A clover-shaped nuclear fuel element optimization method, system and storage medium

Abstract

This invention discloses a method, system, and storage medium for optimizing clover-shaped nuclear fuel elements, comprising: acquiring parameterized geometric design variables of the fuel element and generating a corresponding three-dimensional geometric model; establishing a thermofluid-solid multiphysics coupling model on the three-dimensional geometric model, wherein the multiphysics coupling model includes at least turbulent flow, conjugate heat transfer, and structural response caused by thermal expansion; and completing solver settings, mesh generation, and simulation calculations to obtain temperature and pressure fields; extracting convective heat transfer characterization indices, coolant pressure drop, power density indices, and temperature gradients based on simulation results, and constructing a multi-objective optimization problem in conjunction with structural safety constraints; using a multi-objective evolutionary optimization algorithm to drive the multiphysics coupling model to perform joint simulation iterations, obtaining the Pareto optimal solution set, and determining and outputting the optimal parameter combination from it.

Description

Technical Field

[0001] This invention relates to the field of nuclear fuel element design, specifically to a method, system, and storage medium for optimizing clover-shaped nuclear fuel elements. Background Technology

[0002] With the continuous development of nuclear energy technology, reactor design is facing increasingly stringent requirements for safety, economy, and power density. The optimization of nuclear reactor core performance is highly dependent on the geometric design and heat transfer performance of fuel elements. However, traditional circular fuel elements have significant limitations in heat exchange efficiency, coolant mixing capacity, and structural stability, making it difficult to meet the demands of efficient and clean energy development.

[0003] The cloverleaf-shaped nuclear fuel element is a novel type of fuel element. A typical cloverleaf-shaped nuclear fuel element has a cruciform cross-section, with its blades twisted axially to form a helical structure. The tips of adjacent helical fuel elements contact at a specific height, forming a "self-positioning" structure that eliminates the need for a positioning grid. The twisted blades force the coolant to form a continuous swirling flow within the assembly, enhancing mixing and heat transfer between subchannels and improving the core safety margin. Based on these advantages, helical fuel elements have great potential for increasing the core power density of small reactors.

[0004] Traditional design methods rely on experience or single-objective optimization, making it difficult to simultaneously consider multiple performance indicators (such as heat transfer performance, flow resistance, and power density). In addition, simulations of complex geometries are usually computationally intensive and time-consuming, limiting design efficiency. Summary of the Invention

[0005] To achieve the above and other related objectives, this invention discloses a method for optimizing clover-shaped nuclear fuel elements, comprising: The parametric geometric design variables of the fuel element are obtained and the corresponding three-dimensional geometric model is generated. The parameters of the fuel element include the helix radius. Pitch petal width Petal thickness and coolant passage clearance The three-dimensional geometric model is a model of a clover-shaped fuel element with a spiral structure formed by the axial twisting of the blades, and its coolant channels, including an inlet section for ensuring sufficient development of inlet flow and an outlet section for stabilizing the outlet boundary. A thermofluid-solid multiphysics coupling model is established on the three-dimensional geometric model. The multiphysics coupling model includes at least turbulent flow, conjugate heat transfer, and structural response caused by thermal expansion. Solver settings, mesh generation, and simulation calculations are completed to obtain the temperature field and pressure field. Based on the simulation results, the convective heat transfer characterization index, coolant pressure drop, power density index and temperature gradient are extracted, and a multi-objective optimization problem is constructed in combination with structural safety constraints; A multi-objective evolutionary optimization algorithm is used to drive the multi-physics coupling model to perform joint simulation iterations, obtain the Pareto optimal solution set, and determine and output the optimal parameter combination from it.

[0006] Preferably, the fluid dynamics settings of the multiphysics coupling model include: Time-averaged simulation using incompressible Reynolds And select Algebraic turbulence models; The turbulent boundary layer wall treatment is performed in automatic mode. The boundary conditions include at least velocity inlet boundary conditions, pressure outlet boundary conditions, no-slip wall boundary conditions, and symmetric boundary conditions, and a gravity term is set to characterize the force effect on the coolant in the vertical direction.

[0007] Preferably, the heat transfer configuration of the multiphysics coupling model includes: A bulk heat source is installed in the fuel core, and a cosine power distribution is adopted. Establish the heat conduction process from the fuel core to the cladding and the forced convection heat transfer process from the cladding to the coolant; Given temperatures and thermal equilibrium conditions are set at the inlet and outlet respectively, and the fluid domain outlet adopts outflow boundary conditions. The Kays-Crawford model is used in the turbulent heat transfer setting, and the heat generated by the viscous dissipation of the fluid flow is ignored.

[0008] Preferably, the structural mechanics settings of the multiphysics coupling model include: fuel assembly is made of Encasing and Fuel core composition; The fuel core adopts a linear elastic material model, and the cladding adopts an elastic-plastic material model, both of which take into account the thermal expansion effect; The equivalent thermal conductivity of the fuel assembly was determined using the Maxwell model, and the equivalent thermal expansion coefficient was determined using the Turner model.

[0009] Preferably, the multiphysics coupling settings include setting fluid-structure interaction, non-isothermal flow, and thermal expansion in the multiphysics coupling node of the simulation software; The fluid-structure interaction is set to full coupling, and the fluid domain is set as a moving mesh region. The moving mesh is smoothed using Yeoh smoothing. The thermal expansion coefficient is calculated using the secant coefficient of thermal expansion as the thermal expansion coefficient, and the thermal expansion coefficient is anisotropic.

[0010] Preferably, the multi-objective evolutionary optimization algorithm includes: The parametric geometric design variables are constructed into a design variable vector, and a set of multi-objective objective functions is extracted based on the multiphysics coupling simulation results of each candidate variable vector. The set of objective functions includes: maximizing the convective heat transfer characterization index, minimizing the coolant pressure drop, maximizing the power density, and minimizing the temperature gradient. The constraints include structural safety constraints and system allowable pressure drop constraints, so that only candidate solutions that meet the constraints or have been constrained participate in subsequent population updates.

[0011] Preferred, including: The objective function of the convective heat transfer characterization index Defined as convective heat transfer coefficient And calculated by the following formula: in, For the heat exchange on the surface of the fuel element, For the heat exchange area of ​​the fuel element, The surface temperature of the fuel element. The average temperature of the coolant; and and They respectively satisfy: in, This is the coolant mass flow rate. The specific heat capacity of the coolant. and These are the coolant inlet temperature and outlet temperature, respectively. The objective function of the coolant pressure drop Defined as voltage drop And calculated by the following formula: in, This refers to the coolant inlet pressure. This refers to the coolant outlet pressure. The objective function of power density It is determined by the total thermal power of the core and the total volume of the core fuel elements, and is calculated by the following formula: in, This represents the total thermal power of the reactor core. This is the total volume of the reactor core fuel elements, and It can be determined by the number of fuel elements and the volume of a single fuel element. Power density; The objective function of the temperature gradient Used to characterize the non-uniformity of temperature distribution inside or on the surface of a fuel element, and calculated by the following formula: in, and The temperature gradient is the temperature at any two points inside or on the surface of the fuel element, and the temperature gradient is obtained by extracting the maximum temperature difference from the finite element temperature field results. For temperature gradient; Based on the above objective functions, a fitness function is established, and the formula for the fitness function is: in, , , and Each item has its own weight. , , and This serves as a reference value for a specific target.

[0012] Preferably, the multi-objective optimization co-simulation is performed iteratively according to the following process: Given a range of design variables, an initial population is randomly generated. Each candidate solution in the initial population corresponds to a set of geometric and operational parameter combinations. For each candidate scheme, a simulation tool is called to run a multiphysics model simulation calculation and extract the parameters required for the objective function; fitness is calculated based on the objective function value, and crossover and mutation are performed to generate offspring population; after merging the offspring population with the parent population, individuals with better performance and maintaining diversity are selected according to fitness to enter the next generation population, and this process is repeated until the iteration termination condition is met.

[0013] Secondly, the present invention discloses a cloverleaf-shaped nuclear fuel element optimization system, comprising: The geometric model building module is used to obtain the parametric geometric design variables of the fuel element and generate the corresponding three-dimensional geometric model. The parameters of the fuel element include the helix radius. Pitch petal width Petal thickness and coolant passage clearance The three-dimensional geometric model is a model of a clover-shaped fuel element with a spiral structure formed by the axial twisting of the blades, and its coolant channels, including an inlet section for ensuring sufficient development of inlet flow and an outlet section for stabilizing the outlet boundary. The multiphysics coupling model building module is used to build a thermofluid-solid multiphysics coupling model on the three-dimensional geometric model. The multiphysics coupling model includes at least turbulent flow, conjugate heat transfer and structural response caused by thermal expansion, and completes solver settings, mesh generation and simulation calculation to obtain temperature field and pressure field. The multi-objective optimization module is used to extract convective heat transfer characterization indicators, coolant pressure drop, power density indicators and temperature gradient based on simulation results, and construct a multi-objective optimization problem in combination with structural safety constraints. The multi-objective evolutionary optimization algorithm is used to drive the multi-physics coupling model to perform joint simulation iteration, obtain the Pareto optimal solution set, determine the optimal parameter combination from it and output it.

[0014] Thirdly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0015] By adopting the above technical solutions, this invention can be applied to the design of nuclear reactors, especially the development of small reactors, to improve core power density and thermal efficiency. Nuclear reactor design and energy utilization: By optimizing the geometric design of the clover-shaped fuel element, reducing coolant flow resistance, improving heat exchange efficiency, and optimizing core power density, it is widely applicable to the development and upgrading of various nuclear energy systems. Improved energy efficiency and reduced operating costs: By optimizing the design of the clover-shaped nuclear fuel element, this technology can significantly improve the thermal efficiency, power density, and fuel utilization rate of nuclear reactors, thereby enhancing the overall economic benefits of nuclear power plants. Reducing coolant flow resistance lowers energy consumption, and the optimized design also makes reactor operation more stable, reducing maintenance and operating costs. The optimized geometry and improved fuel utilization rate will help reduce energy waste, improve resource utilization efficiency, and reduce fuel consumption and neutron leakage, which has significant economic benefits in nuclear energy applications. This technology can not only improve the efficiency of existing nuclear reactors but also play an important role in the design of next-generation nuclear reactors, potentially leading to new nuclear energy technology standards and market demands, promoting the further development of the nuclear energy industry, and creating significant economic returns. Attached Figure Description

[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is an overall modeling diagram of a reactor core composed of multiple fuel elements, according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the optimal solution set in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0018] Reference Figure 1 This invention provides a method for optimizing clover-shaped nuclear fuel elements, comprising: The parametric geometric design variables of the fuel element are obtained and the corresponding three-dimensional geometric model is generated. The parameters of the fuel element include the helix radius. Pitch petal width Petal thickness and coolant passage clearance The three-dimensional geometric model is a model of a clover-shaped fuel element with a spiral structure formed by the axial twisting of the blades, and its coolant channels, including an inlet section for ensuring sufficient development of inlet flow and an outlet section for stabilizing the outlet boundary. A thermofluid-solid multiphysics coupling model is established on the three-dimensional geometric model. The multiphysics coupling model includes at least turbulent flow, conjugate heat transfer, and structural response caused by thermal expansion. Solver settings, mesh generation, and simulation calculations are completed to obtain the temperature field and pressure field. Based on the simulation results, the convective heat transfer characterization index, coolant pressure drop, power density index and temperature gradient are extracted, and a multi-objective optimization problem is constructed in combination with structural safety constraints; A multi-objective evolutionary optimization algorithm is used to drive the multi-physics coupling model to perform joint simulation iterations, obtain the Pareto optimal solution set, and determine and output the optimal parameter combination from it.

[0019] Preferably, in this embodiment of the invention, COMSOL software is used to accurately construct a three-dimensional geometric model of the fuel element, simulate its thermal-hydraulic characteristics, and obtain high-precision performance data; advanced multi-objective optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) are adopted to comprehensively consider multiple performance indicators, perform global optimization of design parameters, find the best design scheme, and organically combine COMSOL simulation results with optimization algorithms to construct a feedback loop, thereby improving optimization efficiency and accuracy through rapid iteration.

[0020] In this embodiment of the invention, the combination of multi-objective optimization algorithm and simulation is implemented using COMSOL Multiphysics 5.5 with MATLAB software.

[0021] Preferably, the fluid dynamics settings of the multiphysics coupling model include: Time-averaged simulation using incompressible Reynolds And select Algebraic turbulence models; The turbulent boundary layer wall treatment is performed in automatic mode. The boundary conditions include at least velocity inlet boundary conditions, pressure outlet boundary conditions, no-slip wall boundary conditions, and symmetric boundary conditions, and a gravity term is set to characterize the force effect on the coolant in the vertical direction.

[0022] Preferably, in this embodiment of the invention, the fluid dynamics aspects are configured as follows: In cloverleaf-shaped nuclear fuel elements, fluid flow must satisfy the continuity equation and the incompressible Navier-Stokes equation, taking into account the turbulence effect of the fluid.

[0023] For numerical calculations of flow and heat transfer under turbulent conditions, this model selects the Reynolds Time-Averaged Simulation (RANS) method. In the RANS method, by time averaging the unsteady-state governing equations, equations containing the time-averaged products of fluctuating quantities are obtained. These equations have fewer unknowns and are suitable for numerical solutions of complex turbulent flows.

[0024] During the optimization process, for the geometric parameters of the cloverleaf-shaped nuclear fuel element, in order to improve computational efficiency and ensure accuracy, this patented model selected the robust L-VEL algebraic turbulence model, which is widely used in complex flows. This model can calculate the turbulent viscosity coefficient based on the local velocity of the liquid and its distance from the wall, without needing to solve additional transfer equations. The L-VEL model is particularly suitable for applications in the flow region of cloverleaf-shaped nuclear fuel elements, as it can efficiently calculate the turbulence effects throughout the entire flow region and accurately simulate the flow and heat transfer characteristics of the coolant under complex geometric conditions.

[0025] This method enables precise simulation of coolant flow behavior between clover-shaped nuclear fuel elements, optimizing design, improving reactor performance, and effectively reducing coolant flow resistance, thereby enhancing the system's thermal-hydraulic performance.

[0026] The turbulent boundary layer wall treatment employs an "automatic" mode. L-VEL calculates the turbulent viscosity coefficient based on the local velocity value at each node and the distance to the nearest wall, thus allowing the equations to be closed. For the boundary conditions, fully developed flow rates and given pressures are set for both the inlet and outlet. Furthermore, no-slip walls and symmetric boundaries are also provided.

[0027] For boundary layer treatment, turbulent boundary layers are more complex than laminar boundary layers. Currently, turbulent boundary layers are considered to be divided into a viscous sublayer, a buffer layer, a logarithmic law layer, and a region of intense turbulence. Therefore, when detailed simulations of flow and heat transfer within the turbulent boundary layer are required, a large number of mesh nodes need to be arranged near the wall, leading to increased computational load. To ensure high computational accuracy and short computation time, the wall function method is often used to calculate shear force and heat flux within the turbulent boundary layer.

[0028] In the L-VEL turbulence model, the treatment of the turbulent boundary layer adopts an "automatic" mode. The shear stress and heat flux density on the wall are calculated automatically using either a fully analytical method or the wall function method, based on the distance between the wall and the center of the first-layer mesh. The "automatic" wall treatment assumes a gap δw between the computational domain mesh and the real physical wall, as shown in the formula. .when When the value approaches zero, a fully analytical method is used. The vertical or horizontal dimension of the grid.

[0029] To avoid flow distribution issues, this model includes separate fluid inlet and outlet sections, as shown in the top view of a standard fuel assembly with a helical cross fuel element. Considering the symmetry of the fuel assembly, the flow boundary conditions are set as follows: Inlet boundary conditions: To ensure the full development of coolant flow, fully developed velocity inlet boundary conditions are set to guarantee the stability of inlet flow velocity and the physical rationality of fluid flow; Outlet boundary conditions: Set pressure outlet boundary conditions to ensure that the coolant can flow out smoothly after passing through the fuel element and maintain the flow balance of the system; Wall boundary conditions: Considering the contact between the surface of the fuel element and the coolant, a no-slip boundary condition is set on the wall to ensure that the fluid does not slip on the surface of the fuel element, thereby improving the heat transfer effect; Symmetrical boundary conditions: For the fuel assemblies on the left and right sides, considering their symmetry, left and right symmetric boundary conditions are set respectively to simplify the calculation of the model and improve the simulation efficiency; Gravity boundary conditions: Considering the influence of gravity on coolant flow, a vertically downward gravity is set to simulate the real flow state of coolant under natural convection.

[0030] Preferably, the heat transfer configuration of the multiphysics coupling model includes: A bulk heat source is installed in the fuel core, and a cosine power distribution is adopted. Establish the heat conduction process from the fuel core to the cladding and the forced convection heat transfer process from the cladding to the coolant; Given temperatures and thermal equilibrium conditions are set at the inlet and outlet respectively, and the fluid domain outlet adopts outflow boundary conditions. The Kays-Crawford model is used in the turbulent heat transfer setting, and the heat generated by the viscous dissipation of the fluid flow is ignored.

[0031] Preferably, heat transfer in the fuel assembly of this invention mainly involves two methods. The heat generated by the fuel pellets is transferred to the cladding surface via thermal conduction, and finally removed via forced convection heat transfer. Viscous dissipation of the fluid flow is neglected compared to the heat transfer rate. Furthermore, the influence of coolant turbulence on heat transfer is considered. Given temperatures and thermal balances are established at the inlet and outlet, respectively. Symmetrical boundaries are also considered; given the coolant inlet temperature, an "outflow" boundary condition is used at the fluid domain outlet. The model satisfies the energy conservation equations for both the solid domain and the fluid domain. The heat transfer equations for the multiphysics model are as follows, with a cosine power distribution set for the fuel pellets.

[0032] Preferably, the structural mechanics settings of the multiphysics coupling model include: fuel assembly is made of Encasing and Fuel core composition; The fuel core adopts a linear elastic material model, and the cladding adopts an elastic-plastic material model, both of which take into account the thermal expansion effect; The equivalent thermal conductivity of the fuel assembly was determined using the Maxwell model, and the equivalent thermal expansion coefficient was determined using the Turner model.

[0033] The key to the geometry of the cloverleaf-shaped nuclear fuel element lies in its torsional helical structure, which enhances fluid mixing and heat transfer through optimized geometric parameters. The fuel assembly consists of an Al cladding and U3Si2-Al fuel pellets. The equivalent thermal conductivity of the fuel assembly was calculated using the Maxwell model. The equivalent thermal expansion coefficient was calculated using the Turner model. Figure 2 As shown, the solid area represents multiple fuel elements, while the void area represents coolant channels.

[0034] The fuel assembly's core and cladding are modeled in three dimensions. The core is modeled using a linear elastic material, while the cladding uses an elastoplastic material model. As typical deformations of metallic structures, the plastic deformation of the cladding after it reaches the yield stage is considered. Thermal expansion effects are taken into account for both parts. In practice, COMSOL uses the second-order Piola-Kirchhoff stress tensor, combined with the Green-Lagrange strain tensor, the generalized Hooke's law, and the corresponding constitutive relations, to derive the displacement partial differential equations based on the original configuration. These equations are then solved numerically using the finite element method.

[0035] Preferably, the multiphysics coupling settings include setting fluid-structure interaction, non-isothermal flow, and thermal expansion in the multiphysics coupling node of the simulation software; The fluid-structure interaction is set to full coupling, and the fluid domain is set as a moving mesh region. The moving mesh is smoothed using Yeoh smoothing. The thermal expansion coefficient is calculated using the secant coefficient of thermal expansion as the thermal expansion coefficient, and the thermal expansion coefficient is anisotropic.

[0036] Preferably, in the COMSOL multiphysics coupling node settings, add three multiphysics coupling nodes: "fluid-structure interaction", "non-isothermal flow", and "thermal expansion".

[0037] Fluid-structure interaction (FSI): When performing bidirectional coupled calculations of coolant flow and fuel assembly structural deformation, the coupling type in the FSI nodes should be set to "Full Coupled," and the fluid domain should be set as a moving mesh region. COMSOL provides four mesh smoothing types: Laplace smoothing, Winslow smoothing, hyperelastic smoothing, and Yeoh smoothing.

[0038] The main purpose of this patent optimization is to optimize the geometric parameters of the fuel element, so Yeoh smoothing is chosen.

[0039] Non-isothermal flow simulations depict convective heat transfer between fuel elements and coolant, where the coolant is heated and the fuel element is cooled—a process known as fluid-structure conjugate heat transfer. This requires adding a "non-isothermal flow" multiphysics node. The heat transfer turbulence model uses the default Kays-Crawford model, which, compared to the heat flux transferred from the fuel plate to the coolant, does not consider the minute heat dissipation due to viscous flow during fluid flow.

[0040] Thermal expansion is used to simulate the thermal expansion of fuel elements due to temperature increases, and a "thermal expansion" multiphysics node is added. The secant coefficient of thermal expansion is used as the coefficient of thermal expansion to calculate the thermal strain caused by temperature increases. The coefficient of thermal expansion is anisotropic. Since the calculation process is steady-state, the thermoelastic damping process that occurs during transients is not considered. At the same time, since the mechanical losses caused by processes such as plastic deformation are small, the heat source effect of mechanical losses is ignored.

[0041] Preferably, in this embodiment of the invention, the multiphysics model contains a series of nonlinear equations, and the equations are discretized using finite element methods in the spatial domain, thus forming nonlinear algebraic equations. To achieve robustness, this patent employs a direct solver based on logical unit decomposition to solve the linear equation system.

[0042] The solver settings are shown in Table 1.

[0043] Table 1 Summary of Solver Settings

[0044] When performing mesh generation, the preferred embodiment of the present invention is as follows: Create separate components for the inlet / outlet surfaces and internal constraint sections, and establish a continuous segmented three-dimensional topology along the axial direction (using O-type center partitioning and deleting the central topology block).

[0045] Based on the geometric characteristics of the cross-shaped fuel rod, establish the correspondence between the points and edges on the inlet and outlet surfaces and internal constraint sections and the irregular planar structured topology, and determine the number and distribution of mesh nodes on each edge.

[0046] Based on the torsional characteristics of the cross-shaped fuel rods, the axial correspondence between the axial guide line and the continuous three-dimensional topology is established, and the number and distribution of mesh nodes in each segment along the axial direction are determined.

[0047] Based on the determined number of grid nodes and node distribution characteristics, a continuous three-dimensional structured grid for the fluid region of a single fuel rod is generated.

[0048] The automatic adjustment of the wall surface calculation, using either a fully analytical method or the wall function method, is based on the distance between the outer surface of the fuel element and the center of the first layer mesh. Automatic wall processing employs the wall function method, which avoids the surge in boundary layer mesh size required for high-precision simulation of turbulent flow and heat transfer within the boundary layer.

[0049] The simulation calculations include determining the temperature distribution, pressure distribution, and convective heat transfer coefficient of the fuel assembly, which will serve as target parameters for subsequent optimization.

[0050] Preferably, the multi-objective evolutionary optimization algorithm includes: The parametric geometric design variables are constructed into a design variable vector, and a set of multi-objective objective functions is extracted based on the multiphysics coupling simulation results of each candidate variable vector. The set of objective functions includes: maximizing the convective heat transfer characterization index, minimizing the coolant pressure drop, maximizing the power density, and minimizing the temperature gradient. The constraints include structural safety constraints and system allowable pressure drop constraints, so that only candidate solutions that meet the constraints or have been constrained participate in subsequent population updates.

[0051] The above-mentioned maximization of convective heat transfer characterization indicators is used to improve the heat transfer efficiency between coolant and fuel elements; minimization of coolant pressure drop is used to reduce the pressure drop of coolant flow and reduce pump power requirements; maximization of power density is used to increase the power per unit volume of the reactor core; and minimization of temperature gradient is used to avoid local overheating and ensure thermal safety.

[0052] In the geometric parameters selected in this invention, the helix radius R is used to influence the flow path of the coolant and the heat exchange area.

[0053] The pitch (P) determines the density of the fuel element's helical twist, which affects the fluid mixing effect.

[0054] The petal width (W) and thickness (T) determine the overall heat exchange area and structural strength of the fuel element.

[0055] The coolant passage clearance (G) affects the coolant flow resistance and heat transfer performance.

[0056] These variables are the input parameters that the optimization algorithm adjusts, and ultimately finds the optimal combination through optimization.

[0057] The constraints in this embodiment are: Flow and heat conduction satisfy the continuity equation, the Navier-Stokes equation, and the law of conservation of energy; The stress and deformation of fuel element materials are kept within safe limits; The voltage drop shall not exceed the allowable value of the system design.

[0058] Preferred, including: The objective function of the convective heat transfer characterization index Defined as convective heat transfer coefficient And calculated by the following formula: in, For the heat exchange on the surface of the fuel element, For the heat exchange area of ​​the fuel element, The surface temperature of the fuel element. The average temperature of the coolant; and and They respectively satisfy: in, This is the coolant mass flow rate. The specific heat capacity of the coolant. and These are the coolant inlet temperature and outlet temperature, respectively. The objective function of the coolant pressure drop Defined as voltage drop And calculated by the following formula: in, This refers to the coolant inlet pressure. This refers to the coolant outlet pressure. The objective function of power density It is determined by the total thermal power of the core and the total volume of the core fuel elements, and is calculated by the following formula: in, This represents the total thermal power of the reactor core. This is the total volume of the reactor core fuel elements, and It can be determined by the number of fuel elements and the volume of a single fuel element. Power density; The objective function of the temperature gradient Used to characterize the non-uniformity of temperature distribution inside or on the surface of a fuel element, and calculated by the following formula: in, and The temperature gradient is the temperature at any two points inside or on the surface of the fuel element, and the temperature gradient is obtained by extracting the maximum temperature difference from the finite element temperature field results. For temperature gradient; Based on the above objective functions, a fitness function is established, and the formula for the fitness function is: in, , , and Each item has its own weight. , , and This serves as a reference value for a specific target.

[0059] Preferably, the multi-objective optimization co-simulation is performed iteratively according to the following process: Given a range of design variables, an initial population is randomly generated. Each candidate solution in the initial population corresponds to a set of geometric and operational parameter combinations. For each candidate scheme, a simulation tool is called to run a multiphysics model simulation calculation and extract the parameters required for the objective function; fitness is calculated based on the objective function value, and crossover and mutation are performed to generate offspring population; after merging the offspring population with the parent population, individuals with better performance and maintaining diversity are selected according to fitness to enter the next generation population, and this process is repeated until the iteration termination condition is met.

[0060] Preferably, this patent selects the NSGA architecture, which is more suitable for optimization with multiple optimization parameters. The two-stage multi-objective evolutionary algorithm used can make up for the shortcomings of existing multi-objective evolutionary algorithms in balancing objective optimization and constraint satisfaction when dealing with more complex constrained multi-objective optimization problems. During the evolution process, it adaptively balances objective optimization and constraint satisfaction, and performs about 300-5000 optimization iterations through our objective function, fitness function and constraints to find the most suitable optimal solution set.

[0061] Preferably, in an embodiment of the present invention, the optimization process stage includes: Initialize the population, including: Given a range of design variables, an initial population (e.g., 30-100 candidate solutions) is randomly generated, with each candidate solution corresponding to a combination of geometric and operational parameters.

[0062] Simulation calculations include: For each candidate scheme, the simulation tool (COMSOL Multiphysics 5.5 with MATLAB) is invoked to run the multiphysics model simulation calculation, calculate and extract the corresponding parameter values ​​(such as heat transfer coefficient, pressure drop, etc.).

[0063] Fitness calculation includes: Calculate fitness using the objective function value: For Pareto optimization, the individual's non-dominance level and crowd distance are evaluated.

[0064] Generate the next generation population, including: Crossover and Mutation: Perform crossover and mutation operations on the design variables to generate a new population. Crossover: Swap the design variables of two individuals to create a new individual; Variation: Randomly adjust the design variables (keeping them within a reasonable range) to introduce randomness and avoid getting trapped in local optima.

[0065] Population merging: Merge the newly generated offspring population with the parent population to form a candidate solution set.

[0066] Population updates include: Based on the fitness function value, individuals with better performance are selected to enter the next generation of the population, thus preserving diversity.

[0067] The iterative process includes: The process of repeated simulation calculations, fitness calculations, generating new populations, and updating existing populations typically involves 300-5000 iterations, gradually approximating the optimal solution set, such as... Figure 3 As shown, each point represents an optimal solution.

[0068] Secondly, the present invention discloses a cloverleaf-shaped nuclear fuel element optimization system, comprising: The geometric model building module is used to obtain the parametric geometric design variables of the fuel element and generate the corresponding three-dimensional geometric model. The parameters of the fuel element include the helix radius. Pitch petal width Petal thickness and coolant passage clearance The three-dimensional geometric model is a model of a clover-shaped fuel element with a spiral structure formed by the axial twisting of the blades, and its coolant channels, including an inlet section for ensuring sufficient development of inlet flow and an outlet section for stabilizing the outlet boundary. The multiphysics coupling model building module is used to build a thermofluid-solid multiphysics coupling model on the three-dimensional geometric model. The multiphysics coupling model includes at least turbulent flow, conjugate heat transfer and structural response caused by thermal expansion, and completes solver settings, mesh generation and simulation calculation to obtain temperature field and pressure field. The multi-objective optimization module is used to extract convective heat transfer characterization indicators, coolant pressure drop, power density indicators and temperature gradient based on simulation results, and construct a multi-objective optimization problem in combination with structural safety constraints. The multi-objective evolutionary optimization algorithm is used to drive the multi-physics coupling model to perform joint simulation iteration, obtain the Pareto optimal solution set, determine the optimal parameter combination from it and output it.

[0069] Thirdly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0070] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0071] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0072] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing clover-shaped nuclear fuel elements, characterized in that, include: The parametric geometric design variables of the fuel element are obtained and the corresponding three-dimensional geometric model is generated. The parameters of the fuel element include the helix radius. Pitch petal width Petal thickness and coolant passage clearance The three-dimensional geometric model is a model of a clover-shaped fuel element with a spiral structure formed by the axial twisting of the blades, and its coolant channels, including an inlet section for ensuring sufficient development of inlet flow and an outlet section for stabilizing the outlet boundary. A thermofluid-solid multiphysics coupling model is established on the three-dimensional geometric model. The multiphysics coupling model includes at least turbulent flow, conjugate heat transfer, and structural response caused by thermal expansion. Solver settings, mesh generation, and simulation calculations are completed to obtain the temperature field and pressure field. Based on the simulation results, the convective heat transfer characterization index, coolant pressure drop, power density index and temperature gradient are extracted, and a multi-objective optimization problem is constructed in combination with structural safety constraints; A multi-objective evolutionary optimization algorithm is used to drive the multi-physics coupling model to perform joint simulation iterations, obtain the Pareto optimal solution set, and determine and output the optimal parameter combination from it.

2. The method according to claim 1, characterized in that, The fluid dynamics settings of the multiphysics coupling model include: Time-averaged simulation using incompressible Reynolds And select Algebraic turbulence models; The turbulent boundary layer wall treatment is performed in automatic mode. The boundary conditions include at least velocity inlet boundary conditions, pressure outlet boundary conditions, no-slip wall boundary conditions, and symmetric boundary conditions, and a gravity term is set to characterize the force effect on the coolant in the vertical direction.

3. The method according to claim 1, characterized in that, The heat transfer settings of the multiphysics coupling model include: A bulk heat source is installed in the fuel core, and a cosine power distribution is adopted. Establish the heat conduction process from the fuel core to the cladding and the forced convection heat transfer process from the cladding to the coolant; Given temperatures and thermal equilibrium conditions are set at the inlet and outlet respectively, and the fluid domain outlet adopts outflow boundary conditions. The Kays-Crawford model is used in the turbulent heat transfer setting, and the heat generated by the viscous dissipation of the fluid flow is ignored.

4. The method according to claim 1, characterized in that, The structural mechanics settings of the multiphysics coupling model include: fuel assembly is made of Encasing and Fuel core composition; The fuel core adopts a linear elastic material model, and the cladding adopts an elastic-plastic material model, both of which take into account the thermal expansion effect; The equivalent thermal conductivity of the fuel assembly was determined using the Maxwell model, and the equivalent thermal expansion coefficient was determined using the Turner model.

5. The method according to claim 1, characterized in that, The multiphysics coupling settings include setting fluid-structure interaction, non-isothermal flow, and thermal expansion in the multiphysics coupling node of the simulation software; The fluid-structure interaction is set to full coupling, and the fluid domain is set as a moving mesh region. The moving mesh is smoothed using Yeoh smoothing. The thermal expansion coefficient is calculated using the secant coefficient of thermal expansion as the thermal expansion coefficient, and the thermal expansion coefficient is anisotropic.

6. The method according to claim 1, characterized in that, Multi-objective evolutionary optimization algorithms include: The parametric geometric design variables are constructed into a design variable vector, and a set of multi-objective objective functions is extracted based on the multiphysics coupling simulation results of each candidate variable vector. The set of objective functions includes: maximizing the convective heat transfer characterization index, minimizing the coolant pressure drop, maximizing the power density, and minimizing the temperature gradient. The constraints include structural safety constraints and system allowable pressure drop constraints, so that only candidate solutions that meet the constraints or have been constrained participate in subsequent population updates.

7. The method according to claim 6, characterized in that, include: The objective function of the convective heat transfer characterization index Defined as convective heat transfer coefficient And calculated by the following formula: in, For the heat exchange on the surface of the fuel element, For the heat exchange area of ​​the fuel element, The surface temperature of the fuel element. The average temperature of the coolant; and and They respectively satisfy: in, This is the coolant mass flow rate. The specific heat capacity of the coolant. and These are the coolant inlet temperature and outlet temperature, respectively. The objective function of the coolant pressure drop Defined as voltage drop And calculated by the following formula: in, This refers to the coolant inlet pressure. This refers to the coolant outlet pressure. The objective function of power density It is determined by the total thermal power of the core and the total volume of the core fuel elements, and is calculated by the following formula: in, This represents the total thermal power of the reactor core. This is the total volume of the reactor core fuel elements, and It can be determined by the number of fuel elements and the volume of a single fuel element. Power density; The objective function of the temperature gradient Used to characterize the non-uniformity of temperature distribution inside or on the surface of a fuel element, and calculated by the following formula: in, and The temperature is the temperature at any two points inside or on the surface of the fuel element. The temperature gradient is obtained by extracting the maximum temperature difference from the finite element temperature field results. Based on the above objective functions, a fitness function is established, and the formula for the fitness function is: in, , , and Each item has its own weight. , , and This serves as a reference value for a specific target.

8. The method according to claim 7, characterized in that, The multi-objective optimization joint simulation is performed iteratively according to the following process: Given a range of design variables, an initial population is randomly generated. Each candidate solution in the initial population corresponds to a set of geometric and operational parameter combinations. For each candidate solution, the simulation tool is invoked to run a multiphysics model simulation calculation and extract the parameters required for the objective function; Fitness is calculated based on the objective function value, and crossover and mutation are performed to generate offspring populations. After merging the offspring populations with the parent populations, individuals with better performance and maintaining diversity are selected based on fitness to enter the next generation population. This process is repeated until the iteration termination condition is met.

9. A clover-shaped nuclear fuel element optimization system, characterized in that, include: The geometric model building module is used to obtain the parametric geometric design variables of the fuel element and generate the corresponding three-dimensional geometric model. The parameters of the fuel element include the helix radius. Pitch petal width Petal thickness and coolant passage clearance The three-dimensional geometric model is a model of a clover-shaped fuel element with a spiral structure formed by the axial twisting of the blades, and its coolant channels, including an inlet section for ensuring sufficient development of inlet flow and an outlet section for stabilizing the outlet boundary. The multiphysics coupling model building module is used to build a thermofluid-solid multiphysics coupling model on the three-dimensional geometric model. The multiphysics coupling model includes at least turbulent flow, conjugate heat transfer and structural response caused by thermal expansion, and completes solver settings, mesh generation and simulation calculation to obtain temperature field and pressure field. The multi-objective optimization module is used to extract convective heat transfer characterization indicators, coolant pressure drop, power density indicators and temperature gradient based on simulation results, and construct a multi-objective optimization problem in combination with structural safety constraints. The multi-objective evolutionary optimization algorithm is used to drive the multi-physics coupling model to perform joint simulation iteration, obtain the Pareto optimal solution set, determine the optimal parameter combination from it and output it.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1-7.

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