A reliability evaluation method for a multi-layer space radiation shielding design

Abstract

Step 1: Spatial radiation environment analysis and shielding geometry material design; Step 2: Determine boundary conditions; Step 3: Determine the multi-objective optimization model for radiation shielding; Step 4: Shielding design based on genetic algorithm; Step 5: Radiation shielding reliability risk modeling and analysis; Step 6: Specific case analysis; Step 7: Radiation shielding design analysis based on reliability assessment. It adopts a multi-objective optimization strategy aimed at enhancing shielding structure and material selection, giving priority to objectives such as lightweight, compactness, and minimum radiation dose. This strategy utilizes genetic algorithms and realizes the automation of radiation shielding design optimization and the improvement of efficiency through Monte Carlo MCNP6 simulation analysis. Construct a reliability-based shielding design technology, integrate uncertainty into the optimization design process, achieve reliability assessment on the dose equivalent results of radiation shielding materials, and improve the reliability of electronic devices. Solve the problem of integrating uncertain factors into the radiation shielding optimization process, and provide a theoretical basis for effective radiation shielding and reliability assessment.

Description

(1) Technical Field:

[0001] The present invention relates to a reliability evaluation method for multi-layer space radiation shielding design, which adopts a multi-objective optimization strategy aiming to enhance shielding structure and material selection, with priorities given to objectives such as lightweight, compactness, and minimum radiation dose. This strategy utilizes an evolutionary algorithm to achieve the automation of radiation shielding design optimization and improve efficiency. Numerical results show that this strategy effectively balances the mass, weight, and volume of the shield. Finally, based on reliability-based shielding design technology, a detailed analysis of the dose equivalent results of shielding materials is carried out, integrating uncertainties into the optimization design process, and a reliability-based shielding design method can be developed. This method belongs to the field of space radiation shielding reliability evaluation. (2) Background Art:

[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 significant portion of particles with high atomic number (Z>2) form GCR. This has a great impact on electronic devices. In addition, researchers conduct design analysis on radiation shielding materials and find that materials with the highest charge-to-mass ratio are most effective against High-Z and Energetic (HZE) particles. A good passive shielding system should have characteristics such as strong adaptability, reasonable price, non-toxicity, long service life, and low secondary radiation, but there are few methods for reliability evaluation of radiation shielding.

[0003] The design of multi-layer materials needs to consider the reliability evaluation of electronic device identification and radiation effect research. Research shows that for a certain number of materials, multi-layer shielding can reduce the electron penetration rate by about 60%, which is more effective than single-material shielding. Aluminum and polyethylene laminated shielding have been successfully applied to the International Space Station (ISS). According to current experience, a shielding is first proposed, followed by design, simulation, and inspection, which requires designers to manually iterate multiple times. Therefore, the shielding solutions obtained using this traditional method are empirical rather than ideal. Therefore, it is necessary to study innovative methods for radiation shielding design. In view of the lack of research related to the reliability evaluation of radiation shielding and the little consideration of the reliability issues of electronic devices under radiation shielding conditions, designing an effective shielding mechanism for key electronic components is an important aspect that needs further exploration. The investigation will include an examination of various radiation effects on electronic devices, incorporating uncertainties to promote the reliability evaluation of feasible shielding strategies, aiming to reduce the radiation risk of electronic devices.

[0004] Therefore, this method is based on space radiation shielding design. Based on radiation shielding using intelligent algorithms, a radiation shielding reliability method based on shielding analysis software and a computational reliability design process is proposed. Through the intelligent design analysis of multi-layer radiation shielding materials and the study of shielding effects, the problem of difficult quantitative analysis of multi-layer radiation shielding measurement indicators and reliability analysis can be solved, thereby providing a theoretical basis for effective shielding design and improving the radiation resistance of electronic devices, and providing a technical foundation for evaluating the radiation shielding reliability of electronic devices. (III) Summary of the Invention:

[0005] 1. Objective: The objective of the present invention is to provide a reliability evaluation method for multi-layer space radiation shielding design. By adopting a multi-objective optimization algorithm, this method focuses on the objectives of lightweight, compactness, and minimization of radiation dose, thereby realizing the automated optimization and efficiency improvement of radiation shielding design. Through Monte Carlo simulation analysis, this method can effectively balance the mass, weight, and volume of the shielding body, and constructs a reliability-based shielding design technology to achieve the reliability evaluation of the dose equivalent result of radiation shielding materials, reduce radiation effects, and improve the reliability of electronic devices. The present invention solves the problem of integrating uncertain factors into the radiation shielding optimization process, and provides a solid theoretical basis for effective radiation shielding and reliability evaluation.

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

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

[0008] First, determine the space radiation environment, analyze the space radiation environment of electronic devices, and use the GCR spectrum of the minimum solar activity period in 2010 in the BO-2014GCR model. Use the flat plate mode to simulate the shielding effectiveness of various shielding materials, and design the total shielding thickness range. Select the shielding shape as a finite flat plate, analyze the shielding effect and the thickness of the shielding medium, and establish a geometric model of complex multi-layer shielding materials.

[0009] Step 2: Determine the boundary conditions;

[0010] Determine the particle parameters and use a solar modulation parameter of 475 MV. Apply it to different shielding materials and thicknesses within the flat plate geometry to obtain the flux values at the material interfaces. Determine the pulse SPE element abundances and consider the radiation damage of heavy ions.

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

[0012] Establish a multi-objective radiation shielding optimization model based on the parameters in Steps 1 and 2. The overall goal of advanced radiation shielding design is to simultaneously minimize the weight, volume, and radiation dose outside the shielding layer. A multi-objective optimization model is proposed, which covers the mathematical formulations of the shielding design problem, decision variables, and constraints.

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

[0014] Build a genetic algorithm for optimizing and analyzing the radiation shielding design process to obtain candidate shielding solutions for solving multi-objective optimization problems. Establish a multi-objective optimization function to determine the Pareto optimal solutions. Apply range constraints to each variable to determine the optimal solutions for the shielding solutions and conduct experimental analysis. Based on the experimental results, a combined design consisting of five shielding materials is proposed to analyze the shielding effectiveness and determine the optimal solutions.

[0015] Step 5: Modeling and analysis of radiation shielding reliability risk;

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

[0017] Step 6: Specific case analysis;

[0018] Design a five-year Mars mission with a hybrid core electronic configuration. Through the optimization results and reliability analysis, obtain the variation 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 used to effectively describe the risk reduction in mission design.

[0019] Step 7: Analysis of radiation shielding design based on reliability assessment.

[0020] Combined with the case results, the optimization strategy can determine the best shielding solution by minimizing the objective function under specified constraints in the early stage of radiation shielding design, integrating the reliability-based design method into the radiation shielding assessment to manage and evaluate the uncertainty in shielding design. This method 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 lacking shielding design experience. (IV) Description of the drawings:

[0021] Figure 1 Schematic diagram of the implementation step process

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

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

[0024] Figure 4 Overall scheme for radiation shielding design using genetic algorithms

[0025] Figure 5 Variation trend of dose equivalent and areal density of multi - layer radiation shielding structure under GCR spectrum

[0026] Figure 6 Quality factor X Q Probability distribution of

[0027] Figure 7 X DDREF Change in probability distribution of

[0028] Figure 8 Analysis of confidence level of random design using effective shielding materials (V) Specific implementation method:

[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] First, determine the space radiation environment. For the space radiation environment analysis of electronic equipment, GCR is assumed to occur at 1 AU in free space. Use the GCR spectrum of the solar minimum in 2010 in the BO - 2014 GCR model and a solar modulation parameter of 475 MV. Figure 2 Among them, the representative solar minimum GCR environment used for various code comparisons in the past. 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 spectrum is directly collected for MC simulation without using any additional biasing methods. The dose equivalent of different materials (in mSv / year) and different thicknesses (in g / cm 2 ) are simulated.

[0032] The dose rate is determined through the standard procedures outlined in established regulations. The dose rate is converted using the flux - to - dose conversion coefficients specified in ICRP60. These coefficients depend on the energy and nature of the particles involved. From a practical perspective, the dose equivalent is more important. The dose equivalent represents the transformation of the absorbed dose rate and incorporates a weighting factor specific to the type of radiation encountered, thus determining the space radiation environment.

[0033]

[0034] Equivalent dose H T In units of sievert (Sv), it is determined by the absorbed dose (in units of gray, Gy) induced by radiation type D T,R in tissue T, where W R represents the radiation weighting factor specified by the standard.

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

[0036] The flat plate mode was used to simulate various shielding materials (Table 1), where the total shielding thickness ranged from 0 to 100 g / cm 2 . Ten specific shielding materials were analyzed, and those with hydrogen, boron, and nitrogen added showed enhanced shielding effects. In addition, polymer fiber and fiber resin-based shielding materials showed considerable effectiveness in attenuating space radiation.

[0037] Table 1 Names and properties of shielding materials

[0038]

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

[0040] Step 2: Determine the boundary conditions;

[0041] 1. Radiation particle parameters

[0042] Assume that GCR originates at a distance of 1 astronomical unit (AU) in free space, and use the 2010 solar minimum GCR spectrum in the BO-2014 GCR model. The HZETRN code adopted a flat plate geometry in the simulation. GCR boundary conditions were applied to different shielding materials and thicknesses within the flat plate geometry to obtain the flux values at the material interfaces.

[0043] In addition, a parameterization scheme is adopted, where a value of 0 represents including the entire GCR spectrum. Alternatively, an integer from 1 to 28 represents including ions having charges corresponding to the respective integer. In HZETRN, the mission date or the solar modulation parameter (phi) expressed in megavolts (MV) is incorporated.

[0044] 2. Determine the SPE parameters

[0045] Solar particle events (SPEs) consist of clusters of protons accelerated by the solar magnetic field. Impulsive SPEs typically move along the interplanetary magnetic field lines and are regarded as a threat to electronic devices on Earth or spacecraft. Table 2 lists the composition of impulsive SPEs.

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

[0047]

[0048] From the comparison of elemental abundances, proton fluxes account for more than 90% of the total, although their dose equivalent contribution rate only accounts for 8% of the total. Iron nuclei only account for 0.03% of the GCR flux, which is three 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, the radiation damage of heavy ions needs to be considered.

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

[0050] Establish a multi-objective radiation shielding optimization model with reference to the parameters in Steps 1 and 2, and conduct optimization analysis on the established model. The overall goal of advanced radiation shielding design is to simultaneously minimize the weight, volume, and radiation dose outside the shielding layer. However, it should be noted that these goals are essentially contradictory and cannot be optimized simultaneously. To address this challenge, a multi-objective optimization model is proposed, which encompasses the mathematical formulations 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 (0.1 mm silicon material) after shielding, i.e., the radiation Q 0 is the maximum allowable TID. P(X) is the total thickness of the shielding scheme, P 0 is the upper limit of the total thickness. m 1 Q(X) is the equivalent areal density of the shielding material, m 1 Q 0 is the upper limit of the material surface density.

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

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

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

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

[0057] 2. Multi-objective optimization objective function

[0058] After multiple generations of iteration promoted by the genetic algorithm, the simulation finally reaches the Pareto optimal shielding solution. f(X) is the optimized objective function and the fitness function in the genetic algorithm. The lower its value, the more suitable the designed shielding scheme. Among them, m 1 , m 2 , m 3 are defined as follows.

[0059]

[0060] m 1 , m 2 , m 3 are the weight factors of the weight of TID after shielding, the total thickness, and the equivalent surface density.

[0061] 3. Experimental analysis of multi-layer radiation shielding

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

[0063] In this calculation example, the maximum allowable TID is 10 7 rad, and the maximum total thickness T of the shielding scheme 0 is 10 mm. The maximum allowable areal density is defined as the equivalent areal density of Al with a thickness of 10 mm. Taking the shielded TID as the main optimization objective, 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 materials. It is necessary to ensure the consistency of all calculations and maintain the consistency of assumptions such as the radiation transport 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 table of complex multi-layer materials

[0065]

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

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

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

[0069] Step 5: Modeling and analysis of radiation shielding reliability risks

[0070] The NCRP assumes that the excess failure risk is linearly related to the dose equivalent, and the following formula can be obtained

[0071]

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

[0073] The dose and dose rate effectiveness factor (DDREF) is derived from radiation experiments conducted on electronic devices and has been incorporated by the National Council on Radiation Protection and Measurements (NCRP) as a key parameter for determining dose limits. Therefore, the consideration of uncertainty in this case mainly revolves around two main sources, namely Q(L) and DDREF.

[0074] Therefore, the reliability can be evaluated by the method of uncertainty in the above-mentioned representation formula. This method uses the random variables X Q and X DDREF to replace the nominal values of Q(L) and DDREF, as follows:

[0075]

[0076] κ is a statistical variable representing the potential radiation effects of electronic devices due to the uncertainties of Q(L) and DDREF. As Figure 6 shown, the probability function of X Q is related to LET, and the correlation is determined by the relative biological effectiveness (RBE) of LET measurements. Similarly, the probability distribution function of XDDREF is as Figure 7 shown and is provided by the NCRP.

[0077] By studying the impact of these uncertainties on the basic geometric spacecraft design, the aim is to deeply analyze the impact of electronic uncertainties on mission design. The risk range within the aluminum shield is depicted as a function of the shield thickness. This exploration covers the risk range related to the uncertainties of the risk model factors, including the uncertainties of the relevant physical parameters related to shield evaluation.

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

[0079] When evaluating the dose equivalent, uncertainty is introduced while maintaining a direct relationship with the dose limit. Therefore, the dose equivalent is expressed as a random variable, expressed as:

[0080] H(x,κ) = ∫M(L,κ)D L (x)dL (6)

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

[0082] The design process is as follows: For a given shielding thickness x, a random sequence of relevant risks is determined. When the percentage of the risk sequence 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 task using this shielding structure is initiated, the additional fatal risk to the electronic device at CL will not exceed 3%. Thus, a reliability assessment method for electronic devices with radiation shielding is established.

[0083] Step Six: Specific case analysis;

[0084] To demonstrate the radiation reliability shielding method, a five-year Mars mission with a hybrid core electronics configuration was studied. The optimal space volume required for this mission was determined to be 98 cm 3 . Assume the shielding material is the pressure vessel designed in Experiment 5, Figure 8 shows the variation of design quality over time. Although this description does not capture the exact geometry and only represents the shielding layer, it emphasizes the significant impact of uncertainty on the design process.

[0085] From the perspective of engineering design, a reliability-based approach must be adopted to effectively describe the risk reduction aspects in mission design, especially regarding shielding components.

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

[0087] From the results of the case analysis, it can be seen that in aerospace engineering, designing radiation shielding for complex nuclear power plants is a challenging multi-objective, multi-parameter optimization problem. This work created a multi-objective optimization method for radiation shielding design based on evolutionary algorithms to achieve the best shielding solution that is compact, lightweight, and radiation dose-limited under certain conceptual design constraints.

[0088] Integrate the reliability-based design method into the radiation shielding assessment to manage and evaluate the uncertainty in shielding design. This method can quickly analyze the potential shielding optimization results affected by the uncertain parameter space of electronic devices. It has been proven that the reliability-based method can effectively control the risk of electronic devices within the limits of existing knowledge during the design process.

[0089] The optimization strategy can determine the best shielding solution by minimizing the objective function under specified constraints in the early stage of radiation shielding design. The influence of uncertain parameters on the optimization results is analyzed using the reliability method, and the radiation risk to electronic devices is clarified. In addition, this method helps to make optimal radiation shielding design decisions and provides supplementary data in the conceptual design stage, especially for spacecraft lacking 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 impact, and improve the reliability of electronic equipment. 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; NCRP assumes that the excess mortality risk is linearly related to the dose equivalent, from which the formula can be obtained: is the risk factor related to the exposure time A and the absorbed dose G for high dose rate exposure electronic equipment, and the quality factor Q(L) is defined by ICRP based on the observed RBE factor; The reliability can be evaluated by the above-mentioned method of expressing uncertainty in the formula, using 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 radiation effects of electronic equipment due to the uncertainty of Q(L) and DDREF; X Q The probability function of is related to LET, and the correlation is determined by the relative effect (RBE) on LET measurement. The probability distribution function of XDDREF is provided by NCRP; Introducing uncertainty while maintaining a direct relationship to the dose limit, the dose equivalent is expressed as a random variable expressed as: H(x,κ)=∫M(L,κ)D L (x)dL (6) According to formula (5), the requirement to limit the lifetime risk of overdose to less than 3% is equivalent to limiting the dose equivalent value defined by formula (6) to below the nominal exposure limit; For a given shielding thickness x, determine the random sequence of associated risks. When the percentage of risk sequences is less than 3%, the shielding is considered acceptable for a given confidence level. The task is to determine the value of x that meets this criterion. If a mission using this shielding structure is initiated, the additional failure risk to the electronic equipment will not exceed 3% within the confidence interval. This establishes a reliability assessment method for electronic equipment with radiation shielding. 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: To determine the space radiation environment, GCR is assumed to occur at 1AU in free space, using the GCR spectrum of the 2010 solar minimum in the BO-2014GCR model, using the solar modulation parameter of 475MV, and using the flux-to-dose conversion coefficient specified in ICRP60 to convert the dose rate; 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 In sievert, by radiation type D T,R The absorbed dose caused in material T is determined by W R Represents the radiation weight factor specified in the standard; Various shielding materials 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 effects.

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 the shielding design in the radiation environment. The specific process is as follows: The GCR boundary conditions are applied to different shielding materials and thicknesses within the slab geometry, yielding flux values ​​at the material interfaces; a parameterization scheme is used where a value of 0 indicates inclusion of the entire GCR spectrum, and integers from 1 to 28 indicate inclusion of ions with charges corresponding to the respective integers; in HZETRN, the mission date or solar modulation parameter expressed in megavolts is incorporated; pulsed SPEs typically move along interplanetary magnetic field lines and are considered a threat to electronics on Earth or in spacecraft, and the composition of pulsed SPEs is listed.

4. The reliability assessment method for multi-layer space radiation shielding design according to claim 1 is characterized in that: The specific process of determining the radiation shielding multi-objective optimization model described in step 3 is as follows: The overall goal of advanced radiation shielding design is to simultaneously minimize the weight, volume, and radiation dose outside the shielding layer; a multi-objective optimization model is established, covering 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 after shielding, and the radiation Q0 is the maximum permissible TID; P(X) is the total thickness of the shielding scheme, 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 specific process of the shielding design based on genetic algorithm described in step 4 is as follows: Genetic algorithm is used to optimize the radiation shielding design, MCNP6.2 is used to calculate the radiation shielding, and the f(X) fitness function is established; the lower its value, the more suitable the shielding scheme is designed, where m1, m2, and m3 are defined as follows m1=w d / Q0 <h2 style=";text-align:left;direction:ltr">m2 = w<h2 style=";text-align:left;direction:ltr"> t <h2 style=";text-align:left;direction:ltr"> / P0 (3) <h2 style=";text-align:left;direction:ltr">m3 = w<h2 style=";text-align:left;direction:ltr"> l <h2 style=";text-align:left;direction:ltr"> / m1Q0 m1, m2, and m3 are weight factors for the weight of TID after shielding, total thickness, and equivalent surface density; 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, the cumulative probability distribution q is adjusted in the range of 0.1 to 0.4, 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: In the specific case analysis described in step 6, the specific process is as follows: A five-year mission to Mars was studied with a hybrid core-electronic configuration; the optimum volume of space required for this mission was determined to be 98cm 3 ; Assuming that the shielding material is the pressure vessel of Design Experiment 5, the trend of design quality over time is obtained; Although this description does not capture the exact geometry and only represents the shielding layer, it emphasizes the significant impact of uncertainty on the design process.

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