Nuclear fuel assembly stress topological optimization design method considering irradiation effect

Abstract

A nuclear fuel assembly stress topological optimization design method considering an irradiation effect relates to the technical field of nuclear reactors, and comprises the following steps: S1, carrying out parametric geometric modeling on the structural size of a nuclear fuel assembly; s2, constructing a finite element simulation model based on the geometric model according to the real heap condition, and carrying out stress analysis; s3, determining optimization parameters through an intelligent algorithm; s4, setting a topological optimization target; s5, determining a design domain according to the stress data and the optimization parameters; s6, performing topological optimization on the geometric model according to the steps S4 and S5; s7, process adaptability improvement is carried out on the topological optimization structure, and verification and evaluation are carried out; s8, if verification or evaluation is not passed, returning to the step S1 or the step S2, and restarting the optimization process until an optimization scheme passing verification and evaluation is obtained; the method is used for solving the problem that a fuel assembly obtained through a traditional optimization method is poor in service environment adaptability and stress corrosion cracking resistance.

Description

Technical Field

[0001] This invention relates to the field of nuclear reactor technology, and more specifically to a stress topology optimization design method for nuclear fuel assemblies that takes into account irradiation effects. Background Technology

[0002] Nuclear fuel assemblies are one of the core components of a reactor. They are subjected to high temperature, high pressure, irradiation, and complex aquatic environments for extended periods. These extreme conditions place extremely high demands on the mechanical and irradiation performance of the fuel assembly structures.

[0003] Nuclear reactor operation requires fuel assemblies to remain in service for extended periods. The structural components of these fuel assemblies must withstand continuous mechanical loads, environmental stresses, and in-reactor radiation. Therefore, they are often under significant deformation and high internal stress, making them prone to stress failure behaviors such as stress corrosion cracking (SCC) and fatigue fracture. Currently, structural failures or fractures of fuel assemblies have occurred in operating units both domestically and internationally. These accidents not only seriously affect reactor operational safety but also lead to high maintenance and replacement costs, impacting the economics of nuclear reactors.

[0004] Chinese patent CN113255229A discloses a multidisciplinary structural design optimization method for fuel assemblies based on co-simulation. It employs an ISIGHT co-simulation-based fuel assembly optimization design method, fully leveraging the strengths of NX, ICME CFD, FLUENT, and ABAQUS in their respective fields. Compared to manually adjusting and updating geometric models and setting numerical simulation parameters, this method significantly reduces time costs during optimization. The use of approximate model optimization also improves the accuracy and reliability of the optimization design. However, because this method lacks information on the influence of irradiation effects on material properties, the nuclear environment adaptability of the fuel assemblies designed using this method is poor. Furthermore, the optimization objective of this method is temperature uniformity, with only stress constraints applied, resulting in poor resistance to stress corrosion cracking in the optimized fuel assemblies.

[0005] Therefore, we provide an optimized design method that can effectively improve resistance to stress corrosion cracking and fatigue fracture risks. Summary of the Invention

[0006] The purpose of this invention is to provide a stress topology optimization design method for nuclear fuel assemblies that takes into account the irradiation effect, which is used to solve the problem that fuel assemblies obtained by traditional optimization methods have poor adaptability to the nuclear environment and poor resistance to stress corrosion cracking.

[0007] This invention is achieved through the following technical solution: A stress topology optimization design method for nuclear fuel assemblies considering irradiation effects, specifically including: S1. Perform parametric geometric modeling on the structural dimensions of the nuclear fuel assembly to obtain the geometric model; S2. Based on the actual stacking conditions, a finite element simulation model is constructed based on the geometric model, and stress analysis is carried out to obtain stress distribution data; S3. Using intelligent algorithms, obtain the sensitivity of stress distribution data corresponding to each parameter of the geometric model, and determine the optimization parameters; S4. Based on the stress conditions throughout the fuel assembly's lifespan, set the objectives for topology optimization; S5. Determine the design domain based on stress data and optimization parameters; S6. Based on the design domain and under the objective set in S4, perform topology optimization on the geometric model to obtain the topology-optimized structure; S7. Improve the process adaptability of the topology-optimized structure, and perform stress simulation verification and comprehensive evaluation on the improved structure; S8. If the verification or evaluation fails, return to S1 to modify the structural dimension parameters or return to S2 to modify the simulation model calculation conditions, and restart the optimization process until an optimized solution that passes the verification and evaluation is obtained.

[0008] Furthermore, the nuclear fuel assembly in S1 is suitable for a water-cooled reactor.

[0009] Furthermore, the parameters for parametric geometric modeling in S1 include structural parameters, shape parameters, and constraint parameters.

[0010] Furthermore, the actual reactor parameters input into the finite element simulation model in S2 include reactor operating temperature, system pressure, neutron flux, coolant flow rate and density, burnup, hydraulic load, geometric constraints, and changes in material properties due to irradiation effects.

[0011] Furthermore, the stress analysis process in S2 includes geometric modeling, mesh generation, material constitutive definition, boundary condition application, calculation and solution, and post-processing analysis; the geometric model is discretized and simulated using shell elements, beam elements, or solid elements according to the characteristics of the structural components.

[0012] Furthermore, the intelligent algorithms used in S3 include, but are not limited to, whale optimization algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, genetic algorithm, differential algorithm or simulated annealing algorithm.

[0013] Furthermore, the sensitivity in S3 is characterized by mathematical methods: when one parameter changes while other parameters remain unchanged, the ratio of the change in the calculation result compared to the baseline result to the change in the parameter is the sensitivity. The larger the ratio, the stronger the sensitivity.

[0014] Furthermore, the stress condition throughout the entire lifespan in S4 refers to the change in loads and boundary conditions experienced by the fuel assembly during operation over time, with the calculation input in the simulation model being a function of time.

[0015] Furthermore, the topology optimization objective set in S4 is to achieve an overall stress reduction within the design domain while keeping the load and boundary conditions unchanged, thereby reducing the stress peak and stress-sensitive area of ​​the nuclear fuel assembly structure during its service life.

[0016] Furthermore, the process adaptability improvements in S7 include, but are not limited to, adding chamfers, fillets, or hole features to the topology-optimized structure.

[0017] The technical solution of the present invention has at least the following advantages and beneficial effects: This invention discloses a stress topology optimization design method for nuclear fuel assemblies that considers irradiation effects. By systematically introducing key parameters such as neutron flux and burnup into the finite element simulation model and considering the dynamic changes of material properties with irradiation, the stress analysis can truly reflect the actual mechanical state of the assembly throughout its entire service life. This ensures that the optimized design originates from the real service environment, thereby greatly improving the reliability of the design results and their adaptability to the nuclear environment, and reducing the risk of failures such as stress corrosion cracking and fatigue fracture caused by irradiation effects from the source.

[0018] Furthermore, by organically combining parametric modeling, intelligent sensitivity analysis, and topology optimization, the key design parameters that have the greatest impact on stress peaks and stress-sensitive areas are automatically and efficiently identified through intelligent algorithms. This clarifies the optimization direction and avoids the blind trial and error that traditionally relies on engineers' experience. This not only significantly shortens the design cycle and improves optimization efficiency, but more importantly, topology optimization can spontaneously generate the optimal material layout within a given design domain, thereby obtaining a new and efficient structural configuration that surpasses conventional experience. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a method flow of the present invention; Figure 2 This is a flowchart illustrating the specific steps of the intelligent algorithm of the present invention; Figure 3 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Example 1 like Figure 1-2 The present invention provides a stress topology optimization design method for nuclear fuel assemblies considering irradiation effects. This method can not only directly design the overall structure of the fuel assembly, but also design any key subsystem of the fuel assembly in practical applications, and finally combine multiple subsystems into the final fuel assembly. Since the positioning grid is an important structural component of the fuel assembly, the clamping spring and the rigid protrusions punched out on the grid strip together form a clamping structure to clamp the fuel rods and achieve axial positioning. The clamping structure needs to be maintained in a state of large deformation and high stress for a long time during its service life, which makes it prone to stress failure. Therefore, in this embodiment, the positioning grid and spring can be used as an example: Specifically, it includes: S1. Perform parametric geometric modeling on the structural dimensions of the nuclear fuel assembly to obtain the geometric model; Furthermore, the nuclear fuel assembly in S1 is suitable for water-cooled reactors and can be the fuel assembly of nuclear reactors for different application scenarios, including but not limited to existing commercial pressurized water reactors, fourth-generation supercritical water-cooled reactors, and civilian icebreaker power reactors. In addition, the parameters of the parametric geometric modeling include structural parameters, shape parameters, and constraint parameters. The three-dimensional geometric model of the nuclear fuel assembly is obtained based on parametric modeling, and the new structure can be quickly updated by modifying the parameters. This lays the foundation for subsequent optimization iterations and enables rapid response to design changes. Furthermore, during the parametric geometric modeling of the positioning grid and spring, the main structural dimensions of the rigid convex and spring are parameterized, including but not limited to the spring length, width, height, and chord length, and constraint relationships are established between each dimension to create a clamping structure geometric model.

[0022] S2. Based on the actual stacking conditions, a finite element simulation model is constructed based on the geometric model, and stress analysis is carried out to obtain stress distribution data; The finite element simulation model is based on the clamping structure geometric model obtained in S1. It uses the finite element analysis method to simulate the stress state of the structure under the influence of irradiation during service. The principle is to discretize the continuum into a finite number of small elements and solve the governing equations on each element to approximate complex physical phenomena. Considering the "actual reactor condition" is the key to ensuring the authenticity of the simulation results. Its core is to accurately simulate the time-varying effect of irradiation on material properties. In addition, the actual reactor condition parameters input to the finite element simulation model include reactor operating temperature, system pressure, neutron flux, coolant flow rate and density, burnup, hydraulic load, geometric constraints, and changes in material properties under the influence of irradiation. Taking a common double spring with a symmetrical structure design as an example, the spring sheet is a thin shell structure of uniform thickness, suitable for discrete simulation using shell elements; the two ends of the spring are fixed to the strip by welding, restricting out-of-plane displacement and rotation; the strip and other parts of the spring may come into contact during deformation; and the loads and boundary conditions applied in the simulation calculation model include: the two ends of the spring do not have out-of-plane displacement, but can slide along the length direction; the strip provides support to the spring through contact; the fuel rod generates displacement in the strip normal direction and forces the spring to deform through contact. Finally, the stress distribution state of the spring under load can be obtained by calculation.

[0023] It is important to note that the stress analysis process includes geometric modeling, mesh generation, material constitutive definition, boundary condition application, computation and solution, and post-processing analysis. The geometric modeling step is the physical foundation of stress analysis. Based on the parametric geometric model of S1, a precise three-dimensional numerical representation of the fuel assembly structure (such as positioning grids and springs) is obtained. This geometric model defines the physical shape and size of the solution domain and serves as the basis for all subsequent operations. Parametric modeling ensures the repeatability and rapid modification of the geometric model, facilitating optimization iterations. Meshing is the core of the finite element method—the "discretization" process. Its principle is to divide a complex continuous geometry into a large number of simple, finite-sized micro-elements. These elements are interconnected at nodes to form a "mesh." This discretization transforms the boundary value problem of solving complex partial differential equations (governing equations, such as equilibrium equations) into a system of algebraic equations solving a finite number of unknowns (such as displacements) at a given number of nodes. The quality of the mesh (such as element shape and density) directly determines the accuracy, stability, and efficiency of the computation. In stress concentration regions, mesh refinement is necessary to improve computational accuracy. Material constitutive modeling is defined as the analysis of material properties. For example, a material constitutive model defines the stress-strain response relationship of an element. In this invention, the key is defining a material constitutive model that considers irradiation effects. The finite element simulation model is not directly "generated," but rather calls a pre-embedded model that describes the material properties as a function of neutron flux. and temperature Mathematical models of change, such as radiation hardening models:

[0024] In the formula, The initial yield strength of the material. The increment of the material's yield strength is a function of two variables; Specific material constants are assigned to the model. This allows the material properties of each element to be dynamically updated based on the irradiance and temperature environment of its location, which is key to achieving simulation of "real-world stack conditions". Boundary conditions are applied to simulate the actual constraints and stress states of the structure. Based on the actual stacking conditions, physical constraints and loads are transformed into mathematical constraints and applied to the mesh. The computational solution is obtained using a solver based on the equilibrium equations. The solver is grounded in the principle of virtual work or the principle of minimum potential energy. The equilibrium equations are derived by assembling the stiffness characteristics of each element (determined by geometry and material constitutive properties) into the overall stiffness matrix of the entire structure, forming a system of linear or nonlinear equations with nodal displacements as unknowns. The solution typically employs iterative methods, such as the Newton-Raphson method, to progressively approximate the true solution. This process is automated by a computer, ultimately determining the displacements of all nodes. Post-processing analysis aims to transform numerical results into conclusions with engineering significance. Based on the obtained nodal displacements, the strain of each element is calculated back using the shape function and material constitutive relation of each element. Then, the stress is calculated according to the stress-strain relationship, such as the von Mises equivalent stress. The numerical results are usually visualized in the form of contour plots, curves or animations, so as to intuitively identify stress peaks, deformation modes and stress-sensitive areas, providing a direct basis for subsequent optimization.

[0025] Furthermore, the geometric model is discretized and simulated using shell elements, beam elements, or solid elements, depending on the structural component. Solid elements divide a three-dimensional solid spring into smaller blocks (such as tetrahedrons or hexahedrons). Each solid element has dimensions in three spatial directions, which can describe the complex stress state inside the structure in detail. When it is necessary to accurately analyze areas with significant three-dimensional stress concentration effects, such as the root of the spring, near holes, or rigid protrusions, this is the most accurate but computationally inefficient method. Shell elements are used when the size (thickness) of a spring in one direction is much smaller than the size in the other two directions. They can be simplified to a mid-surface and a thickness property can be assigned to this mid-surface. Shell elements can describe tensile / compressive stress in the plane and bending stress out of the plane at the same time. The computational efficiency of this method is much higher than that of solid elements because it greatly reduces the number of elements and nodes while ensuring sufficient accuracy. Beam elements are used when the structure of a spring is idealized as a line with cross-sectional properties (such as area and moment of inertia). It mainly bears axial force, shear force, and bending moment. If the width and thickness of the spring are similar, and its main mechanical behavior is bending about the weak axis, it can be simplified to a beam element. This simplification is the highest and the calculation is the fastest, but it may not be able to capture the stress distribution in the width direction.

[0026] S3. Using intelligent algorithms, obtain the sensitivity of stress distribution data corresponding to each parameter of the geometric model, and determine the optimization parameters; Sensitivity analysis is used to quantify the impact of each design parameter on the output result (such as peak stress). Through intelligent algorithms, the parameter space can be sampled efficiently and the sensitivity can be calculated. The most critical design variables can be identified, thereby narrowing the dimensions of the optimization problem and improving efficiency. In particular, intelligent algorithms include, but are not limited to, whale optimization algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, genetic algorithm, differential algorithm or simulated annealing algorithm; Taking the genetic algorithm as an example, the specific steps are as follows: S31. Obtain the stress distribution data corresponding to each parameter of the geometric model; S32. Select a genetic algorithm and set its parameters, including population size (the number of schemes evaluated per generation) and maximum number of iterations; S33. Randomly generate the first generation population within the parameter space, i.e., multiple parameter combinations. This is equivalent to performing a space-filling sampling (such as Latin hypercube sampling), laying the foundation for the analysis. S34. Based on stress distribution data, identify combinations with low stress and use their inherent mechanisms, such as selection, crossover, or variation, to generate a new generation of parameter combinations; the aim is to search for regions with better performance while maintaining global exploration. S35. Save every evaluated parameter combination and its corresponding stress results in each generation to form a huge dataset; S36. After the algorithm iteration is completed, the optimal solution found by the algorithm is not used directly, but the complete dataset collected throughout the process is used for statistical analysis. S37. The dataset is processed using the variance-based Sobol index method. This method can calculate the first-order sensitivity index of each parameter. The index value is between 0 and 1. The larger the value, the greater the average effect of the individual change of the parameter on the stress, that is, the higher the sensitivity. In addition, sensitivity is characterized by mathematical methods, specifically the normalized partial derivative method: when one parameter changes while other parameters remain unchanged, the ratio of the change in the calculated result compared to the baseline result to the change in the parameter is the sensitivity. The larger the ratio, the stronger the sensitivity. S38. Based on the calculated sensitivity index, sort all parameters and select the 6 most sensitive parameters as the change parameters for subsequent topology optimization.

[0027] The intelligent algorithm used in this step can automatically and purposefully sample a large amount of data in the parameter space. Then, all the sampled data, not just the final result, is processed using a specialized statistical tool, namely the Sobol exponent method, to ultimately extract the sensitivity ranking of each parameter.

[0028] Compared to the traditional method of changing only one parameter at a time, this method has the advantage of being able to efficiently handle multi-parameter problems and capture the nonlinear interaction effects between parameters, thus obtaining more comprehensive and reliable sensitivity analysis results.

[0029] S4. Based on the stress conditions throughout the fuel assembly's lifespan, set the objectives for topology optimization; Considering the stress conditions throughout the entire lifespan, the loads and boundary conditions borne by the structural components of the fuel assembly during in-core operation vary depending on parameters such as temperature and neutron flux at different times of the lifespan. The corresponding calculation input in the simulation model should be a function of time. The goal of topology optimization is to iteratively optimize the initial design parameters using topology optimization design methods. While keeping the load and boundary conditions of the positioning grid and spring unchanged, the overall stress should be reduced within the specified optimization area. This will reduce the peak stress of the clamping spring during its lifespan and decrease the stress-sensitive area. These are user-defined topology optimization objectives. In addition, areas on the spring with stress higher than 80% of the spring material's yield strength are considered stress-sensitive areas, which are represented by area in the shell element model.

[0030] S5. Determine the design domain based on stress data and optimization parameters; The design domain refers to the entire three-dimensional space region that is predefined at the start of topology optimization, allowing materials to exist or disappear. Subsequent optimization algorithms will freely redistribute materials within this space to find the optimal structure. In the structure of positioning grids and springs, the main body of the spring sheet can be set as the design domain, while the welding area between it and the grid strip can be set as the non-design domain to ensure that the connection function remains unchanged.

[0031] S6. Based on the design domain and under the objective set in S4, perform topology optimization on the geometric model to obtain the topology-optimized structure; Specifically, by using topology optimization algorithms, such as the variable density method or SIMP method, the artificial density variable (between 0 and 1) of each cell is iteratively adjusted to redistribute the material from inefficient regions to efficient regions, eventually converging to a clear material layout. S7. Improve the process adaptability of the topology-optimized structure, and perform stress simulation verification and comprehensive evaluation on the improved structure; Taking the fuel assembly positioning grid clamping spring as an example: Original structure: It may be a single curved spring sheet of uniform thickness; The new structure after topology optimization may be a non-uniform three-dimensional mesh structure with reinforcing ribs and weight-reducing holes optimized from the spring sheet, with the material concentrated at the fixed end and in the high-stress area in contact with the fuel rod.

[0032] The final new structure after process improvement: Engineers interpreted this grid structure as a variable cross-section "I" or "double arch" spring structure and added rounded corners for smooth transition; this new spring, with the same or even lighter weight, has a much lower peak stress and stress-sensitive area than the original design.

[0033] S8. If the verification or evaluation fails, return to S1 to modify the structural dimension parameters or return to S2 to modify the simulation model calculation conditions, and restart the optimization process until an optimized solution that passes the verification and evaluation is obtained.

[0034] Example 2 As one embodiment, the specific steps for performing stress simulation verification and comprehensive evaluation of the improved structure, as well as the judgment of verification and evaluation, are as follows: If the stress simulation results show that the peak stress and the area of ​​the stress-sensitive region are reduced compared to the structure before topology optimization, then it meets expectations. At this point, a comprehensive evaluation of the topology-optimized structure is conducted, with evaluation indicators including economy, safety, and environmental protection. If all three evaluation indicators are higher than the threshold, the evaluation is considered passed, and the optimized scheme is the final scheme. However, if the peak stress and the area of ​​the stress-sensitive region are larger than the structure before topology optimization, or if any evaluation index is lower than the threshold, the verification and evaluation are considered to have failed. The process will then return to S1 to modify the structural dimension parameters or return to S2 to modify the simulation model calculation conditions, and the optimization process will restart.

[0035] Example 3 As attached Figure 3 An electronic device shown includes: Processor, memory, communication interface; The memory is used to store the executable instructions of the processor; The processor is configured to execute the above-described lattice spring topology optimization design method by executing the executable instructions.

[0036] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described lattice spring topology optimization design method.

[0037] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A stress topology optimization design method for nuclear fuel assemblies considering irradiation effects, specifically comprising: S1. Perform parametric geometric modeling on the structural dimensions of the nuclear fuel assembly to obtain the geometric model; S2. Based on the actual stacking conditions, a finite element simulation model is constructed based on the geometric model, and stress analysis is carried out to obtain stress distribution data; S3. Using intelligent algorithms, obtain the sensitivity of stress distribution data corresponding to each parameter of the geometric model, and determine the optimization parameters; S4. Based on the stress conditions throughout the fuel assembly's lifespan, set the objectives for topology optimization; S5. Determine the design domain based on stress data and optimization parameters; S6. Based on the design domain and under the objective set in S4, perform topology optimization on the geometric model to obtain the topology-optimized structure; S7. Improve the process adaptability of the topology-optimized structure, and perform stress simulation verification and comprehensive evaluation on the improved structure; S8. If the verification or evaluation fails, return to S1 to modify the structural dimension parameters or return to S2 to modify the simulation model calculation conditions, and restart the optimization process until an optimized solution that passes the verification and evaluation is obtained.

2. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The nuclear fuel assembly in S1 is suitable for a water-cooled reactor.

3. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The parameters for parametric geometric modeling in S1 include structural parameters, shape parameters, and constraint parameters.

4. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The parameters of the actual reactor conditions input into the finite element simulation model in S2 include reactor operating temperature, system pressure, neutron flux, coolant flow rate and density, burnup, hydraulic load, geometric constraints, and changes in material properties due to irradiation effects.

5. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The stress analysis process in S2 includes geometric modeling, mesh generation, material constitutive definition, boundary condition application, calculation and solution, and post-processing analysis; the geometric model is discretized and simulated using shell elements, beam elements, or solid elements according to the characteristics of the structural components.

6. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The intelligent algorithms used in S3 include whale optimization algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, genetic algorithm, differential algorithm or simulated annealing algorithm.

7. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The sensitivity in S3 is characterized by mathematical methods: when one parameter changes while other parameters remain unchanged, the ratio of the change in the calculation result compared to the baseline result to the change in the parameter is the sensitivity. The larger the ratio, the stronger the sensitivity.

8. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The stress condition throughout the entire lifespan in S4 refers to the load and boundary conditions that the fuel assembly experiences during operation, which change over time. The calculation input in the simulation model is a function of time.

9. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The topology optimization objective set in S4 is to reduce the overall stress within the design domain while keeping the load and boundary conditions unchanged, thereby reducing the peak stress and stress-sensitive area of ​​the nuclear fuel assembly structure during its service life.

10. The method for stress topology optimization design of nuclear fuel assemblies considering irradiation effects according to claim 1, characterized in that: The process adaptability improvement in S7 includes adding chamfer, fillet, or hole features to the topology-optimized structure.

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