Reliability evaluation method for multilayer space radiation shielding design

Abstract

The method comprises the following steps: 1, analyzing a space radiation environment and designing a shielding geometric material; 2, determining boundary conditions; 3, determining a radiation shielding multi-objective optimization model; 4, shielding design based on a genetic algorithm; step 5, carrying out radiation shielding reliability risk modeling analysis; step 6, analyzing specific cases; and step 7, carrying out radiation shielding design analysis based on reliability evaluation. The method adopts a multi-objective optimization strategy, and aims to enhance the selection of a shielding structure and materials, and preferentially consider the objectives of light weight, compactness, minimum radiation dose and the like. According to the strategy, the genetic algorithm is utilized, and the automation and efficiency of radiation shielding design optimization are improved through Monte Carlo MCNP6 simulation analysis. A reliability-based shielding design technology is constructed, uncertainty is integrated into an optimization design process, reliability evaluation on a dose equivalent result of a radiation shielding material is realized, and the reliability of electronic equipment is improved. The problem of integrating uncertain factors into the radiation shielding optimization process is solved, and a theoretical basis is provided for effective radiation shielding and reliability evaluation.

Description

(I) Technical field:

[0001] The present invention relates to a reliability assessment method for multi-layer space radiation shielding design, which adopts a multi-objective optimization strategy to enhance the shielding structure and material selection, giving priority to objectives such as lightweight, compactness and minimum radiation dose. The strategy uses evolutionary algorithms to achieve automation and efficiency improvement in radiation shielding design optimization. Numerical results show that the strategy effectively balances the mass, weight and volume of the shielding body. Finally, based on the reliability-based shielding design technology, the shielding material dose equivalent results are analyzed in detail, and the uncertainty is integrated into the optimization design process, so that a reliability-based shielding design method can be developed. This method belongs to the field of reliability assessment of space radiation shielding. (II) Background technology:

[0002] Galactic cosmic radiation (GCR) and solar particle events (SPE) are the main sources of space radiation. High-energy protons, electrons, alpha particles and a considerable number of particles with high atomic numbers (Z>2) form GCR. This has a great impact on electronic equipment. In addition, researchers have designed and analyzed radiation shielding materials and found that materials with the highest charge-to-mass ratio are most effective against high atomic number and energy (HZE) particles. A good passive shielding system should have the characteristics of strong adaptability, reasonable price, non-toxicity, long service life, and low secondary radiation, but there are few methods to evaluate the reliability of radiation shielding.

[0003] The design of multi-layer materials needs to consider the reliability assessment of electronic equipment identification and the study of radiation effects. Studies have shown that for a certain amount of materials, multi-layer shielding can reduce electron penetration by about 60%, which is more effective than single-material shielding. Aluminum and polyethylene layered shielding has been successfully used on the International Space Station (ISS). According to current experience, a shield is first proposed, followed by design, simulation and inspection, which requires designers to manually iterate many times. Therefore, the shielding solution obtained using this traditional method is empirical rather than ideal. Therefore, it is necessary to study innovative methods for radiation shielding design. There is a lack of research related to the reliability assessment of radiation shielding, and the reliability of electronic equipment under radiation shielding conditions is rarely considered. Designing effective shielding mechanisms for key electronic components is an important aspect that needs further exploration. The investigation will include an examination of various radiation effects on electronic equipment, incorporating uncertainties to facilitate the reliability assessment of feasible shielding strategies, and reduce the radiation risk of electronic equipment.

[0004] Therefore, this method is based on the design of space radiation shielding, based on radiation shielding based on intelligent algorithms, and proposes a radiation shielding reliability method based on shielding analysis software and calculation reliability design process. Through the intelligent design analysis and shielding effect research of multi-layer radiation shielding materials, the problem of multi-layer radiation shielding metrology indicators and reliability analysis that are difficult to quantify can be solved, thereby providing a theoretical basis for effective shielding design and improving the radiation resistance of electronic equipment, and providing a technical basis for evaluating the reliability of radiation shielding of electronic equipment. (III) Summary of the invention:

[0005] 1. Purpose: The purpose of the present invention is to provide a reliability assessment method for multi-layer space radiation shielding design. This method adopts a multi-objective optimization algorithm and focuses on the goals of lightweight, compactness and minimizing radiation dose, thereby achieving automated optimization and efficiency improvement of radiation shielding design. Through Monte Carlo simulation analysis, this method can achieve an effective balance between the mass, weight and volume of the shielding body, and construct a reliability-based shielding design technology to achieve reliability assessment on the dose equivalent results of radiation shielding materials, reduce radiation effects, and improve the reliability of electronic equipment. The present invention solves the problem of integrating uncertainty factors into the radiation shielding optimization process, and provides a solid theoretical basis for effective radiation shielding and reliability assessment.

[0006] 2. Technical solution: The present invention is a reliability assessment method for multi-layer space radiation shielding design, which comprises the following steps:

[0007] Step 1: Space radiation environment analysis and shielding geometry material design;

[0008] First, the space radiation environment is determined, and the space radiation environment of electronic equipment is analyzed, using the GCR spectrum of the 2010 solar minimum in the BO-2014GCR model. The shielding effectiveness of various shielding materials is simulated using the flat plate model, and the total shielding thickness range is designed. The shielding shape is selected as a finite flat plate, the shielding effect and the thickness of the shielding medium are analyzed, and the geometric model of complex multi-layer shielding materials is established.

[0009] Step 2: Determine boundary conditions;

[0010] Determine particle parameters, using solar modulation parameters of 475 MV. Apply to different shielding materials and thicknesses within the flat panel geometry to derive flux values ​​at material interfaces. Determine pulsed SPE element abundances, accounting for radiation damage from heavy ions.

[0011] Step 3: Determine the radiation shielding multi-objective optimization model;

[0012] The multi-objective radiation shielding optimization model is established with reference to the parameters of steps one and two. The overall goal of advanced radiation shielding design is to minimize the weight, volume, and radiation dose outside the shielding layer simultaneously. A multi-objective optimization model is proposed, which covers the mathematical formulation of the shielding design problem, decision variables, and constraints.

[0013] Step 4: Shielding design based on genetic algorithm;

[0014] A genetic algorithm was used to optimize the radiation shielding design and analysis process to obtain candidate shielding solutions for multi-objective optimization problems. A multi-objective optimization function was established to determine the Pareto optimal solution. Range constraints were imposed on each variable to determine the optimal solution for the shielding scheme, and experimental analysis was performed. Based on the experimental results, a combination design consisting of five shielding materials was proposed to analyze the shielding effectiveness and determine the optimal solution.

[0015] Step 5: Radiation shielding reliability risk modeling analysis;

[0016] Based on the linear relationship between excess lethal risk and dose equivalent, and considering the uncertainty sources Q(L) and DDREF, a radiation shielding reliability risk model is established, and for a given shielding thickness, the random sequence of related risks is determined. Thus, a reliability assessment method for radiation shielding electronic equipment is established.

[0017] Step 6: Specific case analysis;

[0018] A five-year mission to Mars was designed using a hybrid core electronics configuration. The optimization results and reliability analysis were used to obtain the evolution of the design quality over time, highlighting the significant impact of uncertainty on the design process. The results show that a reliability-based approach is effective in describing risk reduction in mission design.

[0019] Step 7: Radiation shielding design analysis based on reliability assessment.

[0020] Combined with the case results, the optimization strategy is able to determine the best shielding solution in the early stage of radiation shielding design by minimizing the objective function under specified constraints, integrating reliability-based design methods into radiation shielding assessment to manage and evaluate uncertainties in shielding design. This approach clarifies the radiation risk to electronic equipment. In addition, this method helps to make optimal radiation shielding design decisions and provides supplementary data in the conceptual design stage, especially for spacecraft that lack shielding design experience. (IV) Description of the drawings:

[0021] Figure 1 Implementation steps flow chart

[0022] Figure 2Z=1, 2, 8, 26, annual GCR spectrum of ions with solar modulation parameter of 475MV

[0023] Figure 3 Schematic diagram of spherical geometry for isotropic radiation

[0024] Figure 4 A general approach to radiation shielding design using genetic algorithms

[0025] Figure 5 Trends of dose equivalent and surface density of multi-layer radiation shielding structures under GCR spectrum

[0026] Figure 6 Quality factor X Q The probability distribution of

[0027] Figure 7 X DDREF The probability distribution changes

[0028] Figure 8 Confidence Level Analysis of Randomized Designs Using Effective Shielding Materials (V) Specific implementation methods:

[0029] The reliability evaluation method of the multi-layer space radiation shielding design of the present invention will be further described in detail below in conjunction with the accompanying drawings. The specific steps are as follows:

[0030] Step 1: Space radiation environment analysis and shielding geometry material design;

[0031] Firstly, the space radiation environment is determined. For the space radiation environment analysis of electronic equipment, GCR is assumed to occur at 1AU in free space. The GCR spectrum of the 2010 solar minimum in the BO-2014GCR model is used, and the solar modulation parameter of 475MV is used. Figure 2 A representative solar minimum GCR environment that has been used in the past for comparisons of various codes is shown in Figure 1. All transmission estimates in the energy range from 1 keV / n to 100 GeV / n include particles between Z = 1 and Z = 28. The GCR energy spectra were collected directly for MC simulations without any additional biasing methods. Dose equivalents for different materials (in mSv / year) and different thicknesses (in g / cm 2 ).

[0032] Dose rates are determined through standard procedures outlined in established regulations. Dose rates are converted using the flux-to-dose conversion factors specified in ICRP60. These factors depend on the energy and properties of the particles involved. From a practical point of view, the dose equivalent is more important. The dose equivalent represents a transformation of the absorbed dose rate, combined with weighting factors specific to the type of radiation encountered, thus determining the space radiation environment.

[0033]

[0034] Equivalent dose H T In sievert (Sv), by radiation type D T,R The absorbed dose (in gray) caused in tissue T is determined by W R Represents the radiation weighting factor specified by the standard.

[0035] Most modern spacecraft are made of aluminum, while some modules of the International Space Station are shielded with PE. As potential solid hydrogen storage materials for the future "hydrogen economy" (such as hydroxides), hydrides and composite hydrides have been developed.

[0036] Various shielding materials (Table 1) were simulated using the plate model, with total shielding thickness ranging from 0 to 100 g / cm 2 Ten specific shielding materials were analyzed, among which materials with the addition of hydrogen, boron and nitrogen showed enhanced shielding effectiveness. In addition, polymer fiber and fiber resin-based shielding materials showed comparable effectiveness in attenuating space radiation.

[0037] Table 1 Shielding material names and properties

[0038]

[0039] In order to better understand the effectiveness of radiation shielding, the possibility of complex hydrides was studied in addition to various metal compounds. The best composite materials should be multifunctional and multilayered for radiation shielding and strong, lightweight spacecraft structures and components to maximize the quality of spacecraft. The shielding shape of the present invention is selected as a finite flat plate, which only receives one side of space radiation, and the shielding effect is related to the thickness of the shielding medium. Figure 3 A schematic diagram of a complex multi-layer shielding material is given.

[0040] Step 2: Determine boundary conditions;

[0041] 1. Radiation particle parameters

[0042] The GCR is assumed to originate at a distance of 1 astronomical unit (AU) in free space, and the 2010 solar minimum GCR spectrum from the BO-2014GCR model is used. A slab geometry is used in the simulations by the HZETRN code. GCR boundary conditions are applied to different shielding materials and thicknesses within the slab geometry to derive flux values ​​at the material interfaces.

[0043] Furthermore, a parameterization scheme was adopted where a value of 0 means that the entire GCR spectrum is included. Alternatively, integers from 1 to 28 indicate that ions with charges corresponding to the respective integers are included. In HZETRN, the mission date or the solar modulation parameter (phi) expressed in megavolts (MV) is incorporated.

[0044] 2. Determine SPE parameters

[0045] Solar particle events (SPEs) consist of clusters of protons accelerated by the Sun's magnetic field. Pulsed SPEs typically travel along interplanetary magnetic field lines and are considered a threat to electronics on Earth or in spacecraft. Table 2 lists the composition of pulsed SPEs.

[0046] Table 2 Element abundances of the photosphere, coronal mass ejections (CMEs), impulsive SPEs and galactic cosmic rays (GCRs)

[0047]

[0048] From the comparison of element abundance, proton flux accounts for more than 90% of the total, although their dose equivalent contribution is only 8% of the total. Iron nuclei only account for 0.03% of the GCR flux, which is 3 orders of magnitude lower than the proton flux, but their dose equivalent contribution is as high as 20%. Therefore, when studying the shielding performance characteristics of spacecraft materials, radiation damage from heavy ions needs to be considered.

[0049] Step 3: Determine the radiation shielding multi-objective optimization model;

[0050] A multi-objective radiation shielding optimization model is established with reference to the parameters of steps one and two, and the established model is optimized and analyzed. The overall goal of advanced radiation shielding design is to minimize the weight, volume, and radiation dose outside the shielding layer simultaneously. However, it is worth noting that these goals are inherently contradictory and cannot be optimized simultaneously. To address this challenge, a multi-objective optimization model is proposed, which covers the mathematical formulation of the shielding design problem, decision variables, and constraints.

[0051]

[0052] Q(X) is the radiation absorbed dose TID of the first layer closest to the protected material (0.1mm silicon material) after shielding, that is, radiation Q0 is the maximum allowable TID. P(X) is the total thickness of the shielding solution, and P0 is the upper limit of the total thickness. m1Q(X) is the equivalent surface density of the shielding material, and m1Q0 is the upper limit of the material surface density.

[0053] By defining the shielding shape as a circular finite plate, the surface density is used to represent the thickness of the shielding material. The equivalent surface density can be used to measure the quality of the shielding material. X represents each shielding scheme, n represents the number of layers of the shielding scheme, and X i represents the i-th layer in the shielding scheme, ρ i is the density of the material.

[0054] Step 4: Shielding design based on genetic algorithm;

[0055] 1. Construction of multi-objective radiation shielding genetic algorithm process

[0056] Genetic algorithm is used to optimize the radiation shielding design and obtain candidate shielding solutions to solve the multi-objective optimization problem. Through iterative evolution, the optimal radiation shielding solution is obtained. In this work, Monte Carlo simulation method (MCNP)6.2 is used to calculate the radiation shielding, such as Figure 4 . The shielding design process using genetic algorithms is described to explore multi-objective optimization radiation shielding solutions.

[0057] 2. Multi-objective optimization objective function

[0058] After multiple generations of iterations driven by the genetic algorithm, the simulation finally reached the Pareto optimal shielding solution. f(X) is the objective function of the optimization and the fitness function in the genetic algorithm. The lower its value, the more suitable the designed shielding solution. Among them, m1, m2, and m3 are defined as follows.

[0059]

[0060] m1, m2, and m3 are weight factors of the weight of TID after shielding, total thickness, and equivalent surface density.

[0061] 3. Multi-layer radiation shielding experimental analysis

[0062] Range constraints are imposed on each variable to determine the optimal solution for the shielding scheme. Specifically, the domain constraint for each variable is in the range of 0 to 1, corresponding to 0 to 100 g / cm 2 The total thickness of the population. Depending on the number of variables, the scale is dynamically set between 50 and 300, usually around 10 times the number of variables. In addition, the cumulative probability distribution q is adjusted in the range of 0.1 to 0.4 according to the population size, and the higher the q value, the greater the selection pressure. After many experimental iterations, the number of generations was fixed at 150 to achieve convergence within a reasonable time frame. In addition, in order to speed up the solution process, appropriate distance protection measures were implemented to reduce the shielding thickness.

[0063] In this calculation example, the maximum allowed TID is 10 7rad, the maximum total thickness T0 of the shielding scheme is 10 mm, and the maximum allowable surface density is defined as the equivalent surface density of Al with a thickness of 10 mm. The TID after shielding is the main optimization target, and the weighting factors, and are set to 0.7, 0.2, and 0.1, respectively. Using the multi-layer radiation shielding genetic algorithm, a combination of different material plates is proposed, taking into account the type and performance of the shielding material. It is necessary to ensure the consistency of all calculations and maintain the consistency of assumptions such as radiation transfer scheme, geometry, GCR radiation input boundary conditions, and damage assessment model. The experimental data are shown in Table 3.

[0064] Table 3 Experimental analysis data of complex multilayer materials

[0065]

[0066] 4. Analysis of multi-layer radiation shielding test results

[0067] The shielding performance and parameters of various materials are determined based on genetic algorithms. Based on the experimental results, a combination design consisting of five shielding materials is proposed, and the shielding effectiveness is as follows Figure 5 After analysis, the best design among the five material sandwich structures is the fifth group, which is characterized by the composition of Al+C 21 H 25 CIO5+LiH+PBO+BN+Al. Figure 5 The structure in the fifth group shows higher efficiency than the other structures. In this optimized structure, the addition of LiH significantly attenuates GCR and secondary radiation when interacting with aluminum. However, the addition of the PBO layer may lead to the reproduction of secondary particles, thereby reducing the shielding effect.

[0068] Throughout the radiation shielding optimization design process, it was observed that hydrogen-rich materials showed superior efficacy in reducing radiation dose compared to single aluminum layers. To evaluate the efficiency of different layer sequences and placements, experiments were conducted to evaluate variable multilayer structures. Notably, experiments 3 and 5 showed a significant reduction in the secondary neutron rate. It was found that when the thickness was 82 g / cm 2 When , the dose equivalent dropped sharply to 0.46 and 0.42 mSv / year respectively, which is the most effective shielding design in all experiments.

[0069] Step 5: Radiation shielding reliability risk modeling analysis;

[0070] NCRP assumes that the excess mortality risk is linearly related to the dose equivalent, from which the formula can be obtained:

[0071]

[0072] is the risk factor for high dose rate exposure of electronic equipment related to exposure time A and absorbed dose G. The quality factor Q(L) is defined by ICRP based on the observed RBE factor.

[0073] Dose and dose rate effect factors (DDREF) are derived from radiation experiments performed on electronic equipment and are included by the National Council on Radiation Protection and Measurements (NCRP) as key parameters for determining dose limits. Therefore, the consideration of uncertainty in this context revolves around two main sources, namely Q(L) and DDREF.

[0074] Therefore, the reliability can be evaluated by the proposed method of expressing uncertainty in the above formula. This method uses random variables X Q and X DDREF Substitute the nominal values ​​for Q(L) and DDREF as follows:

[0075]

[0076] κ is a statistical variable that represents the potential radiation effects of electronic equipment due to the uncertainty of Q(L) and DDREF. Figure 6 As shown, X Q The probability function of XDDREF is related to LET, and the correlation is determined by the relative biological effect (RBE) measured for LET. Similarly, the probability distribution function of XDDREF is as follows Figure 7 Shown, courtesy of NCRP.

[0077] The aim is to gain insight into the impact of electronic uncertainties on mission design by studying the impact of these uncertainties on basic geometrical spacecraft design. The risk range within aluminum shielding is plotted as a function of shield thickness. This exploration covers the risk range associated with uncertainties in risk model factors, including uncertainties in relevant physical parameters associated with shielding assessment.

[0078] In addition, for the case where DDREF is equal to 2, where represents the dose distribution of the LET spectrum inside a spherical shell of thickness x, the physics-related uncertainties are assumed to be minimal. This function is used as a correction function for non-nominal LET dependence and is numerically equivalent to Q(L) for standard risk assessment.

[0079] In evaluating the dose equivalent, uncertainty is introduced while maintaining a direct relationship to the dose limit. The dose equivalent is therefore represented as a random variable expressed as:

[0080] H(x,κ)=∫M(L,κ)D L (x)dL (6)

[0081] According to formula (5), the requirement to limit the lifetime risk of fatal overdose to below 3% is equivalent to limiting the dose equivalent value defined by formula (6) to below the nominal exposure limit.

[0082] The design process is as follows: for a given shielding thickness x, a random sequence of associated risks is determined. When the percentage of risk sequences is below 3%, the shielding is considered acceptable for a given confidence level (CL). The task is to determine the value of x that meets this criterion. Therefore, if a mission with this shielding structure is initiated, the additional fatal risk to electronic equipment at the CL will not exceed 3%. Thus, a reliability assessment method for electronic equipment with radiation shielding is established.

[0083] Step 6: Specific case analysis;

[0084] To demonstrate the radiation reliability shielding method, a five-year mission to Mars was studied using a hybrid core electronics configuration. The optimum volume of space required for this mission was determined to be 98 cm 3 Assume that the shielding material is the pressure vessel of Design Experiment 5. Figure 8 shows how the quality of the design changes over time. While this description does not capture the exact geometry and only represents the shielding, it highlights the significant impact that uncertainty has on the design process.

[0085] From an engineering design perspective, a reliability-based approach must be adopted to effectively characterize the risk reduction aspects of mission design, particularly with respect to shielded components.

[0086] Step 7: Radiation shielding design analysis based on reliability assessment.

[0087] The case study results show that designing radiation shielding for complex nuclear power plants is a challenging multi-objective, multi-parameter optimization problem in aerospace engineering. This work creates a multi-objective optimization method for radiation shielding design based on an evolutionary algorithm to achieve an optimal shielding solution that is compact, lightweight, and radiation dose-limited under certain conceptual design constraints.

[0088] Reliability-based design methods are integrated into radiation shielding assessments to manage and evaluate uncertainties in shielding design. This approach can quickly analyze potential shielding optimization results affected by the uncertain parameter space of electronic devices. Reliability-based methods have been shown to effectively control risks in electronic devices during the design process within the limitations of existing knowledge.

[0089] The optimization strategy can determine the best shielding solution in the early stage of radiation shielding design by minimizing the objective function under specified constraints. The reliability method is used to analyze the impact of uncertain parameters on the optimization results and clarify the radiation risk to electronic equipment. In addition, this method helps to make optimal radiation shielding design decisions and provide supplementary data in the conceptual design stage, especially for spacecraft that lack shielding design experience.

Claims

1. A reliability assessment method for multi-layer space radiation shielding design, characterized by: A multi-objective optimization genetic algorithm is used to focus on lightweight, compactness and minimizing radiation dose to achieve automated optimization and efficiency improvement of radiation shielding design. Through Monte Carlo simulation analysis, an effective balance can be achieved between the mass, weight and volume of the shielding body, and a reliability-based shielding design technology can be constructed to achieve reliability evaluation on the dose equivalent results of radiation shielding materials, reduce radiation effects, and improve the reliability of electronic equipment. Solving the problem of integrating uncertainty factors into the radiation shielding optimization process provides a solid theoretical basis for effective radiation shielding and reliability evaluation. The specific steps of this method are as follows: Step 1: Space radiation environment analysis and shielding geometry material design; Step 2: Determine boundary conditions; Step 3: Determine the radiation shielding multi-objective optimization model; Step 4: Shielding design based on genetic algorithm; Step 5: Radiation shielding reliability risk modeling analysis; Step 6: Specific case analysis; Step 7: Radiation shielding design analysis based on reliability assessment.

2. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: The space radiation environment analysis and shielding geometric material design described in step 1 analyzes the space radiation environment of the electronic equipment and determines key influencing factors such as the space radiation specific radiation shielding materials and geometric structures. The specific process is as follows: First, the space radiation environment is determined. GCR is assumed to occur at 1AU in free space. The GCR spectrum of the 2010 solar minimum in the BO-2014GCR model is used, and the solar modulation parameter of 475MV is used. The dose rate is converted using the flux-to-dose conversion coefficient specified in ICRP60. The dose equivalent represents the transformation of the absorbed dose rate, combined with a weighting factor specific to the type of radiation encountered, to determine the space radiation environment. Equivalent dose H T The unit is Sievert (Sv), which is determined by the radiation type D T,R The absorbed dose (in gray) caused in tissue T is determined by W R Represents the radiation weighting factor specified by the standard. Various shielding materials (Table 1) were simulated using the plate model, with total shielding thickness ranging from 0 to 100 g / cm 2 Ten specific shielding materials were analyzed, with materials containing hydrogen, boron, and nitrogen showing enhanced shielding effectiveness.

3. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: Determine the boundary conditions as described in step 2 to obtain the key parameters of shielding design in the radiation environment. The specific process is as follows: The HZETRN code uses a slab geometry in the simulations. GCR boundary conditions are applied to different shielding materials and thicknesses within the slab geometry to derive flux values ​​at the material interfaces. A parameterization scheme is used where a value of 0 means that the entire GCR spectrum is included. Alternatively, integers from 1 to 28 indicate that ions with charges corresponding to the respective integers are included. In HZETRN, the mission date or the solar modulation parameter (phi) expressed in megavolts (MV) is incorporated. Pulsed SPEs typically move along interplanetary magnetic field lines and are considered a threat to electronics on Earth or on spacecraft. Table 2 lists the composition of pulsed SPEs.

4. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: Determine the radiation shielding multi-objective optimization model as described in step 3. The specific process is as follows: The overall goal of advanced radiation shielding design is to simultaneously minimize the weight, volume, and radiation dose outside the shield. However, it is worth noting that these goals are inherently contradictory and cannot be optimized simultaneously. To address this challenge, a multi-objective optimization model is proposed, which covers the mathematical formulation of the shielding design problem, decision variables, and constraints. Q(X) is the radiation absorbed dose TID of the first layer closest to the protected material (0.1mm silicon material) after shielding, that is, radiation Q0 is the maximum allowable TID. P(X) is the total thickness of the shielding solution, and P0 is the upper limit of the total thickness. m1Q(X) is the equivalent surface density of the shielding material, and m1Q0 is the upper limit of the material surface density.

5. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: The shielding design based on genetic algorithm described in step 4. The specific process is as follows: Genetic algorithm is used to optimize the radiation shielding design, and Monte Carlo simulation method (MCNP)6.2 is used to calculate the radiation shielding, and explore the multi-objective optimization radiation shielding scheme. The f(X) fitness function is established. The lower its value, the more suitable the shielding scheme is designed. Among them, m1, m2, and m3 are defined as follows. m1, m 2 , m3 is the weight of TID after shielding, total thickness and equivalent surface density. Range constraints are imposed on each variable to determine the optimal solution for the shielding scheme. The domain constraints of each variable are in the range of 0 to 1, corresponding to 0 to 100 g / cm 2 The total thickness of the population is adjusted with a cumulative probability distribution q ranging from 0.1 to 0.4 according to the population size. The higher the q value, the greater the selection pressure.

6. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: The specific process of the radiation shielding reliability risk modeling analysis described in step 5 is as follows: NCRP assumes that the excess mortality risk is linearly related to the dose equivalent, from which the formula can be obtained: It is the risk coefficient related to the exposure time A and absorbed dose G of electronic equipment exposed at high dose rates. The quality factor Q(L) is defined by ICRP based on the observed RBE factor. The reliability can be evaluated by the proposed method of expressing uncertainty in the above formula. This method uses random variables X Q and X DDREF Substitute the nominal values ​​for Q(L) and DDREF as follows: κ is a statistical variable that represents the potential electronic equipment radiation effect due to the uncertainty of Q(L) and DDREF. As shown in Figure 6, X Q The probability function of is related to LET, and the correlation is determined by the relative biological effect (RBE) measured for LET. Similarly, the probability distribution function of XDDREF is shown in Figure 7 and is provided by NCRP. Uncertainty is introduced while maintaining a direct relationship to the dose limit. The dose equivalent is expressed as a random variable and is given by: H(x,κ)=∫M(L,κ)D L (x)dL (6) According to formula (5), the requirement to limit the lifetime risk of fatal overdose to below 3% is equivalent to limiting the dose equivalent value defined by formula (6) to below the nominal exposure limit. The design process is as follows: for a given shielding thickness x, a random sequence of associated risks is determined. When the percentage of risk sequences is below 3%, the shielding is considered acceptable for a given confidence level (CL). The task is to determine the value of x that meets this criterion. Therefore, if a mission with this shielding structure is initiated, the additional fatal risk to electronic equipment at the CL will not exceed 3%. Thus, a reliability assessment method for electronic equipment with radiation shielding is established.

7. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: In the specific case analysis described in step 6, the specific process is as follows: A five-year mission to Mars was studied using a hybrid core-electron configuration. The optimum volume of space required for this mission was determined to be 98 cm 3 Assuming that the shielding material is the pressure vessel of Design Experiment 5, Figure 8 shows the evolution of the design quality over time. While this description does not capture the exact geometry and only represents the shielding layer, it highlights the significant impact that uncertainty can have on the design process.

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