A simulation modeling method and device for power coordination control of a multi-module high-temperature gas-cooled reactor

Abstract

The application discloses a kind of multi-module high temperature gas cooled reactor power coordination control simulation modeling method and device, belong to high temperature reactor control simulation modeling field.Through based on neutron dynamics, mass / energy / conservation of momentum law and key equipment dynamic law, hierarchical modular model library is constructed;Using modular drag and assembly method, multi-module system dynamic simulation model is quickly generated;Utilize Simulink to build coordination control system model, and with process system dynamic coupling to form integrated simulation platform;Based on the platform, parameterized modeling is carried out, and node division and parameter adjustment of different reactor type configuration are realized through topology editing;In typical working condition, coordination control strategy is verified, and data synchronization interface is developed to real-time show the trend of key parameter change.The application realizes the efficient, flexible simulation and verification of multi-module high temperature gas cooled reactor coordination control.

Description

Technical Field

[0001] This invention relates to the field of high-temperature reactor control simulation modeling, and in particular to a method, apparatus, equipment and storage medium for simulation modeling of power coordination control of a multi-module high-temperature gas-cooled reactor. Background Technology

[0002] High-temperature gas-cooled reactors (HTGRs), as advanced reactor types possessing the main characteristics of fourth-generation nuclear energy systems, employ chemically stable inert gas helium as the primary coolant. Their working principle involves continuously carrying away the heat generated by nuclear fission in the reactor core through helium circulation, transferring it to the steam generator to heat the feedwater on the secondary loop, producing high-temperature, high-pressure steam. This steam can be directly used to drive turbine generator sets for efficient power generation, or to provide high-quality process heat for industries such as petrochemicals and coal-to-hydrogen. In this energy conversion and transfer chain, the control of the primary loop helium flow rate is central, representing a complex, multi-variable, and dynamically demanding control problem.

[0003] Specifically, the helium flow rate driven by the main helium blower directly and rapidly affects the reactor's neutron power level and the helium temperature distribution at the core inlet and outlet, making it a key variable for reactor power regulation. Simultaneously, it, along with the secondary loop feedwater flow rate, determines the heat exchange conditions within the steam generator. Their ratio (helium-to-water flow rate ratio) is a crucial safety parameter for the reactor protection system, directly impacting the core residual heat removal capacity and the system safety boundary. Therefore, achieving precise, stable, and automatic control of the primary loop helium flow rate is a necessary technological foundation for ensuring the safe and economical operation of the high-temperature gas-cooled reactor and fully leveraging its flexible operation and multi-purpose power supply advantages.

[0004] However, existing control strategies and system designs face significant challenges. First, the reactor power, temperature, and flow rate exhibit strong coupling and nonlinear dynamic relationships, making it difficult for traditional single-variable or simple decoupling control methods to achieve ideal results across the entire operating range. Second, the control process must respond rapidly to changes in power demand while strictly ensuring that safety parameters such as the helium-water flow ratio remain within safe ranges, placing extremely high demands on the robustness and coordination of the control system. Furthermore, power coordination and flow distribution during parallel operation of multiple modular reactors further increase the complexity of system control. Currently, there is a lack of efficient automatic control configuration schemes that can systematically balance dynamic performance, safety constraints, and engineering applicability for this critical aspect, which has become one of the bottlenecks restricting the improvement of automation levels and optimized operation of this advanced reactor type. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] To address this, this invention proposes a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor. By constructing a hierarchical modular model library and dragging and dropping to assemble the system dynamic simulation model, the process and control systems are coupled to form an integrated simulation platform. Parametric modeling and topology editing are used to adapt to different reactor configurations, ultimately enabling the verification of the coordination control strategy and the real-time display of key parameters.

[0007] Another objective of this invention is to provide a simulation modeling device for power coordination control of a multi-module high-temperature gas-cooled reactor.

[0008] The third objective of this invention is to provide a computer device.

[0009] The fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, this invention proposes a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor, comprising:

[0011] S1, based on neutron dynamics, the law of conservation of mass / energy / momentum and the dynamic evolution law of key equipment, constructs a hierarchical modular model library architecture that includes a basic model library, a device model library and system-level modules; S2 allows for the rapid generation of dynamic simulation models of multi-module high-temperature gas-cooled reactor systems by dragging and assembling modules from the basic model library and the equipment model library. S3 utilizes Simulink's basic computational modules to construct a coordinated control system model, and dynamically couples the process system model with the control system model to form a multi-module coordinated control simulation platform. S4 uses a multi-module coordinated control simulation platform for parametric modeling, and topology editing is used to divide model nodes and adjust parameters for different stack configurations. S5 verifies the coordinated control strategy under typical operating conditions and develops a secondary interface for data synchronization to display key parameters and their corresponding trends in real time.

[0012] The simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention may also have the following additional technical features: In one embodiment of the present invention, the hierarchical modular model library architecture, which includes a basic model library, a device model library, and system-level modules, is constructed based on neutron dynamics, the law of conservation of mass / energy / momentum, and the dynamic evolution law of key equipment. S11, the basic model library includes neutron dynamics models, thermal component models, three-equation fluid models, and heat-work conversion models; S12, the equipment model library includes core models, steam generator models, pump models, control valve models, and fan models.

[0013] In one embodiment of the present invention, the step of constructing a coordinated control system model using Simulink basic computing modules and dynamically coupling the process system model with the control system model to form a multi-module coordinated control simulation platform includes: S31, the coordinated control system model includes a PID controller module and a fuzzy control module; S32, dynamic coupling enables real-time data interaction between models through Simulink's S-Function interface.

[0014] In one embodiment of the present invention, the step of verifying the coordinated control strategy under typical operating conditions and developing a data synchronization secondary interface to display key parameters and corresponding changing trends in real time includes: S51, the data synchronization secondary interface was developed using MATLAB's APP Designer, and includes a process flow interface and a dynamic curve interface. S52, the real-time synchronization function is based on the MATLAB timer module to periodically read and write simulation data.

[0015] In one embodiment of the present invention, it further includes: S6, based on a multi-module coordinated control simulation platform, develops a model parameter adaptive adjustment module to dynamically correct the control gain parameters in the control system model according to real-time simulation results, so as to optimize the power distribution balance among multiple modules.

[0016] To achieve the above objectives, another aspect of the present invention proposes a simulation modeling device for power coordination control of a multi-module high-temperature gas-cooled reactor, comprising: The hierarchical modular model library construction module is used to build a hierarchical modular model library architecture that includes a basic model library, a device model library, and system-level modules, based on neutron dynamics, the law of conservation of mass / energy / momentum, and the dynamic evolution law of key equipment. The module assembly generation module is used to quickly generate dynamic simulation models of multi-module high-temperature gas-cooled reactor systems by dragging and assembling modules from the basic model library and the equipment model library. The Simulink control system model building module is used to build a coordinated control system model using the basic Simulink computing modules, and dynamically couple the process system model with the control system model to form a multi-module coordinated control simulation platform. The parametric modeling and topology editing module is used for parametric modeling based on a multi-module coordinated control simulation platform. It enables the division of model nodes and adjustment of parameters for different stack configurations through topology editing. The coordinated control strategy verification and interface development module is used to verify the coordinated control strategy under typical operating conditions and develop a secondary interface for data synchronization to display key parameters and their corresponding changing trends in real time.

[0017] In one embodiment of the present invention, it further includes: The model parameter adaptive adjustment module is used to develop a model parameter adaptive adjustment module based on a multi-module coordinated control simulation platform. It dynamically corrects the control gain parameters in the control system model according to the real-time simulation results in order to optimize the power distribution balance among multiple modules.

[0018] This invention discloses a simulation modeling method and apparatus for power coordination control of a multi-module high-temperature gas-cooled reactor, effectively overcoming the limitations of existing technologies, such as complex modeling processes, poor system flexibility, and difficulty in balancing real physical dynamics with control strategy verification. By constructing a hierarchical and modular unified model library, it achieves efficient and flexible construction and integration from basic principles to system-level simulation, and supports rapid adaptation and parameter adjustment for different reactor configurations. This significantly improves the convenience and accuracy of simulation modeling, as well as the verification capability of multi-module coordinated control strategies, providing an efficient and reliable digital twin platform for the design optimization and safe operation analysis of high-temperature gas-cooled reactor control systems.

[0019] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor as described in the first aspect embodiment.

[0020] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor as described in the first aspect embodiment.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention; Figure 2 This is a model architecture diagram of a multi-module high-temperature gas-cooled reactor power coordination control simulation modeling system according to an embodiment of the present invention; Figure 3This is a schematic diagram of the primary loop system model node division of a multi-module high-temperature gas-cooled reactor power coordination control simulation modeling system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the node division of the second loop system model of a multi-module high-temperature gas-cooled reactor power coordination control simulation modeling system according to an embodiment of the present invention; Figure 5 This is a comparison chart of the efficiency of high-temperature electrolysis hydrogen production and iodine-sulfur cycle hydrogen production in an application scenario of a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention. Figure 6 This is a diagram showing the relevant industry thermal demand temperature range for an application scenario of a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention. Figure 7 This is a schematic diagram of a multi-module high-temperature gas-cooled reactor power coordination control simulation modeling device according to an embodiment of the present invention; Figure 8 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] 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.

[0025] The following description, with reference to the accompanying drawings, describes a simulation modeling method, apparatus, equipment, and storage medium for power coordination control of a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention.

[0026] The core idea of ​​this invention is to construct a hierarchical and modular model library, from fundamental physical models to system-level architectures, by deeply integrating nuclear physics, thermal hydraulics, and control theory. Based on this architecture, users can quickly generate dynamic simulation models of multi-module high-temperature gas-cooled reactor process systems through intuitive drag-and-drop assembly. Furthermore, a precisely dynamically coupled coordinated control system model can be built using the mature Simulink platform, forming a unified simulation platform integrating process simulation and control verification. This platform supports parametric modeling and topology editing, flexibly adapting to node partitioning and parameter settings for different reactor configurations. This allows for thorough verification of the designed coordinated control strategy under typical operating conditions. A secondary interface for data synchronization enables real-time visualization monitoring and trend analysis of key operating parameters. This overall solution transforms the traditionally discrete and rigid modeling and verification process into a highly integrated, flexibly configurable, and iteratively optimized digital simulation closed loop, significantly improving the efficiency, realism, and engineering applicability of multi-module high-temperature gas-cooled reactor control system design and verification.

[0027] Example 1 To achieve the above invention, embodiments of the present invention provide a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor, such as... Figure 1 As shown, it includes: S1, based on neutron dynamics, the law of conservation of mass / energy / momentum, and the dynamic evolution of key equipment, constructs a hierarchical modular model library architecture that includes a basic model library, an equipment model library, and system-level modules.

[0028] Specifically, the technical implementation of this step is based on neutron dynamics, the law of conservation of mass / energy / momentum, and the dynamic evolution of key equipment, thereby achieving flexibility and high fidelity in system-level modeling.

[0029] Specifically, the model library is divided into three levels: a basic model library, a device model library, and system-level modules. The basic model library contains models describing the dynamic behavior of neutrons in the reactor core, as well as models of thermal components, three-equation fluid dynamics, and thermo-work conversion processes. These models are based on conservation laws, such as the mass conservation equation. Energy conservation equation and the momentum conservation equation It is used to accurately describe physical processes such as fluid flow, heat transfer, and neutron diffusion within the reactor. In addition, the basic model library also integrates dynamic response models of actuators such as pumps, regulating valves, and fans to support the simulation and verification of control strategies.

[0030] Furthermore, all modules in the model library support parameterized configuration, such as the core neutron diffusion coefficient. Heat capacity of thermal components Fluid density ,speed ,pressure Key parameters can be adjusted according to the reactor design. The reactor core and steam generator models in the equipment model library are realized by combining basic models, and their dynamic response time constant, heat exchange efficiency, flow resistance coefficient and other parameters must meet the requirements of IEC 61284 or ASME standards for the accuracy of nuclear power system modeling.

[0031] Furthermore, this model library can be widely applied to the design and verification of control systems for multi-module high-temperature gas-cooled reactors, especially under typical operating conditions such as step load changes and linear load changes. It supports dynamic simulation of the coordination within NSSS modules, the coordination between modules, and the coordination between the unit and the grid. Through Simulink's graphical modeling tools, users can drag and connect modules from the model library to quickly build a dynamic simulation model of the entire plant, significantly improving modeling efficiency and model reusability.

[0032] Specifically, this hierarchical modular model library architecture achieves unified and integrated multi-physics and multi-scale modeling, supporting rapid modeling and simulation of different reactor configurations. By layering and encapsulating neutron dynamics, thermal processes, and control logic, the maintainability and scalability of the model are improved, providing a high-fidelity, high-real-time simulation platform for power coordination control strategies of high-temperature gas-cooled reactors, demonstrating significant engineering practical value and innovation.

[0033] Furthermore, S1 includes: S11, the basic model library includes neutron dynamics models, thermal component models, three-equation fluid models, and heat-work conversion models.

[0034] Specifically, through a modular and parameterized design concept, this model library achieves high-fidelity modeling of key physical processes in high-temperature gas-cooled reactor systems, providing a foundation for the construction of subsequent equipment model libraries and system-level modules.

[0035] Specifically, the neutron dynamics model describes the change of neutron density in the reactor core over time. Its modeling basis includes neutron diffusion equations, point-pile models, or spatial distribution models, typically employing six sets of delayed neutron models for dynamic response analysis. The thermal component model, based on the fundamental equations of heat conduction and convection, describes the temperature distribution and thermal response characteristics of core components (such as fuel elements and moderators). Parameters such as heat capacity, thermal conductivity, and density need to be precisely set according to material properties. The three-equation fluid model simulates the flow behavior of helium in the reactor core and primary coolant loop, including mass, momentum, and energy conservation equations. It is suitable for non-equilibrium phase change processes and can accurately reflect the pressure, temperature, and flow rate changes of helium under different operating conditions. The heat-work conversion model, based on the first and second laws of thermodynamics, describes the energy transfer process between high-temperature helium and the secondary coolant water, typically involving key parameters such as heat transfer coefficient, heat transfer area, and working fluid properties.

[0036] Furthermore, the neutron lifetime needs to be defined in the neutron dynamics model. Delayed neutron share , rate of change of reactivity The heat capacity needs to be defined in the thermal component model. thermal conductivity ,density In a three-equation fluid model, the gas density needs to be set. Specific heat capacity Viscosity ,pressure In the heat-to-work conversion model, heat exchange efficiency must be considered. Heat transfer temperature difference ,flow Key parameters, etc. All models must meet the accuracy and stability requirements for nuclear power system modeling as specified in IEC 61284 or ASME standards.

[0037] Specifically, this basic model library can be used to build simulation models of key equipment such as reactor cores and steam generators, and can also serve as the underlying support module for dynamic control systems in the Simulink environment. Through the parametric design of the model library, users can quickly adjust reactor configuration, material properties, or operating conditions to achieve flexible modeling and simulation of different modular reactor types.

[0038] Specifically, by constructing a basic model library covering multiple physical processes, the modularity, parameterization, and scalability of the simulation model of the high-temperature gas-cooled reactor system were realized. This provides a high-precision and high-efficiency modeling tool for the development and verification of multi-module coordinated control strategies, significantly improving the flexibility and engineering practicality of simulation modeling.

[0039] S12, the equipment model library includes core models, steam generator models, pump models, control valve models, and fan models.

[0040] Specifically, this step aims to achieve flexibility and high fidelity in system-level modeling by abstracting key equipment in the high-temperature gas-cooled reactor system into independent simulation modules.

[0041] Furthermore, the construction of the equipment model library relies on existing neutron dynamics, thermal component, three-equation fluid, and heat-work conversion models in the basic model library. For example, the reactor core model, through the coupling of the neutron dynamics equation and the thermal model, simulates the release of fission energy and the heat exchange process of the helium coolant; its heat balance equation can be expressed as: ; in, This represents the core fission heat power. , The first The density and specific heat capacity of each fuel assembly, Its temperature, For the first Mass flow rate of each coolant channel For specific heat capacity at constant pressure, , These are the inlet and outlet temperatures of the coolant, respectively. The steam generator model, based on the mass and energy conservation equations, simulates the heat exchange process between helium and water, and its heat transfer efficiency can be calculated using the Nusselt number. With Reynolds number The relationship is modeled parametrically.

[0042] Furthermore, all equipment models support parametric configuration; for example, the pump model allows setting the flow rate. Yangcheng ,efficiency Key parameters such as valve opening degree can be set in the control valve model. Flow coefficient Pressure reduction For example, the fan model needs to define the air volume. Wind pressure ,power These are the operating parameters. These parameters can be dynamically adjusted according to the actual equipment model and operating conditions to meet the modeling needs of different reactor types and operating scenarios.

[0043] Specifically, this equipment model library is widely used in the design and dynamic simulation verification of control systems for high-temperature gas-cooled reactors. For example, in the primary loop system modeling, the core and steam generator models are coupled through helium flow and temperature signals to simulate the impact of core power changes on secondary loop steam parameters. In control system simulation, regulating valve and blower models can act as actuators to participate in load regulation, achieving closed-loop control of key variables such as main steam pressure and temperature.

[0044] Specifically, by modularly encapsulating key equipment models, modeling efficiency and model reusability are significantly improved, while ensuring simulation accuracy and the realism of dynamic response. Its innovation lies in decoupling the physical processes and control logic of complex equipment, enabling parameterization and configurability of the model, and providing a solid foundation for verifying power coordination control strategies for multi-module high-temperature gas-cooled reactors.

[0045] S2 allows for the rapid generation of dynamic simulation models of multi-module high-temperature gas-cooled reactor systems by dragging and assembling modules from the basic model library and the equipment model library.

[0046] Specifically, this step, based on the Simulink platform and incorporating multiphysics modeling concepts, encapsulates key processes such as neutron dynamics, thermal-hydraulic processes, and energy transfer involved in the system into reusable modular units, thereby achieving efficient modeling and simulation of complex systems.

[0047] Furthermore, the basic model library includes neutron dynamics models, thermal component models, three-equation fluid models, and heat-work conversion models. These models are all built upon the laws of conservation of mass, energy, and momentum, featuring clear physical mechanisms and accurate mathematical descriptions. The equipment model library is composed of multiple basic models. For example, the reactor core model is constructed by coupling the neutron dynamics model and the thermal component model, while the steam generator model combines the fluid model and the heat exchange model. Users can drag and drop these modules from the model library to the workspace through Simulink's graphical interface and connect them via signal lines to achieve rapid assembly of system-level models.

[0048] Furthermore, each module supports parameterized configuration; for example, the neutron diffusion coefficient can be set in the core model. Absorption cross section Fission cross section Key parameters; flow velocity can be set in the fluid model. ,pressure ,temperature ,density Variables such as these must be considered. The connections between modules must meet the thermal-hydraulic boundary conditions and control logic requirements. For example, in the helium flow control loop, the regulating valve model needs to be dynamically coupled with the pump model and the core model to ensure the stability and response characteristics of the system under different operating conditions.

[0049] Specifically, this step is widely used in the design and verification of control systems for multi-module high-temperature gas-cooled reactors. For example, under step load or linear load conditions, users can quickly build a plant-wide dynamic simulation model containing multiple NSSS modules to verify key performance indicators such as power coordination between modules, system response time, and control loop stability. Furthermore, this method can also be used for modeling different reactor configurations, such as single-module, dual-module, or triple-module reactors, enabling flexible system simulation by adjusting the number of modules and their connection methods.

[0050] Specifically, through modular design, users can complete the modeling and simulation of complex systems without repeatedly writing the underlying physical equations, shortening the modeling cycle and reducing development costs. At the same time, the parameterized interfaces between modules support flexible configuration, enhancing the adaptability and scalability of the model, and providing an efficient and high-fidelity simulation platform for the optimization and verification of power coordination control strategies for high-temperature gas-cooled reactors.

[0051] S3 utilizes Simulink's basic computational modules to construct a coordinated control system model, and dynamically couples the process system model with the control system model to form a multi-module coordinated control simulation platform.

[0052] Specifically, the core of this step lies in using modular modeling to dynamically couple the control logic with the process system model, thereby forming a multi-module coordinated control simulation platform with high fidelity and real-time response capabilities.

[0053] Specifically, the construction of the control system model must comply with nuclear power control system standards and specifications such as IEC 60880 and IEEE 399 to ensure the engineering applicability and safety of the control strategy. The module parameters in Simulink need to be precisely configured according to the actual control system design, such as the parameters of the PID controller. , , Tuning is required based on the system's dynamic response characteristics to achieve closed-loop control of key variables such as helium flow rate, core power, and steam pressure. Simultaneously, custom control algorithms, such as coordinated control strategies based on fuzzy logic or model predictive control (MPC), can be embedded through S-Function or MATLAB Function modules to improve the system's response accuracy and stability under varying load conditions.

[0054] Furthermore, the control system model and the process system model interact with each other through Simulink's signal connection mechanism. For example, the helium flow rate setpoint output by the control system... As an input signal, the regulating valve module in the primary loop fluid model is transmitted to the regulating valve. The regulating valve adjusts its opening degree according to the set value, thereby affecting the core's thermal-hydraulic characteristics and power output. Simultaneously, the core power... The feedback signal is returned to the control system to adjust the control strategy in real time, achieving closed-loop control. This coupling process needs to meet the sampling period. With simulation step size The matching requirements are usually set. This is to ensure the synchronization of control signals with the simulation process.

[0055] Specifically, this simulation platform can be used to verify the coordinated control strategy of high-temperature gas-cooled reactors under typical operating conditions such as step load changes and linear load changes. It supports the analysis of dynamic coordination within and between NSSS modules, as well as between the unit and the grid. This platform can significantly improve the efficiency and accuracy of control system design, providing strong support for the engineering commissioning and operational optimization of high-temperature gas-cooled reactors.

[0056] Furthermore, S3 includes: S31, the coordinated control system model includes a PID controller module and a fuzzy control module.

[0057] Specifically, in some implementations, the control system model is built using the Simulink platform, combining classical control theory with intelligent control strategies to form a composite control structure to cope with the complex dynamic characteristics of high-temperature gas-cooled reactors under different operating conditions.

[0058] Furthermore, the PID controller module is based on a proportional-integral-derivative control algorithm, which adjusts the control output... To maintain system settings Compared with actual output To minimize the deviation between them, the control law can be expressed as: ; in, For error signals, , , These are the proportional, integral, and derivative gain coefficients, respectively. PID controllers are suitable for linear, time-invariant systems, capable of rapidly responding to changes in setpoints, and perform exceptionally well in steady-state regulation.

[0059] Furthermore, the fuzzy control module, based on fuzzy logic theory, achieves control of nonlinear and uncertain systems through three stages: fuzzification, fuzzy inference, and defuzzification. In this invention, the input to the fuzzy controller is typically the power deviation. and rate of change of deviation The output is a control signal. Its control rules are constructed from expert experience or historical data, and have strong robustness and adaptability.

[0060] Specifically, the gain parameters of the PID controller need to be tuned according to the dynamic response characteristics of the system, and are usually optimized using the Ziegler-Nichols method or a model identification method based on step response. The fuzzy set partitioning of the input and output variables of the fuzzy controller, the shape of the membership function (such as triangular, trapezoidal, or Gaussian), and the construction of the control rule base all need to be designed in conjunction with the thermal-hydraulic characteristics of the high-temperature gas-cooled reactor to ensure control accuracy and response speed.

[0061] Specifically, this step is mainly used in practical applications for the coordinated power control of the primary and secondary loop systems, especially under typical operating conditions such as step load changes and linear load changes, to achieve dynamic adjustment of key variables such as core power, helium flow rate, and steam parameters. By integrating PID and fuzzy control modules into the control system model, the stability and response performance of the system under different operating conditions can be effectively improved, providing high-fidelity and modular technical support for the simulation verification and control strategy optimization of high-temperature gas-cooled reactors.

[0062] S32, dynamic coupling enables real-time data interaction between models through Simulink's S-Function interface.

[0063] Specifically, S-Function (System Function) is a user-defined module interface provided by Simulink, which allows developers to write low-level algorithms in C, C++, or MATLAB to achieve precise control and data interaction of complex dynamic behaviors in simulation models.

[0064] Specifically, S-Function encapsulates and invokes the dynamic behavior within the model by defining interface functions such as module input / output ports, state variables, initialization functions, output functions, and update functions. In this invention, S-Function is used to connect simulation models of different modules (such as reactor core, steam generator, and turbine) to achieve real-time data exchange between the primary and secondary loop systems. Specifically, during Simulink simulation, the S-Function module periodically calls its output function to transmit the current control signals (such as helium flow rate, temperature, and pressure) to the target module and receive its feedback dynamic response data, thereby achieving closed-loop control and multi-module collaborative simulation.

[0065] Furthermore, the sampling time of the S-Function is set to... To ensure the real-time performance of the control signals and the accuracy of the simulation, the dimensions of the input and output ports must be consistent with the interfaces of the connected modules. For example, the helium flow control signal is a scalar input, while the core temperature feedback signal is a vector output, the length of which depends on the number of core partitions. Furthermore, the state variables of the S-Function need to be appropriately configured according to the dynamic characteristics of the model, such as the rate of change of core neutron flux and the hysteresis term of the steam generator outlet temperature, to support high-fidelity dynamic simulation.

[0066] Furthermore, this dynamic coupling mechanism is widely applied to the simulation verification of multi-module high-temperature gas-cooled reactor systems under different operating conditions, including typical conditions such as step load changes and linear load changes. Through the S-Function interface, the control system model can respond to the output of the core physical model in real time, realizing closed-loop regulation of key parameters such as helium flow rate and steam pressure, thereby verifying the effectiveness and robustness of the coordinated control strategy.

[0067] Specifically, by using S-Function to achieve real-time data interaction between modules, the dynamic response accuracy of the simulation model is improved, and the modularity and parameterization capabilities of the system are enhanced, providing an efficient, flexible and high-fidelity simulation platform for verifying the coordinated control strategy of multi-module high-temperature gas-cooled reactors.

[0068] S4 uses a multi-module coordinated control simulation platform for parametric modeling, and achieves the division of model nodes and adjustment of parameters for different stack configurations through topology editing.

[0069] Specifically, this step relies on the existing multi-scale mechanism / simulation model library, including a basic model library, an equipment model library, and system-level modules. By combining parametric modeling with topology editing, it enables flexible configuration and high-fidelity simulation of different stack structures.

[0070] Specifically, parametric modeling primarily involves defining the physical parameters, boundary conditions, and control logic of each node in the model to achieve configurable modeling of key equipment such as the reactor core, steam generator, and primary and secondary loop systems. For example, in the reactor core model, parameters such as neutron flux distribution, fuel temperature coefficient, and coolant inlet and outlet temperatures can be set. and Helium flow rate Key parameters such as module number, connection method, and control strategy are specified. Simulink's modular modeling mechanism allows users to drag and drop sub-modules from the base model library through a graphical interface to form a complete system model. Topology editing allows users to dynamically adjust the model structure according to the actual stack configuration (such as the number of modules, connection method, control strategy, etc.), enabling rapid reconstruction of different stack types (such as single-module, dual-module, and multi-module parallel operation).

[0071] Furthermore, the model node partitioning must meet the coupling accuracy requirements of thermal-hydraulic and neutron dynamics. For example, a reactor core model is typically divided into several fuel assembly nodes, each containing neutron dynamics equations and heat conduction equations, with its time step generally set to... The time limit is set to ensure the accuracy of the transient process simulation. In the helium flow control model, the relationship between the valve opening and the flow rate can be fitted based on empirical formulas or experimental data, such as... ,in For flow coefficient, For the valve orifice area, This refers to the pressure difference.

[0072] Specifically, this step is widely applied in scenarios such as control system design, variable load response analysis, and safety protection logic verification for high-temperature gas-cooled reactors. For example, under conditions of sudden changes in grid load, the power allocation coefficients of each module are adjusted. It can achieve coordinated power control of multiple NSSS modules, thereby improving system response speed and stability.

[0073] Specifically, by combining parametric modeling with topology editing, modeling efficiency and simulation flexibility are significantly improved, supporting rapid modeling and dynamic simulation verification under different reactor configurations, and providing a solid technical foundation for optimizing the coordinated control strategy of high-temperature gas-cooled reactors.

[0074] S5 verifies the coordinated control strategy under typical operating conditions and develops a secondary interface for data synchronization to display key parameters and their corresponding trends in real time.

[0075] Specifically, the technical implementation principle of this step is based on the coupled design of dynamic simulation model and human-computer interaction interface, which aims to improve the efficiency and intuitiveness of control strategy verification.

[0076] Specifically, firstly, a full-plant dynamic simulation model built in the Simulink environment was used to simulate the response characteristics of the high-temperature gas-cooled reactor under typical operating conditions such as step load changes (e.g., load abrupt changes of ±10%) and linear load changes (e.g., a linear load change rate of 5% per minute). During this process, the control system model and the process system model were coupled through an interface module to ensure that the primary and secondary loop system parameters (e.g., helium flow rate, core power, steam pressure, etc.) remained dynamically consistent throughout the simulation. Secondly, a secondary data synchronization interface was developed based on the MATLAB App Designer tool. This interface uses a data mapping mechanism to synchronize key parameters in the simulation model (e.g., ... , , (etc.) Read and display in real time in the graphical user interface (GUI), supporting users to perform graphical analysis of parameter change trends.

[0077] Furthermore, the update frequency of key parameters in the secondary interface is typically set to 100ms to 500ms to ensure a balance between real-time performance and data stability. The data sequence is stored in a timestamp and value pair structure, such as... ,in Indicates the simulation time point, This indicates the corresponding parameter value. The image curve is plotted using an interpolation algorithm (such as linear interpolation or cubic spline interpolation) to improve the smoothness and readability of the trend display.

[0078] Specifically, this step is widely used in the design and commissioning phases of nuclear power plant control systems, particularly in multi-module high-temperature gas-cooled reactor systems, to verify the coordinated control performance within modules, between modules, and between the unit and the power grid. Through the secondary interface, engineers can intuitively observe the dynamic response of key parameters such as helium flow ratio, core temperature distribution, and steam pressure fluctuations, thereby optimizing control logic and parameter settings.

[0079] Furthermore, the technical effect of this step is that it not only improves the accuracy and efficiency of control strategy verification, but also enhances the interpretability and visualization of simulation results through the "combination of numbers and shapes", providing strong support for the safe and stable operation of high-temperature gas-cooled reactor systems.

[0080] Furthermore, S5 includes: S51, the data synchronization secondary interface was developed using MATLAB's APP Designer, and includes a process flow interface and a dynamic curve interface.

[0081] Specifically, the interface consists of two core modules: a process flow interface and a dynamic curve interface, which are used to display the time-series changes of the system structure and key parameters, respectively.

[0082] Specifically, the process flow interface adopts a graphical layout, based on the system topology of the primary and secondary loops of the high-temperature gas-cooled reactor. Modular flowcharts are constructed using MATLAB's UI graphical components (such as UIAxes, UIFigure, and UIPanel). Each module node (such as the reactor core, steam generator, and main pump) is bound to its corresponding entity in the simulation model. Real-time status values ​​(such as temperature, pressure, and flow rate) output from the model are synchronized to the interface through a data mapping mechanism. The interface uses color coding and state labels to visually identify the operating status of equipment, allowing users to intuitively judge the system's operating status.

[0083] Furthermore, the dynamic curve interface is based on MATLAB's graphics engine, using real-time data streaming to display key parameters during the simulation process (such as reactor power). Helium flow rate Steam pressure (e.g., data) are displayed as dynamic curves. This interface supports simultaneous display of multi-channel data and has interactive functions such as zooming, panning, and data export, meeting the needs of real-time monitoring and analysis of parameter change trends during simulation.

[0084] Furthermore, the `timer` object in MATLAB is used to implement a timed data acquisition and update mechanism. The sampling period is set. The refresh rate is dynamically adjusted between 0.1 and 1 second based on the calculation step size of the simulation model to ensure that the interface refresh rate matches the model output rate. Data writing and reading are implemented through MATLAB's `workspace` or `datastore`, supporting efficient processing of structured data (such as time series matrices).

[0085] Specifically, in terms of UI interaction, the callback functions and event listeners provided by the App Designer are used to enable switching and interaction between different windows in the interface (such as the process flow window and the dynamic curve window). Users can trigger interface switching events through buttons (UIButton) or menus (UIMenu), and the system loads the corresponding data view according to the current simulation state, improving the convenience and real-time performance of the operation.

[0086] Specifically, this data synchronization secondary interface plays a crucial role in the high-temperature gas-cooled reactor coordinated control simulation platform. It not only provides intuitive visualization support for control strategy verification but also enhances the interpretability and analytical efficiency of simulation results through a combination of numerical and graphical methods. Its modular design and parametric configuration capabilities allow the interface to flexibly adapt to different reactor types and operating conditions, improving the versatility and practicality of the simulation platform.

[0087] S52, the real-time synchronization function is based on the MATLAB timer module to periodically read and write simulation data.

[0088] Specifically, the technical principle behind this step is to set a timed triggering mechanism to dynamically acquire and update key parameters during the simulation process, thereby meeting the real-time monitoring and analysis requirements for process data in the simulation modeling of power coordination control of high-temperature gas-cooled reactors.

[0089] Furthermore, MATLAB's timer module provides a time-driven callback function execution mechanism. Users can create timer objects using the `timer` function and set their trigger period (`Period`), trigger mode (`ExecutionMode`), and other properties. In this invention, `ExecutionMode` can optionally be set to `fixedRate` to ensure that the timer triggers at fixed time intervals, such as every 0.1 seconds. Perform a data synchronization operation. Upon each trigger, access the signal output port or working area variable in the Simulink simulation model via the callback function (`TimerFcn`), and input the current simulation data (such as core power). Helium flow rate Steam temperature The system reads and writes data to the MATLAB workspace or database, and simultaneously maps this data to the secondary interface in real time, displaying it in the form of graphs and numerical tables.

[0090] Specifically, the key parameters involved in this step include the timer's trigger period. Data sampling frequency Data buffer size and interface refresh delay To ensure data continuity and interface responsiveness, It is usually set to the simulation step size. Matches or multiples thereof, for example This is to avoid data loss or interface lag. Additionally, to prevent memory overflow, the data buffer size... It is usually set to the level of 10^4 to 10^5, and dynamically adjusted according to the simulation duration and data dimensions.

[0091] Specifically, this real-time synchronization function is widely used in the dynamic verification process of coordinated control strategies for high-temperature gas-cooled reactors. For example, under step load conditions, the response of the control system to key parameters such as core power, steam pressure, and helium flow rate needs to be collected and visualized in real time to facilitate evaluation and optimization of control performance by operators or researchers. Through a secondary interface developed using MATLAB APP Designer, users can intuitively observe the coupling behavior between modules and achieve synchronous monitoring of the operating status of the primary and secondary loop systems.

[0092] Specifically, this step enables efficient data communication between the simulation model and the human-computer interface, ensuring the real-time performance and accuracy of key parameters. Through a periodic read-write mechanism, the system can complete data updates within millisecond-level response time, significantly improving the efficiency of simulation verification and the real-time performance of visualization analysis, providing strong support for the commissioning and optimization of the high-temperature gas-cooled reactor coordinated control system.

[0093] S6, based on a multi-module coordinated control simulation platform, develops a model parameter adaptive adjustment module to dynamically correct the control gain parameters in the control system model according to real-time simulation results, so as to optimize the power distribution balance among multiple modules.

[0094] Specifically, this module introduces a feedback mechanism and an online parameter identification algorithm to dynamically adjust the control system model, thereby improving the control accuracy and response performance of the simulation platform under complex operating conditions.

[0095] Specifically, this adaptive adjustment module is typically integrated into the Simulink simulation environment, employing an adaptive algorithm based on Model Predictive Control (MPC) or Proportional-Integral-Derivative (PID) control. During simulation, the system continuously collects key parameters such as output power, temperature, and pressure from each module and compares them with set target values ​​to calculate the deviation signal. This deviation signal is then processed by a pre-defined adaptive algorithm model to dynamically adjust the control gain parameters. , , This enables real-time optimization of power allocation. Specifically, online identification methods such as recursive least squares (RLS) or Kalman filtering can be used to estimate and update model parameters.

[0096] Furthermore, the adjustment range of the control gain is typically set to... , , This is to ensure the system's stability and response speed under different operating conditions. Meanwhile, the sampling frequency of the deviation signal is recommended to be no less than [missing information]. This ensures the real-time adjustment of control parameters. In addition, the model update cycle... Typically set to Adjustments are made based on the dynamic characteristics of the system.

[0097] Specifically, this module is suitable for the coordinated control simulation of multi-module high-temperature gas-cooled reactor systems under typical operating conditions such as step load changes and linear load changes. By dynamically adjusting the control gain, the system can maintain a balanced power distribution during sudden or gradual load changes, avoiding response lag or overshoot caused by fixed control parameters, thereby improving the overall system's operating efficiency and safety.

[0098] Specifically, by introducing an adaptive control mechanism, the dynamic response capability and control accuracy of the multi-module coordinated control simulation platform are significantly improved, providing strong support for the optimization of control strategies for high-temperature gas-cooled reactor systems under complex operating conditions. This has high engineering practical value and innovation.

[0099] This invention discloses a simulation modeling method for coordinated power control of a multi-module high-temperature gas-cooled reactor. By constructing a hierarchical modular model library and achieving dynamic coupling between the process system and the control system, it effectively overcomes the shortcomings of existing technologies, such as cumbersome modeling processes, insufficient system flexibility, and difficulty in efficiently verifying coordinated control strategies. This method achieves integrated simulation across the entire process, from mechanistic model construction, rapid modular assembly, parametric topology editing to real-time strategy verification and visualization. It significantly improves modeling efficiency, model fidelity, and control strategy verification capabilities, providing a highly reliable and adaptable digital twin platform for the design optimization and safe operation analysis of high-temperature gas-cooled reactor coordinated control systems.

[0100] Example 2 To achieve the above invention, embodiments of the present invention also provide a multi-module high-temperature gas-cooled reactor power coordination control simulation modeling system, comprising: Multi-scale mechanism / simulation model library architecture for multi-module high-temperature gas-cooled reactor systems, such as Figure 2 As shown, it contains three levels: 1. Basic model library, developed based on fundamental physics / thermal processes such as neutron dynamics, mass / energy / momentum conservation, etc., covering fundamental physics / thermal process models such as neutron dynamics, thermal components, three-equation fluids, heat-work conversion, etc., as well as actuator mechanism models such as pumps, regulating valves, fans, etc.

[0101] 2. Equipment model library: This library covers large-scale, complex equipment involved in multi-level energy transfer and conversion within the system, and models can be built based on the basic model library. For example, core and steam generator models, as well as core physics and thermal models, can be constructed based on neutron dynamics, thermal components, and fluid fundamental models. Based on the basic model library and equipment model library, dynamic simulation models of high-temperature gas-cooled reactor systems can be quickly built through drag-and-drop and assembly methods.

[0102] Furthermore, the core physics-thermal model is a core tool in nuclear reactor design and analysis, used to comprehensively describe the coupling relationship between neutron physical processes and thermal-hydraulic behavior within the reactor core. This model must simultaneously consider the energy distribution from nuclear fission (physical component) and coolant flow, heat transfer, and phase transition processes (thermal component) to ensure the safe and efficient operation of the reactor. The core physics-thermal model is typically divided into two main parts: a physical model and a thermal model. The physical model focuses on neutron dynamics and burnup evolution, including solving the neutron diffusion equation, control rod response, and nuclear fuel burnup calculation; that is, using the neutron dynamics equation (Equation 1) and the reactivity equation (Equation 3) to express the neutron motion behavior within the reactor. The thermal model focuses on coolant flow, heat transfer, and phase transition phenomena, for example, solving the mass, momentum, and energy conservation equations through subchannel analysis or full-core simulation methods; that is, using the thermal dynamics equation (Equation 2) and the bypass channel equation (Equation 4). These equations are used to simulate the actual conditions of the reactor. The specific formulas are as follows: Neutron dynamics equations: (1) Thermodynamic equations: (2) Reactivity equation: (3) Bypass channel equation: (4) 3. System-level modules: Based on the principles and structure of the coordinated control system of high-temperature gas-cooled reactors, simulation models of the relevant control systems are constructed using Simulink's basic computing modules. Building upon this, and following the process flow and coordinated control principles of the primary and secondary loops of a multi-module high-temperature gas-cooled reactor system, the simulation models of the process system and control system are coupled to develop a coordinated control simulation platform for multi-module high-temperature gas-cooled reactors.

[0103] Furthermore, the simulation model construction process includes requirements analysis and mathematical modeling, building the core open-loop structure, configuring module parameters and signals, adding controllers and feedback, setting simulation parameters and running the simulation, and analyzing results and debugging and optimization.

[0104] Based on the aforementioned model library, a full-plant dynamic simulation model of a multi-module high-temperature gas-cooled reactor system can be quickly constructed. According to the structure and operating principle of the multi-module high-temperature gas-cooled reactor, the model node decomposition for its single NSSS module and secondary loop system is shown below. Figure 3 and Figure 4 .

[0105] Specifically, the construction of the primary loop involves establishing coupling relationships between its components (such as the reactor core, headers, hot gas ducts, main helium blower, evaporator, etc.) through various parameters. The construction of the secondary loop follows the same method, establishing coupling relationships between its components (low-pressure cylinder, high-pressure cylinder, condenser, high-pressure heater, low-pressure heater, etc.) through parameters. The specific formulas represent the input-output relationships for each component. If a corresponding module exists in the MATLAB program, it can be used directly. If no such module exists, it needs to be constructed manually. The construction method essentially involves using formulas to express the input-output relationships.

[0106] Coordinated Control Strategy Verification and Data Synchronization Secondary Interface Development: Based on the developed multi-module high-temperature gas-cooled reactor (NSSS) plant-wide dynamic simulation model, the developed coordinated control scheme for the NSSS was verified under typical operating conditions such as step load changes and linear load changes. This included dynamic simulation verification of coordination within NSSS modules, coordination between NSSS modules, and coordination between the unit and the grid. Verification methods included module-level verification and integration testing. Module-level verification involved testing each basic module (such as integrators and transfer functions) individually, inputting known signals, and verifying whether the output met mathematical expectations. Integration testing was conducted based on module-level verification, i.e., determining whether the output results met expectations after inputting a step input and checking whether the output could maintain stable following. It also included dynamic simulation verification of coordination within NSSS modules, coordination between NSSS modules, and coordination between the unit and the grid.

[0107] Furthermore, considering the process flow and operational characteristics of the high-temperature gas-cooled reactor (HTGR), a data synchronization secondary interface was developed based on the modular dynamic simulation software interface. This interface utilizes a "digital-graphical combination" approach, combining data sequences and image curves to synchronously display key parameter values ​​and their trends during the HTGR system simulation process in real time, and allows for analysis and processing. First, based on the structure and operating principle of the multi-module HTGR system, the process flow interface of the data synchronization secondary interface was designed. Second, based on the developed coordination control simulation software, the secondary interface was developed using MATLAB's APP Designer system. According to the characteristics and actual needs of the HTGR system simulation model, key data sequences and simulation result images were mapped to the secondary interface. Then, using MATLAB's timer function, the data synchronization function on the secondary interface was developed by writing and reading process data generated during the simulation process in real time. Finally, using the UI callback events and listening functions provided by APP Designer, the switching between different window interfaces in the secondary interface was realized, developing a complete data synchronization secondary interface.

[0108] This invention discloses a simulation modeling system for the coordinated power control of a multi-module high-temperature gas-cooled reactor. By constructing a hierarchically integrated multi-scale mechanistic model library, it effectively solves the problems of fragmented modeling tools, disjointed processes, and difficulty in supporting integrated design and verification of multi-module coordinated control in existing technologies. This system achieves integrated simulation across the entire process, from basic physical process modeling, rapid integration of equipment and systems, dynamic coupling of control strategies, to full-condition verification and real-time visualization, significantly improving the efficiency, fidelity, and engineering applicability of simulation modeling. While providing a highly reliable digital twin platform for complex nuclear energy systems, it also greatly enhances the scientific rigor and decision support effectiveness of control strategy design.

[0109] Example 3 To achieve the above invention, this embodiment also provides an application scenario for the simulation modeling method of power coordination control of a multi-module high-temperature gas-cooled reactor, including: Specifically, the application scenarios of multi-module high-temperature gas-cooled reactors (HTGRs) vary depending on the application scenario. For example, industrial steam requires steam parameters at various levels, including high pressure (9.8 MPa / 510℃), medium pressure (2.5 MPa / 400℃), and low pressure (1.5 MPa / 250℃). Iodine-sulfur cycle hydrogen production requires heat sources above 800℃, while SOEC requires heat sources in the range of 570℃ to 750℃. The coupling characteristics between different application scenarios and the reactor also differ. This project will consider typical applications in the future development of HTGRs and study their coupling issues. Within a reasonable range, the research object will be simplified, and a coordinated control strategy for multi-module HTGRs will be studied under the coupling of multiple application scenarios on the secondary / tertiary sides to cope with changes in demand or outages in different scenarios.

[0110] Further research will be conducted on steam parameters, operating modes, and protection strategies under typical application scenarios. Nuclear hydrogen production: Hydrogen is an important industrial raw material and an ideal secondary energy source or energy carrier for the future; as a secondary energy source, hydrogen is easy to store and transport and can be used directly as fuel. Besides traditional ammonia synthesis, methanol synthesis, and petroleum refining, hydrogen can be utilized on a large scale in hydrogen metallurgy, coal liquefaction, and fuel cell vehicles. Nuclear hydrogen production can achieve efficient, large-scale, and carbon-free hydrogen production. From the perspective of nuclear reactors, the outlet temperature of (ultra)high temperature gas-cooled reactors exceeds 700℃, and the process heat provided can meet the needs of high-temperature hydrogen production processes. Its system efficiency is highly correlated with the temperature of the heat energy provided by the reactor (e.g., ...). Figure 5 As shown in the figure, there are currently two main ways to produce hydrogen using nuclear energy: thermochemical cycle hydrogen production and high-temperature electrolysis hydrogen production.

[0111] Specifically, thermochemical hydrogen production is achieved through a high-temperature thermochemical cycle process involving the thermal cracking of steam. This process primarily utilizes the high-temperature heat provided by the reactor. Among hundreds of thermochemical cycle routes, the IS cycle, Cu-Cl cycle, Ca-Br cycle, and UC cycle are the main technologies compatible with Generation IV reactors. However, the efficiency of the IS cycle is significantly affected by temperature; above 900℃, the efficiency can exceed 50%, but it drops sharply below 800℃.

[0112] Specifically, high-temperature steam electrolysis for hydrogen production (HTSE) uses a solid oxide electrolyzer (SOEC) as the core reactor to achieve efficient decomposition of steam to produce hydrogen. Due to its advantages such as high efficiency, cleanliness, and simple process, HTSE technology has received considerable attention from researchers and enterprises both domestically and internationally in recent years, and has become an important technology for hydrogen production in conjunction with clean energy sources such as nuclear, wind, and solar power.

[0113] Furthermore, high-temperature process heat applications: such as Figure 6 As shown, in applications requiring low-temperature heat, such as seawater desalination and nuclear heating, the waste heat from the secondary loop of a high-temperature gas-cooled reactor (HTGR) can provide a high-quality heat source. Chemical processes such as ammonia synthesis, coal gasification, and methane steam reforming require temperatures above 700°C. These traditional chemical industries consume enormous amounts of energy, and the demand for ammonia synthesis, coal liquefaction, and petroleum cracking products (such as ethylene) is gradually increasing. Faced with increasingly stringent carbon emission requirements and the growing scarcity of traditional energy resources, exploring new industrial energy supply and coupling methods is crucial. The helium outlet temperature of the HTGR primary loop can reach 750°C, while the outlet temperature of the ultra-high-temperature gas-cooled reactor (UHTGR) primary loop can reach 950°C, fully covering the high-temperature process heat requirements and significantly reducing the environmental impact of carbon emissions, while also fully leveraging the economic advantages of HTGRs.

[0114] This invention also provides an application scenario for a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor. By constructing a simulation model covering the coupling characteristics of multiple application scenarios, it studies the coupling characteristics and coordinated control strategies between the reactor and different heat sources such as industrial steam, high-temperature hydrogen production, and high-temperature process heat. By establishing a multi-module collaborative simulation framework, it effectively solves the problems of insufficient control adaptability and economic optimization difficulties caused by diverse application scenarios, complex operating conditions, and differences in coupling characteristics. It achieves integrated modeling from typical application scenario analysis and multi-parameter coordinated optimization to the generation of overall system control strategies, significantly improving the system adaptability, operational economy, and safety reliability of the high-temperature gas-cooled reactor under multi-scenario coupled operation, and enhancing the guiding value and engineering applicability of the simulation model in real industrial applications.

[0115] Example 4 To achieve the above invention, such asFigure 7 As shown, this embodiment also provides a multi-module high-temperature gas-cooled reactor power coordination control simulation modeling device 10, which includes: The hierarchical modular model library construction module 100 is used to construct a hierarchical modular model library architecture that includes a basic model library, a device model library, and system-level modules, based on neutron dynamics, the law of conservation of mass / energy / momentum, and the dynamic evolution law of key equipment.

[0116] Module Assembly Generation Module 200 is used to quickly generate dynamic simulation models of multi-module high-temperature gas-cooled reactor systems by dragging and assembling modules from the basic model library and the equipment model library.

[0117] Simulink Control System Model Building Module 300 is used to build a coordinated control system model using Simulink basic computing modules, and dynamically couple the process system model with the control system model to form a multi-module coordinated control simulation platform.

[0118] The parametric modeling and topology editing module 400 is used for parametric modeling based on a multi-module coordinated control simulation platform. It enables the division of model nodes and adjustment of parameters for different stack configurations through topology editing.

[0119] The Coordination Control Strategy Verification and Interface Development Module 500 is used to verify the coordination control strategy under typical operating conditions and develop a secondary interface for data synchronization to display key parameters and their corresponding changing trends in real time.

[0120] In one embodiment of the present invention, it further includes: a model parameter adaptive adjustment module, used to develop a model parameter adaptive adjustment module based on a multi-module coordinated control simulation platform, and dynamically correct the control gain parameters in the control system model according to the real-time simulation results, so as to optimize the power distribution balance among multiple modules.

[0121] This invention discloses a simulation modeling device for power coordination control of a multi-module high-temperature gas-cooled reactor. By constructing a hierarchical modular model library and an integrated simulation platform, it effectively solves the inherent defects of existing technologies, such as scattered modeling tools, lengthy processes, and difficulty in efficiently and comprehensively verifying complex coordinated control strategies. This device achieves fully automated integrated simulation from mechanistic model construction, rapid modular assembly, control coupling, parameterized configuration to strategy verification and real-time visualization. It significantly improves the efficiency, flexibility, and model fidelity of simulation modeling, providing a highly reliable integrated solution for the design, optimization, and safety assessment of coordinated control systems for multi-module high-temperature gas-cooled reactors, enhancing its decision support capabilities and practical value in complex nuclear energy system engineering.

[0122] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 8As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor described above.

[0123] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor as described in the foregoing embodiments.

[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0125] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor, characterized in that, include: S1, based on neutron dynamics, the law of conservation of mass / energy / momentum and the dynamic evolution law of key equipment, constructs a hierarchical modular model library architecture that includes a basic model library, a device model library and system-level modules; S2 allows for the rapid generation of dynamic simulation models of multi-module high-temperature gas-cooled reactor systems by dragging and assembling modules from the basic model library and the equipment model library. S3 utilizes Simulink's basic computational modules to construct a coordinated control system model, and dynamically couples the process system model with the control system model to form a multi-module coordinated control simulation platform. S4 uses a multi-module coordinated control simulation platform for parametric modeling, and topology editing is used to divide model nodes and adjust parameters for different stack configurations. S5 verifies the coordinated control strategy under typical operating conditions and develops a secondary interface for data synchronization to display key parameters and their corresponding trends in real time.

2. The method as described in claim 1, characterized in that, The aforementioned hierarchical modular model library architecture, based on neutron dynamics, the laws of conservation of mass / energy / momentum, and the dynamic evolution of key equipment, constructs a system-level module library architecture comprising a basic model library, an equipment model library, and system-level modules. S11, the basic model library includes neutron dynamics models, thermal component models, three-equation fluid models, and heat-work conversion models; S12, the equipment model library includes core models, steam generator models, pump models, control valve models, and fan models.

3. The method as described in claim 1, characterized in that, The aforementioned method utilizes Simulink's basic computational modules to construct a coordinated control system model, and dynamically couples the process system model with the control system model to form a multi-module coordinated control simulation platform, including: S31, the coordinated control system model includes a PID controller module and a fuzzy control module; S32, dynamic coupling enables real-time data interaction between models through Simulink's S-Function interface.

4. The method as described in claim 1, characterized in that, The verification of the coordinated control strategy under typical operating conditions and the development of a data synchronization secondary interface to display key parameters and their corresponding changing trends in real time include: S51, the data synchronization secondary interface was developed using MATLAB's APP Designer, and includes a process flow interface and a dynamic curve interface. S52, the real-time synchronization function is based on the MATLAB timer module to periodically read and write simulation data.

5. The method as described in claim 1, characterized in that, Also includes: S6, based on a multi-module coordinated control simulation platform, develops a model parameter adaptive adjustment module to dynamically correct the control gain parameters in the control system model according to real-time simulation results, so as to optimize the power distribution balance among multiple modules.

6. A simulation modeling device for power coordination control of a multi-module high-temperature gas-cooled reactor, characterized in that, include: The hierarchical modular model library construction module is used to build a hierarchical modular model library architecture that includes a basic model library, a device model library, and system-level modules, based on neutron dynamics, the law of conservation of mass / energy / momentum, and the dynamic evolution law of key equipment. The module assembly generation module is used to quickly generate dynamic simulation models of multi-module high-temperature gas-cooled reactor systems by dragging and assembling modules from the basic model library and the equipment model library. The Simulink control system model building module is used to build a coordinated control system model using the basic Simulink computing modules, and dynamically couple the process system model with the control system model to form a multi-module coordinated control simulation platform. The parametric modeling and topology editing module is used for parametric modeling based on a multi-module coordinated control simulation platform. It enables the division of model nodes and adjustment of parameters for different stack configurations through topology editing. The coordinated control strategy verification and interface development module is used to verify the coordinated control strategy under typical operating conditions and develop a secondary interface for data synchronization to display key parameters and their corresponding changing trends in real time.

7. The apparatus as claimed in claim 6, characterized in that, Also includes: The model parameter adaptive adjustment module is used to develop a model parameter adaptive adjustment module based on a multi-module coordinated control simulation platform. It dynamically corrects the control gain parameters in the control system model according to the real-time simulation results in order to optimize the power distribution balance among multiple modules.

8. An electronic device, comprising: processor; The memory stores executable instructions; when the processor executes the instructions, it implements the simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor as described in any one of claims 1-5.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements a simulation modeling method for power coordination control of a multi-module high-temperature gas-cooled reactor as described in any one of claims 1-5.

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