Aluminum alloy electronic range estimation method, system and equipment and storage medium
By generating a particle transport calculation model and Monte Carlo simulation, and combining it with an interpolation algorithm to construct a range-energy mapping relationship, the problem of insufficient accuracy in estimating the electronic range of aluminum alloys in traditional methods is solved, and high-precision and efficient range estimation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-10
AI Technical Summary
In existing technologies, traditional methods for estimating the electron range of aluminum alloys ignore the influence of components other than aluminum in the aluminum alloy on electron scattering and energy loss, resulting in a large deviation between the estimation results and the actual situation, which makes it difficult to meet the requirements of high-precision aerospace missions.
By generating a particle transport calculation model based on the composition ratio and geometric properties of aluminum alloy, and combining it with the Monte Carlo simulation method, the raw range data is obtained. Then, the range-energy mapping relationship and continuous estimation function are constructed through interpolation algorithm, and the real-time range estimation results are output.
It significantly improves the accuracy of electron range estimation in aluminum alloys, and can truly reflect the complex scattering process of electrons in aluminum alloys, meeting the needs of aerospace missions for high-precision radiation protection design.
Smart Images
Figure CN121835333A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of space radiation protection technology, and more specifically, to a method, system, device, and storage medium for estimating the electronic range of aluminum alloys. Background Technology
[0002] In the aerospace field, aluminum alloys, especially 6061 aluminum alloy, are widely used in the manufacture of spacecraft structural components due to their excellent mechanical properties and lightweight characteristics. Because the space environment contains a large number of high-energy electrons, the range characteristics of these electrons when interacting with aluminum alloy components directly affect the radiation protection design and structural safety of the spacecraft. Therefore, it is necessary to estimate the range of electrons in aluminum alloys.
[0003] In related technologies, traditional electron range estimation methods typically simplify the problem to a one-dimensional energy loss model of electrons in pure aluminum, ignoring the influence of other components in aluminum alloys such as silicon, iron, and copper on electron scattering and energy loss. Furthermore, the simplified model cannot simulate the complex scattering process of electrons in the alloy, resulting in a large deviation between the estimation results and the actual situation, making it difficult to meet the requirements of high-precision aerospace missions. Summary of the Invention
[0004] The problem addressed by this invention is how to improve the accuracy of electron range estimation in aluminum alloys.
[0005] To address the aforementioned problems, this invention provides a method, system, device, and storage medium for estimating the electronic range of aluminum alloys.
[0006] In a first aspect, the present invention provides a method for estimating the electronic range of aluminum alloys, comprising: Based on the component ratios and geometric properties of the aluminum alloy, a particle transport calculation model for the aluminum alloy is generated. Based on the particle transport calculation model, the original range data after the electron interacts with the aluminum alloy with the preset incident energy data is obtained by Monte Carlo simulation method; Based on the original range data, the range-energy mapping relationship of the aluminum alloy is obtained; A continuous estimation function is determined using an interpolation algorithm based on the range-energy mapping relationship. By using the continuous estimation function and combining it with the real-time incident energy of the target electron, the real-time range estimation result of the target electron in the aluminum alloy is output.
[0007] Optionally, generating a particle transport calculation model for the aluminum alloy based on its component ratios and geometric properties includes: The material properties of the aluminum alloy are determined based on the composition ratio of each element in the aluminum alloy. Based on the material properties, the electron incident direction, target thickness, and boundary conditions corresponding to the aluminum alloy are determined. Based on the electron incident direction, the target thickness, and the boundary conditions, a geometric model of the aluminum alloy is constructed. Based on the geometric model, and combined with the preset electromagnetic physical process mechanism, preset data capture rules, and preset number of running particles, the particle transport calculation model corresponding to the aluminum alloy is generated.
[0008] Optionally, the step of obtaining the raw range data after the electron interacts with the aluminum alloy at a preset incident energy using a Monte Carlo simulation method based on the particle transport calculation model includes: Based on the particle transport calculation model, the energy range and geometric boundaries of electron incident are determined; Based on the energy range and the geometric boundary, multiple preset incident energy data are determined; Based on all the preset incident energy data, generate an electron incident energy sequence; Based on the electron incident energy sequence, a Monte Carlo simulation is performed in the particle transport calculation model to obtain the raw range data of each electron after interacting with the aluminum alloy with the corresponding preset incident energy data.
[0009] Optionally, determining multiple preset incident energy data based on the energy range and the geometric boundary includes: Within the energy range, with the maximum range corresponding to the geometric boundary as a constraint, a preset number of discrete energy points are selected uniformly at logarithmic intervals. Each discrete energy point corresponds to a preset incident energy data.
[0010] Optionally, obtaining the range-energy mapping relationship of the aluminum alloy based on the raw range data includes: The original range data corresponding to each discrete energy point is filtered for validity to obtain the filtered effective range value. The effective range value is subjected to multi-dimensional statistical processing to obtain multiple statistical indicators corresponding to the discrete energy point. The statistical indicators include the particle rebound ratio, the average range, and the percentile range ratio. The range-energy mapping relationship of the aluminum alloy is obtained based on the particle rebound ratio, the average range, and the percentile range ratio.
[0011] Optionally, determining the continuous estimation function based on the range-energy mapping relationship using an interpolation algorithm includes: Using the discrete energy points as independent variables and the statistical indicators corresponding to the discrete energy points as dependent variables, a discrete mapping pair is constructed. By using a quadratic interpolation algorithm, with the independent variable as the X-axis and the dependent variable as the Y-axis, a continuous interpolation function for the statistical index is obtained. The continuous estimation function is obtained by integrating the continuous interpolation functions corresponding to all the statistical indicators.
[0012] Optionally, the step of outputting the real-time range estimation result of the target electron in the aluminum alloy by combining the continuous estimation function with the real-time incident energy of the target electron includes: Determine whether the real-time incident energy is within the energy range; If the real-time incident energy is within the energy range, substitute the real-time incident energy into the continuous estimation function to obtain the particle bounce data, average range and percentile range data corresponding to the real-time incident energy. The particle bounce data, average range, and percentile range data corresponding to the real-time incident energy are used as the real-time range estimation results.
[0013] In a second aspect, the present invention provides an aluminum alloy electronic range estimation system, comprising: The model building unit is used to generate a particle transport calculation model of the aluminum alloy based on the corresponding component ratio and geometric properties. The simulation unit is used to obtain the original range data of the electron after it interacts with the aluminum alloy with preset incident energy data, based on the particle transport calculation model and the Monte Carlo simulation method. The mapping relationship determination unit is used to obtain the range-energy mapping relationship of the aluminum alloy based on the original range data. The function construction unit is used to determine the continuous estimation function based on the range-energy mapping relationship using an interpolation algorithm; The calculation unit is used to output the real-time range estimation result of the target electron in the aluminum alloy by combining the continuous estimation function with the real-time incident energy of the target electron.
[0014] Thirdly, an electronic device according to the present invention includes: a processor and a memory, the memory being used to store a computer program; When the computer program is loaded by the processor, it causes the processor to execute the aluminum alloy electronic range estimation method as described above.
[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aluminum alloy electronic range estimation method as described above.
[0016] The present invention provides an aluminum alloy electron range estimation method, system, device, and storage medium that significantly improves the accuracy of electron range estimation in aluminum alloys. Specifically, firstly, by generating a particle transport calculation model based on the composition ratio and geometric characteristics of the aluminum alloy, the influence of each element in the aluminum alloy on electron scattering and energy loss can be accurately reflected, overcoming the limitation of traditional methods that only consider pure aluminum, and reducing estimation deviations caused by compositional and geometric simplifications at the fundamental level. Secondly, the raw range data obtained using Monte Carlo simulation can simulate the complex scattering process of electrons in the aluminum alloy, thus more realistically reflecting electron behavior and further improving estimation accuracy. Next, by statistically analyzing the raw range data, a range-energy mapping relationship is obtained, transforming discrete simulation data into a regular mapping relationship. Then, a continuous estimation function is determined through an interpolation algorithm, enabling the estimation process to cover a wider energy range and quickly respond to real-time input energy values, improving estimation efficiency and flexibility. Finally, the real-time range estimation result corresponding to the aluminum alloy is output using the continuous estimation function. This invention considers the actual composition and geometry of the aluminum alloy and provides high-precision range estimation results using the obtained continuous estimation function. It not only relies on the accurate model and data foundation in the early stage to ensure the accuracy of the results, but also achieves efficient calculation through interpolation algorithm. It effectively solves the defects of traditional methods that ignore the influence of alloy composition and complex scattering process, and meets the needs of aerospace missions for high-precision radiation protection design. Attached Figure Description
[0017] Figure 1 This is a flowchart illustrating the electronic range estimation method for aluminum alloys according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the aluminum alloy electronic range estimation system according to an embodiment of the present invention. Detailed Implementation
[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.
[0019] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.
[0020] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.
[0021] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".
[0022] Combination Figure 1 As shown, an embodiment of the present invention provides a method for estimating the electronic range of aluminum alloys, comprising: A particle transport calculation model for the aluminum alloy is generated based on the corresponding component ratio and geometric properties.
[0023] Specifically, firstly, by generating a particle transport calculation model based on the component ratios and geometric properties of the aluminum alloy, the influence of each element in the aluminum alloy on electron scattering and energy loss can be accurately reflected. Traditional methods typically simplify the aluminum alloy to pure aluminum for calculations, neglecting the effects of other components (such as silicon, iron, and copper). However, these components play a crucial role in electron scattering and energy loss. By generating a particle transport calculation model based on the actual component ratios, this invention fundamentally reduces estimation bias caused by component simplification, providing a reliable model foundation for subsequent high-precision estimations.
[0024] Based on the particle transport calculation model, the original range data after the electron interacts with the aluminum alloy at a preset incident energy is obtained through Monte Carlo simulation.
[0025] Specifically, the Monte Carlo simulation method was used to obtain the raw range data after electrons interact with aluminum alloy at a preset incident energy. The Monte Carlo method is a numerical simulation technique based on random sampling, capable of simulating the complex scattering process of electrons in aluminum alloys. By simulating a large number of random events, the Monte Carlo method can more realistically reflect the behavior of electrons, capturing the random scattering and energy loss processes of electrons in the alloy. Compared with traditional one-dimensional energy loss models, Monte Carlo simulations provide results closer to the actual physical process, thus significantly improving the accuracy of the estimation.
[0026] Based on the original range data, the range-energy mapping relationship of the aluminum alloy is obtained.
[0027] Specifically, by statistically analyzing the raw range data, the range-energy mapping relationship of aluminum alloys was obtained. This transformed discrete simulation data into a regular mapping relationship, providing a data foundation for subsequent estimations. Through statistical processing of a large amount of simulation data, characteristic values of electron range at different incident energies (such as mean, median, percentile, etc.) can be extracted, thus constructing a clear range-energy mapping table. This mapping relationship can intuitively reflect the relationship between electron range and incident energy, providing accurate data support for subsequent interpolation estimations.
[0028] The continuous estimation function is determined by using an interpolation algorithm based on the range-energy mapping relationship.
[0029] Specifically, a continuous estimation function is determined based on the range-energy mapping relationship using an interpolation algorithm. This continuous estimation function is not a single function, but rather an integration of quadratic interpolation sub-functions constructed based on interp1d, using statistical indicators such as particle bounce ratio and average range. Each sub-function corresponds to a continuous fitting relationship for a statistical indicator. The interpolation algorithm transforms discrete mapping data into a continuous functional relationship, enabling the estimation process to cover a wider energy range. This continuous function not only responds quickly to real-time input energy values but also provides accurate estimation results at energy points not directly simulated. Through the interpolation algorithm, this invention significantly improves computational efficiency while maintaining estimation accuracy, making the estimation process more flexible and efficient.
[0030] By using the continuous estimation function and combining it with the real-time incident energy of the target electron, the real-time range estimation result of the target electron in the aluminum alloy is output.
[0031] Specifically, the final step utilizes a continuous estimation function to output real-time range estimates of the target electrons in the aluminum alloy. By combining previously generated models, simulation data, and interpolation algorithms, high-precision range estimates are provided. By considering the actual composition and geometry of the aluminum alloy, this invention can rapidly output corresponding range estimates for different incident energies. This real-time estimation capability not only meets the requirements of aerospace missions for high-precision radiation protection design but also responds in real-time to dynamic changes during spacecraft operation in orbit, providing strong assurance for the safe operation of spacecraft.
[0032] The electron range estimation method for aluminum alloys of this invention significantly improves the accuracy of electron range estimation in aluminum alloys. Specifically, firstly, by generating a particle transport calculation model based on the composition ratio and geometric characteristics of the aluminum alloy, the influence of each element in the aluminum alloy on electron scattering and energy loss can be accurately reflected, overcoming the limitation of traditional methods that only consider pure aluminum, and reducing estimation deviations caused by composition and geometric simplification at the fundamental level. Secondly, the raw range data obtained by Monte Carlo simulation can simulate the complex scattering process of electrons in aluminum alloys, thus more realistically reflecting electron behavior and further improving the accuracy of estimation. Next, by statistically analyzing the raw range data, the range-energy mapping relationship is obtained, transforming discrete simulation data into a regular mapping relationship. Then, a continuous estimation function is determined through an interpolation algorithm, enabling the estimation process to cover a wider energy range and quickly respond to real-time input energy values, improving the efficiency and flexibility of estimation. Finally, the real-time range estimation result corresponding to the aluminum alloy is output using the continuous estimation function. This invention considers the actual composition and geometry of the aluminum alloy and provides high-precision range estimation results using the obtained continuous estimation function. It not only relies on the accurate model and data foundation in the early stage to ensure the accuracy of the results, but also achieves efficient calculation through interpolation algorithm. It effectively solves the defects of traditional methods that ignore the influence of alloy composition and complex scattering process, and meets the needs of aerospace missions for high-precision radiation protection design.
[0033] Optionally, generating a particle transport calculation model for the aluminum alloy based on its component ratios and geometric properties includes: The material properties of the aluminum alloy are determined based on the composition ratio of each element in the aluminum alloy. Based on the material properties, the electron incident direction, target thickness, and boundary conditions corresponding to the aluminum alloy are determined. Based on the electron incident direction, the target thickness, and the boundary conditions, a geometric model of the aluminum alloy is constructed. Based on the geometric model, and combined with the preset electromagnetic physical process mechanism, preset data capture rules, and preset number of running particles, the particle transport calculation model corresponding to the aluminum alloy is generated.
[0034] Specifically, based on the proportions of each element in the aluminum alloy, this embodiment of the invention uses the element creation interface of the Geant4 framework to construct basic models of each element, mixes them according to their mass proportions to form a multi-element alloy material model, and simultaneously constructs a vacuum environment material model by setting parameters such as atomic number, molar mass, and density, clarifying core material properties such as alloy density. Based on the material properties, the electron incident direction, target thickness, and boundary conditions are determined through the physical parameter configuration function of Geant4, where the boundary conditions include the interface characteristics setting between vacuum and alloy. Subsequently, using the geometry construction tool of Geant4, based on a cubic structure, the size ratio between the world body and alloy components is set, establishing the association between the material model and the geometric structure, enabling the geometric overlap detection function, and delineating a dedicated collection area for range data to complete the geometric model construction. Finally, combined with the built-in electromagnetic physical process mechanism of electron scattering and energy loss in Geant4, the preset data capture rules, and the preset number of running particles of no less than 1,000,000, a complete particle transport calculation model is formed.
[0035] In a preferred embodiment of the present invention, for 6061 aluminum alloy, a model of nine elements—aluminum, silicon, iron, copper, manganese, magnesium, chromium, zinc, and titanium—is first created using the Geant4 framework. These elements are then mixed according to the following mass percentages: aluminum 96.0%, silicon 0.8%, iron 0.7%, copper 0.4%, manganese 0.15%, magnesium 1.2%, chromium 0.35%, zinc 0.25%, and titanium 0.15%, with a set density of 2.70 g / cm³. 3 A material model of 6061 aluminum alloy was constructed, along with a model of atomic number 1, molar mass 1.008 g / mole, and density 1e-25 g / cm³. 3 A vacuum environment material model was constructed. Based on the material properties, the boundary conditions were determined: electrons were incident along the positive Z-axis, the target thickness was set according to the actual application scenario, and the vacuum-alloy interface was non-reflective. The cube geometry of the world body and alloy parts was constructed using Geant4's G4Box class. The size of the world body was set to a fixed ratio multiple of the alloy parts. The 6061 aluminum alloy material model was associated with the geometry of the alloy parts. The checkOverlaps parameter was enabled for geometric overlap detection. The range data collection area was bound using the SteppingAction class. Combining Geant4's built-in electromagnetic physics process library, data capture rules (recording the final Z-axis position of the particles), and the number of running particles in each group of 1,000,000, a particle transport calculation model for 6061 aluminum alloy was generated.
[0036] In this embodiment of the invention, by restoring the material properties and geometric structure of aluminum alloys, the problem of composition and structural distortion in traditional simplified pure aluminum models is avoided. The preset electromagnetic physical process mechanism can realistically reproduce the complex interaction between electrons and alloys. The data capture rules ensure the effectiveness and accuracy of the range data. The sufficient number of running particles reduces the impact of random errors on the simulation results. The organic integration of multiple elements improves the reliability of the model from the basic level, providing solid support for obtaining high-quality raw data, constructing accurate mapping relationships and achieving high-precision estimation. It effectively solves the estimation deviation problem caused by neglecting alloy composition and complex physical processes in traditional methods.
[0037] Optionally, the step of obtaining the raw range data after the electron interacts with the aluminum alloy at a preset incident energy using a Monte Carlo simulation method based on the particle transport calculation model includes: Based on the particle transport calculation model, the energy range and geometric boundaries of electron incident are determined; Based on the energy range and the geometric boundary, multiple preset incident energy data are determined; Based on all the preset incident energy data, generate an electron incident energy sequence; Based on the electron incident energy sequence, a Monte Carlo simulation is performed in the particle transport calculation model to obtain the raw range data of each electron after interacting with the aluminum alloy with the corresponding preset incident energy data.
[0038] Specifically, relying on the preset configuration of the particle transport calculation model, the geometric boundaries are determined by directly reading the geometric structural parameters defined in the model (such as the dimensions of the world body and aluminum alloy components, and the data collection area). At the same time, the energy range of electron incidence is defined based on the application scenario of electron energy in the aerospace field. In this embodiment, the math and numpy libraries of Python can be used to generate multiple sets of uniformly distributed preset incident energy data within the energy range through logarithmic transformation and antilogarithmic inverse operation to ensure comprehensive energy coverage. All preset incident energy data are arranged in numerical order to generate an ordered electron incident energy sequence, providing standardized input for batch simulation. Based on the energy sequence, Monte Carlo simulation is started in the particle transport calculation model. The interaction between electrons and aluminum alloy is simulated through the physical processes built into the model. The range data or non-incident / rebound markers of each electron are recorded according to the data capture rules. Finally, the data is summarized to form the raw data containing single-particle range information.
[0039] In this embodiment of the invention, the geometric boundaries and energy ranges are accurately obtained through a particle transport calculation model, ensuring that the simulation conditions are consistent with the actual application scenario. A logarithmic distribution is used to generate energy data, achieving balanced sampling across different energy ranges and avoiding incomplete data coverage. Monte Carlo simulation technology can realistically reproduce the complex scattering and energy loss processes of electrons and aluminum alloys, and the generated raw data accurately reflects the electron motion characteristics. Ordered energy sequences and batch simulation modes improve the efficiency of data acquisition, providing comprehensive and reliable basic data for subsequently constructing accurate range-energy mapping relationships, effectively compensating for the accuracy defects caused by the simplification of the simulation process and insufficient data sampling in traditional methods.
[0040] Optionally, determining multiple preset incident energy data based on the energy range and the geometric boundary includes: Within the energy range, with the maximum range corresponding to the geometric boundary as a constraint, a preset number of discrete energy points are selected uniformly at logarithmic intervals. Each discrete energy point corresponds to a preset incident energy data.
[0041] Specifically, firstly, the range of energy values is defined, and the maximum range corresponding to the geometric boundary is used as a constraint to ensure that the electron range corresponding to the selected energy point does not exceed the effective range of the geometric model. Through logarithmic transformation with offset and inverse antilogarithmic operation, the energy range is converted into a logarithmic space. A preset number of values are uniformly selected in the logarithmic space and then restored to the actual energy value. This achieves sampling of discrete energy points uniformly distributed at logarithmic intervals. Each discrete energy point is directly used as a preset incident energy data, ensuring that the data sampling density of the low-energy and high-energy ranges is balanced and covering the key characteristics of the entire energy range. In a preferred embodiment of the present invention, based on the particle transport calculation model of 6061 aluminum alloy, the electron incident energy range is first determined to be 0.01 MeV to 10 MeV. The maximum range corresponding to the geometric boundary is the maximum distance that electrons may reach in the 6061 aluminum alloy within this energy range. This constraint avoids the range corresponding to the energy point from exceeding the geometric range of the model. Using a Python script, the math and numpy libraries are imported, and the offset logbias=0.108 is set to perform a logarithmic transformation with offset on the energy range of 0.01 MeV to 10 MeV. In the logarithmic space, 100 uniformly distributed values are generated by the np.linspace function. Then, the inverse antilogarithmic operation is used to restore these values to the actual incident energy, resulting in 100 discrete energy points uniformly distributed at logarithmic intervals. Each discrete energy point is a preset incident energy data, which completely covers the target energy range and has balanced sampling.
[0042] In this embodiment of the invention, the maximum range of the geometric boundary is used as a constraint to ensure that the range of the electron motion corresponding to the selected preset incident energy data is within the effective detection range of the model, thus avoiding invalid sampling. By using logarithmically spaced uniform sampling, sufficient data sampling is ensured in both low-energy and high-energy ranges, while also conforming to the nonlinear law of electron range variation with energy. This allows the sampled data to comprehensively reflect the characteristics of electron range in different energy segments, providing a scientific energy input basis for obtaining high-quality raw data in subsequent Monte Carlo simulations. This effectively makes up for the shortcomings of traditional uniform sampling in terms of insufficient data in low-energy or high-energy ranges.
[0043] Optionally, obtaining the range-energy mapping relationship of the aluminum alloy based on the raw range data includes: The original range data corresponding to each discrete energy point is filtered for validity to obtain the filtered effective range value. The effective range value is subjected to multi-dimensional statistical processing to obtain multiple statistical indicators corresponding to the discrete energy point. The statistical indicators include the particle rebound ratio, the average range, and the percentile range ratio. The range-energy mapping relationship of the aluminum alloy is obtained based on the particle rebound ratio, the average range, and the percentile range ratio.
[0044] Specifically, the original range data corresponding to each discrete energy point is first filtered for validity, identifying and removing invalid data marked "-inf" where particles did not hit or bounce off, retaining valid range values that reflect the actual trajectory of electrons. Then, multi-dimensional statistical processing is performed on the valid range values using statistical functions from the NumPy library. The proportion of invalid data to the total data of the corresponding group is calculated to obtain the particle bounce ratio. The average range is obtained using an arithmetic mean algorithm, and the 5%, 50%, and 95% quantiles are extracted using a quantile calculation function to obtain percentile range data. Finally, Python's dictionary serialization function is used to associate and bind each group of discrete energy points with the corresponding particle bounce ratio, average range, and percentile range data, forming a structured range-energy mapping relationship and storing it to provide a standardized dataset for subsequent interpolation calculations.
[0045] In a preferred embodiment of the present invention, taking the raw range data corresponding to 100 discrete energy points of 6061 aluminum alloy as an example, a Python script is used to import the NumPy library and read the raw result file corresponding to each set of preset incident energy data. Each line in the file records the range data of a single electron, with "-inf" indicating invalid data. The NumPy logic judgment function is used to filter out the valid range values that are not "-inf". For the raw data corresponding to a certain discrete energy point (e.g., 0.1 MeV), 20,000 invalid "-inf" data are removed, resulting in 980,000 valid range values. The NumPy mean function is then called to calculate the valid range values. The mean range is obtained by calculating the average range. The percentile function is then used to calculate the 5th, 50th, and 95th percentiles, respectively, to obtain the 5th percentile range, 50th percentile range, and 95th percentile range. At the same time, the ratio of 20,000 to 1,000,000 is calculated to obtain the particle bounce ratio. The discrete energy point of 0.1 MeV and the corresponding particle bounce ratio, average range, and range data of each percentile are stored in a dictionary. This process is repeated to complete the data processing of 100 sets of discrete energy points, and finally an array containing 100 dictionaries is formed, which is the range-energy mapping relationship of 6061 aluminum alloy. The data is serialized in binary format and stored as a file named "resDictDumps.bin".
[0046] In this embodiment of the invention, invalid data is accurately removed through data filtering and statistical analysis, avoiding interference from noise on the accuracy of the mapping relationship. The introduction of multi-dimensional statistical indicators comprehensively depicts the distribution characteristics of electron range, which, compared with traditional single-dimensional range data, more realistically reflects the movement law of electrons in aluminum alloy. The structured mapping relationship construction method realizes the accurate correlation between discrete energy points and statistical indicators, providing high-quality and standardized data support for the subsequent fitting of interpolation functions and ensuring the accuracy of interpolation calculation. The entire data processing process is highly automated, effectively reducing human operation errors, while adapting to the processing needs of large-scale simulation data.
[0047] Optionally, determining the continuous estimation function based on the range-energy mapping relationship using an interpolation algorithm includes: Using the discrete energy points as independent variables and the statistical indicators corresponding to the discrete energy points as dependent variables, a discrete mapping pair is constructed. By using a quadratic interpolation algorithm, with the independent variable as the X-axis and the dependent variable as the Y-axis, a continuous interpolation function for the statistical index is obtained. The continuous estimation function is obtained by integrating the continuous interpolation functions corresponding to all the statistical indicators.
[0048] Specifically, firstly, all discrete energy points are extracted from the structured range-energy mapping relationship as independent variables, and corresponding statistical indicators such as particle bounce ratio, average range, and percentile range are extracted as dependent variables. Discrete mapping pairs are constructed according to the correspondence between energy and statistical indicators. In this embodiment, the interp1d class of the SciPy library in Python is used as the core technical tool, and the quadratic interpolation mode is selected. Fitting operations are performed with the independent variables as the X-axis and each dependent variable as the Y-axis to obtain a continuous interpolation function specific to each statistical indicator. Finally, the interpolation functions corresponding to all statistical indicators are integrated to form a continuous estimation function that can simultaneously output multi-dimensional range parameters. The continuous estimation function can quickly calculate any incident energy within the effective energy range.
[0049] In a preferred embodiment of the present invention, the range-energy mapping relationship of 6061 aluminum alloy is used as the basis. This mapping relationship includes 100 sets of discrete energy points and corresponding statistical indicators such as particle rebound ratio, average range, 5th percentile range, 50th percentile range, and 95th percentile range. First, 100 sets of discrete energy points are extracted from the mapping relationship data to form an independent variable array X. Then, the statistical indicator data are extracted to form 5 dependent variable arrays (Y). m For the average array, Y r For the rebound ratio array, Y min For the percentile 5 range array, Y mid (Ymax is the range array for the 50th percentile and Ymax is the range array for the 95th percentile); call the interp1d class, using X and Y respectively. m X and Y r X and Y min X and Y mid X and Y max As input, the kind='quadratic' parameter is set to perform quadratic interpolation, resulting in 5 independent continuous interpolation functions; these 5 interpolation functions are then encapsulated and integrated into a complete continuous estimation function.
[0050] Specifically, taking the interpolation function of particle rebound ratio as an example, 100 sets of discrete data are mapped onto a two-dimensional coordinate system, with energy as the horizontal axis and rebound ratio as the vertical axis to form a discrete data point set; then the algorithm performs quadratic polynomial fitting on three adjacent discrete points as a group (i.e., constructs a form like y=ax). 2The algorithm uses a quadratic function (a + bx + c), where a is the curvature coefficient, b is the coefficient of the first term, and c is the constant term, to fit a quadratic curve that closely approximates the distribution of the three points in the set. For 100 discrete points, the algorithm performs local quadratic fitting group by group and smoothly connects all the local fitted curves to form a continuous nonlinear curve covering the entire range of 0.01 MeV to 10 MeV. After fitting, the output is an independent continuous interpolation function that only applies to the energy-particle bounce ratio. This function has the ability to output the corresponding bounce ratio when given any valid energy value. The interpolation functions corresponding to other statistical indicators all use energy as the x-axis and the corresponding statistical indicator as the y-axis to generate the corresponding interpolation functions.
[0051] In this embodiment of the invention, the nonlinear law of electron range variation with energy is accurately captured by a quadratic interpolation algorithm, significantly improving estimation accuracy. The construction of discrete mapping pairs ensures accurate correspondence between independent and dependent variables, providing a reliable foundation for interpolation fitting. The integration of interpolation functions with multiple statistical indices enables continuous estimation functions to comprehensively output key parameters reflecting electron range characteristics, meeting diverse engineering application needs. Simultaneously, the interpolation function does not require repeated execution of complex Monte Carlo simulations, enabling real-time calculation of any effective energy, balancing estimation accuracy and efficiency. This effectively resolves the contradiction between high accuracy and rapid response that traditional methods cannot simultaneously satisfy, providing efficient and practical technical support for spacecraft radiation protection design.
[0052] Optionally, the step of outputting the real-time range estimation result of the target electron in the aluminum alloy by combining the continuous estimation function with the real-time incident energy of the target electron includes: Determine whether the real-time incident energy is within the energy range; If the real-time incident energy is within the energy range, substitute the real-time incident energy into the continuous estimation function to obtain the particle bounce data, average range and percentile range data corresponding to the real-time incident energy. The particle bounce data, average range, and percentile range data corresponding to the real-time incident energy are used as the real-time range estimation results.
[0053] Specifically, the system acquires the real-time incident energy of the target electron (e.g., the target electron itself), compares this energy with the boundary values of a preset energy range using conditional statements, and verifies the energy's validity. If the energy is within the valid range, it is used as an input parameter in a continuous estimation function. This function simultaneously calculates multi-dimensional range-related data by calling corresponding continuous interpolation functions for particle bounce ratio, average range, and percentile range. Finally, the real-time incident energy and the corresponding particle bounce data, average range, and percentile range data are organized in a standardized format to form a complete real-time range estimation result, which is then presented to the user. If the real-time incident energy is not within the energy range, a range exceedance warning is output, clearly informing the user of the valid energy input range.
[0054] In a preferred embodiment of the invention, a continuous estimation function for 6061 aluminum alloy is used as the basis. This function covers the energy range of 0.01 MeV to 10 MeV and includes five interpolation sub-functions: particle rebound rate, average range, 5th percentile range, 50th percentile range, and 95th percentile range. An interactive program written in Python receives user input. When the user inputs a real-time incident energy of "0.123 MeV", the program first determines that the energy is within the valid range, then substitutes it into the continuous estimation function, and calls each interpolation sub-function to calculate the rebound rate of 12.81%, the average range of 0.0385 mm, the 5th percentile range of 0.0100 mm, the 50th percentile range of 0.0399 mm, and the 95th percentile range of 0.0623 mm, respectively. Finally, the results are output in the format of "energy + multi-dimensional range data". If "11 MeV" is input, the program determines that it exceeds the valid range and outputs a prompt message.
[0055] In this embodiment of the invention, relying on the previously constructed continuous estimation function, multi-dimensional range data can be quickly output without repeatedly executing complex Monte Carlo simulations, achieving real-time response, greatly improving estimation efficiency, and effectively balancing the accuracy, efficiency and practicality of estimation.
[0056] Combination Figure 2 As shown, another embodiment of the present invention provides an aluminum alloy electronic range estimation system, comprising: The model building unit is used to generate a particle transport calculation model of the aluminum alloy based on the corresponding component ratio and geometric properties. The simulation unit is used to obtain the original range data of the electron after it interacts with the aluminum alloy with preset incident energy data, based on the particle transport calculation model and the Monte Carlo simulation method. The mapping relationship determination unit is used to obtain the range-energy mapping relationship of the aluminum alloy based on the original range data. The function construction unit is used to determine the continuous estimation function based on the range-energy mapping relationship using an interpolation algorithm; The calculation unit is used to output the real-time range estimation result of the target electron in the aluminum alloy by combining the continuous estimation function with the real-time incident energy of the target electron.
[0057] The advantages of the aluminum alloy electronic range estimation system of the present invention compared with the prior art are the same as those of the above-mentioned aluminum alloy electronic range estimation method compared with the prior art, and will not be repeated here.
[0058] Another embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store a computer program; When the computer program is loaded by the processor, it causes the processor to execute the aluminum alloy electronic range estimation method as described above.
[0059] The electronic device of the present invention has the same advantages over the prior art as the above-mentioned aluminum alloy electronic range estimation method, and will not be repeated here.
[0060] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aluminum alloy electronic range estimation method as described above.
[0061] The computer-readable storage medium of the present invention has the same advantages over the prior art as the above-described method for estimating the electronic range of aluminum alloys, and will not be repeated here.
[0062] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.
Claims
1. A method for estimating the electronic range of aluminum alloys, characterized in that, include: Based on the component ratios and geometric properties of the aluminum alloy, a particle transport calculation model for the aluminum alloy is generated. Based on the particle transport calculation model, the original range data after the electron interacts with the aluminum alloy with the preset incident energy data is obtained by Monte Carlo simulation method; Based on the original range data, the range-energy mapping relationship of the aluminum alloy is obtained; A continuous estimation function is determined using an interpolation algorithm based on the range-energy mapping relationship. By using the continuous estimation function and combining it with the real-time incident energy of the target electron, the real-time range estimation result of the target electron in the aluminum alloy is output.
2. The method for estimating the electron range of aluminum alloys according to claim 1, characterized in that, The step of generating a particle transport calculation model for the aluminum alloy based on its component ratios and geometric properties includes: The material properties of the aluminum alloy are determined based on the composition ratio of each element in the aluminum alloy. Based on the material properties, the electron incident direction, target thickness, and boundary conditions corresponding to the aluminum alloy are determined. Based on the electron incident direction, the target thickness, and the boundary conditions, a geometric model of the aluminum alloy is constructed. Based on the geometric model, and combined with the preset electromagnetic physical process mechanism, preset data capture rules, and preset number of running particles, the particle transport calculation model corresponding to the aluminum alloy is generated.
3. The method for estimating the electron range of aluminum alloys according to claim 1, characterized in that, The method of obtaining the raw range data of the electron after interacting with the aluminum alloy at a preset incident energy, based on the particle transport calculation model and using the Monte Carlo simulation method, includes: Based on the particle transport calculation model, the energy range and geometric boundaries of electron incident are determined; Based on the energy range and the geometric boundary, multiple preset incident energy data are determined; Based on all the preset incident energy data, generate an electron incident energy sequence; Based on the electron incident energy sequence, a Monte Carlo simulation is performed in the particle transport calculation model to obtain the raw range data of each electron after interacting with the aluminum alloy with the corresponding preset incident energy data.
4. The method for estimating the electron range of aluminum alloys according to claim 3, characterized in that, The step of determining multiple preset incident energy data based on the energy range and the geometric boundary includes: Within the energy range, with the maximum range corresponding to the geometric boundary as a constraint, a preset number of discrete energy points are selected uniformly at logarithmic intervals. Each discrete energy point corresponds to a preset incident energy data.
5. The method for estimating the electron range of aluminum alloys according to claim 4, characterized in that, The step of obtaining the range-energy mapping relationship of the aluminum alloy based on the original range data includes: The original range data corresponding to each discrete energy point is filtered for validity to obtain the filtered effective range value. The effective range value is subjected to multi-dimensional statistical processing to obtain multiple statistical indicators corresponding to the discrete energy point. The statistical indicators include the particle rebound ratio, the average range, and the percentile range ratio. The range-energy mapping relationship of the aluminum alloy is obtained based on the particle rebound ratio, the average range, and the percentile range ratio.
6. The method for estimating the electron range of aluminum alloys according to claim 5, characterized in that, The step of determining the continuous estimation function based on the range-energy mapping relationship using an interpolation algorithm includes: Using the discrete energy points as independent variables and the statistical indicators corresponding to the discrete energy points as dependent variables, a discrete mapping pair is constructed. By using a quadratic interpolation algorithm, with the independent variable as the X-axis and the dependent variable as the Y-axis, a continuous interpolation function for the statistical index is obtained. The continuous estimation function is obtained by integrating the continuous interpolation functions corresponding to all the statistical indicators.
7. The method for estimating the electron range of aluminum alloys according to claim 4, characterized in that, The step of outputting the real-time range estimate of the target electron in the aluminum alloy by combining the continuous estimation function with the real-time incident energy of the target electron includes: Determine whether the real-time incident energy is within the energy range; If the real-time incident energy is within the energy range, substitute the real-time incident energy into the continuous estimation function to obtain the particle bounce data, average range and percentile range data corresponding to the real-time incident energy. The particle bounce data, average range, and percentile range data corresponding to the real-time incident energy are used as the real-time range estimation results.
8. An electronic range estimation system for aluminum alloys, characterized in that, include: The model building unit is used to generate a particle transport calculation model of the aluminum alloy based on the corresponding component ratio and geometric properties. The simulation unit is used to obtain the original range data of the electron after it interacts with the aluminum alloy with preset incident energy data, based on the particle transport calculation model and the Monte Carlo simulation method. The mapping relationship determination unit is used to obtain the range-energy mapping relationship of the aluminum alloy based on the original range data. The function construction unit is used to determine the continuous estimation function based on the range-energy mapping relationship using an interpolation algorithm; The calculation unit is used to output the real-time range estimation result of the target electron in the aluminum alloy by combining the continuous estimation function with the real-time incident energy of the target electron.
9. An electronic device, characterized in that, include: Processor and memory, the memory being used to store computer programs; When the computer program is loaded by the processor, it causes the processor to execute the aluminum alloy electronic range estimation method as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for estimating the electronic range of aluminum alloys as described in any one of claims 1-7.