A screening method for comprehensive radiation shielding performance of materials for deep space exploration

Abstract

The present invention discloses a method for screening the comprehensive radiation shielding performance of materials for deep space exploration, and belongs to the field of radiation protection. This method comprehensively considers the radiation shielding performance of materials in the radiation environment of deep space exploration and the optimal design of shielding materials. The screening process includes determining the radiation environment, constructing a charged particle model that simulates the radiation environment, constructing a geometric structure model of the radiation shielding material, simulating the transport behavior of charged particles in the shielding material, and constructing a database based on the data of multiple radiation shielding material samples to achieve the optimal design of the material. The present invention calculates the influence of the deep space exploration space radiation environment on the radiation shielding performance of the material, obtains the change in the radiation shielding performance of the material system under different structures, and then establishes the theoretical relationship between the radiation environment, the structure of the material, and the performance, so as to provide important theoretical guidance for designing materials with good shielding performance and avoid a large number of trial and error experiments.

Description

Technical Field

[0001] The invention relates to a screening method based on the comprehensive radiation shielding performance of a material for deep space exploration, and belongs to the field of radiation protection. Background Art

[0002] The space radiation environment mainly comes from the Earth's radiation belt, galactic cosmic rays, solar proton events, etc. Due to the existence of these radiation sources, the composition of the space radiation environment is quite complex. Radiation belt particles and solar high-energy particles not only interact on the surface of the material, but are also likely to penetrate the material and enter the interior of the material, affecting the normal operation of the internal payload of the spacecraft. In the space radiation environment, the electronic systems of satellites or spacecraft often suffer from performance degradation or failure due to space radiation, and even eventually lead to catastrophic consequences for satellites or spacecraft. Therefore, it is of great significance to study and analyze the radiation effects of the space environment for the radiation protection of spacecraft. With the increase of radiation-sensitive materials and devices on satellites and the increase in the requirements for long-life and high reliability of satellites, the radiation dose analysis method also requires more precision. The most important factors to consider in the study of radiation damage are: the total dose, energy deposition rate and dose rate deposited during the material mission period. The ground simulation test of the space environment effect is costly and it is difficult to truly simulate the space environment. In order to save costs and shorten the development cycle, a screening method for the comprehensive radiation shielding performance of materials for deep space exploration is urgently needed to accurately evaluate the space radiation effect and take appropriate radiation-resistant reinforcement protection measures on this basis. Summary of the invention

[0003] The present invention proposes a method for screening the comprehensive radiation shielding performance of materials for deep space exploration to solve the problems existing in the prior art.

[0004] A method for evaluating and screening radiation shielding materials for deep space exploration, the method for evaluating and screening radiation shielding materials for deep space exploration comprising the following steps:

[0005] S100. Determine the space radiation environment for deep space exploration and establish an omnidirectional differential flux energy spectrum of charged particles to simulate the energy distribution and flux of particles in the space radiation environment;

[0006] S200, constructing a charged particle model simulating a radiation environment according to the charged particle omnidirectional differential flux energy spectrum established in S100, including the energy, position and incident direction settings of a particle source;

[0007] S300, using the charged particle model for simulating the radiation environment constructed in S200, constructing a geometric structure model of the radiation shielding material, including defining the shape, size and composition of the material;

[0008] S400, simulating the behavior of charged particles in the geometric structure model of the radiation shielding material constructed in S300 through simulation software, and calculating the position distribution, range, ionization energy loss and displacement energy loss of the particles in the material;

[0009] S500: Based on the results of charged particle behavior simulated in S400, evaluate the performance of radiation shielding materials, consider radiation resistance, protection performance and structural strength requirements, so as to improve the safety and stability of satellites in space environment;

[0010] S600 uses the data obtained in S400 and S500, combined with optimization algorithms and artificial intelligence technology, to analyze and select multiple candidate materials to achieve the optimal design of radiation shielding materials.

[0011] Furthermore, in S100, the following steps are included:

[0012] S110. Define the planned orbit of the spacecraft, including the orbital semi-major axis, orbital eccentricity, orbital inclination, equatorial longitude of ascending node, argument of perigee and true perigee angle;

[0013] S120, using formula (1) to calculate the spatial position of the satellite in the equatorial inertial coordinate system according to the input orbital parameters to determine its specific position on the orbit, the formula (1) is:

[0014]

[0015] Where a is the semi-major axis of the orbit, e is the orbit eccentricity, i is the orbit inclination, Ω is the equatorial longitude of the ascending node, w is the argument of perigee, and f is the true perigee angle;

[0016] S130, according to the orbital position of the spacecraft, obtaining radiation environment data of the position from a relevant radiation environment model, including the type, energy distribution and flux information of charged particles;

[0017] S140. Based on the radiation environment model, derive the omnidirectional differential flux energy spectrum of charged particles in the orbit of the spacecraft, that is, the number of particles per unit area of ​​space, per unit time, in all directions, and per unit energy interval.

[0018] Furthermore, after S140, it also includes:

[0019] S150, normalizing and sampling the omnidirectional differential energy spectrum of the charged particles to facilitate subsequent simulation and calculation.

[0020] Furthermore, in S200, the following steps are included:

[0021] S210. Select particle physics software that can simulate deep space radiation environment and determine the required modules;

[0022] S220, according to the determined radiation environment and the omnidirectional differential flux energy spectrum of charged particles, use simulation software to set the energy distribution of the particle source to simulate the particle characteristics in the space radiation environment;

[0023] S230. Configure the incident direction of the charged particles in the simulation software. Usually, it can be set to omnidirectional or parallel incidence to simulate the impact of particles on the material in different directions.

[0024] S240, generating random numbers and performing linear interpolation according to the normalized charged particle differential energy spectrum to determine the energy of the incident particle, thereby completing the particle sampling process;

[0025] S250, assigning a weight to each sampled particle, where the weight of each particle is 1.

[0026] Furthermore, in S240, based on the differential energy spectrum of the charged particles, the cumulative particle energy spectrum is obtained according to formula (2) and normalized:

[0027]

[0028] Among them, F is the probability distribution function, f is the differential flux of charged particles, and E is the energy of charged particles.

[0029] Furthermore, in S300, the following steps are included:

[0030] S310. Determine the application scenarios and functional requirements of radiation shielding materials based on the specific requirements of deep space exploration missions;

[0031] S320, based on actual usage, selecting a suitable geometric configuration to simulate the physical form of the shielding material;

[0032] S330, creating a detailed geometric structure model of the shielding material using the application internal C++ code and related geometric building blocks in the simulation software;

[0033] S340, defining the physical and chemical properties of each material in the detailed geometric structure model of the shielding material, including density, atomic composition and electron density, as well as the thickness and sequence of different material layers, to ensure the accuracy of the model;

[0034] S350, setting the relationship between the various components in the detailed geometric structure model of the shielding material;

[0035] S360. Check whether the geometric structure model correctly reflects the actual design and intended function of the shielding material, and ensure that the model has no geometric errors or missing components.

[0036] Furthermore, in S400, the following steps are included:

[0037] S410. Define the physical processes of the interaction between charged particles and shielding materials, including ionization, excitation, scattering and nuclear reaction, and form a physical list;

[0038] S420, selecting a suitable physical model to describe the interaction according to the type, incident direction, shape and energy range of the charged particles to ensure the accuracy of the simulation;

[0039] S430, using the configured physics list and the selected interaction model, simulating the penetration, range, energy loss and angular distribution behavior of charged particles in the shielding material;

[0040] S440, in order to count the characteristic information of charged particles after penetrating the shielding material, the surface of the penetrated material is divided into fine grid units, and the number, energy and angular distribution of particles in each grid are calculated;

[0041] S450, analyzing the simulated charged particle behavior data and evaluating the shielding effectiveness of the material;

[0042] S460. Based on the simulation results, verify and make necessary adjustments to the model to ensure that it reflects the actual physical process and the performance of the shielding material.

[0043] Furthermore, in S600, the following steps are included:

[0044] S610, using a random construction method and a Monte Carlo algorithm to generate multiple sets of different radiation shielding material data samples as input for subsequent optimization design;

[0045] S620, dividing the multiple groups of different radiation shielding material data samples into a training set and a test set, wherein the training set is used to train the neural network model, and the test set is used to evaluate the generalization ability and performance of the model;

[0046] S630, designing and constructing a neural network model, wherein the model includes an appropriate number of hidden layers and nodes to process input samples and predict the radiation shielding performance of the material;

[0047] S640, selecting appropriate activation functions for the input layer and output layer of the neural network, and using appropriate methods to initialize network weights to promote rapid convergence of the model;

[0048] S650, training the neural network model using the training set data, and verifying and evaluating the model using the test set data to ensure the accuracy and reliability of the model;

[0049] S660, use the trained neural network model to predict the shielding performance of new material component solutions, and use optimization techniques such as Monte Carlo algorithm to select the best material design solution;

[0050] S670. Evaluate different material design schemes based on the model prediction results and actual application requirements, and select the best scheme for practical application.

[0051] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for evaluating and screening radiation shielding materials for deep space exploration.

[0052] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned method for evaluating and screening radiation shielding materials for deep space exploration.

[0053] Beneficial effects of the present invention: The screening method of the comprehensive radiation shielding performance of materials for deep space exploration of the present invention reduces the dependence on physical experiments through simulation and calculation, greatly improves the efficiency of material screening and reduces costs. Secondly, the method uses advanced technologies such as neural networks and Monte Carlo algorithms to quickly and accurately predict and optimize the performance of materials, thereby providing more reliable radiation protection solutions for deep space exploration missions. In addition, the method helps to improve the safety and stability of spacecraft in extreme radiation environments, extend their service life, and reduce launch costs. Therefore, the method supports long-term and longer-distance deep space exploration missions and promotes mankind's in-depth exploration and scientific research of space. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A method flow chart of a method for screening comprehensive radiation shielding performance of materials for deep space exploration according to the present invention;

[0055] Figure 2 is the electron flux spectrum of a certain orbital environment;

[0056] Figure 3 is the proton flux spectrum of a certain orbital environment;

[0057] Figure 4 Shielding materials of different structures;

[0058] Figure 5 This is a schematic diagram of charged particles incident on a material in parallel;

[0059] Figure 6 This is a schematic diagram of a point source of charged particles incident on a material;

[0060] Figure 7It is the basic structure of BP neural network. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Reference Figure 1 As shown, a method for evaluating and screening radiation shielding materials for deep space exploration, the method for evaluating and screening radiation shielding materials for deep space exploration comprises the following steps:

[0063] S100. Determine the space radiation environment for deep space exploration and establish an omnidirectional differential flux energy spectrum of charged particles to simulate the energy distribution and flux of particles in the space radiation environment;

[0064] S200, constructing a charged particle model simulating a radiation environment according to the charged particle omnidirectional differential flux energy spectrum established in S100, including the energy, position and incident direction settings of a particle source;

[0065] S300, using the charged particle model for simulating the radiation environment constructed in S200, constructing a geometric structure model of the radiation shielding material, including defining the shape, size and composition of the material;

[0066] S400, simulating the behavior of charged particles in the geometric structure model of the radiation shielding material constructed in S300 through simulation software, and calculating the position distribution, range, ionization energy loss and displacement energy loss of the particles in the material;

[0067] S500: Based on the results of charged particle behavior simulated in S400, evaluate the performance of radiation shielding materials, consider radiation resistance, protection performance and structural strength requirements, so as to improve the safety and stability of satellites in space environment;

[0068] S600 uses the data obtained in S400 and S500, combined with optimization algorithms and artificial intelligence technology, to analyze and select multiple candidate materials to achieve the optimal design of radiation shielding materials.

[0069] Specifically, the method for evaluating and screening radiation shielding materials for deep space exploration of the present invention provides an effective way to accurately evaluate and screen radiation shielding materials by comprehensively simulating the deep space radiation environment and the behavior of charged particles. It can simulate real deep space radiation conditions, analyze the shielding effectiveness of materials in detail, and combine optimization algorithms and artificial intelligence technology to conduct comprehensive performance evaluation, thereby achieving the optimal design of materials. This method not only improves the accuracy and efficiency of material selection, but also helps to improve the safety and stability of spacecraft in deep space environments, while reducing R&D costs and launch costs, providing important technical support for the success of deep space exploration missions.

[0070] Further, see Figure 2 , Figure 3 As shown, in S100, the following steps are included:

[0071] S110. Define the planned orbit of the spacecraft, including orbital semi-major axis, orbital eccentricity, orbital inclination, equatorial longitude of ascending node, argument of perigee and true perigee angle, etc.;

[0072] S120, using formula (1) to calculate the spatial position of the satellite in the equatorial inertial coordinate system according to the input orbital parameters to determine its specific position on the orbit, the formula (1) is:

[0073]

[0074] Where a is the semi-major axis of the orbit, e is the orbit eccentricity, i is the orbit inclination, Ω is the equatorial longitude of the ascending node, w is the argument of perigee, and f is the true perigee angle;

[0075] S130, according to the orbital position of the spacecraft, obtaining radiation environment data of the position from a relevant radiation environment model, including information such as the type, energy distribution and flux of charged particles;

[0076] S140. Based on the radiation environment model, derive the omnidirectional differential flux energy spectrum of charged particles in the orbit of the spacecraft, that is, the number of particles per unit area of ​​space, per unit time, in all directions, and per unit energy interval.

[0077] Specifically, the goal of S100 is to analyze the orbital environment and obtain the omnidirectional differential flux energy spectrum of charged particles in the orbit. First, it is necessary to input the preset orbital parameters to obtain the orbital radiation environment data, and derive the omnidirectional differential flux energy spectrum of charged particles based on the corresponding radiation environment model, that is, the number of particles per unit area in space, per unit time, from all directions, and within a unit energy interval. Taking the Earth orbit as an example, the spatial position of the satellite in the equatorial inertial coordinate system is calculated by formula (1).

[0078] S100 provides an accurate method for deep space exploration missions to analyze and determine the specific radiation environment faced by spacecraft in orbit. By defining the predetermined orbital parameters of the spacecraft, using formula (1) to calculate the precise spatial position of the satellite, and combining the radiation environment model to obtain detailed radiation data, this step can generate a comprehensive omnidirectional differential flux energy spectrum of charged particles. This energy spectrum is the basis for subsequent material shielding performance evaluation and optimization design, ensuring that shielding materials can be accurately designed and evaluated for actual radiation conditions.

[0079] Furthermore, after S140, it also includes:

[0080] S150, normalizing and sampling the omnidirectional differential energy spectrum of the charged particles to facilitate subsequent simulation and calculation.

[0081] Specifically, by normalizing and sampling the omnidirectional differential energy spectrum of charged particles, step S150 significantly improves the efficiency and accuracy of simulation and calculation. This process not only simplifies the subsequent simulation process and ensures the accuracy of statistical analysis, but also optimizes the simulation results to more realistically reflect the deep space radiation environment. In addition, the sampling method also helps to reduce computing costs and resource requirements, making the simulation of complex radiation environments more efficient and feasible.

[0082] Further, see Figure 5 , Figure 6 As shown, in S200, the following steps are included:

[0083] S210. Select particle physics software that can simulate deep space radiation environment and determine the required modules;

[0084] S220, according to the determined radiation environment and the omnidirectional differential flux energy spectrum of charged particles, use simulation software to set the energy distribution of the particle source to simulate the particle characteristics in the space radiation environment;

[0085] S230. Configure the incident direction of the charged particles in the simulation software. Usually, it can be set to omnidirectional or parallel incidence to simulate the impact of particles on the material in different directions.

[0086] S240, generating random numbers and performing linear interpolation according to the normalized charged particle differential energy spectrum to determine the energy of the incident particle, thereby completing the particle sampling process;

[0087] S250, assigning a weight to each sampled particle, where the weight of each particle is 1.

[0088] Furthermore, in S240, based on the differential energy spectrum of the charged particles, the cumulative particle energy spectrum is obtained according to formula (2) and normalized:

[0089]

[0090] Among them, F is the probability distribution function, f is the differential flux of charged particles, and E is the energy of charged particles.

[0091] Specifically, in S200, according to the characteristics of the omnidirectional differential flux energy spectrum of charged particles, a charged particle model simulating the radiation environment of deep space exploration is constructed. Based on the G4SPSEneDistribution, G4SPSPosDistribution and G4SPSAngDistribution modules provided by Geant4, the energy, position and incident direction of the charged particle source are set respectively. Usually, the incident direction of the charged particles is set to omnidirectional or parallel incidence.

[0092] The particle energy and flux in the space radiation environment cover multiple orders of magnitude, which will result in a very small probability of high-energy, low-density charged particles being sampled. Therefore, it is necessary to normalize the omnidirectional differential energy spectrum of charged particles for sampling. The specific operations are as follows:

[0093] First, based on the differential energy spectrum of charged particles, the cumulative particle energy spectrum is obtained according to formula (2) and normalized.

[0094] Generate a random number between 0 and 1, search for the energy segment corresponding to the random number in the normalized cumulative particle energy spectrum, obtain the energy of the incident particle through linear interpolation and other methods, complete the sampling, and the particle weight is 1.

[0095] In S200, by carefully selecting appropriate particle physics software and required modules, and setting the energy distribution of the particle source according to the actual radiation environment and the omnidirectional differential flux energy spectrum of charged particles, this method ensures the accuracy and applicability of the simulation environment. Configuring the incident direction of charged particles and performing particle energy sampling further enhances the authenticity and comprehensiveness of the simulation, so that the simulation results can cover the potential impact of particles from different directions on the material. By assigning a weight to each sampled particle, it is ensured that the importance of each particle in the simulation process is properly reflected. In addition, the use of normalization and linear interpolation improves the efficiency and accuracy of data processing, so that the simulation can truly reflect the behavior of particles in the actual deep space environment.

[0096] Further, see Figure 4 , Figure 5 and Figure 6 As shown, in S300, the following steps are included:

[0097] S310. Determine the application scenarios and functional requirements of radiation shielding materials based on the specific requirements of deep space exploration missions;

[0098] S320, based on actual usage, selecting a suitable geometric configuration to simulate the physical form of the shielding material;

[0099] S330, creating a detailed geometric structure model of the shielding material using the application internal C++ code and related geometric building blocks in the simulation software;

[0100] S340, defining the physical and chemical properties of each material in the detailed geometric structure model of the shielding material, including density, atomic composition and electron density, as well as the thickness and sequence of different material layers, to ensure the accuracy of the model;

[0101] S350, setting the relationship between the various components in the detailed geometric structure model of the shielding material;

[0102] S360. Check whether the geometric structure model correctly reflects the actual design and intended function of the shielding material, and ensure that the model has no geometric errors or missing components.

[0103] Specifically, according to the actual use of radiation shielding materials, a geometric structure model of radiation shielding materials is constructed. The radiation shielding material model is used to simulate the composite materials corresponding to the satellite structure. Usually the shielding material configuration is a flat plate configuration or a spherical configuration. Based on the internal C++ code of the Geant4 application and modules such as G4Box, G4LogicalVolume, G4PhysicalVolume, and G4VUserDetectorConstruction, the geometric structure model of the radiation shielding material is created to realize the setting of the shape, material, position of the shielding material structure, and the mutual inheritance relationship of each structure.

[0104] In S300, by clarifying the application scenarios and functional requirements, selecting appropriate geometric configurations, and using advanced simulation software and geometric building blocks, this step ensures the detailedness and physical authenticity of the shielding material model. Defining the physical and chemical properties of each material and setting the relationships between the components further enhances the accuracy and applicability of the model. Finally, by checking and verifying the integrity of the model, this method ensures that the model can accurately reflect the actual design and function of the shielding material, providing a reliable basis for subsequent performance evaluation and optimization design.

[0105] Furthermore, in S400, the following steps are included:

[0106] S410. Define the physical processes of the interaction between charged particles and shielding materials, including ionization, excitation, scattering and nuclear reaction, and form a physical list;

[0107] S420, selecting a suitable physical model to describe the interaction according to the type, incident direction, shape and energy range of the charged particles to ensure the accuracy of the simulation;

[0108] S430, using the configured physics list and the selected interaction model, simulating the penetration, range, energy loss and angular distribution behavior of charged particles in the shielding material;

[0109] S440, in order to count the characteristic information of charged particles after penetrating the shielding material, the surface of the penetrated material is divided into fine grid units, and the number, energy and angular distribution of particles in each grid are calculated;

[0110] S450, analyzing the simulated charged particle behavior data and evaluating the shielding effectiveness of the material;

[0111] S460. Based on the simulation results, verify and make necessary adjustments to the model to ensure that it reflects the actual physical process and the performance of the shielding material.

[0112] Specifically, the G4VUserPhysicsList module in the Geant4 application is used to describe the physical interactions of charged particles, select appropriate interaction models, and realize the calculation of charged particle transport behavior. It is necessary to establish a geometric model based on multi-component material primitives to calculate the position distribution, range, ionization energy loss, and displacement energy loss of charged particles in the material. In order to count the characteristic information of charged particles penetrating the shielding material and entering the detector, the surface of the charged particles after penetrating the shielding material is divided into 1×1μm grid units to count the number of particles, energy, angle, and other surface distribution information in each grid after the charged particles pass through the shielding material.

[0113] This method significantly improves the accuracy of evaluating the shielding effectiveness of materials by simulating the interaction between charged particles and shielding materials in detail. Defining physical processes and selecting appropriate physical models ensure the accuracy and reliability of the simulation. By simulating the penetration, range, energy loss, and angular distribution of particles, and using grid cells to perform statistics on the characteristic information after penetration, this method can comprehensively analyze the impact of particle behavior on shielding performance. In addition, the analysis of simulation results and the verification and adjustment of the model further ensure the authenticity of the evaluation results and the practicality of the model. This step provides solid data support for optimizing the design of radiation shielding materials and helps to develop more effective shielding solutions to protect spacecraft from the impact of high-energy particles in deep space environments.

[0114] Furthermore, in S500, the total shielding residual dose of the radiation shielding material and the weight of the satellite are used as indicators for performance evaluation. It is necessary to comprehensively consider the radiation resistance, protection performance and structural strength of the radiation shielding material to improve the safety and stability of the satellite in the space environment, thereby extending its service life. By reducing the weight of the satellite, reducing the launch cost and increasing the carrying capacity, more space exploration missions will become possible.

[0115] Further, see Figure 7 As shown, in S600, the following steps are included:

[0116] S610, using a random construction method and a Monte Carlo algorithm to generate multiple sets of different radiation shielding material data samples as input for subsequent optimization design;

[0117] S620, dividing the multiple groups of different radiation shielding material data samples into a training set and a test set, wherein the training set is used to train the neural network model, and the test set is used to evaluate the generalization ability and performance of the model;

[0118] S630, designing and constructing a neural network model, wherein the model includes an appropriate number of hidden layers and nodes to process input samples and predict the radiation shielding performance of the material;

[0119] S640, selecting appropriate activation functions for the input layer and output layer of the neural network, and using appropriate methods to initialize network weights to promote rapid convergence of the model;

[0120] S650, training the neural network model using the training set data, and verifying and evaluating the model using the test set data to ensure the accuracy and reliability of the model;

[0121] S660, use the trained neural network model to predict the shielding performance of new material component solutions, and use optimization techniques such as Monte Carlo algorithm to select the best material design solution;

[0122] S670. Evaluate different material design schemes based on the model prediction results and actual application requirements, and select the best scheme for practical application.

[0123] Specifically, multiple groups of radiation shielding materials calculated by random construction and Monte Carlo algorithm are used as training samples to quickly establish a neural network model with good convergence to achieve the optimal design of shielding materials. The specific operations are as follows:

[0124] First, the training samples are input with 4 layers of data of different material types. The input space is a 4-dimensional space, and the training sample points are 4-dimensional vectors. 500 groups of different material component schemes are randomly sampled. By comparing the consistency of the statistical distribution of each data in the 500 samples, the representativeness and uniformity of the samples are qualitatively judged. Part of the training samples is used to train the network (90%), and the other part is used for testing and verification (10%), which is used to test the generalization of the network during the training process.

[0125] Next, set up the neural grid. Use one hidden layer; the number of hidden layer nodes is the number of intermediate variables, which is set to 25 based on experience and debugging. After comparing the network training results, the activation functions of the input layer and the output layer are both selected as tansig functions:

[0126]

[0127] The optimization design process in 600 greatly improves the efficiency and accuracy of radiation shielding material design by combining random construction methods and Monte Carlo algorithms, as well as utilizing neural network models. The multiple sets of data samples generated provide rich training and test data for the neural network, enabling the model to learn and predict the shielding performance of different material composition schemes. By carefully designing the neural network structure and selecting appropriate activation functions, the model can converge quickly and provide reliable predictions. In addition, the accuracy and reliability of the model have been rigorously verified and evaluated to ensure the credibility of the prediction results. Finally, combined with optimization technology, this method can select the best material design from multiple candidate schemes to meet actual application needs, thereby achieving more effective radiation protection in deep space exploration missions. These beneficial effects not only accelerate the iterative process of material design, but also improve the performance of the final product, providing key technical support for deep space exploration.

[0128] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for evaluating and screening radiation shielding materials for deep space exploration.

[0129] Specifically, the storage medium stores a specific computer program so that when the processor executes the program, it can effectively implement the evaluation and screening method of radiation shielding materials for deep space exploration. The implementation of this method brings significant benefits: it improves the accuracy and efficiency of material screening, reduces dependence on physical experiments, and thus reduces costs and time consumption. In addition, through the application of simulation and optimization algorithms, this method can accurately predict the performance of different materials in deep space radiation environments, providing a scientific basis for material selection and design. Ultimately, this will help improve the radiation protection capabilities of spacecraft, ensure the success rate of deep space exploration missions and the long-term stable operation of spacecraft.

[0130] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned method for evaluating and screening radiation shielding materials for deep space exploration.

[0131] Specifically, the computer device provides an efficient and integrated solution for evaluating and screening radiation shielding materials suitable for deep space exploration missions by integrating memory, processors and specially designed computer programs. When the processor executes the program, it can accurately simulate and analyze the performance of materials in complex deep space radiation environments, thereby optimizing material selection and design. This integrated equipment not only improves the accuracy and efficiency of the evaluation process, but also reduces dependence on traditional physical experiments and reduces R&D costs and time. In addition, it supports rapid iteration and real-time adjustments, which helps to develop more effective radiation protection measures to ensure the success of deep space exploration missions and the long-term stable operation of spacecraft.

[0132] The present invention calculates the influence of the deep space radiation environment on the radiation shielding performance of materials, obtains the changes in the radiation shielding performance of material systems under different structures, and then establishes the theoretical relationship between the radiation environment, the structure and performance of the material, providing important theoretical guidance for designing materials with good shielding performance and avoiding a large number of trial and error experiments. The invention can meet the relevant application fields of aerospace in extreme space environments.

[0133] The above is a detailed introduction to a method for evaluating and screening radiation shielding materials for deep space exploration provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for evaluating and screening radiation shielding materials for deep space exploration, characterized in that: The method for evaluating and screening radiation shielding materials for deep space exploration comprises the following steps: S100. Determine the space radiation environment for deep space exploration and establish the omnidirectional differential flux energy spectrum of charged particles to simulate the energy distribution and flux of particles in the space radiation environment; In S100, the following steps are included: S110. Define the planned orbit of the spacecraft, including the orbital semi-major axis, orbital eccentricity, orbital inclination, equatorial longitude of ascending node, argument of perigee and true perigee angle; S120, using formula (1) to calculate the spatial position of the satellite in the equatorial inertial coordinate system according to the input orbital parameters to determine its specific position on the orbit, the formula (1) is: Where a is the semi-major axis of the orbit, e is the orbit eccentricity, i is the orbit inclination, Ω is the equatorial longitude of the ascending node, w is the argument of perigee, and f is the true perigee angle; S130, according to the orbital position of the spacecraft, obtaining radiation environment data of the position from a relevant radiation environment model, including the type, energy distribution and flux information of charged particles; S140. Based on the radiation environment model, derive the omnidirectional differential flux energy spectrum of charged particles in the orbit where the spacecraft is located, that is, the number of particles per unit area in space, per unit time, in all directions, and per unit energy interval; S200, constructing a charged particle model simulating a radiation environment according to the charged particle omnidirectional differential flux energy spectrum established in S100, including the energy, position and incident direction settings of a particle source; S300, using the charged particle model for simulating the radiation environment constructed in S200, constructing a geometric structure model of the radiation shielding material, including defining the shape, size and composition of the material; S400, simulating the behavior of charged particles in the geometric structure model of the radiation shielding material constructed in S300 through simulation software, and calculating the position distribution, range, ionization energy loss and displacement energy loss of the particles in the material; S500: Based on the results of charged particle behavior simulated in S400, evaluate the performance of radiation shielding materials, consider radiation resistance, protection performance and structural strength requirements, so as to improve the safety and stability of satellites in space environment; S600 uses the data obtained in S400 and S500, combined with optimization algorithms and artificial intelligence technology, to analyze and select multiple candidate materials to achieve the optimal design of radiation shielding materials.

2. A method for evaluating and screening radiation shielding materials for deep space exploration according to claim 1, characterized in that: After S140, it also includes: S150, normalizing and sampling the omnidirectional differential energy spectrum of the charged particles to facilitate subsequent simulation and calculation.

3. A method for evaluating and screening radiation shielding materials for deep space exploration according to claim 2, characterized in that: In S200, the following steps are included: S210. Select particle physics software that can simulate deep space radiation environment and determine the required modules; S220, according to the determined radiation environment and the omnidirectional differential flux energy spectrum of charged particles, use simulation software to set the energy distribution of the particle source to simulate the particle characteristics in the space radiation environment; S230. Configuring the incident direction of the charged particles in the simulation software, which can be set to omnidirectional or parallel incidence, to simulate the impact of particles on the material in different directions; S240, generating random numbers and performing linear interpolation according to the normalized charged particle differential energy spectrum to determine the energy of the incident particle, thereby completing the particle sampling process; S250, assigning a weight to each sampled particle, where the weight of each particle is 1.

4. A method for evaluating and screening radiation shielding materials for deep space exploration according to claim 3, characterized in that: In S240, based on the differential energy spectrum of the charged particles, the cumulative particle energy spectrum is obtained according to formula (2) and normalized: Among them, F is the probability distribution function, f is the differential flux of charged particles, and E is the energy of charged particles.

5. A method for evaluating and screening radiation shielding materials for deep space exploration according to claim 4, characterized in that: In S300, the following steps are included: S310. Determine the application scenarios and functional requirements of radiation shielding materials based on the specific requirements of deep space exploration missions; S320, based on actual usage, selecting a suitable geometric configuration to simulate the physical form of the shielding material; S330, creating a detailed geometric structure model of the shielding material using the application internal C++ code and related geometric building blocks in the simulation software; S340, defining the physical and chemical properties of each material in the detailed geometric structure model of the shielding material, including density, atomic composition and electron density, as well as the thickness and sequence of different material layers, to ensure the accuracy of the model; S350, setting the relationship between the various components in the detailed geometric structure model of the shielding material; S360. Check whether the geometric structure model correctly reflects the actual design and intended function of the shielding material, and ensure that the model has no geometric errors or missing components.

6. A method for evaluating and screening radiation shielding materials for deep space exploration according to claim 5, characterized in that: In S400, the following steps are included: S410. Define the physical processes of the interaction between charged particles and shielding materials, including ionization, excitation, scattering and nuclear reaction, and form a physical list; S420, selecting a suitable physical model to describe the interaction according to the type, incident direction, shape and energy range of the charged particles to ensure the accuracy of the simulation; S430, using the configured physics list and the selected interaction model, simulating the penetration, range, energy loss and angular distribution behavior of charged particles in the shielding material; S440, in order to count the characteristic information of charged particles after penetrating the shielding material, the surface of the penetrated material is divided into fine grid units, and the number, energy and angular distribution of particles in each grid are calculated; S450, analyzing the simulated charged particle behavior data and evaluating the shielding effectiveness of the material; S460. Based on the simulation results, the model is verified and adjusted to ensure that it reflects the actual physical processes and performance of the shielding materials.

7. A method for evaluating and screening radiation shielding materials for deep space exploration according to claim 6, characterized in that: In S600, the following steps are included: S610, using a random construction method and a Monte Carlo algorithm to generate multiple sets of different radiation shielding material data samples as input for subsequent optimization design; S620, dividing the multiple groups of different radiation shielding material data samples into a training set and a test set, wherein the training set is used to train the neural network model, and the test set is used to evaluate the generalization ability and performance of the model; S630, designing and constructing a neural network model, wherein the model includes an appropriate number of hidden layers and nodes to process input samples and predict the radiation shielding performance of the material; S640, selecting appropriate activation functions for the input layer and output layer of the neural network, and using appropriate methods to initialize network weights to promote rapid convergence of the model; S650, training the neural network model using the training set data, and verifying and evaluating the model using the test set data to ensure the accuracy and reliability of the model; S660, use the trained neural network model to predict the shielding performance of new material component solutions, and use Monte Carlo algorithm optimization technology to select the best material design solution; S670. Evaluate different material design schemes based on the model prediction results and actual application requirements, and select the best scheme for practical application.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating and screening radiation shielding materials for deep space exploration as described in any one of claims 1 to 7 is implemented.

9. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for evaluating and screening radiation shielding materials for deep space exploration as described in any one of claims 1 to 7.

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