Simulation method for studying life period mechanical property of aluminum alloy material for reactor

Abstract

The invention relates to a mechanical property simulation method for researching the service life of an aluminum alloy material for a reactor. The simulation method for the life period mechanical property of the aluminum alloy material comprises the following steps: S1, preparing initial, middle and last stage aluminum alloy samples according to a < 27 > Al-< 28 > Si transmutation path of the aluminum alloy material; s2, representing the sample by using a transmission electron microscope, obtaining precipitated particle parameters, calculating a volume fraction f, fitting f and operation time t, and constructing a precipitation evolution model f (t); s3, performing high-energy ion irradiation on the sample, and performing nanoindentation experiment and load-depth curve fitting to obtain nano hardness H; s4, based on the f (t), establishing a relation model of the nano hardness H and the irradiation damage D; and S5, calculating the change quantity of the nano hardness H, converting the change quantity into the change quantity of the yield strength, associating the irradiation damage D with the service time T, and establishing a life period yield strength prediction model. According to the method, the mechanical property of the aluminum alloy in the full service cycle of the research reactor can be accurately evaluated.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation technology for nuclear reactor structural materials, and in particular to a simulation method for studying the life-cycle mechanical properties of aluminum alloy materials used in reactors. Background Technology

[0002] Aluminum alloys are widely used as key structural materials in research on reactor weight tanks, fuel cladding supports, and other critical components due to their excellent neutron economy, high thermal conductivity, low density, and good formability. However, during long-term service near the reactor core, aluminum alloys are continuously subjected to neutron irradiation, leading to two major problems:

[0003] Irradiation damage effect: Neutron bombardment of aluminum alloy atoms causes atomic displacement, forming microscopic defects such as dislocation loops, vacancy clusters, and bubbles. These defects hinder the movement of dislocations inside the material, triggering "irradiation hardening", which leads to an increase in the yield strength and a decrease in the plasticity of the material.

[0004] Compositional transmutation effect: Major elements in aluminum alloys 27 Al will transform through a neutron capture reaction into 28 As the service life of the material increases, the Si content gradually increases. Once it exceeds the solid solubility, it will nucleate and grow to form Si-based precipitates, which will further change the microstructure and mechanical properties of the material.

[0005] Currently, the mainstream method for evaluating the mechanical properties of aluminum alloys after irradiation is the neutron irradiation experiment: the sample is placed in an existing research reactor and subjected to neutron irradiation consistent with actual service, and then the performance evolution is analyzed through offline testing. However, this method has significant drawbacks:

[0006] Long cycle: Irradiation experiments simulating the neutron flux of aluminum alloy materials in a reactor until the end of their lifespan can take months to years;

[0007] High cost: It relies on a dedicated research reactor, and the cost of experimental equipment and maintenance is high;

[0008] Poor controllability: Parameters such as neutron flux and irradiation temperature are difficult to control precisely, and the effects of "damage effect" and "transmutation effect" cannot be separated.

[0009] In existing technologies, although some studies have attempted to simulate neutron damage using ion irradiation, the accuracy of mechanical property prediction is low, making it difficult to meet the needs of research reactor safety assessment. Summary of the Invention

[0010] To address the aforementioned problems in the prior art, this invention proposes a simulation method for studying the life-cycle mechanical properties of aluminum alloy materials used in reactors, which can accurately assess the mechanical properties of aluminum alloys throughout the entire service life of the research reactor.

[0011] Specifically, this invention proposes a simulation method for studying the life-cycle mechanical properties of aluminum alloy materials used in reactors, comprising the following steps:

[0012] S1, based on aluminum alloy materials used in service 27 Al→ 28 The transmutation path of Si was investigated, and aluminum alloy samples simulating different service stages were designed and prepared. The aluminum alloy samples included initial state samples, mid-life state samples, and end-life state samples. The mass percentage of Si added to the aluminum alloy samples was selected based on the transmutation calculation results of the aluminum alloy during service.

[0013] S2. The microstructure of each sample was characterized using transmission electron microscopy to obtain the size and number density of the precipitated particles. Based on the size and number density of the precipitated particles, the volume fraction f of the precipitated phase for each sample was calculated. Then, the volume fraction f of the precipitated phase was fitted with the running time t to construct a precipitation evolution model. ;

[0014] S3. High-energy ion irradiation was applied to each sample. Nanoindentation experiments were performed on the samples before and after irradiation. The load-depth curve of the indentation was fitted to obtain the nanohardness value H.

[0015] S4, based on the aforementioned evolutionary model A relationship model between the nanohardness H and the irradiation damage D was established, and the nanohardness value H of each sample under different irradiation damage values ​​D was fitted according to the relationship model.

[0016] S5, calculate the change in the nanohardness value H and convert it into the change in yield strength ∆σ, establish the yield strength-irradiation damage prediction model for the aluminum alloy sample, and use the defect saturation dose D sat Characterizes the end of service life.

[0017] According to one embodiment of the present invention, the diameter range of the precipitated particles characterized by the transmission electron microscope is 8 nm to 150 nm, a typical size is selected within the diameter range, and the number density of precipitated particles of each typical size is statistically analyzed.

[0018] According to an embodiment of the present invention, in step S2, a JMAK model is used to fit the precipitated phase volume fraction f with the running time t, wherein the precipitation evolution model... for: ,in, The volume function of saturated precipitates at the end of the simulated service life of the sample. The growth rate constant of the precipitated phase is... The Avrami index is the value of the precipitated phase.

[0019] According to one embodiment of the present invention, in step S3, the dose range of high-energy ion irradiation covers the initial stage to the precipitation saturation stage, and no less than 6 irradiation dose points are set, and the irradiation damage peak and the ion deposition peak do not overlap; in the nanoindentation experiment, no less than 20 indentation points are set for each sample, and the load-depth curve of the indentation points is fitted using the Nix-Gao model to obtain the nanohardness value H, the indentation depth is greater than the thickness of the irradiated layer and the fitting region avoids the ion deposition peak range.

[0020] According to one embodiment of the present invention, the indentation depth of the nanoindentation experiment is 20% to 50% greater than the thickness of the irradiated layer.

[0021] According to one embodiment of the present invention, in step S3, the irradiation temperature is within the range of room temperature or ±5°C of the actual operating temperature of the aluminum alloy material.

[0022] According to an embodiment of the present invention, in step S4, the relationship model expression between the nanohardness H and the irradiation damage D is as follows: ;

[0023] in, The hardness of the unirradiated sample. and , respectively, are the strengthening coefficients of the matrix and the precipitated phase, and The nanohardness was obtained by fitting experimental data from multiple sets of samples with different compositions, comparing samples containing Si and those without Si. Value differentiation settings.

[0024] According to one embodiment of the present invention, in step S5, the change in hardness... Converted to change in yield strength The conversion formula is: , where constant =0.266 MPa / GPa.

[0025] According to one embodiment of the present invention, the end of service is defined using a defect saturation stage, with the corresponding defect saturation dose D. sat Determined based on at least one of the following criteria:

[0026] When the growth rate of the number density, average size, or barrier effect of dislocation loops or dislocation networks is lower than a preset threshold in a continuous dose range, it exhibits a stable plateau.

[0027] The nanohardness, or the yield strength derived from it, enters a plateau region within the continuous dose range, with the increase per unit dose increment being lower than a preset threshold.

[0028] According to an embodiment of the present invention, the expression of the lifetime yield strength prediction model is as follows: ;

[0029] Where α is the irradiation dislocation loop growth rate constant. The Avrami index is the result of irradiation of dislocation loops.

[0030] This invention provides a simulation method for studying the mechanical properties of aluminum alloy materials used in reactors throughout their service life. By combining composition design to simulate transmutation with high-energy ion irradiation to simulate damage, and by combining microstructure characterization with mathematical models, the mechanical properties of aluminum alloys can be efficiently and accurately evaluated throughout the entire service life of the research reactor.

[0031] It should be understood that the above general description and the following detailed description of the present invention are exemplary and illustrative, and are intended to provide further explanation of the present invention. Attached Figure Description

[0032] The accompanying drawings are included to provide further explanation of the invention; they are incorporated into and constitute a part of this application. The drawings illustrate embodiments of the invention and, together with this specification, serve to explain the principles of the invention. In the drawings:

[0033] Figure 1 A flowchart illustrating a simulation method for studying the life-cycle mechanical properties of aluminum alloy materials for reactors, according to an embodiment of the present invention, is shown.

[0034] Figure 2 The diagram illustrates the evolution and fitting results of nanohardness measured by nanoindentation with irradiation damage according to an embodiment of the present invention.

[0035] Figure 3 The diagram illustrates the evolution and fitting results of the aluminum precipitate volume fraction f versus running time t according to an embodiment of the present invention.

[0036] Figure 4 A schematic diagram showing the evolution and fitting results of nanohardness H and irradiation damage D according to an embodiment of the present invention is presented.

[0037] Figure 5 The diagram illustrates the evolution of the yield strength variation Δσ with the defect accumulation index in one embodiment of the present invention. Detailed Implementation

[0038] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this application or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0040] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0041] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0042] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0043] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0044] In this invention, unless otherwise specified, the terms used should be understood in the sense that are commonly understood by those skilled in the art.

[0045] In this invention, "ion irradiation" refers to the use of high-energy ions (such as Si) 5+ Accelerated irradiation of materials simulates the displacement damage effect induced by neutron irradiation. "Irradiation damage" refers to the cumulative atomic displacement caused by ion irradiation, usually expressed in dpa (displacement per atom), which is used to characterize the degree of energy impact on the material during irradiation.

[0046] In this invention, "transmutation" refers to the process by which elements in materials are transformed into other elements through nuclear reactions such as neutron capture during the operation of a nuclear reactor. 27 Al is converted into... through thermal neutron capture. 28 Si. Mass percentage (wt.%) refers to the percentage content of a certain element in an alloy by mass, and is often used to characterize the chemical composition of each component in an alloy.

[0047] In this invention, "nanoindentation" refers to using an instrument with an indentation depth at the nanoscale to perform indentation deformation tests on local areas of a material, obtain nanohardness values, and thus characterize the material's resistance to deformation.

[0048] In this invention, "yield strength" refers to the minimum stress value at which a material undergoes plastic deformation during tension or compression, and its unit is MPa. It can usually be estimated using the empirical conversion relationship between nanohardness and yield strength.

[0049] In this invention, "precipitated phase" refers to the second phase particles that spontaneously form in aluminum alloys through nucleation and growth mechanisms after the solute element (such as Si) has a solid solubility exceeding the limit or is locally enriched by irradiation.

[0050] In this invention, "dislocation loop" refers to a closed-loop dislocation structure formed during irradiation due to the migration and aggregation of point defects (vacancies or interstitial atoms). "Irradiation hardening" refers to the phenomenon that the mechanical properties (such as yield strength) of a material increase with increasing damage dose after irradiation, mainly due to the hindering effect of irradiation defects such as dislocation loops. "Evolution" refers to the changes in morphology, size, distribution, and quantity of the internal microstructure of a material (such as precipitates, dislocation loops, etc.) under irradiation with varying doses, times, or temperatures.

[0051] In this invention, the "JMAK model" refers to the Johnson–Mehl–Avrami–Kolmogorov model, which is used to describe the dynamic behavior of nucleation and growth processes in crystals. In this invention, it is used to fit the relationship between the volume fraction of precipitated phase and the evolution of irradiation damage.

[0052] Figure 1 A flowchart illustrating a simulation method for studying the life-cycle mechanical properties of aluminum alloy materials for reactors, according to an embodiment of the present invention, is shown. As shown, a simulation method for studying the life-cycle mechanical properties of aluminum alloy materials for reactors includes the following steps:

[0053] S1, based on aluminum alloy materials used in service 27 Al→ 28 The transmutation path of Si was investigated, and aluminum alloy samples simulating different service stages were designed and prepared. These samples included initial state, mid-life state, and end-life state. The mass percentage of Si added to the aluminum alloy samples was selected based on the transmutation calculations of the aluminum alloy during service. This design is based on the following: within the reactor... 27 Al transforms into neutron-captured form 28 During the Si process, the mass percentage of Si gradually increases with the extension of service time and the accumulation of irradiation damage, and the Si content is linearly related to the operating time t. By adjusting the Si content, the composition characteristics of materials at different service stages can be accurately reproduced, providing a basic sample for subsequent microscopic and mechanical property simulation.

[0054] S2. The microstructure of each sample was characterized using transmission electron microscopy to obtain the size and number density of the precipitated particles. The volume fraction f of the precipitated phase for each sample was calculated by observing and statistically analyzing the size and number density of the precipitated particles. Then, the volume fraction f of the precipitated phase was fitted with the running time t to construct a precipitation evolution model. The core function of this model is to quantify the evolution of microscopic precipitates over time, providing a microscopic quantitative basis for subsequent correlation with macroscopic mechanical properties. During the fitting process, it is necessary to ensure an accurate description of the entire process of "nucleation-growth-saturation" of the precipitates, and to ensure that the model fit meets the engineering accuracy requirements, thus ensuring the reliability of the microscopic evolution law.

[0055] S3. High-energy ion irradiation was applied to each sample, and nanoindentation experiments were performed on the samples before and after irradiation. The load-depth curve of the indentation was fitted to obtain the nanohardness value H. High-energy ion irradiation and nanoindentation experiments were used to achieve "damage effect simulation" and "mechanical property characterization". The goal of high-energy ion irradiation is to simulate the displacement damage effect induced by reactor neutron irradiation. The irradiation dose range needs to cover from the initial no-damage stage to the saturation stage of the precipitated phase. By setting a sufficient number of irradiation dose points, the continuous characteristics of damage evolution can be captured. At the same time, the irradiation parameters need to be strictly controlled to ensure that the irradiation damage peak and the ion deposition peak do not overlap. The irradiation damage peak is the region where the atomic displacement damage caused by ion bombardment is the largest. The ion deposition peak is the region where the ion concentration (or mass) is the largest after high-energy incident ions travel through the material, are depleted of energy, and are deposited. This avoids the local compositional anomalies caused by ion deposition from interfering with the damage assessment results. Nanoindentation experiments are conducted on samples before and after irradiation. Sufficient indentation points are set on each sample to reduce test errors. The load-depth curve of the indentation is fitted with a model to obtain an accurate nanohardness value H. The indentation depth must be greater than the thickness of the irradiated layer, and the fitting region must avoid the ion deposition peak range to ensure that the hardness data only reflects the effect of irradiation damage on the mechanical properties of the material and eliminates irrelevant interference factors.

[0056] S4, based on analytical evolution model A model was established to show the relationship between nanohardness H and irradiation damage D. Based on the model, the nanohardness H values ​​of each sample under different irradiation damage values ​​D were fitted.

[0057] S5, calculate the change in nanohardness value H (i.e., the difference between hardness after irradiation and hardness before irradiation), and convert it into the change in yield strength ∆σ using commonly used engineering conversion relationships. Establish a yield strength-irradiation damage prediction model for aluminum alloy samples, and use the defect saturation dose D. sat Characterizes the end of service life.

[0058] In some examples, in step S2, the diameter range of the precipitated particles characterized by transmission electron microscopy is 8 nm to 150 nm. Typical sizes within this diameter range are selected, and the number density is statistically analyzed for each typical size. Specifically, in the simulation of the mechanical properties of aluminum alloy materials over their service life, the characterization of precipitated particles by transmission electron microscopy needs to focus on a diameter range of 8 nm to 150 nm, and typical sizes within this range need to be selected and their number densities statistically analyzed. This operational design highly matches the actual evolution of aluminum alloy precipitates under reactor service conditions, and provides a quantitative basis for subsequent correlation between microscopic and macroscopic properties. Because aluminum alloys in reactors... 27 Al→ 28Si transmutation is a gradual process; the enrichment of Si gradually induces the nucleation and growth of precipitates. In the early stages of service, when Si content is low, the precipitates are mainly small particles around 20 nm in size (nucleation stage). As service time increases, these small particles merge and grow through the Ostwald ripening mechanism, gradually forming medium-sized particles of 50 nm to 100 nm (growth stage). Towards the end of the service life, some particles will further grow to around 150 nm (stabilization stage). Therefore, a diameter range of 8 nm to 150 nm can completely cover the size evolution range of precipitates in aluminum alloys throughout their entire service life, avoiding the omission of information on key evolution stages (such as the initial nucleation of small particles and the later stabilization of large particles) due to an overly narrow size range, ensuring that the characterization data accurately reflects the microstructural changes during actual service.

[0059] Selecting typical sizes within this range (e.g., 20nm, 100nm, etc.) and statistically analyzing their number densities is crucial for ensuring the accuracy of the precipitate volume fraction f. The contribution of precipitates of different sizes to the volume fraction varies significantly. Small particles (e.g., 20nm), although small in individual volume, have high number densities, and their total contribution may be comparable to that of large particles (e.g., 100nm). If statistical analysis is not performed according to typical sizes, and only a rough calculation method of "average size + total number density" is used, errors in the volume fraction calculation will occur because small particles are averaged out by larger particles, or large particles are ignored due to their small number. Obtaining an accurate volume fraction f is essential for subsequently constructing the precipitate evolution model. The core input parameters and the model's fitting accuracy directly depend on the quantitative density data of the classification statistics, which in turn affects the reliability of the subsequent nanohardness-damage model and the life yield strength prediction model, and ultimately determines the accuracy of the entire simulation method.

[0060] In some examples, step S2 uses a JMAK model to fit the precipitate volume fraction f to the running time t, thus deriving an evolutionary model. for: ,in, The volume function of saturated precipitates at the end of the simulated service life of the sample. This is the precipitate growth rate constant, and its magnitude is closely related to the reactor temperature and the aluminum alloy composition: the higher the temperature, the stronger the atomic diffusion ability, and the faster the precipitate growth. The larger the value, the higher the initial Si content, and the greater the nucleation driving force. The value will also increase accordingly—obtained through fitting. It can quantify the growth rate of precipitates under different service conditions, providing a basis for assessing the "evolution rate of precipitates under different reactor conditions"; The Avrami index represents the precipitate phase, reflecting its nucleation mode and growth dimension. From a model adaptability perspective, the core advantage of the JMAK model lies in its accurate description of the "nucleation-growth-saturation" kinetic process. The evolution of precipitates in aluminum alloys within a reactor follows this pattern, meaning that as service time increases... 27 Al→ 28 The Si element produced by Si transmutation gradually accumulates. When the Si content exceeds the solid solubility, the precipitated phase begins to nucleate. Subsequently, the nucleation rate accelerates, and the particles grow rapidly, entering a rapid growth stage. When Si transmutation reaches saturation, f tends to stabilize, entering a saturation stage. The exponential form of the JMAK model can reproduce this evolutionary characteristic, therefore the JMAK model is the optimal choice for this scenario.

[0061] In some examples, step S3 requires high-energy ion irradiation to cover the dose range from the initial undamaged stage to the precipitate saturation stage. This range is designed to fully reproduce the damage evolution process of the aluminum alloy throughout the entire service life of the research reactor. From the initial minor damage, to the rapid growth of precipitates due to damage accumulation in the middle stage, and finally to the stabilization of the precipitates when the damage saturates in the later stage, this ensures that subsequent performance analysis covers the critical stages of the entire lifespan. Setting no fewer than six irradiation dose points is to capture the nonlinear relationship between damage and performance through a sufficient number of discrete data points, avoiding model fitting distortion due to insufficient data density and ensuring the accuracy of the "damage-performance" correlation. Simultaneously, irradiation parameters must be strictly controlled to ensure that the irradiation damage peak does not overlap with the ion deposition peak. This aims to eliminate interference from local compositional anomalies caused by ion deposition, ensuring that the damage assessment only reflects the atomic displacement effect induced by ion bombardment, consistent with the real scenario where reactor neutron irradiation only produces displacement damage, thus avoiding irrelevant factors affecting the reliability of the simulation results.

[0062] Correspondingly, nanoindentation experiments need to be conducted on samples before and after irradiation, with no fewer than 20 indentation points set for each sample. The purpose of this design is to reduce the random interference of local micro-defects (such as single vacancy clusters or small-sized precipitates) on hardness data through statistical testing, and to improve the representativeness of hardness values ​​through averaging of multiple indentation data. The Nix-Gao model is used to fit the load-depth curve to obtain the nanohardness value H, because this model can effectively eliminate systematic errors caused by the geometric effects of the indenter tip, significantly improving the accuracy of hardness calculations compared to simple fitting methods, ensuring the accuracy of mechanical property data. Furthermore, the indentation depth must be greater than the thickness of the irradiated layer. This requirement ensures that the indentation test area is completely within the irradiation influence range, avoiding dilution of hardness data by the unirradiated matrix, and ensuring that the test results truly reflect the impact of irradiation damage on the material's mechanical properties. Simultaneously, the fitting region must avoid the ion deposition peak region to further eliminate interference from the ion deposition layer on the hardness test, ensuring that the final obtained nanohardness value H is directly related only to irradiation damage, providing an experimental data basis for subsequently establishing a quantitative model of hardness and damage.

[0063] Preferably, the indentation depth in nanoindentation experiments is 20%–50% greater than the irradiated layer thickness. This setting balances testing effectiveness and accuracy. The lower limit of 20% covers variations in irradiated layer thickness, ensuring the indentation remains entirely within the irradiated layer and preventing dilution of hardness data by the unirradiated substrate; the upper limit of 50% prevents excessive indentation from introducing deep-seated defects or edge effects, reducing additional errors. This range is compatible with the precision of conventional equipment, ensuring the reliability of experimental data.

[0064] In some examples, in step S3, the irradiation temperature is set to room temperature or the actual operating temperature of the aluminum alloy material ±5°C. Room temperature facilitates laboratory operation, while a setting close to the actual operating temperature can reproduce the damage effects under real service conditions, ensuring the accuracy of the irradiation simulation and providing realistic damage samples for subsequent mechanical property testing.

[0065] In some examples, the model expression for the relationship between nanohardness H and irradiation damage D in step S4 is: This model correlates the evolution of microscopic precipitates with macroscopic hardness properties through irradiation damage. Among other things, The hardness of the unirradiated sample. and These are the matrix strengthening coefficient and the precipitated phase strengthening coefficient, respectively. They were obtained by fitting experimental data from multiple sets of samples with different compositions, including samples containing Si and samples without Si. Value differentiation settings were implemented to match the impact of Si content variations on the enhancement mechanism. The terms align with the strengthening patterns of irradiation defects (such as dislocation loops). By integrating microstructural parameters and damage variables, this model achieves a quantitative conversion from irradiation damage to macroscopic hardness, laying the foundation for subsequent yield strength prediction.

[0066] In some examples, in step S5, the change in hardness Converted to change in yield strength The conversion formula is: , where constant =0.266 MPa / GPa. This constant The results were obtained by fitting multiple sets of measured data on the hardness-yield strength of aluminum alloys before and after irradiation. This method can accurately match the correlation between the mechanical properties of this type of material and avoid conversion errors caused by general numerical values.

[0067] In some examples, step S5 establishes a yield strength prediction model with irradiation damage D (dpa) as the sole independent variable; if a correspondence with service history is required, the equivalent dose growth rate (unit: dpa / year) can be obtained based on the operating history or calibrated monitoring samples, and an engineering correspondence can be provided as needed. In order to achieve The result is expressed.

[0068] In some examples, the expression for the lifetime yield strength prediction model is: This expression integrates microscopic strengthening mechanisms with damage evolution over time, enabling quantitative prediction of yield strength changes in aluminum alloy materials used in reactors throughout their entire service life. This represents the saturation damage value for radiation hardening, corresponding to the critical point where the yield strength no longer increases with radiation damage. (Exponential term) This describes the evolution trend of yield strength from initial growth to final saturation, which conforms to the physical law that radiation hardening is initially rapid and then slows down. The Avrami index represents the irradiation dislocation loop, reflecting the effect of the nucleation-growth mode of the dislocation loop on the hardening rate.

[0069] The following is a specific embodiment of the present invention, described in detail below:

[0070] S1, based on aluminum alloy materials used in service 27 Al→ 28 Based on the Si transmutation path, Al-2.64Mg was selected as the base alloy, and four groups of aluminum alloy samples simulating different service stages were prepared.

[0071] Figure 2This diagram illustrates the evolution and fitting results of nanohardness measured by nanoindentation as a function of irradiation damage, according to an embodiment of the present invention. As shown in the figure, the horizontal axis represents irradiation damage (unit: dPa), indicating the average number of times atoms in the material are displaced due to irradiation (ion / neutron bombardment). The vertical axis represents nanohardness (H0, unit: GPa), measured through nanoindentation experiments, reflecting the material's ability to resist localized plastic deformation. The aluminum alloy samples in the figure are specifically: Al-2.64Mg (no Si, initial state); Al-2.64Mg-1.57Si (simulated service life approximately 20 years); Al-2.64Mg-2.28Si (simulated service life approximately 30 years); Al-2.64Mg-2.98Si (simulated end of service life approximately 40 years). The Si mass percentage of each Si-containing sample was selected based on the neutron transport and transmutation calculations of the reactor core, corresponding to the typical transmutation accumulation level of aluminum alloy at different operating times t (in years), to simulate the compositional state at the initial, intermediate, and end-of-life stages. This invention does not limit the relationship between Si content and irradiation damage D to a specific linear or other functional relationship.

[0072] Select a sample under saturated dose of irradiation on dislocation loops (denoted as D). sat In this example, approximately 10 dPa is used to simulate the irradiation state of the aluminum alloy at the end of its core service life. Therefore, the nanohardness of the Al-2.64Mg-2.98Si alloy at 10 dPa can be considered equivalent to the material properties at the end of its service life; while the hardness of the undoped Al-2.64Mg sample at 0 dPa represents the performance level at the beginning of its service life. In this example, Al-2.64Mg, Al-2.64Mg-1.57Si, Al-2.64Mg-2.28Si, and Al-2.64Mg-2.98Si can be assigned service times t≈0 a, 20 a, 30 a, and 40 a, respectively, to construct a precipitation evolution and mechanical property prediction model that varies with operating time. The numbers preceding the sample elements represent the mass ratio of the corresponding elements in the sample. For example, Al-2.64Mg-2.98Si means that the main components are 94.38 wt.% Al, 2.64 wt.% Mg and 2.98 wt.% Si.

[0073] S2, Constructing the analytical evolution model :

[0074] The microstructure of each unirradiated sample was characterized using transmission electron microscopy. Focusing on precipitated particles with diameters ranging from 8 nm to 150 nm, statistical number densities were calculated for two typical sizes: 20 nm and 100 nm. The volume fraction f of the precipitated phase was then calculated using the formula... Estimate the volume fraction, and sum the volume fractions of precipitates of different sizes to obtain the overall integral. For number density, The average size is shown in Table 1. As the Si content is higher, the density of the precipitated phase is greater, and the volume fraction is also significantly increased, which is consistent with the reactor operating conditions.

[0075] Table 1. Density and volumetric molecular weight of precipitated phases of different sizes in the electrolytic double-jet samples of the examples.

[0076]

[0077] Figure 3 The diagram illustrates the evolution and fitting results of the aluminum precipitate volume fraction f versus running time t according to an embodiment of the present invention. As shown in the figure, the JMAK model is used to fit the precipitate volume fraction f versus running time t to obtain the precipitation evolution model. ,in The volume function of the saturated precipitate phase at the end of its lifespan. The growth rate constant of the precipitated phase is... The Avrami index represents the precipitated phase, corresponding to a runtime of t = 40 years. In this example, the fitting result... =1.83×10 -4 , =2.59, goodness of fit R 2 >0.98. The derived evolutionary model is:

[0078] .

[0079] S3, High-energy ion irradiation and nanoindentation testing:

[0080] In a high-energy ion accelerator equipped with a high-vacuum system, the above-mentioned sample was subjected to ex-situ irradiation, with the irradiation dose range covering the initial stage to the precipitation saturation stage, and no fewer than six irradiation dose points set from low to high. (Reference) Figure 2 In this example, the maximum saturation dose for different component samples is 10 dPa; therefore, the irradiation dose points are set at 0 dPa, 0.5 dPa, 1.0 dPa, 3.0 dPa, 5.0 dPa, and 10.0 dPa. High-energy ex-situ irradiation is performed at room temperature. Preferably, the high-energy ex-situ irradiation conditions should be as close as possible to the actual reactor operating temperature of the sample. Preferably, it is ensured that the irradiation damage peak does not overlap with the ion deposition peak.

[0081] Nanoindentation experiments were performed on the samples before and after irradiation: no fewer than 20 indentation points were set for each sample, and the average value of the hardness-depth curve was taken. The load-depth curve was fitted using the Nix–Gao model to obtain the nanohardness value H. The indentation depth was greater than the thickness of the irradiated layer, and 20%–50% greater than the thickness of the irradiated layer. The fitting region avoided the ion deposition peak range.

[0082] S4, Establish a hardness-damage relationship model:

[0083] The evolutionary model constructed based on step S2 A model was established to establish the relationship between nanohardness H and irradiation damage D. ,in The hardness of the unirradiated sample. and These are the strengthening coefficients of the matrix and the precipitated phase, respectively, obtained by fitting experimental data from multiple sets of samples with different compositions. The irradiation dislocation ring nuclei velocities near the precipitated phase and in the matrix of the Si-containing sample differ, therefore... and The values ​​are different.

[0084] Based on the fitting results, the nanohardness of different samples under various typical irradiation damage levels was calculated. In this example, the saturation dose D of the irradiated dislocation loop was... sat ≈10 dpa. The unirradiated state of Al-2.64Mg (0 dpa) serves as the baseline state for the initial service phase. Al-2.64Mg-2.98Si in D... sat Under these conditions, representing states with sufficient precipitation and near-saturated radiation hardening, the test results for Al-2.64Mg-1.57Si and Al-2.64Mg-2.28Si fall between these two values ​​and can be considered as D≈0.5D. sat and D≈0.8D sat These represent intermediate damage levels. Under the experimental conditions described above, the four representative states were at 0 dPa and approximately 0.5D. sat Approximately 0.8D sat and D sat The corresponding nanohardnesses under the given conditions are approximately 0.67 GPa, 1.16 GPa, 1.24 GPa, and 1.26 GPa. Therefore, the nanohardness of this aluminum alloy system increases with irradiation damage in the range of D≈D. sat The area near the saturation point is approaching saturation. To verify this result, the cumulative damage level at the end of the study's life (approximately 220 dpa) was substituted into the calculation, and the nanohardness was found to be approximately 1.26 GPa, consistent with the nanohardness at the saturation dose of the irradiated dislocation ring (10 dpa).

[0085] Figure 4A schematic diagram illustrating the evolution and fitting results of nanohardness H and irradiation damage D according to an embodiment of the present invention is shown. As shown in the figure, the horizontal axis represents irradiation damage D, which indicates the average number of times atoms in the material are displaced due to irradiation, and is an indicator for quantifying the "degree of damage" caused by irradiation. The vertical axis represents nanohardness, reflecting the material's ability to resist localized plastic deformation.

[0086] S5, Establishing Lifetime Prediction:

[0087] Calculate the change in nanohardness of three Si-containing samples relative to an unirradiated silicon-free sample (Al-2.64Mg). Through empirical formulas ( Calculate the corresponding change in yield strength (e.g., 0.266 MPa / GPa). In this example, the yield strength changes of Al-2.64Mg-1.57Si, Al-2.64Mg-2.28Si, and Al-2.64Mg-2.98Si at simulated room temperature for 0 dpa, 5.27 dpa, 7.65 dpa, and 10 dpa are 130.34 MPa, 151.62 MPa, and 155.55 MPa, respectively.

[0088] Construct a life-cycle yield strength prediction model ,in This represents the irradiation saturation damage value, expressed in dPa. Using the Avrami index of irradiated dislocation loops, this model can predict the evolution trend of the yield strength of aluminum alloys during the reactor's service life.

[0089] Figure 5 This diagram illustrates the evolution of yield strength variation Δσ with defect accumulation in one embodiment of the present invention. As shown, the horizontal axis represents the fault / defect density or equivalent barrier strength index, which is positively correlated with irradiation damage D. The vertical axis represents the yield strength variation, reflecting the change in yield strength of the aluminum alloy due to service effects such as irradiation. The curve exhibits an evolutionary characteristic of rapid initial increase followed by gradual saturation. In the initial stage, as service time increases, damage induced by reactor neutron / ion irradiation (such as dislocation loop accumulation and precipitate evolution) intensifies, strongly hindering dislocation movement and causing the aluminum alloy to "harden," resulting in a rapid increase in yield strength. In the saturation stage, when the service time is sufficiently long, the evolution of irradiation damage or microscopic precipitates tends to saturate, and the increase in yield strength gradually stagnates, eventually maintaining a stable level (the stage marked "Saturation" in the figure). This curve intuitively reflects the evolution law of yield strength of the aluminum alloy material used in the reactor throughout its entire service life, from "rapid strengthening in the early stage of service" to "saturated stability in the later stage," providing a key basis for reactor structure life assessment and safety redundancy design.

[0090] In this example, the change in yield strength of Al-2.64Mg under simulated transmutation irradiation throughout its service life follows the following relationship:

[0091]

[0092] Alternatively, if the change in yield strength is not a concern, the result of nanohardness can be directly substituted into the simulated transmutation irradiation strengthening model to replace the change in yield strength.

[0093] The beneficial effects of the simulation method for studying the life-cycle mechanical properties of aluminum alloy materials for reactors provided by this invention are as follows:

[0094] 1. Improved efficiency: High-energy ion irradiation is used instead of neutron irradiation, which greatly shortens the experimental cycle and allows for rapid acquisition of full-lifetime performance data;

[0095] 2. Ensure accuracy: through simulation 27 Al essence 28 The dual effects of Si transmutation and irradiation damage fully reproduce the actual service performance evolution, ensuring prediction accuracy;

[0096] 3. Engineering Application: The constructed life-cycle yield strength prediction model can directly output performance data at any service time point, providing a quantitative basis for the safety design and life assessment of aluminum alloy materials;

[0097] 4. Operational feasibility: Each step uses conventional experimental equipment (transmission electron microscope, nanoindentation instrument, etc.) and can be implemented without special modifications.

[0098] It will be apparent to those skilled in the art that various modifications and variations can be made to the exemplary embodiments described above without departing from the spirit and scope of the invention. Therefore, it is intended that this invention cover modifications and variations falling within the scope of the appended claims and their equivalents.

Claims

1. A simulation method for studying the mechanical properties of an aluminum alloy material during its service life in a nuclear reactor, comprising the steps of: S1, based on the aluminum alloy material during service 27 Al→ 28 The transmutation path of Si, design and prepare aluminum alloy samples simulating different service stages, including initial state samples, mid-life state samples and end-of-life state samples, and the mass percentage of added Si in the aluminum alloy samples is selected according to the calculation results of the transmutation of aluminum alloy during service; S2, microstructure characterization of each sample is carried out by using a transmission electron microscope to obtain the size and number density of precipitated particles, and based on the size and number density of the precipitated particles, the precipitated phase volume fraction f of each sample is calculated, and then the precipitated phase volume fraction f is fitted with the running time t to construct a precipitated evolution model ; S3. performing high-energy ion irradiation on each sample, performing nanoindentation experiments on the samples before and after irradiation, fitting the load-depth curve of the indentation to obtain the nano-hardness value H; S4, based on the evolution model of precipitation , a relationship model between the nano-hardness H and the irradiation damage D is established, and the nano-hardness value H of each sample at different irradiation damage values D is fitted according to the relationship model; S5, calculating the change amount of the nano-hardness value H and converting it into the change amount of the yield strength ∆σ, establishing the yield strength-irradiation damage prediction model of the aluminum alloy sample, and taking the defect saturation dose D sat characterized at the end of service.

2. The method of Claim 1, wherein The diameter of the precipitated particles characterized by the transmission electron microscope ranges from 8 nm to 150 nm, a typical size is selected within the diameter range, and the number density of each typical size of the precipitated particles is counted.

3. The method of Claim 1, wherein The JMAK model is used in step S2 to fit the precipitate volume fraction f to the running time t, and the precipitate evolution model is wherein wherein is the saturation precipitate volume fraction of the sample at the end of the simulated service life, is the precipitate growth rate constant, is the Avrami exponent of the precipitate.

4. The method of Claim 1, wherein In step S3, the dose range of high-energy ion irradiation covers the initial stage to the saturation stage of precipitation, and not less than 6 dose points of irradiation are set, and the irradiation damage peak and the ion deposition peak are not overlapped; in the nanoindentation experiment, not less than 20 indentation points are set for each sample, the load-depth curve of the indentation point is fitted by using the Nix-Gao model to obtain the nano-hardness value H, the indentation depth is greater than the thickness of the irradiation layer, and the fitting region avoids the ion deposition peak interval.

5. The method of Claim 4, wherein the aluminum alloy material life mechanics performance simulation method is characterized by, The indentation depth of the nanoindentation experiment is 20% to 50% greater than the thickness of the irradiation layer.

6. The method of Claim 1, wherein In step S3, the irradiation temperature is within ±5℃ of the room temperature or the actual operating temperature of the aluminum alloy material.

7. The method of Claim 1, wherein In step S4, the relationship model expression of the nano-hardness H and the irradiation damage D is: ; wherein, H0is the hardness of the unirradiated sample, and are the strengthening coefficients of the matrix and the precipitate, respectively, and are obtained by fitting the nano-hardness experimental data of a plurality of samples with different compositions, and the values of are set differently.

8. The method for simulating the life-cycle mechanical properties of aluminum alloy materials as described in claim 7, characterized in that, In step S5, the hardness change amount converted into the yield strength change amount is calculated by the conversion formula where the constant = 0.266 MPa / GPa.

9. The method of Claim 8, wherein The end of service life is defined by the defect saturation phase, corresponding to the defect saturation dose D sat is determined according to at least one of the following criteria: The number density, average size or obstacle effect of the dislocation loop or dislocation network has a growth rate lower than a preset threshold in a continuous dose interval, which shows a stable platform; The nano-hardness or the yield strength converted therefrom enters a platform region in a continuous dose interval, and the corresponding improvement per unit dose increment is lower than a preset threshold.

10. The method of Claim 9, wherein the aluminum alloy material is selected from the group consisting of AA 2xxx, AA 5xxx, AA 6xxx, AA 7xxx, and AA 8xxx alloys. 10 The expression of the service-life yield strength prediction model is: ; where a is the irradiation dislocation loop growth rate constant, is the Avrami exponent for irradiation dislocation loops.

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