Ground simulation test method and device based on ground irradiation source combination parameter optimization

Abstract

The invention provides a ground simulation test method and device based on ground irradiation source combination parameter optimization. The method comprises the steps that an in-orbit space irradiation environment is simulated, an orbit environment particle energy spectrum of a to-be-tested space orbit is obtained through calculation, and an in-orbit PKA distribution spectrum in a to-be-tested material is calculated based on the orbit environment particle energy spectrum; based on the obtained on-orbit PKA distribution spectrum, determining optimal combination parameters of one or more groups of ground irradiation sources through a specified optimization algorithm and fitting calculation, so that a PKA energy spectrum generated by the ground irradiation sources in the to-be-measured space material is matched with the on-orbit PKA distribution spectrum, and an optimal fitting energy spectrum is obtained; and calculating and determining a critical dose rate, and calculating to obtain an acceleration fluence and a maximum acceleration multiplying power for guiding a ground acceleration test so as to carry out a ground simulation test of the to-be-tested space material. According to the application, the combined parameters of the ground irradiation source are optimized, and a more accurate ground simulation test is realized.

Description

Technical Field

[0001] This application relates to the field of space environment engineering technology, and more specifically, to a ground simulation test method and apparatus based on the optimization of ground irradiation source combination parameters. Background Technology

[0002] In space applications, spacecraft and their onboard electronic devices are exposed to the space radiation environment for extended periods. Irradiation by high-energy charged particles (such as electrons and protons) can cause displacement damage to materials, where lattice atoms leave their equilibrium positions, forming "primary knock-on atoms" (PKAs). The energy, quantity, and spatial distribution of PKAs are crucial in determining the degradation of macroscopic properties (such as electrical and optical properties) of materials. Spacecraft are subjected to various extreme environmental effects during spaceflight. Among these, charged particle irradiation has the greatest impact on surface materials. In actual orbital environments, electrons and protons carrying various energies (hundreds to megaeV) continuously bombard materials, causing damage and accumulation to the material's microstructure, leading to performance degradation. Due to cost limitations of on-orbit testing, ground-based simulation experiments are often used to evaluate and study the effects of charged ions.

[0003] However, due to technical limitations, the charged particles (electrons, protons, and heavy ions) used in ground-based experiments are all of single energy, and their energies are relatively low compared to the actual orbital environment (e.g., geostationary orbit ions can reach several MeV, while ground-based irradiation sources are generally ≤200 keV). Furthermore, space applications typically require surface materials to withstand on-orbit environmental effects for several to several decades without significant performance degradation. This necessitates evaluating the long-term irradiation effects of these materials. Ground-based experiments using actual dose rates are too time-consuming to meet mission requirements, necessitating acceleration by increasing the dose rate. Excessively high acceleration rates can produce significant additional thermal and coupling effects, requiring limitations on the acceleration rate.

[0004] In related technologies, a method for simulating the comprehensive radiation effects of surface functional materials for spacecraft in space has been disclosed. This method uses the dose-depth distribution of the irradiation environment in the material as an equivalent simulation basis to conduct ground simulation experiments of the comprehensive irradiation effects. Equivalent simulation is achieved by calculating the dose-depth distribution of the orbital environment and the dose-depth distribution of electrons / protons at a given energy, and by adjusting the fluence to achieve consistency between the two curves. However, the dose-depth distribution cannot accurately reflect the damage effect of irradiation on the material, and existing technologies do not design for acceleration rates. Furthermore, existing methods often neglect dose rate effects (recombination effects) and the annealing effect of the material, leading to a mismatch between ground test results and actual on-orbit failure mechanisms, and even erroneous conclusions. For example, the simple equivalent total dose method cannot accurately reflect the generation, recombination, and annealing dynamics of defects at different dose rates, making the calculation of acceleration factors lack a physical basis. Existing simulation methods also often suffer from limitations in energy range, contradictions between computational efficiency and accuracy, and other technical problems, making it difficult to comprehensively describe the real damage process.

[0005] Therefore, it is necessary to provide a new ground simulation test method and apparatus based on the optimization of ground irradiation source combination parameters to solve one of the above-mentioned technical problems. Summary of the Invention

[0006] The purpose of this application is to provide a ground simulation test method, apparatus, medium, and electronic equipment based on the optimization of ground irradiation source combination parameters, which can solve at least one of the aforementioned technical problems. The specific solution is as follows:

[0007] According to a specific embodiment of this application, this application provides a ground simulation test method based on the optimization of ground-based irradiation source combination parameters, comprising: simulating the in-orbit space irradiation environment, calculating and obtaining the orbital environment particle energy spectrum of the space orbit to be tested, and calculating the in-orbit PKA distribution spectrum in the material to be tested based on the orbital environment particle energy spectrum; based on the obtained in-orbit PKA distribution spectrum, using a specified optimization algorithm, fitting calculations to determine the optimal combination parameters of one or more ground-based irradiation sources, such that the PKA energy spectrum generated by the ground-based irradiation sources in the space material to be tested matches the in-orbit PKA distribution spectrum, thereby obtaining the optimal fitted energy spectrum; based on the obtained optimal fitted energy spectrum, calculating and determining the critical dose rate based on the defect-related data obtained from the molecular dynamics calculation of the space material to be tested, and calculating and obtaining the acceleration flux and maximum acceleration ratio for guiding ground-based acceleration tests; and conducting ground-based simulation tests on the space material to be tested based on the obtained acceleration flux and maximum acceleration ratio.

[0008] According to a specific embodiment of this application, this application also provides a ground simulation test device based on the optimization of ground irradiation source combination parameters. This device executes the ground simulation test method based on the optimization of ground irradiation source combination parameters described in this application, comprising: a simulation calculation module for simulating the on-orbit space irradiation environment, calculating the orbital environment particle energy spectrum of the space orbit to be tested, and calculating the on-orbit PKA distribution spectrum in the material to be tested based on the orbital environment particle energy spectrum; a fitting calculation module for determining the optimal combination parameters of one or more ground irradiation sources by using a specified optimization algorithm based on the obtained on-orbit PKA distribution spectrum, such that the PKA energy spectrum generated by the ground irradiation source in the space material to be tested matches the on-orbit PKA distribution spectrum, thereby obtaining the optimal fitted energy spectrum; a parameter calculation module for calculating and determining the critical dose rate based on the obtained optimal fitted energy spectrum and according to the defect dynamics model of the space material to be tested, and calculating the acceleration flux and maximum acceleration ratio for guiding ground acceleration tests; and a test parameter determination module for conducting ground simulation tests on the space material to be tested based on the obtained acceleration flux and maximum acceleration ratio.

[0009] According to a specific embodiment of this application, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the ground simulation test method based on the optimization of ground irradiation source combination parameters as described in any of the preceding claims.

[0010] According to a specific embodiment of this application, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the ground simulation test method based on the optimization of ground irradiation source combination parameters as described in any of the preceding claims.

[0011] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0012] This application simulates the in-orbit space irradiation environment to calculate the orbital environment particle energy spectrum of the space orbit under test. Based on the orbital environment particle energy spectrum, it calculates the in-orbit PKA distribution spectrum in the test material. By specifying an optimization algorithm, it fits and calculates to determine the optimal combination parameters of one or more ground irradiation sources, so that the PKA energy spectrum generated by the ground irradiation source in the test space material matches the in-orbit PKA distribution spectrum, which can accurately obtain the optimal fitted energy spectrum. By effectively combining ground single-energy irradiation sources, it can better approximate and accelerate the evaluation of the real space energy spectrum irradiation environment effect. Furthermore, based on the defect dynamics model of the test space material and the corrected critical dose rate, it calculates the optimized critical dose rate, which further optimizes the acceleration fluence and maximum acceleration ratio of the ground acceleration test, enabling a more accurate and effective ground simulation test of the test space material. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0014] Figure 1 This is a flowchart illustrating an example of the ground simulation test method based on the optimization of ground irradiation source combination parameters according to this application.

[0015] Figure 2 This is an example PKA energy spectrum obtained by proton irradiation of ZnO with an energy of 100 keV according to this application;

[0016] Figure 3 This is a flowchart illustrating the process of obtaining the optimal combination parameters in the ground simulation test method based on the optimization of ground irradiation source combination parameters in this application.

[0017] Figure 4 This is a structural framework diagram of an embodiment of the ground simulation test device based on the optimization of ground irradiation source combination parameters of this application;

[0018] Figure 5 This is a schematic diagram of the electronic device structure shown in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0021] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0022] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0023] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0024] In view of the above problems, this invention conducts ground simulation experiments based on the principle of displacement damage effect equivalence. According to the cause of displacement damage, given information such as the type, quantity, energy, and direction of PKA (primary displaced atoms), the variation with irradiation depth is quantified, i.e., the PKA spectrum, to quantitatively determine displacement damage. Therefore, the displacement damage effect is equivalent to the PKA spectrum equivalence. Based on the PKA spectrum, the PKA spectrum under real orbital conditions and the PKA spectrum under single-energy combination conditions of ground particles (e.g., electrons and / or protons) are calculated. By adjusting the energy and flux of ground particles (e.g., electrons and / or protons) to make the two PKA spectra consistent, the equivalence of the material irradiation effect of the space material under test is achieved.

[0025] The following is combined with Figures 1 to 3 The optional embodiments of this application are described in detail below.

[0026] like Figure 1 As shown, in step S101, the on-orbit space irradiation environment is simulated, the energy spectrum of the orbital environment particles of the space orbit to be tested is calculated, and the on-orbit PKA distribution spectrum in the material to be tested is calculated based on the energy spectrum of the orbital environment particles.

[0027] Specifically, the energy spectrum of orbital environment particles of the space material to be tested is obtained, wherein the space material to be tested includes semiconductor materials. The semiconductor materials include gallium arsenide (GaAs) or zinc oxide (ZnO).

[0028] It should be noted that, in this application, the orbital environment particle energy spectrum refers to the actual or calculated proton / electron energy spectrum (energy-integral flux) of different protons / electrons, which is composed of discrete points. The particle energy corresponding to each point is calculated in existing software to obtain a monoenergetic PKA energy spectrum, which specifically includes information such as PKA type, three-dimensional position, and energy. For example, this is a txt file. Each particle energy yields a txt file after calculation. Therefore, the continuous PKA spectrum corresponding to the orbital energy can be obtained.

[0029] From the perspective of particle energy, high-energy particles are distinguished from low-energy particles. For protons: less than 100 keV is the low-energy region, 100 keV-10 MeV is the medium-energy region, and 10 MeV-100 GeV is the high-energy region. For electrons: less than 10 keV is the low-energy region, 10 keV-1 MeV is the medium-energy region, and greater than 1 MeV is the high-energy region.

[0030] Specifically, orbital environment particles (e.g., high-energy particles) are high-energy charged particles, including electrons, protons, or other particles. Orbital environment particles correspond to ground-based irradiation source particles, i.e., the ground-based irradiation source particles include electrons, protons, or other particles.

[0031] In one specific implementation, a solar cell containing gallium arsenide (GaAs) on a geostationary orbit (GEO, orbital altitude approximately 36,000 km) communications satellite is taken as an example. Gallium arsenide (GaAs), the space material under test, is exposed to high-energy electron and proton radiation environment for a long time, and its performance degradation is mainly affected by displacement damage effects.

[0032] Specifically, the actual irradiance environment parameters of the orbit are obtained, and specialized space irradiance environment models such as AE8, AP8, and JPL1991 solar flare models are used to calculate the energy spectrum distribution of electrons and protons (i.e., ion energy-ion orbital integral flux relationship curves) of the corresponding orbit under quiet and worst conditions, and the ion energy range is further determined.

[0033] Optionally, the NASA AE8 / AP8 radiation belt model (or the newer AE9 / AP9 / SPM model is recommended for more accurate results) can be used to calculate the energy spectrum distributions of electrons and protons. For short-term extreme environments caused by solar flares, the JPL 1991 solar flare model is used as a supplementary calculation for worst-case conditions.

[0034] For example, under quiescent conditions, based on the AE8MAX (electron maximum mode) and AP8MAX (proton maximum mode) models, and inputting the environmental parameters of the aforementioned orbitals (GEO), the orbital integral fluxes of electrons and protons over their full energy range (in particles / cm²) can be calculated. 2 The distribution curve of ·s with energy (MeV), i.e., the energy spectrum of particles in the orbital environment.

[0035] For example, under worst-case conditions, the extreme environment energy spectrum is formed by combining the calculation results of the AP8MAX model with the extreme solar proton event energy spectrum given by the JPL 1991 model (such as extreme events within a century).

[0036] Next, the particle energy range is determined. Based on the calculated particle energy spectrum of the orbital environment, the particle energy range used for ground simulation experiments is determined.

[0037] To determine the range of electron energy, the energy spectrum of particles in the corresponding orbital environment is analyzed, and the simulated lower limit and upper limit of electron energy are determined. For example, the electron energy range is 0.4 MeV to 10 MeV.

[0038] For example, analysis of the energy spectrum curves of particles in the orbital environment shows that electrons with energies below 0.4 MeV have difficulty penetrating the satellite shielding layer and battery cover. Meanwhile, the flux of electrons with energies above 10 MeV drops sharply, and their contribution to the total damage is negligible. Therefore, the electron simulation energy range is determined to be 0.4 MeV to 10 MeV.

[0039] To determine the proton energy range, the energy spectrum of particles in the corresponding orbital environment is analyzed to determine the lower and upper limits of the proton energy simulation, for example, the proton energy range is 0.1 MeV to 100 MeV.

[0040] For example, analysis of the particle energy spectrum curves in the orbital environment reveals that protons with energies below 0.1 MeV, while possessing high flux, have extremely short ranges in the space materials under test. They are typically shielded by satellite bulkheads and battery glass covers, preventing them from effectively penetrating the GaAs active region and causing damage. Therefore, the lower limit for proton energy simulation is set at 0.1 MeV. Protons with energies above 100 MeV, however, have fluxes more than three orders of magnitude lower than the peak flux, contributing less than 1% to the total displacement damage dose. From the perspective of engineering practice and simulation efficiency, the upper limit for proton energy simulation is set at 100 MeV.

[0041] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0042] Based on the energy data obtained from the particle energy spectrum of the orbital environment, the in-orbit PKA distribution spectrum of the space material under test is calculated. The in-orbit PKA distribution spectrum is a three-dimensional data relationship with PKA quantity, energy and depth, or energy and spatial location as variables.

[0043] The calculation of the on-orbit PKA distribution spectrum of protons was performed. After obtaining the proton irradiation energy range, the cascade collision data of the space material under test was calculated using SRIM software, including the position, energy, particle type, and other information of primary and secondary collision atoms.

[0044] A self-developed model and software were used to calculate the PKA spectra of the space material under test at different energies. Based on the input particle type and orbital environment particle energy spectrum, the energy and quantity distribution of PKA at different depths of the irradiated material can be quickly calculated (i.e., the relationship between irradiation depth and PKA energy is expressed as NPKA(d,EPKA), with units of p·cm). -3 ·day -1 The PKA spectra for all incident particle types were calculated using the above method and then summed to obtain the orbital PKA distribution spectrum within the space material under test.

[0045] Specifically, the equivalent PKA spectrum of the space material to be tested is calculated using a self-built model. This includes grouping PKA energies, calculating the integral flux corresponding to continuous energy irradiation (i.e., the integral flux in the energy spectrum of particles in the orbital environment), calculating single-energy equivalence, and calculating continuous energy spectrum equivalence.

[0046] First, the PKA energy needs to be divided into grids.

[0047] It should be noted that the PKA energy generated by proton irradiation of the space material under test is non-uniform, with more low-energy areas and fewer high-energy areas. Therefore, the mesh is denser in areas of lower energy and coarser in areas of higher energy. The mesh is also evenly divided along the depth direction based on the deepest distance.

[0048] Next, the differential flux corresponding to continuous energy irradiation (i.e., the differential flux in the particle energy spectrum of the orbital environment) is calculated. In the real space environment, different proton energies correspond to a differential flux. By plotting proton energy as the x-axis and differential flux as the y-axis, a function curve corresponding to the continuous energy spectrum can be obtained. Dividing the curve according to proton energy points and integrating the area under each proton energy, the number of particles passing through each square centimeter per day within the proton irradiation energy range is obtained.

[0049] Next, for the calculation of single-energy equivalence, the number of PKAs obtained is based on the irradiation of N particles, not the number of PKAs and depth data obtained under the actual proton energy flux. Therefore, after dividing the grid, we obtain the number of PKAs x at a certain PKA energy and irradiation depth after irradiation of N particles. x / N is the number of PKAs X for each proton at a certain PKA energy and irradiation depth. Multiplying X by the incident particle flux gives the value of the number of PKAs generated per square centimeter per day at each PKA energy and corresponding irradiation depth within the proton energy range.

[0050] Next, for the calculation of the equivalent of the continuous energy spectrum, after obtaining the equivalent at each PKA energy, the spectra are superimposed to obtain the energy deposition of the continuous energy spectrum.

[0051] The PKA spectrum of electrons is calculated using the same method as that for protons, and the PKA energy of electrons is obtained as the amount of energy deposited in the space material to be tested.

[0052] It should be noted that in this example, the calculation method for the PKA spectrum of electrons is roughly the same as that for protons; therefore, the explanation of the same content is omitted. Additionally, the depth of electron irradiation is greater than that of proton irradiation, and the grid used for electrons in the depth direction is coarser.

[0053] In one specific implementation, taking ZnO, i.e. the space material to be tested, as an example, for instance, 100,000 protons are set to bombard ZnO (i.e. the space material to be tested) in the SRIM software, and the energy deposition of the PKA energy of the output electrons or protons in the space material to be tested is the energy deposition of atomic absorption or the distribution of PKA of different energies in the space material to be tested.

[0054] For protons, set 100,000 protons to bombard ZnO in the SRIM software and output relevant information about PKA.

[0055] For ZnO, the energy of PKA produced by proton irradiation is mostly concentrated below 200 eV, with relatively little PKA above 200 eV. The irradiation depth is divided into 5 eV intervals for 0 eV-200 eV, 50 eV intervals for 200 eV-1000 eV, and 1 keV intervals for above 1000 eV. It is one grid.

[0056] By integrating the proton energy with the captured proton integral flux, the number of particles passing through each square centimeter per day at each energy level in real space can be obtained.

[0057] By dividing the grid and calculating the number of particles passing through at each energy level, the relationship between the number of O-PKA particles (irradiation depth and PKA energy) can be obtained by superimposing the models. Figure 2 The PKA energy spectrum was obtained by proton irradiation of ZnO at an energy of 100 keV. Figure 2 As can be seen from this, most PKA atoms have energies below 200 eV.

[0058] In another embodiment, taking PI (i.e., the space material to be tested) as an example, the PKA spectrum of electrons is calculated. First, preliminary PKA-related information is obtained by calculating electron irradiation of PI using Geant4 software.

[0059] Specifically, the PKA atoms produced by electron irradiation of PI have mostly energies below 50 eV and are relatively few in number. Electrons are divided into 10 eV intervals between 0 eV and 50 eV, and 100 eV intervals above 50 eV. The injection depths of protons and electrons are also on the order of magnitude: protons are on the order of micrometers, while electrons are on the order of centimeters. Therefore, the grid density for protons and electrons differs. Electrons are divided into grids of 200 μm to 500 μm.

[0060] By integrating the captured electron flux with the electron energy, the number of particles passing through each square centimeter per day in real space at each electron energy can be obtained.

[0061] By dividing the data into grids and calculating the number of particles passing through at each energy level, the relationship between the number of O-PKA particles (irradiation depth and PKA energy) can be obtained through model overlay. By overlaying the PKA particles (irradiation depth and PKA energy) for all energies, the on-orbit PKA distribution spectrum can be obtained.

[0062] Next, in step S102, based on the obtained on-orbit PKA distribution spectrum, an optimization algorithm is specified to fit and calculate to determine the optimal combination parameters of one or more ground irradiation sources, so that the PKA spectrum generated by the selected ground irradiation source in the space material to be tested matches the on-orbit PKA distribution spectrum, so as to obtain the optimal fitted energy spectrum.

[0063] Specifically, the on-orbit PKA distribution spectrum refers to the on-orbit PKA distribution spectrum corresponding to the continuous energy spectrum in the actual orbit.

[0064] Specifically, the incident particles are ground-based radiation sources, specifically protons, electrons, and other particles.

[0065] The match between the PKA spectrum generated by the ground-based irradiation source in the selected space material under test and the on-orbit PKA distribution spectrum is determined by calculating the degree of agreement. This degree of agreement is assessed by comparing the differences between the on-orbit PKA distribution spectrum and the PKA spectrum generated by the selected ground-based irradiation source in the space material under test.

[0066] Based on the obtained PKA energy spectra corresponding to different energy particles, the optimal combination parameters of one or more ground-based irradiation sources are determined through fitting calculations using a specified optimization algorithm. Specifically, the optimal combination parameters of one or more ground-based irradiation sources are determined through fitting calculations using a genetic algorithm or simulated annealing algorithm, and include the following steps.

[0067] Step S201: Perform iterative optimization.

[0068] Specifically, a specific number of iterative optimization processes are performed, with each iteration generating a set of combined parameters to calculate the fitted PKA spectrum generated in the space material under test. The obtained combined parameters include particle type (i.e., the particle type of the ground irradiation experiment), energy, and the flux ratio of each particle.

[0069] Step S202: Perform fitting calculation.

[0070] Specifically, the similarity between the fitted PKA spectrum generated in each iteration of the optimization process and the on-orbit PKA distribution spectrum is calculated to determine the optimal combination parameters of the ground irradiation source and obtain the optimal fitted energy spectrum.

[0071] Using the calculated on-orbit PKA distribution spectrum as the target, experimental particles with electrons and protons as ground irradiation sources were used. The on-orbit PKA distribution spectrum was fitted by selecting and optimizing the number or energy of electron sources, the number or energy of proton sources, and the flux ratio of electron and proton sources, so as to obtain the optimal fitted energy spectrum.

[0072] Specifically, first determine the available electron source energy and proton source energy and their adjustable range, such as 50keV to 200keV adjustable electrons; select the number of source terms, such as 3 different energy electrons and 2 different energy protons; calculate the corresponding PKA spectrum for each single energy electron and proton, and accumulate them according to the flux ratio; compare the accumulated PKA spectrum with the on-orbit PKA distribution spectrum, and adjust the source energy and flux ratio to make the two as consistent as possible.

[0073] It should be noted that in this embodiment, optimization is performed using automated methods such as genetic algorithms. In other embodiments, the iteration process can be manually adjusted multiple times. The above description is provided as an optional example and should not be construed as limiting the present invention.

[0074] In a preferred embodiment, a combination of nonlinear least squares and randomized OMP is used to optimize the combined parameters to obtain the optimal combined parameters. Specifically, the following steps are included: selecting at least two particle energies or particle types as the initial combination matrix; setting the number of iterations for iterative optimization; and in each iteration optimization process, calculating the combination coefficients and the initial combination matrix, and calculating the current error between the PKA spectrum generated by the selected ground irradiation source in the space material under test and the on-orbit PKA distribution spectrum.

[0075] If the calculated current error is less than the historical error, update the solution for the combination coefficients and combination matrix. If the calculated current error is greater than or equal to the historical error, do not update the solution for the combination coefficients and combination matrix. Repeat the above iterative optimization process until the configured number of iterations is reached, and output the optimal solution. See details below. Figure 3 .

[0076] For example, there are 20 sets of PKA energy spectra generated by monoenergetic proton irradiation. These PKA energy spectra are superimposed to obtain a cumulative PKA energy spectrum. From these 20 sets, 3 sets are selected and their corresponding coefficients are assigned to approximate the on-orbit PKA distribution spectrum. The combination coefficients include the optimal source energy and flux ratio.

[0077] Suppose there are 20 particle energy matrices {A1, A2, A3…A20} forming the in-orbit PKA spectrum B, i.e., B = A1 + A2 + A3 + … A20. Three particle energy matrices are randomly selected from these 20 candidate matrices. For example, matrices A1, A2, and A3 are chosen to fit the in-orbit PKA spectrum B, and the optimal coefficients k = {x1, x2, x3} are calculated such that C = x1*A1 + x2*A2 + x3*A3. The difference between C and B is then calculated. Then, three more matrices are selected from the candidate matrices, and the above process is repeated, ultimately yielding x1*A1 + x2*A2 + x3*A3 ≈ B. The combination coefficients correspond to k = {x1, x2, x3}.

[0078] If the current error is less than the historical error, then update the solution of the combination coefficients and the combination matrix; if the current error is greater than or equal to the historical error, then do not update the solution of the combination coefficients and the combination matrix; repeat the iterative optimization process until the configured number of iterations is reached, and output the optimal solution.

[0079] It should be noted that in the above iterative optimization process, matrix A in each iteration is the reconstruction matrix. The solution of the combined matrix is ​​the final selected reconstruction matrix and combination coefficients.

[0080] To determine the historical error, an array is set up in the execution code to store the error. Each calculation uses the same method to calculate the error (i.e., the difference between the reconstructed matrix and the target matrix). If the second error is smaller than the first error (historical error), the solution is updated; if the second error is larger than the first error (historical error), the solution is not updated. Therefore, the historical error is not a specific value, but a dynamic value that is continuously updated during the calculation process.

[0081] For example, each matrix in the continuous energy spectrum is listed as a candidate matrix, and all candidate matrices are concatenated end-to-end to form a huge matrix A. A target vector b is defined, which is the vector obtained by flattening the superimposed matrix of the entire continuous energy spectrum. A sparse coefficient vector c is determined such that A*c = b, and the error is minimized.

[0082] Set a maximum number of iterations (e.g., 1000 iterations). Each iteration starts from a different starting point and shuffles all columns of matrix A to escape local optima. Specify the selection of four matrices to combine to fit the continuous energy spectrum. In each iteration, reconstruct y using the selected matrices and the calculated sparse coefficient vector, and calculate the error between y and the true target vector b.

[0083] Optionally, the maximum number of iterations is 150 to 300. After executing the maximum number of iterations, the above iterative optimization process ends.

[0084] After each iteration of optimization, it is determined whether the calculated error is smaller than the historical error. If the calculated error is smaller than the historical error, the solution for the combination coefficients and combination matrix is ​​updated, i.e., the current solution is retained. If the calculated error is greater than or equal to the historical error, the solution for the combination coefficients and combination matrix is ​​not updated, i.e., the calculated result is discarded, and the next iteration of optimization is executed. Finally, the optimal solution that performs best in, for example, 1000 independent trials can be obtained, i.e., the optimal combination parameters (e.g., the optimal source energy and flux ratio), and the optimal fitted energy spectrum can be obtained.

[0085] For example, by using four energies (proton energies of 200keV, 250keV, 300keV, and 350keV) instead of the continuous energy spectrum, the optimal fitted energy spectrum can be obtained.

[0086] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0087] Next, in step S103, based on the optimal fitted energy spectrum and the defect-related data obtained from the molecular dynamics calculation of the space material to be tested, the critical dose rate is calculated and determined, and the acceleration dose and maximum acceleration ratio used to guide the ground acceleration test are calculated.

[0088] Specifically, the defect-related data includes the defect's impact range and annihilation time.

[0089] In one specific implementation, based on the source energy and flux ratio obtained through iterative optimization, and according to the on-orbit service life (e.g., 10 years), a total dose rate and total flux are given, and the dose rate and flux of all irradiation sources are allocated proportionally according to the flux ratio. According to the given dose rate and flux, a finite element model of the temperature change of the sample (corresponding to the space material under test) during the ground simulation test is established based on the ground test conditions of the actual ground simulation test, simulating the maximum temperature rise (which can also be roughly estimated using a simple energy relationship under lower requirements). By gradually increasing the total dose rate, the maximum temperature rise versus dose rate relationship curve is simulated. Given a critical temperature rise, the critical dose rate is obtained based on a given temperature rise criterion (e.g., whether the temperature rise exceeds 50K) and the maximum temperature rise-dose rate curve. Estimate the average distance and average time interval between adjacent incident ions or adjacent incident particles based on the critical dose rate. Compare the defect influence range and annihilation time of electrons or protons at the maximum energy (obtained from molecular dynamics simulations, which can be stored in tabular form after independent simulations for subsequent design reference). If the distance and interval between two adjacent incident ions / particles are less than the theoretically calculated values, reduce the obtained critical dose rate by multiplying the square of the distance ratio by the interval ratio. Adjust the critical dose rate in the manner described above.

[0090] The square of the spacing ratio multiplied by the interval ratio represents the spacing and interval, respectively. Spacing refers to the average spatial distance and average time interval between adjacent incident ions / particles, estimated based on the critical dose rate D_c. Interval refers to the intrinsic defect influence range (e.g., displacement cascade region size) and defect annihilation time (e.g., the characteristic time of vacancy-interstitial recombination), obtained beforehand through molecular dynamics (MD) simulations. If the calculated average spacing is less than the defect influence range, or the average time interval is less than the defect annihilation time, it indicates that at high dose rates, defect regions generated by multiple radiation events will overlap, and the defects will be interfered with by subsequent radiation events before they can recover, leading to an unrealistic defect accumulation effect and distorting the experimental results. Therefore, it is necessary to reduce the dose rate. The correction factor is (S md / S c ) 2 *(t md / t c ), where S md and t mdS represents the characteristic distance and time obtained from molecular dynamics calculations, respectively. c and t c These are the values ​​calculated based on D_c. The corrected critical dose rate is: D_c* = D_c × B (i.e., the correction factor). If both the interval and the spacing are greater than the theoretical value, no correction is needed, i.e., D_c* = D_c.

[0091] The maximum acceleration factor is calculated based on the ratio of the critical dose rate to the orbital integral flux. The critical dose rate is measured in units of the number of particles hitting the test material per unit area per unit time, i.e., the total flux. The orbital integral flux is measured in units of the number of particles passing through a unit area per unit time. When the critical dose rate exceeds the calculated orbital integral flux, the defect will form a critical point for nonlinear acceleration. The dose rate divided by the orbital integral flux gives the acceleration factor at the current time; that is, the ratio of the critical dose rate to the orbital integral flux at the current time is the maximum acceleration factor.

[0092] The defects mentioned are vacancies (in crystals), interstitial atoms, broken bonds (in polymers), etc.

[0093] To determine the flux in ground-based irradiation experiments, specifically, the current intensity I of the particle beam under the current ground-based irradiation simulation scenario is measured using a Faraday cup or beam transformer via beam measurement (specifically representing the amount of charge captured per unit time). Dividing the current intensity I by the charge q carried by a single particle yields the number of particles (particle flux) hitting the space material under test per unit time.

[0094] The following expression represents the flux rate of the particle beam in the current ground-based irradiation simulation test scenario.

[0095]

[0096] in, This represents the flux rate of the particle beam within a unit irradiation time t under the current ground irradiation simulation test scenario, where t represents the unit irradiation time of the particle under the current ground irradiation simulation test scenario; I represents the current intensity of the particle beam under the current ground irradiation simulation test scenario measured using a Faraday cup or beam transformer; q represents the amount of charge carried by a single particle under the current ground irradiation simulation test scenario; and A represents the beam spot area.

[0097] The total flux is represented by the following expression, specifically by the integral of the flux rate over unit irradiation time to characterize the total flux in the current ground irradiation simulation test scenario.

[0098]

[0099] Where Φ represents the total flux under the current ground irradiation simulation test scenario, which is characterized by the integral of the flux rate over the unit irradiation time t, t represents the unit irradiation time of the particles under the current ground irradiation simulation test scenario, and T represents the total irradiation time of the particles under the current ground irradiation simulation test scenario.

[0100] In a preferred embodiment, the specific process of simulating the relationship curve between the maximum temperature rise and the dose rate and determining the critical dose rate is explained in conjunction with a specific ground simulation test (e.g., a ground irradiation simulation test).

[0101] Specifically, it includes the following steps.

[0102] Step S1031: Mapping from on-orbit conditions to ground test conditions.

[0103] Determine the on-orbit service life of the target spacecraft (e.g., 10 years) and the total ionization dose (TID) and total displacement damage dose (DDD, usually expressed as equivalent flux) of its orbit, which is the ultimate target that the ground simulation test needs to simulate.

[0104] The optimal combination parameters of various radiation sources (such as electrons, protons, X-rays, etc.) for simulating a real space composite environment were obtained through preliminary optimization calculations. Specifically, these parameters include energy distributions (e.g., E1, E2, ..., E...). n ) and volume ratio (e.g.) The given total flux is allocated to each radiation source in a specific proportion.

[0105] Step S1032: Establish a thermal dose coupling model and simulate the temperature rise curve.

[0106] Specifically, a finite element model is established to accurately simulate the internal temperature distribution and its changes over time of the space material under test when irradiated at a specific dose rate. Radiative energy deposition causes the space material under test to heat up, and the heating rate is proportional to the absorbed dose rate.

[0107] Based on the shape, size (such as sheet or block) and fixing method (such as sample holder) of the space material to be tested in the actual ground simulation test, a 1:1 model, namely the thermal dose coupling model, is established.

[0108] By inputting the material properties of the space material to be tested into the thermal dose coupling model, the temperature rise of the space material under specific irradiation or energy input can be obtained.

[0109] Specifically, material properties are key thermophysical parameters, including thermal conductivity, specific heat capacity, density, and radiation energy absorption coefficient.

[0110] By defining the heat dissipation method of the material to be tested, the boundary conditions are constrained.

[0111] It should be noted that boundary conditions are crucial to the accuracy of the model. Both radiative heat dissipation (following the Stefan-Boltzmann law) and weak conductive heat dissipation (through the sample holder, etc.) under experimental vacuum conditions must be fully considered.

[0112] The dose rate (Gy / s or rad(Si) / s) is loaded as the in vivo heat generation rate into the established thermo-dose coupling model. The conversion relationship between heat generation rate and dose rate is as follows:

[0113] Q = Dose_Rate * ρ (3)

[0114] Where Q represents the volumetric heat generation power, which is the energy source term for the internal tattooing of materials caused by irradiation deposition energy; Dose_Rate represents the dose rate; and ρ is the material density of the space material to be tested.

[0115] Next, the maximum temperature rise-dose rate relationship curve was simulated.

[0116] With other model parameters unchanged, gradually increase the total input dose rate D (e.g., starting from 100 rad(Si) / s and increasing it in multiples to 10 rad(Si) / s).

[0117] For each dose rate D i The transient thermal analysis simulation is run to calculate the temperature field of the sample (i.e., the space material to be tested) from the initial temperature throughout the assumed irradiation period or until the steady-state temperature (heat input and dissipation are in equilibrium).

[0118] Record each dose rate D i Under these conditions, the highest temperature T_max,i that appears inside the sample is calculated, and the difference between the highest temperature T_max,i and the initial temperature is calculated, that is, the maximum temperature rise ΔT_max,i=T_max,i-T_initial.

[0119] Plot all data points (D) with dose rate on the x-axis and maximum temperature rise ΔT_max on the y-axis. i By calculating ΔT_max,i), the temperature rise-dose rate relationship curve, i.e., the "maximum temperature rise-dose rate relationship curve", can be obtained.

[0120] Step S1033: Determine the initial critical dose rate based on the temperature rise criterion.

[0121] Based on material properties (such as preventing overheating that could lead to annealing, melting, or the introduction of unrealistic thermal effects), a critical temperature rise criterion ΔT_critical is set. For example, it is required that the maximum temperature rise of the sample (i.e., the space material to be tested) during the test must not exceed 50K (i.e., ΔT_max≤50K).

[0122] Determine the initial critical dose rate D_c: On the "maximum temperature rise-dose rate relationship curve" obtained in step 302, find the abscissa value corresponding to the point where the vertical axis ΔT_max = ΔT_critical (50K). This abscissa value is the dose rate value, which is the initial critical dose rate D_c determined only from the perspective of macroscopic thermal management. Conducting experiments at the initial critical dose rate D_c ensures that the sample temperature rise will not exceed the limit.

[0123] Step S1034: Correct the critical dose rate based on the defect range (i.e., annihilation time) calculated by molecular dynamics.

[0124] To achieve a realistic assessment of defect accumulation, and to ensure that accelerated testing can not only control the temperature but also realistically simulate the accumulation process of microscopic defects in materials (which is the essence of performance degradation), it is necessary to compare ground-based high dose rate conditions with microscopic theory.

[0125] Specifically, this includes calculating the average interval. Based on the critical dose rate D_c, the average spatial distance and average time interval between adjacent incident ions or adjacent incident particles are estimated.

[0126] The calculated average spatial spacing and average time interval are compared with the intrinsic defect influence range (such as the size of the displacement cascade region) and defect annihilation time (such as the characteristic time of vacancy-interstitial recombination) obtained in advance through molecular dynamics (MD) simulations to determine whether the critical dose rate needs to be corrected.

[0127] If the calculated average spatial spacing is within the defect's influence range, or if the calculated average time interval is less than the defect annihilation time, it indicates that at high dose rates, the defect areas generated by multiple radiation events will overlap, and the defects will be disturbed by subsequent radiation events before they can recover, resulting in an unrealistic defect accumulation effect and distorting the experimental results.

[0128] The problem of test distortion caused by overlapping defect areas resulting from multiple radiation events can be solved by reducing the dose rate.

[0129] In an alternative implementation, the corrected critical dose rate is calculated using the following expression.

[0130] D_c*=D_c×B (4)

[0131] Where D_c* represents the critical dose rate corrected according to the real-time calculated correction coefficient; D_c represents the critical dose rate before correction; B represents the real-time calculated correction coefficient, B=(S md / S c ) 2 *(t md / t c ), S md and t md S represents the characteristic distance and time given by molecular dynamics. c and t c These are the average spatial spacing and average time interval calculated based on D_c, respectively.

[0132] Optionally, the calculation of D_c is determined for a series of dose rates D i Perform transient heat conduction simulation (volume heat source Q) i =D i ×ρ), record the steady-state or peak temperature rise ΔTmax,i obtained from each simulation. In (D i Interpolate (or fit) the data on ΔTmax,i) to determine the D that satisfies ΔTmax=ΔTcritica. i That is, we get D_c.

[0133] Optionally, for the calculation and determination of D_c, it is assumed that the uniform volumetric heat rate absorbed by the space material under test is Q, where Q = Dose_Rate * ρ, Dose_Rate is in Gy / s, and ρ is in kg / m³. 3 Let ΔT crit The maximum permissible temperature rise (K), h is the equivalent heat transfer coefficient, in W / m³. 2 K; The volume of the object is V, and its surface area is A.

[0134] Under steady-state energy equilibrium, QV=h×A×ΔT ss This yields the following expression:

[0135]

[0136] For thin sheets, blocks and other geometric shapes, V / A is often equal to L, so expression (5) is transformed into the following expression (7).

[0137]

[0138] Furthermore, substituting Q = Dρ, we obtain the following expression.

[0139]

[0140] Where D_c represents the critical dose rate before correction.

[0141] Furthermore, substituting D_c obtained from expression (7) into the above expression (4), we obtain the corrected critical dose rate:

[0142]

[0143] Where D_c*' represents the critical dose rate corrected according to the correction coefficient calculated in real time; D_c represents the critical dose rate before correction. Where h represents the height of the material to be measured, ρ represents the density of the material to be measured, and L represents the length of the material to be measured; S md and t md S represents the characteristic distance and time given by molecular dynamics. c and t c These are the average spatial spacing and average time interval calculated based on D_c, respectively.

[0144] After performing PKA molecular dynamics calculations, defects may appear. In such cases, the characteristic distance and recovery time of these defects can be statistically analyzed. For example, molecular dynamics methods can be used for these calculations.

[0145] The characteristic distance is specifically the range of defect influence. It is calculated from the MD to determine the defect distribution radius or the maximum defect expansion range after a single PKA irradiation, for example, about 5nm to 50nm in ZnO.

[0146] Time refers to the time it takes for the defect to stabilize. The curve of the number of defects changing with time is tracked from the MD dynamic trajectory, and the time scale for reaching steady state or recombination is taken, for example, 1ps to 100ps.

[0147] If the calculated average spatial spacing is not within the defect's influence range, or if the calculated average time interval is greater than or equal to the defect annihilation time interval, then there is no need to correct the critical dose rate, i.e., D_c* = D_c.

[0148] Step S1035: Calculate the maximum acceleration multiplier.

[0149] The critical dose rate that ultimately satisfies both macroscopic and microscopic constraints is divided by the orbital integral flux of the target orbital environment or the current orbital environment to obtain the maximum acceleration factor that can be used in this ground test scheme, i.e., maximum acceleration factor = D_c / orbital integral flux.

[0150] Specifically, the acceleration factor is determined by calculating the displacement damage rate and the critical dose rate.

[0151] The displacement damage rate is the number of atoms ejected per unit time, reflecting the "destruction rate of the structure". It is typically expressed in three ways, as shown in expressions (9), (10), and (11):

[0152]

[0153] W = Φ × NIEL (10)

[0154]

[0155] Where W represents the displacement damage rate; Φ represents the incident particle flux, in units of particles / cm². 2 •s; NIEL represents non-ionizing energy loss, measured in MeV·cm. 2 / g; dpa represents the average number of times each atom is ejected, i.e., displacements per atom; Φ p (E) and Φ e (E) represents the flux of protons (p) and electrons (e), respectively, in particles / cm². 2 ·s; This represents the displacement section by which proton p moves by a displacement d at energy E, in cm. 2 ; This represents the displacement section by which electron e moves by a displacement d at energy E, and the unit is cm. 2 N0 represents the atomic density of the material, with units of atoms / cm³. 3 .

[0156] Specifically, the physical quantities related to displacement damage include at least one of the following: non-ionization energy loss (NIEL), displacement atoms (dpa), and defect concentration.

[0157] It should be noted that the above is only an optional example and should not be construed as a limitation of this application.

[0158] Next, in step S104, a ground simulation test of the space material to be tested is conducted based on the obtained acceleration flux and maximum acceleration ratio.

[0159] The accelerated injection volume is the total injection volume. The critical dose rate can be obtained according to step S1034. The total injection volume, i.e. the accelerated injection volume, can be obtained according to formula (2).

[0160] Determine the ground test conditions for the ground simulation test, including the given dose rate and fluence, beam spot area, size of the space material to be tested, type of radiation source, energy, etc., and then conduct the ground simulation test.

[0161] Based on the acceleration injection volume and maximum acceleration ratio obtained in step S103, a specific ground simulation test plan is determined for conducting ground simulation tests.

[0162] Compared with existing technologies, this application accurately simulates the ground equivalent test of on-orbit damage (PKA spectrum). Using the PKA spectrum and critical dose rate as core physical quantities, it achieves higher physical fidelity and more realistically reflects the damage mechanism compared with the traditional method of using total dose or displacement damage energy as equivalent standards. This results in a more accurate and efficient ground simulation test.

[0163] Furthermore, by optimizing ground simulation experiments through this application, the space irradiation damage effect can be reproduced to the greatest extent possible under limited and feasible experimental conditions, avoiding blind experiments and saving a significant amount of experimental costs and time.

[0164] Furthermore, this application has a wide range of applications and is not limited to specific incident particles, energy ranges, or space materials to be tested. It can be widely used for displacement damage analysis and accelerated test design of various semiconductor or metal materials by different types of particles.

[0165] This application also provides apparatus embodiments that follow the above embodiments, for implementing the method steps described in the above embodiments. The interpretation of the same names is the same as that in the above embodiments, and they have the same technical effects as those in the above embodiments, so they will not be repeated here.

[0166] like Figure 4 As shown, this application provides a ground simulation test device 500 based on the optimization of ground irradiation source combination parameters. The ground simulation test device 500 includes a simulation calculation module 510, a fitting calculation module 520, a parameter calculation module 530, and a test parameter determination module 540.

[0167] In one specific embodiment, the simulation calculation module 510 is used to simulate the on-orbit space irradiation environment, calculate the orbital environment particle energy spectrum of the space orbit to be tested, and calculate the on-orbit PKA distribution spectrum in the material to be tested based on the orbital environment particle energy spectrum. The fitting calculation module 520, based on the obtained on-orbit PKA distribution spectrum, uses a specified optimization algorithm to perform fitting calculations to determine the optimal combination parameters of one or more ground irradiation sources, such that the PKA energy spectrum generated by the ground irradiation source in the space material to be tested matches the on-orbit PKA distribution spectrum, thereby obtaining the optimal fitted energy spectrum. The parameter calculation module 530, based on the obtained optimal fitted energy spectrum, calculates and determines the critical dose rate according to the defect dynamics model of the space material to be tested, and calculates the acceleration flux and maximum acceleration ratio used to guide the ground acceleration test. The test parameter determination module 540, based on the obtained acceleration flux and maximum acceleration ratio, conducts a ground simulation test of the space material to be tested.

[0168] According to optional implementation methods, the optimal combination parameters of one or more ground-based irradiation sources are determined through fitting calculations using a genetic algorithm or simulated annealing algorithm. Specifically, this includes: performing iterative optimization, executing a specific number of iterative optimization processes, with each iteration generating a set of combination parameters, and calculating the PKA spectrum generated in the space material under test; the combination parameters include particle type, energy, and the flux ratio of each particle. Fitting calculations are then performed, comparing the similarity between the PKA spectrum generated in each iteration and the on-orbit PKA distribution spectrum to determine the optimal combination parameters of the ground-based irradiation sources.

[0169] According to an optional implementation, the method includes: selecting at least two particle energies or particle types as an initial combination matrix; configuring the number of iterations for performing iterative optimization; and calculating the combination coefficients and the initial combination matrix in each iteration optimization process, and calculating the error between the PKA spectrum generated by the selected ground irradiation source in the space material under test and the on-orbit PKA distribution spectrum.

[0170] If the current error is less than the historical error, then update the solution of the combination coefficients and the combination matrix; if the current error is greater than or equal to the historical error, then do not update the solution of the combination coefficients and the combination matrix; repeat the iterative optimization process until the configured number of iterations is reached, and output the optimal solution.

[0171] According to the optional implementation method, determining the critical dose rate includes the following steps: mapping from on-orbit conditions to ground test conditions; establishing a thermal dose coupling model and simulating the temperature rise curve; determining the initial critical dose rate based on the temperature rise criterion; and correcting the critical dose rate based on microscopic defect dynamics.

[0172] According to the optional implementation method, the corrected critical dose rate is calculated using the following expression:

[0173] D_c*=D_c×B

[0174] Where D_c* represents the critical dose rate corrected according to the real-time calculated correction coefficient; D_c represents the critical dose rate before correction; B represents the real-time calculated correction coefficient, B=(S md / S c ) 2 *(t md / t c ), S md and t md S represents the characteristic distance and time given by molecular dynamics. c and t c These are the average spatial spacing and average time interval calculated based on D_c, respectively.

[0175] According to the optional implementation, if the calculated average spatial spacing is not within the defect influence range, or if the calculated average time interval is greater than or equal to the defect annihilation time interval, then there is no need to correct the critical dose rate, i.e., D_c* = D_c.

[0176] According to an optional implementation, the critical dose rate is calculated based on the corrected critical dose rate; the average time interval for any particle incident is calculated and compared with the results of molecular dynamics simulations; if the time interval is less than the critical time, the maximum acceleration factor is calculated using the following expression:

[0177] Maximum acceleration rate = critical dose rate / orbital integral flux.

[0178] According to the optional implementation method, the material property parameters of the space material to be tested are determined to establish a defect dynamics model of the space material to be tested; based on the corrected critical dose rate, the average spatial distance and average time interval between adjacent incident ions or adjacent incident particles are calculated.

[0179] Compared with existing technologies, this application accurately simulates the ground equivalent test of on-orbit damage (PKA spectrum). Using the PKA spectrum and critical dose rate as core physical quantities, it achieves higher physical fidelity and more realistically reflects the damage mechanism compared with the traditional method of using total dose or displacement damage energy as equivalent standards. This results in a more accurate and efficient ground simulation test.

[0180] Furthermore, by optimizing ground simulation experiments through this application, the space irradiation damage effect can be reproduced to the greatest extent possible under limited and feasible experimental conditions, avoiding blind experiments and saving a significant amount of experimental costs and time.

[0181] Furthermore, this application has a wide range of applications and is not limited to specific incident particles, energy ranges, or space materials to be tested. It can be widely used for displacement damage analysis and accelerated test design of various semiconductor or metal materials by different types of particles.

[0182] like Figure 5 As shown, this embodiment provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method steps described in the above embodiment.

[0183] This application provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0184] The following is for reference. Figure 5The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The terminal devices in the embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0185] like Figure 5 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0186] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0187] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a storage device 408, or installed from a ROM 402. When the computer program is executed by the processing device 401, it performs the functions defined in the methods of the embodiments of this application.

[0188] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0189] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0190] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0191] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0192] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A ground simulation test method based on ground irradiation source combination parameter optimization, characterized in that, The method comprises the following steps: Simulating the on-orbit space irradiation environment, obtaining the on-orbit particle energy spectrum of the space orbit to be measured, and calculating the on-orbit PKA distribution spectrum in the material to be measured based on the on-orbit particle energy spectrum; Based on the obtained on-orbit PKA distribution spectrum, the optimal combination parameters of one or more ground irradiation sources are determined by specifying an optimization algorithm, and the PKA energy spectrum generated by the ground irradiation source in the space material to be measured is matched with the on-orbit PKA distribution spectrum to obtain the optimal fitting energy spectrum; Based on the obtained optimal fitting energy spectrum, the critical dose rate is calculated and determined according to the defect-related data obtained by molecular dynamics calculation of the space material to be measured, and the accelerated fluence and maximum acceleration ratio for guiding the ground acceleration test are obtained by calculation; Based on the obtained accelerated fluence and maximum acceleration ratio, the ground simulation test of the space material to be measured is carried out.

2. The ground simulation test method according to claim 1, characterized by, The method comprises the following steps: The optimal combination parameters of one or more ground irradiation sources are determined by fitting calculation through a genetic algorithm or a simulated annealing algorithm, which specifically comprises: Performing iterative optimization, performing a specific number of iteration optimization processes, generating a set of combination parameters in each iteration optimization process, and calculating the PKA spectrum generated in the space material to be measured; the combination parameters include particle type, energy and particle fluence ratio; Performing fitting calculation, and calculating the similarity of the PKA spectrum generated in each iteration optimization process and the on-orbit PKA distribution spectrum to determine the optimal combination parameters of the ground irradiation source.

3. The ground simulation test method according to claim 1 or 2, characterized by, The method comprises the following steps: Selecting at least two particle energies or particle types as an initial combination matrix; The number of iterations for performing iterative optimization is set; In each iteration optimization process, the combination coefficient and the initial combination matrix are calculated, and the error between the PKA spectrum generated by the selected ground irradiation source in the space material to be measured and the on-orbit PKA distribution spectrum is calculated; If the current error is less than the historical error, the solution of the combination coefficient and the combination matrix is updated; If the current error is greater than or equal to the historical error, the solution of the combination coefficient and the combination matrix is not updated; The iterative optimization process is repeated until the set number of iterations is reached, and the optimal solution is output.

4. The ground simulation test method according to claim 1, characterized by, The method comprises the following steps: Determining the critical dose rate comprises the following steps: Mapping from on-orbit conditions to ground test conditions; Establishing a thermal dose coupling model and simulating a temperature rise curve; Determining an initial critical dose rate based on a temperature rise criterion; Correcting the critical dose rate based on micro-defect dynamics.

5. The ground simulation test method according to claim 4, characterized by The method comprises the following steps: The corrected critical dose rate is calculated using the following expression: D_c*=D_c×B wherein D_c* represents the critical dose rate corrected according to the correction factor calculated in real time; D_c represents the critical dose rate before correction; B represents the correction factor calculated in real time, B = (S md / S c )2*(t md / t c ), S md and t md are the characteristic distance and time calculated by molecular dynamics, respectively, and S c and t c are the average spatial interval and average time interval calculated according to D_c, respectively.

6. The ground simulation test method according to claim 5, characterized by The method comprises the following steps: If the calculated average space interval is not within the defect influence range, or if the calculated average time interval is greater than or equal to the defect annihilation time interval, the critical dose rate does not need to be corrected, i.e. D_c*=D_c.

7. The ground simulation test method according to claim 5, characterized by, The method comprises the following steps: According to the corrected critical dose rate, the critical dose rate is calculated; The average time interval of any particle incidence is calculated and compared with the molecular dynamics simulation result; If the time interval is less than the critical time, the maximum acceleration ratio is calculated using the following expression: Maximum acceleration ratio=critical dose rate / orbit integrated flux.

8. The ground simulation test method according to claim 5, characterized by, The method comprises the following steps: Determining the material attribute parameters of the space material to be measured to establish a defect dynamics model of the space material to be measured; According to the modified critical dose rate, the average spatial interval and the average time interval between adjacent incident ions are calculated, or the average spatial interval and the average time interval between adjacent incident particles are calculated.

9. A ground-based simulation test device based on ground irradiation source combination parameter optimization, characterized in that, The method for performing the ground simulation test based on the parameter optimization of the ground irradiation source combination according to any one of claims 1 to 8, comprising: a simulation calculation module for simulating an on-orbit space irradiation environment, calculating an on-orbit environmental particle energy spectrum of a to-be-tested space orbit, and calculating an on-orbit PKA distribution spectrum in a to-be-tested material based on the on-orbit environmental particle energy spectrum; a fitting calculation module for fitting and calculating to determine one or more sets of optimal combination parameters of the ground irradiation source based on the obtained on-orbit PKA distribution spectrum by specifying an optimization algorithm, so that the PKA energy spectrum generated by the ground irradiation source in the to-be-tested space material matches the on-orbit PKA distribution spectrum, to obtain an optimal fitting energy spectrum; a parameter calculation module for calculating and determining a critical dose rate based on the obtained optimal fitting energy spectrum according to a defect dynamics model of the to-be-tested space material, and calculating an accelerated fluence and a maximum acceleration ratio for guiding a ground acceleration test; a test parameter determination module for performing a ground simulation test of the to-be-tested space material based on the obtained accelerated fluence and maximum acceleration ratio.

10. The ground-based simulation test device based on the combination of parameters of ground irradiation sources according to claim 9, characterized in that, comprising: determining one or more sets of optimal combination parameters of the ground irradiation source by fitting and calculating through a genetic algorithm or a simulated annealing algorithm, specifically comprising: performing iterative optimization, performing a specific number of iterative optimization processes, each iterative optimization process generating a set of combination parameters, and calculating a PKA spectrum generated in the to-be-tested space material; the combination parameters include particle type, energy, and particle fluence ratio; performing fitting calculation, calculating the similarity of the PKA spectrum generated by each iterative optimization process and the on-orbit PKA distribution spectrum to determine the optimal combination parameters of the ground irradiation source.

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