Radiation shielding scheme optimization design method for star spacecraft

Abstract

The invention relates to an optimization design method of a multilayer material radiation shielding scheme for a wood star radiation environment. The optimization design method comprises the following steps: step 1, analyzing wood star high-energy particle distribution based on an SPENVIS platform; step 2, selecting the type and superposition mode of the shielding material; step 3, genetic algorithm parameter design; step 4, carrying out undefined chromosome coding; 5, establishing a fitness function; and step 6, optimization design of an optimal multilayer material radiation shielding scheme is carried out. According to the method, the problem that it is difficult to consider multiple influence factors and obtain the optimal shielding scheme through a simple mathematical formula analysis method is solved, and theoretical guidance is provided for design and research of the radiation shielding scheme of the wooden star spacecraft.

Description

(I) Technical field:

[0001] The present invention relates to an optimization design method for a multi-layer material radiation shielding scheme for the Jupiter radiation environment. The method first determines an applicable Jupiter radiation environment model by analyzing the Jupiter particle radiation environment, then determines the high-energy particle distribution of a specific Jupiter detection orbit through the SPENVIS numerical analysis platform, takes the Jupiter high-energy particle distribution as the environment input, combines a genetic algorithm and a MULASSIS program, screens and optimizes candidate shielding schemes, and obtains an optimal multi-layer material radiation shielding scheme for the Jupiter detection orbit environment. The method belongs to the technical field of Jupiter spacecraft radiation shielding scheme design. (II) Background technology:

[0002] High-energy particles in the Jupiter environment can cause a variety of radiation effects. Radiation shielding is currently the main method of radiation protection for Jupiter probes. The combination of different materials and the reasonable selection of the number of shielding layers can effectively improve the shielding effect. Therefore, multi-layer material radiation shielding is the main research direction of radiation shielding scheme design today. However, there are many factors that need to be considered in the design of multi-layer material radiation shielding schemes, and it is difficult to design the optimal radiation shielding scheme through simple physical laws.

[0003] At present, there are deficiencies in the research on the design of radiation shielding schemes to cope with the radiation environment of Jupiter. First of all, people have not yet fully understood the influence of the radiation effects caused by high-energy particles in Jupiter's orbit, and there are many factors that affect the shielding effect of the shielding scheme, so the design of the radiation shielding scheme is quite difficult. Jupiter exploration is also an emerging hotspot in the field of aerospace. There is very little research on the design of radiation shielding schemes for Jupiter probes, and it is also necessary to consider the feasibility of the design scheme in engineering. Therefore, this is a difficult problem that has not yet found the correct answer, and an optimization method is needed to provide a reference for the design of radiation shielding structures. (III) Summary of the invention:

[0004] 1. Purpose: The present invention is a method for optimizing the design of a multi-layer material radiation shielding scheme for Jupiter spacecraft, and provides a shielding structure design concept that can be applied to most Jupiter exploration orbit environments. The core content of the method is to quantify the different needs of researchers for spacecraft shielding structures, and optimize the design of multi-layer material shielding schemes for a specified Jupiter exploration orbit by combining genetic algorithms, solving the problem that it is difficult to consider multiple influencing factors and obtain the optimal shielding scheme through simple mathematical analytical methods. This method provides theoretical guidance for the design and research of radiation shielding schemes for Jupiter spacecraft.

[0005] 2. Technical solution: The technology of the present invention mainly includes the following steps:

[0006] Step 1: Analyze the distribution of high-energy particles on Jupiter based on the SPENVIS platform

[0007] The Jupiter radiation belt is the main source of Jupiter's high-energy charged particles. The distribution of high-energy particles for a specified Jupiter exploration orbit is analyzed by the SPENVIS numerical analysis platform. Most models implemented in SPENVIS require a set of points on the spacecraft trajectory or a set of user-defined geographic points as input. These point sets are generated by two tools: the orbit generator and the coordinate grid generator. In order to access the Jupiter radiation model built into the platform, the geographic coordinate information should be entered according to the actual Jupiter exploration mission orbit.

[0008] According to the detection orbit information, select the appropriate radiation model. At present, the radiation environment models of Jupiter that can be used for reference include: the captured electron and proton (DG) model, the Galilean transition radiation electron (GIRE) model, the Jupiter canonical environment (JOSE) model and the Salammbo model.

[0009] Determine the detector orbit data and radiation model, and then analyze and output HTML report files and ASCII tables through SPENVIS. Then calculate the captured particle flux of the detector mission phase based on the output result data and accumulate the mission flux of the segment and the solar proton flux of the entire mission duration to obtain the electron and proton spectra of the specified detection orbit. These data are the environmental inputs for subsequent work.

[0010] Step 2: Selection of shielding material type and stacking method

[0011] The MULASSIS program is used to compare the shielding effects of different shielding schemes. For the defined detection track environment, the radiation dose of the 0.1mm silicon material placed behind the shielding material is used as the measurement benchmark for analysis. The radiation dose is mainly based on the electron flux. The smaller the electron flux, the better the shielding effect of the shielding scheme. The simulation results of the MULASSIS program are analyzed and compared to select the appropriate shielding material, the stacking method of different materials, and the number of stacking layers.

[0012] Step 3: Genetic algorithm parameter design

[0013] The optimal multi-layer material radiation shielding scheme is designed through the GAFortran genetic algorithm program in the SPENVIS numerical analysis platform. The target value and optimization object are defined according to the requirements, the genetic algorithm calculation process is designed, and then the shielding effect is evaluated using the MULASSIS program.

[0014] Step 4: Define chromosome encoding

[0015] In order to enable the program to read the information of each individual, it is necessary to encode each individual, that is, each chromosome. Using decimal encoding, the decimal digits of each chromosome are defined as 8n bits, where n is the number of shielding material layers. Figure 3Shown is an example of chromosome encoding corresponding to a certain masking scheme.

[0016] Each 8-digit number represents the property of a shielding layer material. The leftmost digit in the 8-digit number represents the type of shielding material, and the last 7 digits in the 8-digit number represent the thickness of the shielding layer. Based on the thickness and density of the material, the surface density of each shielding layer can be calculated.

[0017] Step 5: Establish fitness function

[0018] Combined with the analysis of genetic algorithms, the radiation shielding scheme should be evaluated with the help of fitness, which indicates the quality of individuals. Different constraints and their restrictions are defined according to the needs, and the fitness function is formulated with the help of the GAFortran package to evaluate different shielding schemes. The smaller the fitness function value, the closer it is to the optimal radiation shielding scheme.

[0019] Step 6: Optimal design of the optimal multi-layer radiation shielding solution

[0020] The mutation probability, crossover probability and jump mutation probability of each chromosome code are defined, the number of generations optimized by the genetic algorithm is defined, and then the defined fitness function is used to perform genetic algorithm operations. As the number of generations of the genetic algorithm continues to increase, the shielding scheme in each generation is continuously optimized, and ultimately the optimal radiation shielding scheme that meets the needs can be obtained. (IV) Description of the drawings:

[0021] Figure 1 This is a layer of pure aluminum material shielding effect diagram

[0022] Figure 2 This is a shielding effect diagram of a layer of pure tantalum material

[0023] Figure 3 This is the shielding effect diagram of the first layer of aluminum and the second layer of tantalum material

[0024] Figure 4 This is the shielding effect diagram of the first layer being tantalum and the second layer being aluminum.

[0025] Figure 5 This is the shielding effect diagram of the first layer being aluminum and four layers of aluminum and tantalum stacked alternately

[0026] Figure 6 This is the shielding effect diagram of the first layer being tantalum and four layers of aluminum and tantalum stacked alternately

[0027] Figure 7 This is a shielding effect diagram with aluminum as the first layer and five layers of aluminum and tantalum stacked alternately.

[0028] Figure 8 This is the shielding effect diagram of the first layer being aluminum and eight layers of aluminum and tantalum stacked alternately

[0029] Fig. 9 This is the solution process of the genetic algorithm

[0030] Fig.10 It is the logic and processing flow of the GAFortran genetic algorithm program

[0031] Fig.11 A schematic diagram of chromosome coding

[0032] Fig.12 This is the shielding effect diagram of the optimal shielding solution (V) Specific implementation methods:

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and a specific example of a Jupiter exploration orbit environment. The specific steps are as follows:

[0034] Step 1: Analyze the distribution of high-energy particles on Jupiter based on the SPENVIS platform

[0035] Most models implemented in SPENVIS require a set of points on the spacecraft trajectory or a set of user-defined geographic points as input. These point sets are generated by two tools: the orbit generator and the coordinate grid generator. In order to access the platform's built-in Jupiter radiation model, choose to define the orbit data using the orbit generator. Referring to the Juno orbit and NASA's official website, the height of the perigee is set at 2.9×10 5 km, and the altitude of the far tree point is set at 7.25×10 6 km.

[0036] For the Jupiter mission phase, the Jupiter orbit is set to start on January 1, 2022, and the mission duration spans 5 years. The probe's flight orbit is defined as an equatorial orbit, that is, the inclination is 0°, the RA ascending node is 0.00, the period is 20 days, and the eccentricity is 0. During the Jupiter mission period, the solar proton model is used. Under the condition of a period of 5 years, the maximum value of the solar cycle is 4.13 years and the minimum value is 0.87 years, which exceeds the setting of one solar cycle. Therefore, the probability of not exceeding 95% of the particle flow is achieved, which is in line with the overall Jupiter mission design.

[0037] In terms of model selection, SPENVIS has many built-in radiation models. The GIRE model is an omnidirectional equatorial model developed using EPD data, covering the entire equatorial plane of Jupiter. It is applicable to the range of 4-16Rj; the Jupiter Canonical Environment (JOSE) model is a new model of the trapped proton and electron environment near Jupiter based on all relevant data. This model overcomes the inaccuracies and discontinuities found in the previous GIRE model. The spatial range of the model extends from the surface to L=9.5Rj for electrons and L=6Rj for protons; the DG model is based on the measurements of the Pioneer and Voyager flybys of Jupiter and ground-based synchrotron observations. It is the only global model available today and is applicable to the full orbit range.

[0038] Comparative analysis shows that the GIRE model and the DG model are suitable for the defined detection orbit. If the GIRE model is selected, the flux will be discontinuous when R=8Rj. This is because when <8Rj, the model calls the Salammbo model, and in the spatial range of >8Rj, the GIRE model is used for calculation. The DG model is applicable to the entire range and will not be discontinuous. Therefore, the D&G model is selected for the numerical analysis of SPENVIS.

[0039] According to the called D&G trapped particle radiation model, the SPENVIS analysis can output HTML report files and ASCII tables, and then calculate the captured particle flux of the detector mission phase based on the output result data and accumulate it into segmented mission flux and solar proton flux for the entire mission duration, that is, the electron and proton spectra of the specified detection orbit are obtained. These high-energy particle distribution data are the environmental inputs for subsequent work.

[0040] Table 1 Jupiter proton spectrum

[0041]

[0042]

[0043] Table 2 Electronic spectrum of Jupiter

[0044]

[0045]

[0046] Step 2: Selection of shielding material type and stacking method

[0047] The shielding effects of different shielding schemes are analyzed, and the radiation dose level of 0.1mm silicon material placed behind the shielding material is used as a benchmark for analysis. The higher the radiation dose level of the silicon material, the worse the shielding effect of the shielding scheme.

[0048] The total thickness of the shielding scheme is defined as 4mm, and the shielding effects of eight different shielding schemes are simulated. The first is a single-layer shielding scheme defined as a layer of 4mm pure aluminum material; the second is a single-layer shielding scheme defined as a layer of 4mm pure tantalum material; the third is a two-layer shielding scheme in which the front layer is defined as 2mm aluminum material and the back layer is defined as 2mm tantalum material; the fourth is a two-layer shielding scheme in which the front layer is defined as 2mm tantalum material and the back layer is defined as 2mm aluminum material; the fifth is a shielding scheme in which each layer is 1mm, the first layer is aluminum, and aluminum and tantalum materials are alternately stacked for four layers; the sixth is a shielding scheme in which each layer is 1mm, the first layer is tantalum, and aluminum and tantalum materials are alternately stacked for four layers; the seventh is a shielding scheme in which each layer is 0.8mm, the first layer is aluminum, and aluminum and tantalum materials are alternately stacked for five layers; the eighth is a shielding scheme in which each layer is 0.5mm, the first layer is aluminum, and aluminum and tantalum materials are alternately stacked for eight layers.

[0049] According to the MULASSIS program analysis, eight different shielding schemes can be obtained, such as Figures 1 to 8 As shown, the horizontal axis is the serial number of the shielding layer, the vertical axis is the radiation dose level of different shielding layers, and the total radiation dose level uses the radiation dose level of the innermost layer, the silicon material closest to the protected material, as the measure of the shielding effect.

[0050] By comparison, the following simulated shielding effect data can be obtained:

[0051] Table 3 Radiation dose levels after shielding for eight different shielding schemes

[0052]

[0053] It can be seen that the radiation dose of the two-layer shielding scheme with aluminum as the first layer and tantalum as the second layer is the smallest, that is, the shielding effect is the best. Therefore, compared with the shielding scheme of a single metal material, the combination of low atomic number shielding material and high atomic number shielding material can significantly improve the shielding effect.

[0054] Comparing the third scheme with the fourth scheme, it can be seen that in the shielding scheme combining high and low atomic number materials, the shielding effect is most closely related to the first layer of shielding material. When the first layer of material is metal aluminum, the shielding effect is better than that of the shielding scheme in which the first layer of material is metal tantalum. Comparing the fifth scheme with the sixth scheme, it can also be seen that increasing the number of shielding layers will also satisfy the above rule.

[0055] By comparing the third, fifth, seventh and eighth solutions, it can be seen that when the total thickness is the same and the shielding material of the first layer is aluminum, increasing the number of stacked layers cannot significantly improve the shielding effect. On the contrary, too many layers will reduce the shielding effect.

[0056] Step 3: Genetic algorithm parameter design

[0057] The three main constraints are the total radiation dose after shielding, the total thickness of the shielding scheme, and the total mass of the shielding material. The ultimate optimization goal is to minimize the three constraint target values ​​as much as possible, provided that the three target values ​​do not exceed the maximum allowable total radiation dose, maximum total thickness, and maximum allowable mass.

[0058] The optimization model is defined as follows:

[0059] D(X)=D(X1)≤D0

[0060]

[0061] f(X)=min[D(X),T(X),AD(X)]

[0062] Among them, D(X) is the radiation dose value after shielding, D0 is the maximum allowable radiation dose level, the unit is rad, and the radiation dose level D(X) is the radiation dose value of the first layer, which is the shielding layer closest to the protected material; T(X) is the total thickness of the shielding scheme, T0 is the upper limit of the thickness, the unit is mm; AD(X) is the equivalent surface density of the shielding material, AD0 is the upper limit of the material surface density. The material shape is defined as a finite plate and the area of ​​each layer of shielding material is equal. Therefore, the total mass of the shielding scheme is proportional to its surface density, and the equivalent surface density can be used to measure the mass of the shielding material. The units of AD(X) and AD0 are g / cm 2 X represents the shielding scheme, n is the number of shielding layers in the shielding scheme, and X i represents the i-th shielding layer, and ρ is the density of the material.

[0063] At the beginning of the operation of the first generation of GAFortran genetic algorithm program, the program automatically generates initial genotype individuals, which form an initial group. Each individual represents a multi-layer material radiation shielding scheme X. The information of each individual includes the type of shielding material, the number of shielding layers, the thickness of each shielding layer and the superposition method of different shielding materials. The MULASSIS program is used to measure the radiation dose level after shielding for each shielding scheme.

[0064] The detailed operations of the genetic algorithm include crossover, mutation, and individual screening and elimination. The basic principle of screening is that individuals with strong adaptability have a high probability of contributing one or more offspring to the next generation, reflecting the principle of survival of the fittest.

[0065] Through the continuous iterative operation of the genetic algorithm, the radiation shielding scheme is optimized. Once the optimal radiation shielding scheme meets the design requirements, the optimization is terminated, otherwise the next generation of ethnic groups will continue to be generated. It is defined that in each generation, 10 new offspring individuals will be generated through hybridization, mutation and variation, and these newly formed offspring individuals and the parents together constitute 20 candidate individuals for the next generation population, and the best 10 individuals are selected according to the fitness distribution function to form the next generation population. The solution process of the genetic algorithm is as follows: Fig. 9 , the logic and processing flow of the GAFortran genetic algorithm program are as follows Fig.10 shown.

[0066] Step 4: Define chromosome encoding

[0067] Six shielding materials are selected from the materials currently commonly used for radiation shielding, namely aluminum, magnesium, tantalum, iron, lead and titanium. Aluminum, magnesium and titanium are commonly used low atomic number materials, and tantalum, iron and lead are common high atomic number materials. The properties of these six materials are shown in Table 4.

[0068] Table 4 Properties of six shielding materials

[0069]

[0070] The number of shielding layers in the multi-layer radiation shielding solution should not be too many, generally not exceeding 4 layers, and redundant design is given. The number of material layers is defined as 6 layers, the minimum lower limit of the shielding layer thickness is defined as 0.001mm, and the maximum upper limit of the shielding layer thickness is defined as 10mm.

[0071] Then the chromosome code is defined. The decimal digits of each chromosome are defined as 8n digits, where n is the number of shielding material layers, that is, n=6. The code has a total of 48 digits. From left to right, each 8 digits represents the properties of a shielding layer material. Taking the leftmost 8 digits as an example, it represents the properties of the first shielding layer material. The first leftmost digit in the 8 digits represents the type of shielding material, which can be represented by the six numbers 1 to 6, and is identified in order from small to large atomic numbers. 1 represents magnesium with the smallest atomic number, and 6 represents lead with the largest atomic number. The last 7 digits in the 8 digits represent the thickness of the shielding layer. Based on the thickness and density of the material, the surface density of each shielding layer can be calculated.

[0072] by Fig.11 For example, the number 10301425 represented by the first layer means that the material selected is magnesium and the thickness is 3.01425 mm.

[0073] Step 5: Establish fitness function

[0074] During the analysis process, if several consecutive shielding layers are made of the same material, they are considered to be the same shielding layer, and the thickness is the sum of the thicknesses of these layers.

[0075] The radiation dose to the silicon layer, along with the total thickness and areal density of the shielding material, is used to develop a "fitness function" that assigns a numerical value to each shielding solution, which the genetic algorithm optimizer can use to select the best shielding solution.

[0076] Based on the high-energy particle distribution data of Jupiter obtained from the previous SPENVIS numerical analysis as the environmental input, the following parameterized boundary conditions are defined:

[0077] The maximum permissible total radiation dose D0 is 10 7 rad;

[0078] The maximum total thickness T0 of the shielding material is 10 mm.

[0079] The maximum allowable surface density AD0 is defined as the equivalent mass of a given aluminum thickness of 10 mm, in g / cm 2 .

[0080] The fitness function F is defined to have a linear form:

[0081] F=1-[A×D(X)+B×AD(X)+C×T(X)]

[0082] D(X) is the radiation dose level after shielding, in rad, and AD(X) is the surface density of the shielding solution, in g / cm 2 , T(X) is the total thickness of the shielding material, in mm. The larger the value of F, the more suitable the shielding scheme is. A, B, and C represent the weights of these three terms respectively.

[0083] The weight constants A, B and C are defined as:

[0084]

[0085] Set the radiation dose weighting factor W according to the requirements d , surface density weighting factor W m and thickness weighting factor W t That is, the value of F can be used to measure the fitness value of different shielding schemes. Taking the radiation dose level after shielding as the main optimization target, the weighting factor W d Set to 0.8, weighting factor W m and W t Both are set to 0.1.

[0086] Step 6: Optimal design of the optimal multi-layer radiation shielding solution

[0087] (1) Optimal multi-layer material radiation shielding solution

[0088] The number of individuals contained in a single population of the genetic algorithm is 10, the number of shielding layers of the shielding structure is set to a maximum of 6 layers, and the mutation probability of each chromosome coding bit is set to 0.04, the crossover probability is 0.5, the jump mutation probability is 0.02, and the number of generations optimized by the genetic algorithm is defined as 40 generations. Then, the above-mentioned optimization model, fitness function and genetic operation were used for modeling operations. As the number of generations of the genetic algorithm continued to increase, the optimal shielding scheme in each generation was continuously optimized, and finally the optimal shielding scheme was obtained in the 22nd generation. The results are shown in the following table:

[0089] Table 5 Optimal radiation shielding solution

[0090]

[0091] The shielding effect of this shielding scheme was evaluated using the MULASSIS program. The results are as follows: Fig.12 As shown in Figure 2, the shielding scheme can reduce the total radiation dose received by the Jupiter probe in the Jupiter exploration orbit specified in this paper to 0.612×10 5 rad.

[0092] (2) Verification of the superiority of the shielding solution

[0093] For the three single-layer materials of tantalum (JGO satellite, Galileo), titanium (Juno) and aluminum (geosynchronous satellite), in order to achieve the same radiation shielding effect as the optimal shielding scheme obtained in the end, that is, to reduce the total radiation dose to 0.612×105rad for the specific Jupiter exploration orbit environment set in this paper, the required shielding material surface density is 1.47348g / cm 2 , 1.86221g / cm 2 , 1.87397g / cm 2 It can be seen that compared with the use of a single layer of tantalum, titanium, and aluminum materials for radiation shielding, the surface density of this shielding structure is 1.26191g / cm 2 , which can greatly save material weight resources, the data is shown in Figure 6.

[0094] Table 6 Material surface density required for different radiation shielding solutions

[0095]

Claims

1. An optimization design method for a multi-layer material radiation shielding scheme for the Jupiter radiation environment, characterized by: Taking into account the harsh radiation environment of the Jupiter spacecraft exploration orbit, the distribution of high-energy particles in the exploration orbit was determined based on the SPENVIS numerical analysis platform. This was used as the environmental input, and the radiation shielding scheme was optimized and designed in combination with the genetic algorithm and the MULASSIS program. The invention gave the design ideas for the selection of shielding materials and the method of material superposition, the encoding method of chromosomes in the genetic algorithm and the method for formulating the fitness function were given, and finally the optimal radiation shielding scheme that met the needs was obtained through the genetic algorithm calculation. This method combines genetic algorithms to optimize the design of multi-layer material shielding solutions, solving the problem that it is difficult to consider multiple influencing factors and obtain the optimal shielding solution through simple mathematical analytical methods. The specific steps of this method are as follows: Step 1: Determine the detector orbit data and radiation model, and then use SPENVIS to analyze and output HTML report files and ASCII tables. Then, calculate the captured particle flux of the detector mission phase based on the output result data and accumulate the mission flux in segments and the solar proton flux of the entire mission duration to obtain the electron and proton spectra of the specified detection orbit; Step 2: Use the MULASSIS program to compare the shielding effects of different shielding schemes. For the defined detection orbit environment, use the radiation dose of 0.1mm silicon material placed behind the shielding material as the benchmark, analyze and compare the simulation results of the MULASSIS program, and select appropriate shielding materials, stacking methods of different materials, and the number of stacking layers; Step 3: Design the optimal multi-layer material radiation shielding scheme through the GAFortran genetic algorithm program in the SPENVIS numerical analysis platform, define the target value and optimization object according to the radiation shielding requirements, design the genetic algorithm calculation process, and then use the MULASSIS program to evaluate its shielding effect; Step 4: In order to enable the program to read the information of each individual, each chromosome is numerically encoded; Step 5: Define different constraints and their restrictions according to the needs, and use the GAFortran package to develop a fitness function to evaluate different shielding schemes. The smaller the fitness function value, the closer it is to the optimal radiation shielding scheme; Step 6: Define the mutation probability, crossover probability and jump mutation probability of each chromosome code, and the number of generations optimized by the genetic algorithm. Then perform genetic algorithm operations according to the fitness function to finally obtain the optimal radiation shielding solution that meets the requirements.

2. The optimization design method of a multi-layer material radiation shielding scheme for the Jupiter radiation environment according to claim 1, characterized in that: The specific process of genetic algorithm parameter design described in step 3 is as follows: The three main constraints are the total radiation dose after shielding, the total thickness of the shielding scheme, and the total mass of the shielding material. The ultimate optimization goal is to minimize the three constraint target values ​​as much as possible, provided that the three target values ​​do not exceed the maximum allowable total radiation dose, maximum total thickness, and maximum allowable mass. The optimization model is defined as follows: D(X)=D(X1)≤D0 f(X)=min[D(X),T(X),AD(X)] Among them, D(X) is the radiation dose value after shielding, D0 is the maximum allowable radiation dose level, the unit is rad, and the radiation dose level D(X) is the radiation dose value of the first layer, which is the shielding layer closest to the protected material; T(X) is the total thickness of the shielding scheme, T0 is the upper limit of the thickness, the unit is mm; AD(X) is the equivalent surface density of the shielding material, AD0 is the upper limit of the material surface density. The material shape is defined as a finite plate and the area of ​​each layer of shielding material is equal. Therefore, the total mass of the shielding scheme is proportional to its surface density, and the equivalent surface density can be used to measure the mass of the shielding material. The units of AD(X) and AD0 are g / cm 2 X represents the shielding scheme, n is the number of shielding layers in the shielding scheme, and X i represents the i-th shielding layer, and ρ is the density of the material.

3. The optimization design method of a multi-layer material radiation shielding scheme for the Jupiter radiation environment according to claim 1, characterized in that: In step 4, the chromosome encoding is as follows: Define the chromosome encoding, and the decimal digits of each chromosome are defined as 8n digits, where n is the number of shielding material layers. From left to right, each 8-digit number represents the properties of a shielding layer material. Taking the leftmost 8-digit number as an example, it represents the properties of the first shielding layer material. The first leftmost digit in the 8-digit number represents the type of shielding material, which can be identified in order from small to large atomic number. 1 represents the material with the smallest atomic number, and 9 represents the material with the largest atomic number. If there are more than 9 types of materials, the number of digits can be increased according to demand. The number after the number representing the type of material in the 8-digit number represents the thickness of the shielding layer. Based on the thickness and density of the material, the surface density of each shielding layer can be calculated, so that each chromosome contains all the information of the solution.

4. The optimization design method of a multi-layer material radiation shielding scheme for the Jupiter radiation environment according to claim 1, characterized in that: In step five, the fitness function is established. The specific process is as follows: In the low-temperature tensile test, the displacement-tensile strength curve can be obtained based on the stress-strain data measured by the universal testing machine. The toughness and brittleness of the material can be judged by the shape of the tensile curve and the observation results of the scanning electron microscope, and the temperature range of the tough-brittle transition of the lead-free solder, the change of the maximum tensile strength and the fracture displacement can be obtained accordingly.

5. The method for evaluating the low temperature reliability of lead-free component interconnection solder joints according to claim 1, characterized in that: The specific process of reliability evaluation of lead-free solder under low temperature conditions described in step 4 is as follows: During the analysis, if the same material is used for several consecutive shielding layers, they are considered to be the same shielding layer, and the thickness is the sum of the thicknesses of these layers. The radiation dose of the silicon material layer, together with the total thickness and surface density of the shielding material, is used to formulate a "fitness function", which assigns a value to each shielding scheme, and the genetic algorithm optimizer can use this value to select the best shielding scheme. The fitness function F is defined to have a linear form: F=1-[A×D(X)+B×AD(X)+C×T(X)] D(X) is the radiation dose level after shielding, in rad, and AD(X) is the surface density of the shielding solution, in g / cm 2 , T(X) is the total thickness of the shielding material, in mm. The larger the value of F, the more suitable the shielding scheme is. A, B, and C represent the weights of these three terms respectively. The weight constants A, B and C are defined as: Set the radiation dose weighting factor W according to the requirements d , surface density weighting factor W m and thickness weighting factor W t That is, the value of F can be used to measure the fitness value of different shielding schemes.

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