into the CRUD material module, so that the CRUD material module outputs the CRUD information within the second time period; thereby obtaining neutron physics information and CRUD information within the second time period, including fuel burnup, critical boron concentration, CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information.

[0069] Step S814, calculate t i+1 The calculation result at the moment.

[0070] Specifically, first, the fuel consumption, critical boron concentration, CRUD internal boron concentration distribution information and CRUD internal lithium concentration distribution information in the second time period, and t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input to the neutron physics module so that the neutron physics module outputs t i+1The core axial power distribution information and critical boron concentration at time t are then converted into the coolant pressure, inlet temperature, inlet flow rate, i+1 The core axial power distribution information at the moment and the CRUD effective thermal conductivity in the second time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs t i+1 The fuel temperature, cladding temperature, coolant temperature and cladding heat flux density at the moment t i+1 The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached, and t i+1 The calculation result at the moment.

[0071] Simultaneously, the CRUD geometry and composition information are updated in the neutron physics, thermal-hydraulic, and CRUD material modules. This update refers to updating the spatial structure of the CRUD and coolant in these modules after the entire computational step has completed to accommodate the CRUD growth process. Because the CRUD and coolant together occupy all space within the modules (including the neutron physics, thermal-hydraulic, and CRUD material modules), excluding the fuel rods (outside the fuel and cladding), the space occupied by the CRUD increases as the CRUD grows. Therefore, updating the CRUD geometry in these modules further improves the prediction accuracy of the core axial power excursion phenomenon.

[0072] Step S816, determine whether the preset time has been reached, that is, determine whether t i+1 Is the moment the last calculated moment in the preset duration? If so, execute step S818; if not, set i=i+1 and return to step S808 until the preset duration is reached.

[0073] Step S818: Generate prediction information of the core axial power deviation phenomenon based on the core axial power distribution information corresponding to each calculation moment.

[0074] For example, when the values ​​of the various parameters and calculation steps in the calculation parameters are as shown in Table 1 below, the prediction information of the core axial power offset phenomenon is as follows: Figure 9 shown.

[0075] Table 1

[0076] In summary, the method for predicting the core axial power excursion phenomenon of a pressurized water reactor provided by an embodiment of the present invention first constructs a prediction model for the core axial power excursion phenomenon; wherein the prediction model is used to characterize the change of the core axial power excursion phenomenon with the oxidative corrosion deposits CRUD on the cladding surface and fuel burnup, and the prediction model includes a neutron physics module, a thermal hydraulic module, and a CRUD material module with a coupling relationship; then, the prediction model is coupled and calculated within a preset time period to generate prediction information for the core axial power excursion phenomenon. The above prediction method describes the core axial power excursion phenomenon as it changes with fuel burnup and CRUD deposition during the operation of a pressurized water reactor, thereby improving the prediction accuracy of the core axial power excursion phenomenon.

[0077] An embodiment of the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above-mentioned pressurized water reactor core axial power offset phenomenon prediction method.

[0078] See also Figure 10 As shown, the electronic device includes a processor 100 and a memory 101. The memory 101 stores machine executable instructions that can be executed by the processor 100. The processor 100 executes the machine executable instructions to implement the above-mentioned pressurized water reactor core axial power offset phenomenon prediction method.

[0079] Further, Figure 10 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 100 , the communication interface 103 and the memory 101 are connected via the bus 102 .

[0080] Among them, the memory 101 may include high-speed random access memory (RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 103 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 102 can be an ISA (Industrial Standard Architecture, Industrial Standard Architecture bus) bus, PCI (Peripheral Component Interconnect, Peripheral Component Interconnect Standard) bus or EISA (Enhanced Industry Standard Architecture, Extended Industry Standard Architecture) bus, etc. The above-mentioned bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 10Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0081] The processor 100 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 100 or software instructions. The above processor 100 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or register. The storage medium is located in the memory 101. The processor 100 reads the information in the memory 101 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0082] This embodiment also provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the above-mentioned pressurized water reactor core axial power offset phenomenon prediction method.

[0083] The computer program product of the pressurized water reactor core axial power deviation phenomenon prediction method and electronic device provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0084] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0086] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0087] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0088] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for predicting the axial power deviation phenomenon of a pressurized water reactor core, characterized in that: The method comprises: Constructing a prediction model for the core axial power excursion phenomenon; wherein the prediction model is used to characterize how the core axial power excursion phenomenon changes with oxidative corrosion deposits (CRUD) on the cladding surface and fuel burnup. The prediction model includes a coupled neutron physics module, a thermal-hydraulic module, and a CRUD material module. The neutron physics module is used to simulate the relationship between the thickness of the CRUD, the core axial power distribution information, and the fuel burnup. The thermal-hydraulic module is used to simulate the boiling heat transfer process of the fuel rods and coolant through the porous structure of the CRUD. The CRUD material module is used to simulate the growth process of the CRUD as well as the heat transfer, capillary flow, and chemical equilibrium within the CRUD. The prediction model is coupled calculated within a preset time period to generate prediction information of the core axial power offset phenomenon.

2. The method according to claim 1, characterized in that The preset duration includes multiple calculation moments from the initial moment to the final moment, and the time difference between any two adjacent calculation moments is equal to the calculation step length; The step of performing coupling calculation on the prediction model within a preset time period includes: Obtaining calculation parameters; wherein the calculation parameters include: fuel power, coolant pressure, inlet temperature at the coolant channel inlet, inlet flow rate, and inlet corrosion product concentration, as well as the inlet lithium concentration and inlet boron concentration at the initial moment; the inlet corrosion product concentration includes at least one of the following: inlet soluble iron corrosion product concentration, inlet soluble nickel corrosion product concentration, and inlet insoluble iron-nickel particle concentration; The prediction model is coupled calculated based on the calculation step size and the calculation parameters to obtain the core axial power distribution information corresponding to each calculation moment.

3. The method according to claim 2, characterized in that The step of performing coupled calculation on the prediction model based on the calculation step size and the calculation parameters to obtain the core axial power distribution information corresponding to each calculation moment includes: Get the current calculation time t i The calculation results include: core axial power distribution information, critical boron concentration, fuel temperature, cladding temperature, coolant temperature, and cladding heat flux density; the critical boron concentration is used to characterize the boron concentration in the CRUD external coolant when the proliferation factor reaches a critical state; the value range of i is 0 to M, where M represents the total number of calculation moments within the preset time length; In the first time period (t i , t i +t half_step ], according to the calculation parameters and the current calculation time t i The prediction model is coupled with the calculation results until the convergence condition is reached and t i +t half_step The calculation result at the moment; where t half_step represents half of the calculation step; In the second time period (t i +t half_step , t i+1 ], according to the calculation parameters and the t i +t half_step The calculation result of the time is coupled to the prediction model until the convergence condition is reached, and the next calculation time t is obtained. i+1 The calculation results of .

4. The method according to claim 3, characterized in that The calculation parameters and the current calculation time t i The step of performing coupling calculation on the prediction model based on the calculation results includes: In the first time period (t i , t i +t half_step ], firstly input the fuel power into the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the first time period; then, the current calculation time t i The cladding temperature, coolant temperature, and cladding heat flux density are input into the CRUD material module, so that the CRUD material module outputs CRUD information within the first time period; wherein the CRUD information includes: CRUD thickness, CRUD effective thermal conductivity, CRUD internal temperature field, CRUD internal flow field, CRUD internal boron concentration distribution information, and CRUD internal lithium concentration distribution information; Finally, the fuel consumption, critical boron concentration, CRUD internal boron concentration distribution information and CRUD internal lithium concentration distribution information in the first time period, as well as the current calculation time t i The fuel temperature, cladding temperature, and coolant temperature are input to the neutron physics module so that the neutron physics module outputs t i +t half_step The core axial power distribution information and critical boron concentration at the moment; the coolant pressure, the inlet temperature, the inlet flow rate, the t i +t half_step The core axial power distribution information at the time and the CRUD effective thermal conductivity in the first time period are input to the thermal hydraulic module so that the thermal hydraulic module outputs the t i +t half_step The fuel temperature, cladding temperature, coolant temperature and cladding heat flux density at the moment t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached to obtain the t i +t half_step The calculation result at the moment.

5. The method according to claim 4, characterized in that According to the calculation parameters and the t i +t half_step The step of coupling calculation of the prediction model with the calculation result at the time comprises: In the second time period (t i +t half_step , t i+1 ], firstly input the fuel power into the neutron physics module so that the neutron physics module outputs the fuel burnup and critical boron concentration in the second time period; then, the t i +t half_step The cladding temperature, coolant temperature, and cladding heat flux density are input into the CRUD material module, so that the CRUD material module outputs CRUD information within the second time period; Finally, the fuel consumption, critical boron concentration, boron concentration distribution information inside the CRUD, lithium concentration distribution information inside the CRUD, and the t i +t half_step The fuel temperature, cladding temperature, and coolant temperature at the time are input to the neutron physics module so that the neutron physics module outputs t i+1 The core axial power distribution information and critical boron concentration at the moment; the coolant pressure, the inlet temperature, the inlet flow rate, the t i+1 The core axial power distribution information at the time and the CRUD effective thermal conductivity in the second time period are input to the thermal hydraulic module, so that the thermal hydraulic module outputs the t i+1 The fuel temperature, cladding temperature, coolant temperature and cladding heat flux density at the moment t i+1 The fuel temperature, cladding temperature, and coolant temperature at the time are input into the neutron physics module, and the calculation is repeated until the convergence condition is reached to obtain the t i+1 The calculation result at the moment.

6. The method according to claim 5, characterized in that The step of repeatedly calculating until the convergence condition is reached comprises: In the first time period (t i , t i +t half_step ] and the second time period (t i +t half_step , t i+1 ], respectively calculating the power difference between two adjacent core axial power distribution information, the temperature field difference between two adjacent CRUD internal temperature fields, and the boron concentration difference between two adjacent CRUD internal boron concentration distribution information; If the power difference, the temperature field difference, and the boron concentration difference are all smaller than the preset error sum, it is determined that the convergence condition is met.

7. The method according to claim 6, characterized in that The preset error is less than 10 -5 .

8. The method according to claim 2, characterized in that The step of generating prediction information of the core axial power offset phenomenon includes: According to the core axial power distribution information corresponding to each calculation moment, prediction information of the core axial power deviation phenomenon is generated.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are executed.

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