Method and program product for determining a core reactivity state

Abstract

The application discloses a kind of determination methods and program products of core reactivity state, the method includes: the key state parameter in the process of core reaction is collected, key state parameter includes at least one of core power distribution parameter, coolant temperature, coolant density, control rod position, boric acid concentration and burnup;Determine the burnup step of fuel, the evolution process of fissile nuclide and fission product in core is modeled based on the burnup step of fuel and burnup model, to obtain the reactivity parameter related to the burnup of fuel, the burnup model is used to describe the nuclide transmutation and consumption logic of fuel during the operation of core;Corresponding digital twin is constructed with the reactivity state of the core, the reactivity state of the core is mapped by the digital twin based on the reactivity parameter and the key state parameter driving, can realize high-precision, real-time, full life cycle dynamic digital mapping and intelligent detection of core reactivity state.

Description

Technical Field

[0001] This invention relates to the field of digital information technology, and in particular to a method and program product for determining the reactivity state of a reactor core. Background Technology

[0002] Nuclear reactor operation requires real-time monitoring of the reactor core reactivity to ensure safety and efficiency. However, factors such as fuel burnup, poison accumulation, and thermal feedback are highly coupled, making it difficult to achieve high-fidelity, dynamic, and full-lifecycle state mapping.

[0003] In related technologies, offline burnup calculations combined with periodically calibrated simplified neutronics models are commonly used, or core condition assessments are performed using simulation platforms with fixed time steps. These methods rely on pre-defined operating condition assumptions and cannot integrate multi-source heterogeneous detection data in real time. Furthermore, they often neglect the dynamic accumulation of fission products and the thermal-neutron coupling effect in burnup evolution modeling, leading to delayed or distorted predictions of reactivity parameters. In addition, the lack of mechanisms to handle out-of-order, missing, or version-conflicting data makes it difficult to support continuous and reliable updates of digital twins in complex operating scenarios, limiting their application in online security and intelligent operation and maintenance. Summary of the Invention

[0004] This invention provides a method and program product for determining the reactivity state of a reactor core, in order to solve the problem of real-time, high-fidelity dynamic mapping of the reactivity state of a reactor core in related technologies.

[0005] According to one aspect of the present invention, a method for determining the core reactivity state is provided, comprising: Key state parameters during the reactor core reaction process are collected, including at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup rate. The burnup step of the fuel is determined, and the evolution process of fissile nuclides and fission products in the reactor core is modeled based on the burnup step and the burnup model of the fuel to obtain reactivity parameters related to the burnup amount of the fuel. The burnup model is used to describe the transmutation and consumption logic of nuclides in the fuel during reactor core operation. A digital twin corresponding to the reactivity state of the reactor core is constructed, and the digital twin is driven to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters.

[0006] According to another aspect of the present invention, an apparatus for determining the core reactivity state is provided, comprising: The critical state parameter acquisition module is used to acquire critical state parameters during the reactor core reaction process. The critical state parameters include at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup. The reactivity parameter determination module is used to determine the fuel burnup step. Based on the fuel burnup step and the burnup model, the evolution process of fissile nuclides and fission products in the reactor core is modeled to obtain reactivity parameters related to the fuel burnup amount. The burnup model is used to describe the nuclide transmutation and consumption logic of the fuel during reactor core operation. The reactivity state mapping module is used to construct a digital twin corresponding to the reactivity state of the reactor core, and drive the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for determining the core reactivity state according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for determining the core reactivity state as described in any embodiment of the present invention.

[0009] According to another aspect of the present invention, embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements a method for determining the core reactivity state as described in any of the embodiments of this disclosure.

[0010] The technical solution of this invention, by collecting key state parameters during the reactor core reaction process, including at least one of core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup, enables real-time, accurate perception and dynamic tracking of the reactor core reaction state. It determines the fuel burnup step, and based on the fuel burnup step and burnup model, models the evolution process of fissile nuclides and fission products within the reactor core to obtain reactivity parameters related to the fuel burnup. The burnup model describes the nuclide transmutation and consumption logic of the fuel during core operation, accurately predicting burnup-related reactivity changes. It constructs a digital twin corresponding to the reactor core's reactivity state, and drives the digital twin to map the reactor core's reactivity state based on the reactivity parameters and key state parameters, achieving high-fidelity, dynamic digital mapping of the reactor core's reactivity state. This solves the problem of real-time, high-fidelity dynamic mapping of the reactor core's reactivity state in related technologies, enabling high-precision, real-time, full-lifecycle dynamic digital mapping and intelligent detection of the reactor core's reactivity state.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for determining the reactivity state of a reactor core according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of a device for determining the reactivity state of a reactor core according to Embodiment 3 of the present invention; Figure 3 This is a schematic diagram of the boundary between fuel consumption and fuel consumption step according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a graphical image driven by a digital twin according to Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device for implementing the method for determining the core reactivity state according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0018] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0019] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0020] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0021] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0022] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0023] Example 1 Figure 1 The flowchart of a method for determining the reactivity state of a reactor core is provided in Embodiment 1 of the present invention. This embodiment is applicable to real-time status detection and intelligent operation and maintenance of a nuclear reactor throughout its operation. The method can be executed by a device for determining the reactivity state of the reactor core, which can be implemented in hardware and / or software. Optionally, it can be implemented through an electronic device, such as a mobile terminal, a PC, or a server.

[0024] like Figure 1 As shown, the method may specifically include: S110. Collect key state parameters during the core reaction process. The key state parameters include at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup.

[0025] The reactor core can be understood as the core region in a nuclear reactor where a controlled chain reaction of nuclear fission occurs. It contains components such as nuclear fuel (e.g., uranium-235, plutonium-239), moderators, and coolants. It is the key part of the reactor that generates energy, serving as the "birthplace" of the fission reaction and releasing nuclear energy through a controlled chain reaction. Simultaneously, it must maintain reaction stability (avoiding runaway or extinction) and ensure radiation safety through structural design. The reaction process can be understood as the dynamic process of the continuous nuclear fission chain reaction throughout the entire lifecycle of the reactor core, from startup and stable operation to shutdown. Accompanied by changes in parameters such as power and temperature, it forms the basis of the reactor's energy output. Its stability directly affects the reactor's safety (e.g., preventing over-power) and economy (e.g., efficient fuel utilization). The key state parameters can be understood as core physical quantities used to characterize the reactor core's operating state, including but not limited to at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup. These parameters form a "set of indicators" for detecting the reactor core's operating state. By collecting these parameters in real time, it can be determined whether the reactor is in a safe and stable operating condition (e.g., uneven power distribution may cause local overheating, and abnormal control rod position may lead to reactive runaway). The core power distribution parameters can be understood as quantitative indicators (e.g., power peak factor, local power density) of the power density distribution at different spatial locations (e.g., radial, axial) within the reactor core. They are used to reflect the spatial uniformity of the nuclear fission reaction and to optimize fuel loading schemes, avoiding excessive local power that could damage the fuel cladding or cause coolant boiling. The coolant temperature can be understood as the temperature of the coolant (such as water or liquid metal) in the reactor core after absorbing heat from the nuclear reaction during its flow (e.g., the temperature difference between the inlet and outlet or the temperature at critical locations). It directly affects the physical properties of the coolant (such as density and phase transition risk) and its reactivity (increased temperature may reduce reactivity through the Doppler effect); it is also a key monitoring parameter for preventing core meltdown. The coolant density can be understood as the mass per unit volume of the coolant in the reactor core, affected by temperature and pressure (e.g., the density of liquid coolant decreases at high temperatures). It determines the coolant's moderation capability (for thermal neutron reactors) and heat transfer efficiency; changes in density alter the probability of collisions between neutrons and the coolant, thus affecting core reactivity (e.g., a decrease in coolant density in a light water reactor reduces the number of moderated neutrons, thus reducing reactivity). The position of the control rod can be understood as the insertion depth or coordinate position of the control rod (including neutron-absorbing materials such as boron and cadmium) in the reactor core (such as full lift, partial insertion, full insertion, etc.). The reactivity of the reactor core is directly controlled by adjusting the amount of neutron absorption (the deeper the insertion, the more neutrons are absorbed, and the lower the reactivity). It is the core means to realize reactor start-up and shutdown, power regulation and emergency shutdown.The boric acid concentration can be understood as the mass or molar concentration of boric acid (H3BO3) in the coolant (used as a soluble neutron absorber in pressurized water reactors). The boron-10 isotope absorbs neutrons to reduce core reactivity, compensating for the decrease in reactivity caused by burnup or for long-term reactivity control. Concentration adjustments can achieve slow reactivity regulation (such as compensation during refueling cycles). The burnup rate can be understood as the number of nucleons consumed by nuclear fuel during core operation (in "megawatt-days / ton of uranium"), reflecting the degree of fuel utilization and serving as a core indicator for measuring fuel economy. Simultaneously, increased burnup leads to a reduction in fissile nuclides (such as U-235) and an accumulation of fission products (such as Xe-135), thereby altering core reactivity.

[0026] For example, the control rod position, as the primary means of regulating reactivity, is measured in real time by the control rod drive mechanism in the reactor control system. The specific control rod position is determined by position sensors feeding back step-size signals, which are then transmitted to the digital twin system via a data acquisition module. The control rod position directly affects the neutron flux distribution in the reactor core and is a crucial boundary condition for calculating instantaneous reactivity.

[0027] For example, coolant temperature and pressure are acquired in real time by temperature sensors and pressure transmitters located in the primary and secondary loops. Changes in coolant temperature cause changes in neutron moderation characteristics, which in turn affect the core's temperature reactivity coefficient and Doppler effect. These parameters are used in the twin model to update the thermo-hydraulic boundary conditions for neutronics calculations and are combined with the burnup model to estimate local reactivity changes.

[0028] For example, boric acid concentration, as a crucial control variable in the chemical volume control system, is measured using online sampling analysis or density reduction algorithms. Preferably, the sampled values ​​are updated every 5–15 minutes. The boric acid concentration determines the concentration of soluble neutron absorber in the solution, a slowly varying parameter that determines the overall reactivity of the reactor core. By using the collected boric acid concentration values, the absorption cross-section calculation in the burnup model can be corrected, thereby ensuring the accuracy of reactivity prediction at different burnup stages.

[0029] For example, the core power distribution is obtained through inversion of nuclear instrument signals and measurement data. The relevant acquisition equipment includes self-protected detectors, fixed neutron flux monitors, and axial detectors measuring signals. The core power distribution is corrected using energy balance or power inversion methods to ensure it meets measurement constraints. The core power distribution is fundamental for calculating the burnup rate and transmutation source term, and can be used to calculate the fission rate in the burnup model, thereby deriving subsequent nuclide evolution and reactivity corrections. The fission rate can be expressed as: ; in, It is the total fission cross section, that is, the macroscopic fission cross section of all fissile nuclides; Represents nuclide The microscopic fission cross section can be determined experimentally.

[0030] It also includes calculating the fuel burnup BU by means of the following, and further calculating the fuel burnup step to update the minority group constant and nuclide concentration in the burnup model, thereby reflecting the performance changes of nuclear fuel throughout its lifetime.

[0031] Optionally, it also includes calculating the fuel burnup BU using the following formula, and further calculating the fuel burnup step to update the minority group constant and nuclide concentration in the burnup model, thereby reflecting the performance changes of the nuclear fuel throughout its lifetime. (See attached...) Figure 3 As shown, the fuel consumption step is a discrete segmentation with fuel consumption BU as the independent variable, used to control the transmutation solution and the retrieval and updating of the minority group constant library. Fuel consumption BU is a state variable; the fuel consumption step is a discrete stage / index for numerical computation and library retrieval, dividing the continuous BU axis (vertical axis) into several intervals [BUn, BUn+1), updating the nuclide vector, interpolation cross section, or outputting the result once in each interval.

[0032] For example, such as Figure 3 As shown, the fixed step size ΔBU = 0.5MWd / kgU for the fuel consumption step is set, or other step sizes can be set, or an adaptive step size can be used (ΔBU is shortened when there are sudden changes in power / temperature). When BU(t) exceeds the threshold, the next fuel consumption step begins. The fuel consumption BU is defined as: ; in, For the core in time power, This indicates the initial mass of heavy metals in the fuel.

[0033] For the definition of BU, the upper and lower limits of integration and the variables of the power function can also be set as user-defined variables, requiring only changes to the corresponding differential terms. Specifically, with BU as the independent variable, the upper and lower limits of integration in Equation 3 are the current local fuel consumption scale BU. current →Target (Next) Burnout Step Scale BU target .

[0034] During the integral calculation, the power P corresponding to the differential scale of each burnup amount (dBu) can be determined jointly by external system measurements and core physics calculations. First, the total reactor thermal power is measured through the main loop thermal balance as a power reference. Then, combined with the minority group constant library and flux distribution calculations, the relative power factor of each node is obtained, thereby distributing the total power to each component or node to obtain local power values. For power changes within a burnup step, integral averaging or piecewise approximation methods are used to ensure that the cumulative scale of burnup is consistent with the actual energy release.

[0035] Specifically, under core operating conditions, each fuel assembly or node has a corresponding power level and heavy metal mass, and the cumulative scale of burnup (BU) can accurately reflect the degree of fuel consumption. Furthermore, the burnup step (ΔBU) is used as the prediction granularity, that is, when the cumulative core burnup exceeds the preset ΔBU, a prediction state update is triggered.

[0036] In each burnup step, the current burnup scale BU and the corresponding nuclide composition vector N(BU) are first read. Then, the evolution relationship of N with BU is integrated based on the nuclide transmutation equation to obtain the nuclide composition N(BU+ΔBU) at the next burnup step BU+ΔBU. This transmutation process comprehensively considers the fission, capture, and decay effects of nuclides to ensure that the predicted value reflects the actual reactor physics. After the nuclide composition vector is updated, based on the new BU value and the current thermal parameters (e.g., coolant temperature T, coolant density ρ),... m (e.g., boric acid concentration [B], etc.) in the pre-generated minority group constant library Σ g (BU,T,ρ m The corresponding macroscopic cross-sectional data is retrieved or interpolated from [B]). Based on this macroscopic cross-sectional data, key reactivity parameters for future fuel consumption steps can be further generated. These reactivity parameters include the effective proliferation factor k. eff The current core reactivity ρ, critical boron concentration (as a function of BU), control rod value, and the relationship between rod position margin and BU are all important factors.

[0037] The key state parameters are acquired by the state parameter acquisition module. This module comprises several working layers: a field acquisition layer, used for real-time acquisition of key operating parameters at the nuclear power plant site. For example, the field acquisition layer includes multiple sensors, including: a temperature sensor and a pressure transmitter for acquiring coolant temperature and pressure parameters; a flow meter for measuring cooling loop flow; a control rod position sensor for providing the current position and step size signals of the rod assembly; and a neutron flux detector and power monitoring device for acquiring core power distribution and neutron flux information to support the estimation of reactivity parameters. The control system interface layer communicates with multiple sensing devices in the field acquisition layer through a deployed data acquisition unit (DAU) to complete the access, conversion, and standardized encapsulation of sensor signals, and submits the processed data to the data exchange and transmission layer. Preferably, the control system interface layer is configured with analog inputs, digital inputs, and a network interface to standardize the signals acquired by various sensors and detection devices, forming structured data message frames. Preferably, the control system interface layer also provides a timestamp synchronization function to ensure the timing consistency of multi-source data in subsequent processing. The data exchange and transmission layer is used to transmit the data frames output by the control system interface layer through an industrial network. Preferably, the data exchange and transmission layer includes a high-speed data bus, such as OPC UA or Modbus TCP, to achieve communication with the upper-level system. Preferably, to ensure the real-time performance and reliability of data transmission, the data exchange and transmission layer is configured with redundant links and dual-machine hot standby servers, and implements CRC checksum and security encryption in the message protocol to meet communication network security standards. For example, the data exchange and transmission layer can be communicatively coupled with the existing process control system (PCS) and protection system (RPS) of the nuclear power plant itself to provide various field-acquired data to the nuclear power plant's own operating systems. Furthermore, preferably, the data exchange and transmission layer 130 is also communicatively coupled with the digital twin operation module through the twin system processing layer to provide field-acquired data to the digital twin system. For example, the twin system processing layer provides an application programming interface (API) to transmit key state parameters... number The data is simultaneously pushed to the fuel consumption model calculation module and the digital twin construction module. The processing layer of the twin system includes a data preprocessing unit for preprocessing the input data. For example, data preprocessing includes filtering, noise reduction, and outlier removal. After preprocessing, valid data is further sent to the fuel consumption model calculation module, the digital twin construction module, or the update module. The fuel consumption model calculation module updates the fuel consumption step information based on multiple key state parameters and calls Equation 1 to perform nuclide transmutation calculations, generating reactivity-related parameters.

[0038] Furthermore, in an exemplary embodiment, the burnup model calculation module is communicatively connected to the twin system processing layer to calculate the time-varying composition of the core fuel based on a nuclide transmutation model and generate reactivity parameters that evolve with burnup. Preferably, the burnup model calculation module includes support for calculations of short-lived nuclide channels at the second level, and also supports burnup step updates at the minute or hour level, so that the digital twin has medium- to long-term prediction capabilities.

[0039] S120. Determine the fuel burnup step, and model the evolution process of fissile nuclides and fission products in the reactor core based on the fuel burnup step and the burnup model to obtain reactivity parameters related to the fuel burnup amount. The burnup model is used to describe the nuclide transmutation and consumption logic of the fuel during reactor core operation.

[0040] The fuel can be understood as the material in the reactor core that undergoes nuclear fission reactions (such as low-enriched uranium or MOX fuel), which releases nuclear energy (thermal energy) through fission reactions and is the energy source of the reactor. Its composition evolves with burnup (decrease in fissile nuclides and increase in fission products), requiring periodic replacement to maintain reactivity. The burnup step can be understood as a discrete time or energy step that divides the entire fuel operating cycle, used to simulate nuclide evolution in stages, simplifying continuous calculations of the burnup process, improving computational efficiency through segmented modeling, while retaining the characteristics of key stages (such as the initial high reactivity period, the intermediate stable period, and the final low reactivity period). The burnup model can be understood as a mathematical model describing the transmutation (such as neutron capture to generate new nuclides) and consumption (such as fission and decay) of nuclides (fissile nuclides, fission products, etc.) during reactor core operation, used to predict changes in fuel composition with burnup, providing a basis for reactivity calculations, refueling design, and safety analysis. The fissile nuclides in the reactor core can be understood as nuclides that can undergo fission and release energy in the neutron field of the reactor core (such as U-235, Pu-239, U-233, etc.), which are the core materials for maintaining the chain reaction; their concentration decreases with burnup (fission consumption), and reactivity needs to be maintained through breeding (such as U-238→Pu-239) or fuel replenishment. The fission products can be understood as new nuclides generated by the nuclear fuel fission reaction (such as Xe-135, Sm-149, Cs-137), most of which are radioactive nuclides. Some fission products (such as Xe-135) have a high neutron absorption cross section ("poison"), which will significantly reduce reactivity (requiring "xenon elimination" through reactor shutdown or power reduction); at the same time, the accumulation of fission products affects fuel performance (such as swelling and heating). The evolution process can be understood as the dynamic change of fissile nuclides and fission products within the reactor core over time / burnup (including the increase, decrease, and transformation of nuclide types and concentrations). This determines the long-term trend of core reactivity (e.g., rapid decline in reactivity in the early stages of burnup, followed by a gradual plateauing due to Pu-239 breeding in the mid-term), and serves as the basis for reactivity control and refueling strategy formulation. The reactivity parameters can be understood as physical quantities related to fuel burnup that characterize the core's neutron breeding capacity, directly reflecting the core's "potential" to maintain a chain reaction. They are calculated using a burnup model combined with burnup quantity and used to assess the current reactivity level. These reactivity parameters include, but are not limited to, at least one of the following: effective breeding factor, core reactivity, critical boron concentration curve, control rod value, and temperature feedback coefficient.

[0041] The effective multiplication factor can be understood as a physical quantity used to characterize the neutron multiplication capability of the reactor core, which is the ratio of the number of neutrons in a certain generation to the number of neutrons in the previous generation. =Next-generation fission neutron number / Current-generation fission neutron number), directly reflecting the "sustainability" of the reactor core chain reaction. For example, =1: The reactor core is in a critical state (neutron number is stable, power is constant); >1, Supercritical (neutron number increases, power increases); <1, subcritical (neutron number decay, power decrease). Core reactivity can be understood as a physical quantity characterizing the degree to which the core deviates from criticality. This can describe the "degree of deviation" of the reactor core in a more intuitive way. For example, =0, critical (equilibrium state); >0, positive reactivity (must be suppressed, otherwise power will increase); <0, negative reactivity (requires compensation, otherwise power decreases). The critical boron concentration curve can be understood as being used to characterize the core critical state (…). =1), the critical boron concentration value varying with burnup is used to guide the dynamic adjustment of boric acid concentration during operation: as burnup increases (reduction of fissile nuclides, accumulation of fission product "poisons"), the boron concentration needs to be reduced (to reduce neutron absorption) to compensate for reactivity loss and maintain core criticality; this curve is the core basis for setting the boron concentration during the refueling cycle. The control rod value can be understood as the amount of reactivity change caused by one or more control rods being fully inserted into the core (usually a negative value, due to neutron absorption by the control rods). Quantifying the control rods' ability to regulate reactivity is a key parameter for designing the control rod layout (such as number and position) and operating methods (such as how many control rods need to be inserted to achieve sufficient negative reactivity during emergency shutdown); the greater the value, the more sensitive the control rods are to reactivity regulation. The temperature feedback coefficient can be understood as the rate at which the temperature change causes a change in reactivity when the core temperature changes; according to the temperature type, it can be divided into fuel temperature coefficient (Doppler effect), coolant temperature coefficient (density / moderation capability change), etc., reflecting the core's self-stabilization. For example, a negative temperature feedback coefficient indicates that reactivity decreases as temperature increases, suppressing power increase, which is the core mechanism of inherent reactor safety; a positive temperature feedback coefficient indicates that reactivity increases as temperature increases, which may lead to power runaway (such as boiling water reactors which require strict control), and is an important parameter for reactor safety analysis (such as power response under a decooling accident).

[0042] Based on the above scheme, optionally, the step of determining the fuel consumption step includes at least one of the following: determining a preset fuel consumption step as the fuel consumption step; determining the fuel consumption step according to the actual fuel consumption and a preset fuel consumption threshold; determining the fuel consumption step according to a fuel consumption scale, wherein the fuel consumption scale is used to mark the fuel consumption.

[0043] The preset burnup step can be understood as a pre-defined fixed step size (such as a fixed interval divided by time, energy, or burnup amount), serving as a unified standard for fuel burnup steps, simplifying the burnup modeling process. It is suitable for scenarios where the burnup process is relatively uniform or where computational efficiency is critical (such as the preliminary design stage), and the fixed step size can reduce model complexity. The actual burnup amount can be understood as the specific number of nucleons consumed during core operation, reflecting the current utilization level of the fuel. It is the core basis for dynamically adjusting the burnup step size, reflecting the true operating state of the fuel. By combining the actual burnup amount, the step size can be selectively divided (e.g., a smaller step size in high burnup stages to capture details, and a larger step size in low burnup stages to improve efficiency). The preset burnup threshold can be understood as a pre-defined burnup interval value, used to trigger the division of burnup steps, achieving automated and standardized division of burnup steps, avoiding model inconsistencies caused by subjective step size selection; controlling the step size through the threshold can balance the computational needs of different stages. The burnup scale can be understood as discrete markers used to mark the amount of fuel consumed (such as marking by key events or fixed increments, for example, "burnup scale 1: 1000 MW·day / ton of uranium" "burnup scale 2: 2000 MW·day / ton of uranium"). Each scale corresponds to the start or end point of a burnup step, providing clear "milestones" for the burnup process and facilitating the tracking of fuel state evolution.

[0044] By adopting this technical solution, the fuel consumption step can be flexibly determined, taking into account both computational efficiency and modeling accuracy. It can adapt to rapid simulation under fixed working conditions and respond to dynamic changes in fuel consumption during actual operation, thereby improving the timeliness and accuracy of nuclide evolution and reactivity prediction.

[0045] Based on the above scheme, optionally, the evolution process of fissile nuclides and fission products in the reactor core is modeled based on the burnup step of the fuel to obtain reactivity parameters related to the burnup amount, including: for each burnup step, obtaining the burnup amount and nuclide composition vector of the fuel at the first time corresponding to the burnup step; integrating the evolution relationship of the nuclide composition vector with the burnup amount based on the burnup model to obtain the nuclide composition vector at the second time corresponding to the next burnup step; determining the macroscopic cross-sectional data at the second time based on the burnup amount, thermal parameters and a pre-established minority group constant library at the second time; and determining the reactivity parameters related to the burnup amount based on the macroscopic cross-sectional data.

[0046] The first moment can be understood as the starting point of a certain burnup step (such as the start time of the nth burnup step), corresponding to the initial state of the fuel in that step (burnup amount, nuclide composition, etc.), serving as the "initial condition" for the nuclide evolution of the current burnup step and providing benchmark data for subsequent integral calculations. The nuclide composition vector can be understood as a vector describing the density values ​​of various nuclides (fissile nuclides, fission products, breeder nuclides, etc.) in the fuel, serving as the "state carrier" of the nuclide evolution process, with its elements (nuclide concentration) dynamically updated as the burnup step progresses; the vector form allows for batch processing of the transmutation relationships of multiple nuclide couplings. The second moment can be understood as the ending point of a certain burnup step (such as the end time of the nth burnup step, which is also the start time of the (n+1)th burnup step), corresponding to the state of the fuel after its evolution in that step, serving as the "result output point" of the nuclide evolution of the current burnup step, and its nuclide composition vector will serve as the initial state (first moment) of the next burnup step, forming an "iterative" modeling process that ultimately covers the evolution of the entire fuel life cycle. The thermal parameters can be understood as key physical quantities describing the thermal-hydraulic state of the reactor core, including but not limited to coolant temperature, pressure, flow rate, and density. These parameters indirectly alter the nuclide evolution rate and reactivity by influencing the neutron cross section and minority group constant. The minority group constant library can be understood as a database storing neutron cross section data of fuel under different burnup rates and thermal parameters. This database stores the corresponding burnup, thermal parameters, and cross-sectional data of the fuel during the reactor core reaction process, avoiding redundant calculations of complex neutron cross sections (such as solving multi-group transport equations). The minority group constant corresponding to the current state (burnup and thermal parameters at the second moment) can be quickly obtained by looking up the table. The cross-sectional data can be understood as a probability measure of the interaction between nuclides and neutrons, including fission cross section, capture cross section, and scattering cross section. This data forms the basis for calculating the nuclear reaction rate. The reaction rate can be determined through neutron flux and interface, directly determining the neutron multiplication capability. The macroscopic cross-sectional data can be understood as the overall neutron interaction probability of the reactor core obtained by weighted summation of the nuclide composition vector and the corresponding minority cross-section. It reflects the reactor core's ability to "block / absorb" neutrons of a specific energy and is the direct input for calculating the effective multiplication factor.

[0047] By employing this technical solution, the nuclide composition is integrally evolved within each burnup step, and macroscopic cross sections are dynamically generated by combining real-time burnup, thermal parameters, and a minority group constant library. This enables the high-precision and efficient calculation of reactivity parameters coupled with burnup, significantly improving the physical fidelity and timeliness of core neutron behavior prediction, and providing reliable state input for digital twins.

[0048] Based on the above scheme, optionally, the fuel consumption model includes a matrix form model, which is as follows: , Where, matrix vector For containing multiple nuclides Vector and matrix of nuclide number density values ​​at time t. It is a coefficient matrix consisting of the nuclide decay constant and the neutron reaction cross section.

[0049] The matrix vector can be understood as a vector composed of the nuclide number density values ​​of each nuclide at time t (i.e., the nuclide composition vector, such as...). ,in Indicates the time of the kth nuclide The number of atoms per unit volume. The nuclide decay constant can be understood as the probability of a radioactive nuclide spontaneously decaying per unit time, and is a coefficient matrix. The origin of the naturally disappearing term directly affects the natural consumption rate of a nuclide. The neutron reaction cross section can be understood as the probability that the k-th nuclide undergoes a specific nuclear reaction (such as fission, capture, or scattering) with a neutron, and is a coefficient matrix. The source of the neutron-driven term determines the rate at which nuclides are consumed or generated in the neutron field. Neutron flux is the number of neutrons passing through a unit area per unit time and unit volume in the reactor core, reflecting the "density" and activity of neutrons.

[0050] This technical solution employs a matrix-based burnup model, which can uniformly describe the coupling process between nuclide decay and neutron reaction, thereby improving the systematic nature, stability, and numerical solution efficiency of burnup calculations. Based on the above scheme, optionally, the fuel consumption model includes a formulaic model, which is as follows: , in, Indicates nuclide atomic number density, Indicates the parent nuclide atomic number density; Represents nuclide The decay constant; Indicates nuclide The absorption cross section; This represents the neutron flux in the reactor core. Indicates the parent nuclide Transformed into nuclides Conversion rate; Indicates the parent nuclide Nuclide generated through neutron reaction The reaction cross section.

[0051] The parent nuclide can be understood as a nuclide generated through decay or neutron reaction. Precursor nuclides.

[0052] The operation of the fuel consumption model calculation module includes the following steps: S100: Receive a structured data frame from the twin system processing layer; the data frame includes numerical data of multiple key state parameters aligned by timestamps, as well as initial loaded nuclide vectors and minority group constant library indexes input by relevant technical personnel to the fuel consumption model calculation module. All input data is timestamped and dimensionally unified, and a calculation input set is ultimately generated.

[0053] S200: When the system detects that the fuel consumption BU has reached a preset threshold and crosses the current fuel consumption step limit, the fuel consumption model calculation module triggers the execution of the fuel consumption step update program, which performs the following steps: S210: Nuclide transmutation update; Based on the burnup model, re-integrate the transmutation equation for the managed fuel assembly or core partition, and calculate and update the vector N(t) corresponding to multiple nuclide number densities.

[0054] S220: Section library retrieval and interpolation; in the minority group constant library Σ g (Example can be set to Σ) g (BU,T,ρ m [B]) is indexed by the updated BU index, coolant temperature T, and coolant density ρ. m Parameters such as boric acid concentration [B] are used to retrieve the corresponding macroscopic cross-sectional values ​​for the g-th energy group under given fuel consumption, temperature, density, and boron concentration conditions. Furthermore, the macroscopic cross-sectional values ​​are corrected by incorporating control rod insertion rate and spectral effects to form a set of consistent parameters.

[0055] S300: Combining the nuclide vector N(t) obtained in step S210 and the consistency parameter set obtained in step S220, the fuel consumption model calculation module calculates the effective proliferation factor k. eff The current core reactivity ρ, critical boron concentration (as a function of BU), control rod value and position margin in relation to BU, and linearized estimation of temperature feedback coefficient (i.e., estimated values ​​of moderator temperature MTC and fuel temperature ITC).

[0056] The above calculation results are used as reactive parameters that evolve with fuel consumption. They are then passed to the digital twin construction module and synchronized to the update module for online assimilation and medium- to long-term prediction.

[0057] The fuel consumption model calculation module may include two calculation channels: a first channel for short-lived chains and a second channel for long-lived and parent nuclides.

[0058] Using this technical solution, the formulaic burnup model explicitly describes the generation and consumption mechanisms of each nuclide, improving the physical transparency and local accuracy of burnup evolution simulation, and facilitating targeted verification and optimization.

[0059] S130. Construct a digital twin corresponding to the reactivity state of the reactor core, and drive the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters.

[0060] The reactivity state can be understood as the neutron multiplication capability state of the reactor core at a specific moment, determined by reactivity parameters and key state parameters, and is the core state for the safe and stable operation of the reactor. The digital twin can be understood as a virtual mapping model constructed based on multi-source data (such as key state parameters and reactivity parameters) of the physical reactor core. It has functions such as real-time synchronization and simulation prediction. As a "digital mirror" of the physical reactor core, it can map the actual reactivity state of the reactor core in real time, supporting fault diagnosis (such as parameter anomaly tracing), operation simulation (such as predicting the impact of control rod movement), and optimization decision-making (such as burnup compensation strategies).

[0061] Based on the above scheme, optionally, the construction of a digital twin corresponding to the reactivity state of the reactor core includes: acquiring the design drawings and operating parameters of the actual physical reactor core, determining the component distribution, node division, and axial hierarchy division standards of the reactor core, constructing a virtual mesh structure of the actual physical reactor core in virtual space according to a preset mapping ratio; acquiring the key state parameters and the reactivity parameters of the actual physical reactor core, and associating the key state parameters and the reactivity parameters with the corresponding nodes, components, and hierarchy of the virtual mesh to obtain a digital twin.

[0062] The actual physical reactor core can be understood as the core part of an actual operating nuclear reactor, including physical structures such as fuel assemblies, control rods, and coolant channels. It is the site of nuclear fission reactions and the "physical entity" of the digital twin, providing the original data required to build the virtual model. Its dynamic behavior is the target object for mapping and simulation of the digital twin. The design drawings can be understood as engineering drawings (such as mechanical structure drawings, layout drawings, and pipeline diagrams) that record the structural details of the actual physical reactor core. They include static information such as component dimensions, material properties, and spatial positions (such as fuel assembly spacing and control rod guide tube positions). They serve as the geometric reference for building the virtual mesh structure, providing precise geometric constraints for reproducing the physical structure of the physical reactor core in virtual space (such as component distribution and node positions), ensuring the structural consistency between the virtual model and the physical reactor core. The operating parameters can be understood as key configuration and boundary condition data in the actual physical reactor core operation, including rated power, coolant flow rate, pressure, initial boron concentration, and initial control rod position. These define the operating environment of the virtual reactor core, providing the foundation for virtual model initialization (e.g., setting the initial temperature field and neutron flux distribution) and dynamic simulation (e.g., simulating reactivity changes under different operating conditions). The component distribution can be understood as the spatial arrangement rules and positional relationships of various functional components (e.g., fuel assemblies arranged in an M×M grid, control rods inserted into guide tubes of specific fuel assemblies) within the actual physical reactor core. This determines the physical structure and neutron field distribution of the reactor core and serves as the basis for "component-level mapping" in the virtual mesh structure. The node partitioning can be understood as the rules for discretizing the reactor core physical space into tiny units. Each node corresponds to a computational or mapping unit in the virtual space and is the "basic computational unit" of the virtual mesh structure. By discretizing continuous space, precise positioning of key state parameters (e.g., temperature, neutron flux) and reactivity parameters is achieved. The axial hierarchy division standard can be understood as the rule for dividing the core into levels (such as bottom reflector layer, active region, and top reflector layer) along the core height direction (axial direction). It can be determined based on functional or physical characteristics (such as the active region being the main region for nuclear fission, and the reflector layer being used for neutron reflection to reduce leakage). The preset mapping ratio can be understood as the ratio between the virtual space and the actual physical core space. It is used to determine the correspondence between the size of the virtual mesh structure and the actual core, control the "spatial fidelity" of the virtual model, and ensure that the mesh structure constructed in the virtual space is consistent with the size and position of the physical core. The virtual mesh structure can be understood as a discretized geometric framework constructed in the virtual space according to the component distribution, node division, and axial hierarchy division standard, including node coordinates, component virtual models, and hierarchy boundaries.

[0063] Furthermore, in an optional embodiment, the digital twin construction module is configured to construct a virtual digital model, i.e., a digital twin, corresponding to the reactivity state of the nuclear power plant core. The digital twin construction module updates the digital twin using parameters input from an external working module, ensuring it reflects the operating state of the nuclear power plant core at any given time. Simultaneously, combined with the burnup model output, it presents the medium- to long-term evolution trend of the core state. Unlike traditional simulation models, this twin does not directly perform nuclide transmutation, cross-sectional interpolation, or reactivity calculations. Instead, it serves as a carrier for multi-source information fusion, achieving state visualization, parameter correlation, and high consistency throughout its lifecycle.

[0064] For example, the digital twin construction module integrates three types of data—multi-source inputs, including real-time measurements, derived data, and burnup model outputs—to construct a high-fidelity virtual digital model of reactor core reactivity, ensuring its dynamic consistency and predictive capabilities during operation.

[0065] For example, when initializing the virtual digital model, the digital twin construction module includes first establishing a baseline digital twin; the baseline digital twin includes setting the initial distribution of each nuclide, the minority group cross-section library index, and the control rod adjustment boundary.

[0066] For example, the digital twin defines core computing nodes, flux field variables, and thermal boundary conditions in a virtual digital space, and corresponds to the actual core configuration.

[0067] For example, the digital twin includes performing real-time mapping updates by dynamically inputting key state parameters and their processing results into the model to maintain consistency of the current state.

[0068] Furthermore, the digital twin construction module also includes predicting the future state of the digital twin driven by fuel consumption data. By calling the transformation results and BU-related parameters output by the fuel consumption model calculation module, the virtual model can automatically iterate and predictively expand with the fuel consumption step.

[0069] In some exemplary embodiments, the digital twin can be presented in a purely data-driven form. In this form, the digital twin is constructed using an internal data structure in conjunction with an external interface.

[0070] For example, the digital twin presented in the form of data representation includes State Table A, State Table B, and State Table C.

[0071] The first state table uses multiple fields for state mapping, assigning one record to each core node or component, dynamically ensuring the consistency between the digital twin and the real system state. The second state table is a prediction state table, which uses multiple fields to record and update the fuel consumption model results in real time, listing the predicted values ​​of parameters such as reactivity indicators and critical boron concentration for multiple future fuel consumption steps. The third state table is an operation version table, used to record the update history of the digital twin for future data operation traceability.

[0072] In other exemplary embodiments, the digital twin is presented in a graphical form, using concrete images and graphics to display the digital twin so that users can intuitively understand the actual working status of the nuclear power reactor core.

[0073] In an exemplary embodiment, the digital twin is presented using a two-dimensional display method such as a flat screen or planar projection. Alternatively, in some embodiments, the digital twin can be presented using virtual reality glasses, 3D projection, or other stereoscopic display methods.

[0074] For example, such as Figure 4 The figure illustrates one presentation method of a graphical three-dimensional cross-sectional view of the reactor core driven by the digital twin. As shown, the three-dimensional cross-section of the reactor core is represented as a sectional view of a cylindrical core structure or component matrix, clearly indicating the location of fuel assemblies and core channels. For example, the control rod position depth is represented by longitudinal rectangular bars or colored insertion markers; the depth ratio should correspond to the reactive control effect, and the color should be clearly highlighted when the top insertion portion is longer. For example, the coolant temperature gradient is presented using a color gradient or a horizontal color band along the coolant channel to represent temperature changes; a clear gradient is formed between the upper and lower parts of the core, with higher temperatures at the top and lower temperatures at the bottom.

[0075] For example, add a numerical ruler to the graph to indicate the temperature range, and add a percentage label for the insertion depth next to the control bar.

[0076] For example, the presentation also includes a core planar power distribution matrix driven by the digital twin, displayed as a grid of colored blocks in conjunction with digital power values.

[0077] Furthermore, the graphically represented digital twin is driven by three types of keyframe data, including: (1) Operating status frame, the data of which is input by the status parameter acquisition module or the update module. The operating status frame includes the measured or externally corrected values ​​of the core power distribution parameters, control rod positions, boric acid concentration, coolant temperature / density, etc. at the current moment.

[0078] (2) Fuel consumption associated frame. The data in this frame is provided by the fuel consumption model calculation module. The fuel consumption associated frame includes the fuel consumption amount BU, fuel consumption step index, calculated reactivity parameters and other parameter values.

[0079] (3) Structural configuration frame, which is provided by the core engineering configuration and includes information that changes less frequently, such as the arrangement of core components, the number of axial layers, and the correspondence between channels and rod groups.

[0080] Furthermore, the digital twin construction module includes a data access interface, through which timestamped data frames are input sequentially. Upon receiving a new frame, the digital twin construction module may optionally perform the following data processing steps: E100: Checks the frame's source identifier, version number, and checksum to avoid misusing old or corrupt frames.

[0081] E200: If multiple data sources exist at the same time, such as power from measurement and temperature from calculation, the digital twin uses the combination of timestamps closest to the same batch of inputs to update the state of the digital twin once.

[0082] E300: Based on the frame index of the structure configuration frame, such as "Node_ID (node ​​number), Assembly_ID (component number), Axial_Level (axial level)," each value is located at a specific position in the virtual mesh.

[0083] Preferably, the digital twin construction module assigns a corresponding confidence score to the numerical values ​​in the keyframe data. The digital twin construction module can select the latest and most reliable numerical values ​​to cover the object's attributes according to preset rules. For example, if both sensor measurements and externally retrieved values ​​are received simultaneously at the same location, the data value to be used is determined in the order of "confidence priority > time priority".

[0084] For example, the confidence score given for the numerical values ​​in keyframe data can be calculated based on the following rules: Data completeness and freshness: If the measurement data is updated in a timely manner and the signals of key channels are complete, the reliability is high; if some data is missing or there is a delay, the score will be reduced.

[0085] Historical residual performance: By comparing the difference between the previous fuel consumption step or the previous prediction result and the actual measurement value, if the consistency between the prediction and the observation is good, the score is increased; if the difference is large, the score is decreased.

[0086] Prediction stability: If the results obtained by the external module under multiple disturbances or different initial values ​​are relatively concentrated, it indicates that the prediction is reliable; if the differences are large, the score will decrease.

[0087] Operating condition range matching: When the fuel consumption, temperature, coolant density and boron concentration involved in the prediction are all within the effective range of the minority group constant library, the score is higher; if there is an extrapolation beyond the boundary, the score is lower.

[0088] This technical solution integrates physical core design information with measured key states and reactivity parameters to construct a high-fidelity virtual grid structure with spatial correspondence, enabling accurate mapping and visualization of core states in digital space, and providing a reliable digital foundation for real-time detection, fault diagnosis, and operation optimization.

[0089] Optionally, after driving the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters, the method further includes: updating the digital twin based on idempotent update logic and partial coverage rules when the burnup exceeds the preset burnup amount corresponding to the burnup step.

[0090] The preset fuel consumption can be understood as a fuel consumption threshold set in advance for each fuel consumption step, used to define the boundary of the fuel consumption step and serve as a stage marker for the fuel consumption process. By setting the preset threshold, the continuous fuel consumption process is discretized into manageable steps. When the actual fuel consumption exceeds the preset value for the current step, an update of the digital twin is triggered, ensuring that the model reflects the evolution of fuel state in stages. The idempotent update logic can be understood as a data update rule. The idempotent update logic may include ignoring data when the version number of newly received data is not higher than the current version. The partial coverage rule includes updating only the fields carried by new data, while retaining the previous valid values ​​for fields not carried, ensuring that the same data is not repeatedly or incorrectly updated, resolving data redundancy or conflict issues, and avoiding disruption of the digital twin's state consistency due to repeated processing of old data. Version number control ensures that the model is always updated based on the latest data. The partial coverage rule can be understood as a data writing rule, which can improve the efficiency of data updates. For example, new data only updates the fields it explicitly carries (such as only containing "coolant temperature" and "control rod position"), while fields not carried (such as "boric acid concentration") retain the valid values ​​from the previous data, rather than resetting them all.

[0091] By adopting this technical solution, when fuel consumption exceeds the threshold, the continuity, consistency and robustness of the digital twin's state evolution are ensured through idempotent updates and partial overwrite mechanisms, effectively addressing issues such as data delays or missing fields.

[0092] Based on the above scheme, optionally, after driving the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters, the method further includes: in response to a preset interactive operation for the digital twin, generating target response data corresponding to the preset interactive operation based on the digital twin, and displaying the target response data.

[0093] The preset interactive operations can be understood as predefined human-computer interaction actions or instructions used to trigger the digital twin to generate specific responses. These include clicks, drags, parameter inputs, query requests, and simulated operations (such as adjusting the position of a control stick). They serve as the interaction entry point between the user and the digital twin, guiding the user to interact with the virtual model in a clear manner through standardized operation definitions (such as limiting the types of executable actions and parameter ranges), avoiding ineffective operations. The target response data can be understood as customized feedback data generated by the digital twin in response to the preset interactive operations. Its content is directly related to the intent of the interactive operation and is the result output of the interactive operation. It transforms the abstract state within the digital twin (such as nuclide concentration and neutron flux) into a form that the user can understand (such as charts, values, and simulation curves), directly answering the user's questions or verifying hypotheses (such as "What consequences will a certain operation cause?"). It is a key medium connecting the virtual model and the user's decision-making.

[0094] Furthermore, the digital twin construction module pre-sets mapping rules for the digital twin, used to convert input values ​​into attributes of a visual object. For example, the mapping rules include: Rule M1, power mapping rule: Fill the values ​​in the power distribution table into the core plane matrix according to the node coordinates; the value magnitude is mapped to the color block shade, and a short value, such as percentage or normalized value, is displayed in the cell.

[0095] Rule M2, Rod Position Mapping Rule: Write the insertion depth ratio of the control rod position into the vertical attribute of the corresponding component; it is displayed as the length of the vertical bar in the 3D section, and labeled "xx%" next to it.

[0096] Rule M3, temperature gradient mapping rule: maps the interlayer differences in coolant temperature or density to vertical color bands or equivalent color levels; the top, which is warmer, is displayed as a brighter or warmer color, and the bottom, which is cooler, is displayed as a darker or cooler color.

[0097] Optionally, mapping rules for the operating parameters of the digital twin, such as fuel consumption index mapping and reactivity parameter labeling, can also be established. The specific rules depend on the parameter composition of the digital twin and the final effect requirements. This embodiment does not impose strict requirements on this.

[0098] Furthermore, to maintain the stability and consistency of the state of the digital twin, the digital twin construction module 30 includes several efficient and verifiable update rules.

[0099] For example, this includes executing one or more of the following rules: The idempotent update logic means that if the version number of the new frame is not higher than the current version, the frame is ignored to avoid state rollback.

[0100] The partial overwrite rule only updates the fields carried by the new frame; fields not carried retain the previous valid values.

[0101] The batch consistency rule requires that power, temperature, rod position, etc., entered in the same batch should use the same timestamp window to avoid misaligned display caused by "cross-batch splicing".

[0102] Trigger threshold rules: When a specified event marked by an external module is triggered, such as "power step," "significant change in rod position," "temperature cross-range," or "passing the fuel consumption step," the relevant view layer is immediately refreshed. For example, when the fuel consumption step is passed, the next stage curve in the fuel consumption label and the prediction tab is updated simultaneously.

[0103] For untested and outdated rules, if a field is not updated within a set time, the digital twin will display a "Stale" label next to that field, but will not arbitrarily infer a new value; it will be overwritten after an external module provides a new value.

[0104] The above rules ensure that every change to the digital twin is supported by external data and that the changes are traceable.

[0105] Furthermore, in an exemplary embodiment, the fuel consumption model calculation module generates a series of fuel consumption-related frames according to the fuel consumption step. After receiving the prediction data of the fuel consumption-related frames, the digital twin construction module includes causing the digital twin to perform one or more of the following operations: For example, the digital twin construction module creates a predictive version of the digital twin and binds the predictive version to the current construction structure of the digital twin, such as creating a predictive item for the field BU: "Forecast@BU=12.0".

[0106] Then, a prediction view portion of the digital twin is established based on the predicted state version; in an exemplary embodiment, the actual given curves and surfaces are directly drawn according to the coordinate system in a real-time digital twin display view; at the same time, if the prediction data portion provides an uncertainty range or upper and lower bounds of the data, it is displayed as a semi-transparent band.

[0107] For example, the digital twin system allows users to switch between the current version and the predicted version of the digital twin via a button; the switch only changes the displayed data source, but does not change the underlying data calculation.

[0108] For example, when the system determines that a new burn-out step has been passed as the core burns out, the digital twin construction module 30 includes converting the corresponding predicted version of the digital twin to the current version and automatically loading the next prediction package to achieve the rolling forward of the prediction version.

[0109] Ultimately, the overall performance of the digital twin is to gradually receive and display the calculated results of each future fuel consumption step.

[0110] This technical solution allows users to query or intervene in the core status in real time through preset interactive operations, improving the operability and decision support capabilities of the digital twin, and enhancing human-machine collaboration efficiency and operational transparency.

[0111] The technical solution of this invention, by collecting key state parameters during the reactor core reaction process, including at least one of core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup, enables real-time, accurate perception and dynamic tracking of the reactor core reaction state. It determines the fuel burnup step, and based on the fuel burnup step and burnup model, models the evolution process of fissile nuclides and fission products within the reactor core to obtain reactivity parameters related to the fuel burnup. The burnup model describes the nuclide transmutation and consumption logic of the fuel during core operation, accurately predicting burnup-related reactivity changes. It constructs a digital twin corresponding to the reactor core's reactivity state, and drives the digital twin to map the reactor core's reactivity state based on the reactivity parameters and key state parameters, achieving high-fidelity, dynamic digital mapping of the reactor core's reactivity state. This solves the problem of real-time, high-fidelity dynamic mapping of the reactor core's reactivity state in related technologies, enabling high-precision, real-time, full-lifecycle dynamic digital mapping and intelligent detection of the reactor core's reactivity state.

[0112] Example 2 Figure 2 This is a schematic diagram of a device for determining the core reactivity state according to Embodiment 2 of the present invention. Figure 2 As shown, the device includes: a key state parameter acquisition module 210, a reactivity parameter determination module 220, and a reactivity state mapping module 230. Among them, The key state parameter acquisition module 210 is used to acquire key state parameters during the reactor core reaction process. The key state parameters include at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup rate. The reactivity parameter determination module 220 is used to determine the fuel burnup step and model the evolution process of fissile nuclides and fission products in the reactor core based on the fuel burnup step and burnup model to obtain reactivity parameters related to the fuel burnup rate. The burnup model is used to describe the nuclide transmutation and consumption logic of the fuel during reactor core operation. The reactivity state mapping module 230 is used to construct a digital twin corresponding to the reactivity state of the reactor core and drive the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters.

[0113] The technical solution of this invention collects key state parameters during the reactor core reaction process through a key state parameter acquisition module. These key state parameters include at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup rate. This enables real-time, accurate perception and dynamic tracking of the reactor core reaction state. A reactivity parameter determination module determines the fuel burnup step. Based on the fuel burnup step and burnup model, the evolution process of fissile nuclides and fission products within the reactor core is modeled to obtain reactivity parameters related to the fuel burnup rate. This model is used to describe the nuclide transmutation and consumption logic of the fuel during core operation, and can accurately predict burnup-related reactivity changes. A digital twin corresponding to the reactivity state of the core is constructed through a reactivity state mapping module. Based on the reactivity parameters and key state parameters, the digital twin is driven to map the reactivity state of the core, achieving high-fidelity, dynamic digital mapping of the core reactivity state. This solves the problem of real-time, high-fidelity dynamic mapping of core reactivity state in related technologies, enabling high-precision, real-time, full-lifecycle dynamic digital mapping and intelligent detection of the core reactivity state.

[0114] Based on the above scheme, optionally, the reactivity parameter determination module includes a reactivity parameter determination submodule. This reactivity parameter determination submodule is used to, for each burnup step, obtain the fuel burnup amount and nuclide composition vector at a first time point corresponding to that burnup step; integrate the evolution relationship of the nuclide composition vector with the burnup amount based on the burnup model to obtain the nuclide composition vector at a second time point corresponding to the next burnup step; determine the macroscopic cross-sectional data at the second time point based on the burnup amount, thermal parameters, and a pre-established minority group constant library; and determine the reactivity parameters related to the burnup amount based on the macroscopic cross-sectional data. The minority group constant library is used to store the corresponding burnup amount, thermal parameters, and cross-sectional data of the fuel during the core reaction process.

[0115] Based on the above scheme, optionally, the fuel consumption model includes a matrix form model, which is as follows: , Where, matrix vector For containing multiple nuclides Vector and matrix of nuclide number density values ​​at time t. It is a coefficient matrix consisting of the nuclide decay constant and the neutron reaction cross section.

[0116] Based on the above scheme, optionally, the fuel consumption model includes a formulaic model, which is as follows: , in, Indicates nuclide atomic number density, Indicates the parent nuclide atomic number density; Indicates nuclide The decay constant; Indicates nuclide The absorption cross section; This represents the neutron flux in the reactor core. Indicates the parent nuclide Transformed into nuclides Conversion rate; Indicates the parent nuclide Nuclide generated through neutron reaction The reaction cross section.

[0117] Based on the above scheme, optionally, the reactivity parameter determination module includes a fuel consumption step determination submodule. The fuel consumption step determination submodule is used for at least one of the following: determining a preset fuel consumption step as the fuel consumption step; determining the fuel consumption step based on the actual fuel consumption and a preset fuel consumption threshold; determining the fuel consumption step based on a fuel consumption scale, wherein the fuel consumption scale is used to mark the fuel consumption.

[0118] Based on the above scheme, optionally, the reactive state mapping module includes a digital twin determination submodule. The digital twin determination submodule is used to acquire the design drawings and operating parameters of the actual physical reactor core, determine the component distribution, node division, and axial hierarchy division standards of the reactor core, construct a virtual mesh structure of the actual physical reactor core in virtual space according to a preset mapping ratio; acquire the key state parameters and reactive parameters of the actual physical reactor core, and associate the key state parameters and reactive parameters with the corresponding nodes, components, and hierarchy of the virtual mesh to obtain a digital twin.

[0119] Optionally, based on the above scheme, the device for determining the core reactivity state includes a digital twin update module. The digital twin update module is used to update the digital twin based on idempotent update logic and partial coverage rules after the digital twin is mapped to the core reactivity state based on the reactivity parameters and the key state parameters, in response to the burnup exceeding a preset burnup amount corresponding to the burnup step. The idempotent update logic includes ignoring data when the version number of newly received data is not higher than the current version, and the partial coverage rule includes updating only the fields carried by the new data, while fields not carried retain the previous valid values.

[0120] Optionally, based on the above scheme, the device for determining the core reactivity state includes a target response data generation module. The target response data generation module is used to, after the digital twin is driven to map the core reactivity state based on the reactivity parameters and the key state parameters, generate target response data corresponding to a preset interactive operation based on the digital twin in response to a preset interactive operation for the digital twin, and display the target response data.

[0121] Based on the above scheme, optionally, the reactivity parameters include at least one of the following: effective multiplication factor, core reactivity, critical boron concentration curve, control rod value, and temperature feedback coefficient; wherein, the effective multiplication factor is used to characterize the core neutron multiplication capability, the core reactivity is used to characterize the degree to which the core deviates from criticality, and the critical boron concentration curve is used to characterize the critical boron concentration value that varies with burnup.

[0122] The device for determining the core reactivity state provided in this embodiment of the invention can execute the method for determining the core reactivity state provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0123] Example 3 Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0124] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0125] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0126] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for determining the reactivity state of a reactor core.

[0127] In some embodiments, a method for determining a core reactivity state may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining a core reactivity state described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a method for determining a core reactivity state by any other suitable means (e.g., by means of firmware).

[0128] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0129] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0130] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0131] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0132] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0133] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0134] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0135] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0136] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the core reactivity state, characterized in that, include: Key state parameters during the reactor core reaction process are collected, including at least one of the following: core power distribution parameters, coolant temperature, coolant density, control rod position, boric acid concentration, and burnup rate. The burnup step of the fuel is determined, and the evolution process of fissile nuclides and fission products in the reactor core is modeled based on the burnup step and the burnup model of the fuel to obtain reactivity parameters related to the burnup amount of the fuel. The burnup model is used to describe the transmutation and consumption logic of nuclides in the fuel during reactor core operation. A digital twin corresponding to the reactivity state of the reactor core is constructed, and the digital twin is driven to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters.

2. The method for determining the core reactivity state according to claim 1, characterized in that, The burnup step based on the fuel models the evolution of fissile nuclides and fission products within the reactor core to obtain reactivity parameters related to burnup, including: For each of the fuel consumption steps, the fuel consumption amount and nuclide composition vector at the first time corresponding to the fuel consumption step are obtained. Based on the fuel consumption model, the evolution relationship of the nuclide composition vector with the fuel consumption amount is integrated to obtain the nuclide composition vector at the second time corresponding to the next fuel consumption step. Based on the burnup, thermal parameters, and a pre-established minority group constant library at the second time point, the macroscopic cross-sectional data at the second time point are determined. Based on the macroscopic cross-sectional data, the reactivity parameters related to the burnup are determined. The minority group constant library is used to store the corresponding burnup, thermal parameters, and cross-sectional data of the fuel during the reactor core reaction process.

3. The method for determining the core reactivity state according to claim 1, characterized in that, The fuel consumption model includes a matrix form model, which is as follows: ， Where, matrix vector For containing multiple nuclides Vector and matrix of nuclide number density values ​​at time t. It is a coefficient matrix consisting of the nuclide decay constant and the neutron reaction cross section.

4. The method for determining the core reactivity state according to claim 1, characterized in that, The fuel consumption model includes a formulaic model, which is as follows: ， in, Indicates nuclide atomic number density, Indicates the parent nuclide atomic number density; Indicates nuclide The decay constant; Indicates nuclide The absorption cross section; This represents the neutron flux in the reactor core. Indicates the parent nuclide Transformed into nuclides Conversion rate; Indicates the parent nuclide Nuclide generated through neutron reaction The reaction cross section.

5. The method for determining the core reactivity state according to claim 1, characterized in that, The step of determining fuel consumption includes at least one of the following: The preset fuel consumption step is determined as the fuel consumption step; The fuel consumption step is determined based on the actual fuel consumption and the preset fuel consumption threshold. The fuel consumption step is determined according to a fuel consumption scale, which is used to mark the amount of fuel consumed.

6. The method for determining the core reactivity state according to claim 1, characterized in that, The construction of a digital twin corresponding to the reactivity state of the reactor core includes: Obtain the design drawings and operating parameters of the actual physical core, determine the component distribution, node division and axial hierarchy division standards of the core, and construct the virtual mesh structure of the actual physical core in the virtual space according to the preset mapping ratio; The key state parameters and reactivity parameters of the actual physical core are obtained, and the key state parameters and reactivity parameters are associated with the corresponding nodes, components and levels of the virtual mesh to obtain a digital twin.

7. The method for determining the core reactivity state according to claim 1, characterized in that, After driving the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters, the method further includes: When the fuel consumption exceeds the preset fuel consumption corresponding to the fuel consumption step, the digital twin is updated based on idempotent update logic and partial coverage rules. The idempotent update logic includes ignoring the data when the version number of the newly received data is not higher than the current version. The partial coverage rules include updating only the fields carried by the new data and using the previous valid value for fields not carried.

8. The method for determining the core reactivity state according to claim 1, characterized in that, After driving the digital twin to map the reactivity state of the reactor core based on the reactivity parameters and the key state parameters, the method further includes: In response to a preset interactive operation on the digital twin, target response data corresponding to the preset interactive operation is generated based on the digital twin, and the target response data is displayed.

9. The method for determining the core reactivity state according to claim 1, characterized in that, The reactivity parameters include at least one of the following: effective multiplication factor, core reactivity, critical boron concentration curve, control rod value, and temperature feedback coefficient; wherein, the effective multiplication factor is used to characterize the core neutron multiplication capability, the core reactivity is used to characterize the degree to which the core deviates from criticality, and the critical boron concentration curve is used to characterize the critical boron concentration value as a function of burnup.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for determining the core reactivity state as described in any one of claims 1-9.

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