A structural design method, device and medium for an amorphous alloy liner

Abstract

The present invention discloses a structural design method, device, and medium for an amorphous alloy liner, relating to the technical field of liner technology. Multiple initial values for each structural design parameter of the amorphous alloy liner are obtained; an orthogonal experimental design is performed based on the multiple initial values of each structural design parameter to obtain multiple structural design schemes for the amorphous alloy liner; numerical simulations are performed on the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme; a neural network model is constructed based on the multiple structural design schemes and the corresponding at least one jet forming index; and optimization is performed based on the neural network model to obtain the optimal structural design scheme for the amorphous alloy liner. This method can determine the optimal structural design scheme for the amorphous alloy liner and improve the jet forming effect of the amorphous alloy liner.

Description

Technical Field

[0001] The present invention relates to the technical field of liner, and in particular to a structural design method, device and medium of an amorphous alloy liner. Background Art

[0002] Liner materials are a key factor in improving the penetration performance of shaped charge warheads. In recent years, rapidly developing Zr amorphous materials have demonstrated an energy release effect under impact conditions and high density. Their application in liner materials can promote the formation of a stable metal jet, effectively enhancing their armor-piercing capabilities and showing promising application prospects in the defense industry.

[0003] In the existing technology, a new type of charge liner structure is designed using W-Zr based amorphous alloy. This structure has good aftereffect and perforation performance. The energy release characteristics of Zr based amorphous material are used to improve the hole expansion effect of the perforating bullet, and the released energy can achieve the purpose of cleaning the channel.

[0004] However, the amorphous alloy-based liner structures in the prior art are all constructed based on historical experience, and the jet forming effect of the amorphous alloy liner constructed in this way is poor. Summary of the Invention

[0005] Based on this, it is necessary to provide a structural design method, device and medium for an amorphous alloy liner to address the above technical problems. This method can determine the optimal structural design scheme of the amorphous alloy liner and improve the jet forming effect of the amorphous alloy liner.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a structural design method for an amorphous alloy liner, comprising:

[0008] Obtaining multiple initial values of each structural design parameter of the amorphous alloy liner;

[0009] Based on multiple initial values of each structural design parameter, orthogonal experimental design was performed to obtain multiple structural design schemes for the amorphous alloy liner.

[0010] Numerical simulations were performed on the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme.

[0011] Constructing a neural network model based on multiple structural design schemes and corresponding at least one jet forming index;

[0012] The optimal structural design of the amorphous alloy liner was obtained by optimizing the model according to the neural network.

[0013] Preferably, a neural network model is constructed based on a plurality of structural design schemes and corresponding at least one jet forming index, including:

[0014] For any jet forming index, the values of the jet forming index under each structural design scheme are normalized respectively;

[0015] Under each structural design scheme, the normalized values of each jet forming index are weighted according to the weight of each jet forming index to obtain the comprehensive evaluation index under each structural design scheme;

[0016] A neural network model is constructed based on multiple structural design schemes and corresponding comprehensive evaluation indexes.

[0017] Preferably, the jet forming index includes jet length, jet head velocity and jet total energy; the comprehensive evaluation index is calculated as follows:

[0018]

[0019] Among them, W represents the comprehensive evaluation index, W a represents the jet length, represents the weight of the jet length, W b represents the jet head velocity, represents the weight of the jet head velocity, W c represents the total energy of the jet, The weight representing the total energy of the jet.

[0020] Preferably, a neural network model is constructed based on a plurality of structural design schemes and corresponding comprehensive evaluation indices, including:

[0021] For any structural design parameter, the initial value of the structural design parameter under multiple structural design schemes is fitted with the corresponding comprehensive evaluation index to obtain a fitting function under the structural design parameter; the fitting function represents the corresponding relationship between the structural design parameter and the comprehensive evaluation index;

[0022] Determine the training samples through the fitting function under each structural design parameter;

[0023] Train the neural network model through training samples.

[0024] Preferably, the training samples are determined by fitting functions under various structural design parameters, including:

[0025] For any structural design parameter, according to the fitting function under the structural design parameter, multiple sample values of the structural design parameter and the corresponding comprehensive evaluation index are obtained;

[0026] Determine multiple sample structural design schemes based on multiple sample values of each structural design parameter, and determine the average value of the comprehensive evaluation index corresponding to each sample value under each sample structural design scheme as the comprehensive evaluation index of each sample structural design scheme;

[0027] A variety of sample structure design schemes and corresponding comprehensive evaluation indexes are determined as training samples.

[0028] Preferably, the optimal structural design scheme of the amorphous alloy liner is obtained by optimizing according to the neural network model, including:

[0029] The neural network model is used as the objective function of the genetic algorithm, and multiple structural design parameters are used as variables of the genetic algorithm. The structural design parameters of the amorphous alloy charge liner are optimized through the genetic algorithm. The structural design parameter value corresponding to the maximum objective function value in the genetic algorithm iteration process is determined as the optimal structural design scheme.

[0030] Optionally, numerical simulations are performed on the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme, including:

[0031] For any structural design scheme, boundary conditions are added to the air domain and the Euler mesh is divided in the shaped charge finite element model. Then, the amorphous alloy liner is numerically simulated under the structural design scheme through the numerical simulation algorithm.

[0032] Optionally, the material of the amorphous alloy liner is W skeleton / Zr-based amorphous alloy, and the amorphous alloy liner is a conical liner.

[0033] The present invention provides a structural design device for an amorphous alloy liner, comprising:

[0034] An acquisition module, used to obtain multiple initial values of each structural design parameter of the amorphous alloy liner;

[0035] The design module is used to perform orthogonal experimental design based on multiple initial values of each structural design parameter to obtain multiple structural design schemes for the amorphous alloy liner;

[0036] A simulation module is used to numerically simulate the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme;

[0037] A construction module, configured to construct a neural network model based on a plurality of structural design schemes and at least one corresponding jet forming index;

[0038] The optimization module is used to perform optimization based on the neural network model to obtain the optimal structural design scheme of the amorphous alloy charge liner.

[0039] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the structural design method of the amorphous alloy liner is implemented.

[0040] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned structural design method of the amorphous alloy liner is implemented.

[0041] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0042] In the present invention, by obtaining multiple initial values for each structural design parameter of the amorphous alloy liner and conducting orthogonal experimental design, the level combinations of each factor can be evenly dispersed and representative, covering various possible structural design schemes, thereby comprehensively examining the effects of different parameter combinations on the performance of the liner and providing a basis for finding the optimal solution. The jet forming index can quantitatively reflect the jet forming effect of the liner, and the advantages and disadvantages of jet forming under different structural design schemes can be intuitively understood. Then, a neural network model is constructed based on multiple structural design schemes and corresponding jet forming indicators. The complex relationship between the structural design parameters and the jet forming index can be automatically mined from a large amount of data without having to clarify the specific mathematical expression of this relationship in advance, thereby more accurately fitting the data and providing a reliable model basis for optimization. Finally, the constructed neural network model is used for optimization, and the optimal structural design scheme can be quickly searched in the entire design parameter space. This optimization method based on the neural network model can avoid local optimal solutions and find the true global optimal structural design scheme, thereby maximizing the jet forming effect of the amorphous alloy liner. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0044] Figure 1 A schematic flow chart of a structural design method for an amorphous alloy liner provided by the present invention;

[0045] Figure 2 A schematic diagram of a shaped charge structure provided by the present invention;

[0046] Figure 3 A schematic diagram of a finite element model of a shaped charge provided by the present invention;

[0047] Figure 4A schematic diagram of the structure of a neural network provided by the present invention;

[0048] Figure 5 A neural network prediction error graph provided by the present invention;

[0049] Figure 6 A linear regression diagram of the predicted results and the actual fitting results obtained by the neural network provided by the present invention;

[0050] Figure 7 A graph showing changes in mean square error of a training set, validation set, test set, and population versus the number of training times provided by the present invention;

[0051] Figure 8 A schematic diagram of a structural design device for an amorphous alloy liner provided by the present invention;

[0052] Figure 9 A schematic diagram of a computer device for implementing a structural design method for an amorphous alloy liner provided by the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] In the existing technology, a lot of research has been carried out on the materials of the liner. For example, ballistic tests and numerical simulations have been used to describe the impact energy release process of Zr-based amorphous alloy fragments, and the reaction mechanism of Zr-based amorphous alloy fragments has been well explained. A new shaped charge structure has been designed using W-Zr-based amorphous alloy. This structure has good aftereffect and perforation performance, and the energy release characteristics of Zr-based amorphous materials are used to improve the hole expansion effect of perforating bullets. Tungsten is a crystalline metal with extremely excellent mechanical properties. When it is used as a reinforcing phase and combined with Zr-based amorphous alloys, an excellent composite material can be obtained. A W skeleton / Zr-based amorphous alloy composite material with a porosity of 30% was prepared by a hydraulic process, and its compression performance was studied. The test results showed that the maximum compressive strength of the composite material reached 2764Mpa, and the plastic strain reached 39.4%, which were much higher than those of Zr-based amorphous alloys and tungsten skeletons. The mechanical properties of W skeleton / Zr-based amorphous alloy composite materials prepared by hydraulic process and infiltration casting process were compared. The results showed that the composite material prepared by hydraulic process has higher strength, while the cast composite material has better plasticity.

[0055] However, due to the late development of W skeleton / Zr-based amorphous composite materials, their application in warheads is still relatively rare.

[0056] Based on this, the present invention uses W skeleton / Zr-based amorphous alloy composite material as the liner material, studies its jet forming and target penetration characteristics, carries out numerical simulation and experimental research, and optimizes the design of the liner structure to achieve the purpose of improving the liner's jet forming and target penetration capabilities.

[0057] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] Figure 1 The following is a flow chart of a structural design method of an amorphous alloy liner according to the present invention, which specifically includes the following steps:

[0059] S101, obtaining multiple initial values of each structural design parameter of the amorphous alloy liner.

[0060] The liner is a key component of the shaped charge. The material selection and structural design of the liner have an important influence on the jet formation and penetration capability of the shaped charge.

[0061] Among them, the material of the amorphous alloy liner is W skeleton / Zr-based amorphous alloy, and the amorphous alloy liner is a conical liner; the present invention adopts a new energetic material W skeleton / Zr-based amorphous alloy as the liner material, such as Figure 2 As shown, Figure 2 This is a shaped charge structure with a conical liner. The liner has a wall thickness of 1mm, an aperture D1 of 48.59mm, and a liner height H1 of 35mm. The charge aperture D2 is 50mm, the charge height H2 is 60mm, the liner cone angle is 60°, and the inner arc radius R is 8mm.

[0062] The present invention selects three structural design parameters of the liner, namely the cone angle, wall thickness and inner arc radius, as factors affecting the jet forming of the shaped charge structure, and analyzes the influence of different combinations of these three structural design parameters on the jet forming.

[0063] Therefore, multiple initial values of each structural design parameter can be obtained first. The initial values can be determined based on historical experience, as shown in Table 1.

[0064] Table 1

[0065]

[0066]

[0067] S102, performing orthogonal experimental design based on multiple initial values of each structural design parameter to obtain multiple structural design schemes of the amorphous alloy liner.

[0068] Take the example of 3 structural design parameters and 4 values for each structural design parameter, which is equivalent to 3 factors and 4 levels. Therefore, L can be selected. 16 (43) Orthogonal table, which includes 16 experimental combinations, each combination corresponds to a different factor matching level, that is, there are 16 structural design schemes, each structural design scheme corresponds to a different combination of structural design parameter values.

[0069] S103, numerically simulate the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme.

[0070] The numerical simulation of shaped charge jets primarily uses material models for the explosive, liner, airspace, and target. The AUTODYN-2D material library includes a selection of material models. The material models and parameters for the liner and target are shown in Table 2.

[0071] The air domain can be regarded as an ideal polytropic gas and described by the ideal gas state equation, which is as follows:

[0072] p=(γ-1)ρe (1)

[0073] Where p is the pressure, γ is the adiabatic exponent 1.4, and e is the initial energy 2.068×10 5 J / kg, ρ is the density 1.225×10 -3 g / cm 3 .

[0074] During the simulation process, the JWL state equation is used to describe the TNT explosive material model and the expansion work process of the detonation products. Its expression is:

[0075]

[0076] Where p is the pressure of the detonation products; V is the relative specific volume of the detonation products; A, B, R1, R2, ω, and E0 are the parameters to be input. The specific parameters are shown in Table 3 below. Table 3 is the parameters of the projectile-target Johnson-Cook model.

[0077] The warhead and target plate materials are both based on the Johnson-Cook strength-failure model, which is widely used in machining, explosion, and high-speed impact fields. This model takes into account the strain hardening effect of the material and the influence of strain rate and temperature on strength and plasticity. The expanded form of the stress-strain relationship is:

[0078]

[0079] Among them, σ eff , ε p are equivalent stress and equivalent plastic strain respectively; A, B, n, C, and m are all unknown parameters determined by experiments.

[0080] Table 2

[0081]

[0082] Table 3

[0083]

[0084] In one embodiment, numerical simulations are performed on the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme, including:

[0085] For any structural design scheme, boundary conditions are added to the air domain and the Euler mesh is divided in the shaped charge finite element model. Then, the amorphous alloy liner is numerically simulated under the structural design scheme through the numerical simulation algorithm.

[0086] Specifically, the shaped charge finite element model is as follows: Figure 3 As shown, the detonation mode is initiated from the center of the explosive base. To eliminate boundary effects, a "Flow-out" boundary condition is added to the air domain boundary. The Euler mesh is divided into 0.3mm × 0.3mm grids. The Euler algorithm, suitable for large deformation simulation, is used for both the shaped charge and the liner. The Lagrange algorithm is used for the target. The fluid-structure interaction algorithm is used for jet formation and target penetration.

[0087] Therefore, the amorphous alloy liner under each structural design scheme was numerically simulated using the shaped charge finite element model to obtain at least one jet forming index for each structural design scheme.

[0088] The orthogonal design method allows for ranking the importance of various factors and comprehensive optimization of multiple factors. This paper selects jet length, jet head velocity, and total jet energy as performance indicators for jet forming, assigning different weights to each indicator based on its influence on jet forming. Three structural design parameters, namely the cone angle, wall thickness, and inner arc radius of the liner, were selected as factors influencing the jet forming of the shaped charge structure. Orthogonal experiments were conducted to analyze the effects of different combinations of these three structural design parameters on jet forming, resulting in multiple structural design schemes. Numerical simulations were then performed on the amorphous alloy liner under each structural design scheme, yielding at least one jet forming indicator for each structural design scheme, as shown in Table 4. Table 4 shows the values of the jet forming indicators for each structural design scheme at t = 50 μs during the jet process.

[0089] Table 4

[0090]

[0091] S104: Construct a neural network model based on the multiple structural design schemes and the corresponding at least one jet forming index.

[0092] Among them, a neural network model is constructed according to multiple structural design schemes and at least one corresponding jet forming index, including: for any jet forming index, the values of the jet forming index under each structural design scheme are normalized respectively; under each structural design scheme, the values of the normalized jet forming indicators are weighted according to the weight of each jet forming index to obtain a comprehensive evaluation index under each structural design scheme; a neural network model is constructed according to multiple structural design schemes and the corresponding comprehensive evaluation index.

[0093] The jet forming index can be normalized according to formula (4) to obtain the normalized jet forming index.

[0094]

[0095] Among them, Y * represents the value of the normalized jet forming index, y represents the value of the jet forming index to be normalized, y min Indicates the minimum value of all values in the jet forming index, y max It represents the maximum value of all the values of the jet forming index.

[0096] The jet forming index includes jet length, jet head velocity and jet total energy; the comprehensive evaluation index is calculated as follows:

[0097]

[0098] Among them, W represents the comprehensive evaluation index, W a represents the jet length, represents the weight of the jet length, W b represents the jet head velocity, represents the weight of the jet head velocity, W c represents the total energy of the jet, The weight representing the total energy of the jet.

[0099] To obtain a good jet effect, the jet needs to have a large jet length. Jet length is the most important factor affecting the jet forming effect, followed by jet head velocity, while the influence of total jet energy is relatively small. Based on this, different weights are assigned to these indicators, namely Then W = 1.5W a +Wb +0.5W c .

[0100] The values corresponding to the jet forming indicators in Table 4 above are normalized to obtain a data normalization analysis table, as shown in Table 5.

[0101] Table 5

[0102] <![CDATA[W a ]]> <![CDATA[W b ]]> <![CDATA[W c ]]> W 1 1.000 1.000 0.338 2.669 2 0.688 0.569 0.171 1.687 3 0.324 0.385 1.000 1.371 4 0.014 0.200 0.000 0.221 5 0.815 0.902 0.379 2.314 6 0.628 0.629 0.167 1.655 7 0.243 0.234 0.091 0.644 8 0.123 0.000 0.054 0.212 9 0.637 0.852 0.207 1.911 10 0.342 0.441 0.086 0.997 11 0.209 0.295 0.082 0.650 12 0.062 0.089 0.867 0.616 13 0.411 0.731 0.110 1.403 14 0.307 0.484 0.100 0.995 15 0.154 0.233 0.071 0.499 16 0.000 0.048 0.044 0.070

[0103] By analyzing the normalized data of these three factors (structural design parameters) obtained under different schemes of orthogonal test, the main and secondary influencing factors are determined. 16 It can be observed that at each level of A0, the four levels of B0 and C0 are all present, and each level appears only once. Therefore, it can be concluded that in the test combination with only A1, A2, A3, and A4, the effects of other variables on the comprehensive evaluation index W remain unchanged, and thus W A1 、W A2 、W A3 、W A4 Changes in directly reflect the effect of changes in factor A0. This allows us to calculate the numerical changes in jet forming performance indicators based on factor A0. Following this logic, we can also calculate the impact of changes in factors B0 and C0 on W. Specific calculation data is shown in Table 6, which shows the calculation results for each factor.

[0104] Table 6

[0105] <![CDATA[Factor A0]]> <![CDATA[Factor B0]]> <![CDATA[Factor C0]]> 1.333 1.261 1.487 2.074 1.279 1.206 0.791 1.222 1.043 0.280 0.816 0.742

[0106] Calculate the range R of factors A0, B0, and C0 in the table respectively A =1.794, R B =0.463, R C = 0.745. The calculation results show that for this numerical simulation, the liner's cone angle and inner arc radius are the primary factors affecting the jet morphology, while the liner's wall thickness is a secondary factor. The optimal combination is A2, B2, and C1.

[0107] It should be noted that the above-mentioned optimal combination is based on established factor levels and does not represent the optimal liner structural parameters within the parameter range. Therefore, the present invention employs a BP neural network and a genetic algorithm for parameter optimization. To enhance the prediction accuracy of the neural network, numerical simulation results based on orthogonal design experimental schemes are used to fit relationship curves and generate data samples to satisfy the neural network learning requirements.

[0108] Specifically, a neural network model is constructed based on multiple structural design schemes and corresponding comprehensive evaluation indexes, including: for any structural design parameter, the initial value of the structural design parameter under multiple structural design schemes is fitted with the corresponding comprehensive evaluation index to obtain a fitting function under the structural design parameter; the fitting function represents the corresponding relationship between the structural design parameter and the comprehensive evaluation index; training samples are determined through the fitting function under each structural design parameter; and the neural network model is trained through the training samples.

[0109] For any structural design parameter, the comprehensive evaluation index W is fitted with the value point of the structural design parameter to obtain the fitting function of the structural design parameter: Among them, x i Indicates the value of the structural design parameter. Specifically, taking the cone angle as an example, at each level of the cone angle, the four levels of wall thickness and inner arc radius are all present, and each level appears only once. Therefore, it can be concluded that in the test combination involving only the four levels of cone angle A1, A2, A3, and A4, the effects of other variables on the injection molding index remain unchanged, and the corresponding comprehensive evaluation index W is obtained. A1 、W A2 、W A3 、W A4 The change in directly reflects the effect of the change in the cone angle. Therefore, for any cone angle value, the average of the multiple comprehensive evaluation indices corresponding to the cone angle value can be used as the target comprehensive evaluation index of the cone angle at that value to participate in the fitting process of the fitting parameters. In this way, the fitting function of the cone angle is obtained by fitting each cone angle value and the target comprehensive evaluation index at each value. The process of determining the fitting function of the wall thickness and inner arc radius is similar to that of the cone angle and will not be repeated in this embodiment.

[0110] In an exemplary embodiment, based on the above data, the P value and error corresponding to each factor (structural design parameter) can be obtained, as shown in Table 7.

[0111] Table 7

[0112] <![CDATA[A0]]> <![CDATA[B0]]> <![CDATA[C0]]> <![CDATA[P1]]> 582.31 341.25 452.65 <![CDATA[P2]]> -325.854 -632.174 -832.84 <![CDATA[P3]]> 1471.931 981.23 923.65 <![CDATA[P4]]> 252.17 784.59 426.81 SSE <![CDATA[6.548×10 -23 ]]> 2.193E-22 9.217E-24

[0113] In an exemplary embodiment, training samples are determined by fitting functions under various structural design parameters, including: for any structural design parameter, multiple sample values of the structural design parameter and corresponding comprehensive evaluation indexes are obtained according to the fitting functions under the structural design parameters; multiple sample structural design schemes are determined according to the multiple sample values of each structural design parameter, and the average value of the comprehensive evaluation index corresponding to each sample value under each sample structural design scheme is determined as the comprehensive evaluation index of each sample structural design scheme; and the multiple sample structural design schemes and the corresponding comprehensive evaluation indexes are determined as training samples.

[0114] First, multiple sample values of each structural design parameter are obtained, and then each sample value is substituted into the fitting parameter of the corresponding structural design parameter to obtain a comprehensive evaluation index corresponding to each sample value. Based on the multiple sample values of each structural design parameter, multiple sample structural design schemes are determined, including: randomly combining the multiple sample values of each structural design parameter to obtain multiple sample structural design schemes, each sample structural design scheme including one sample value of each structural design parameter.

[0115] The training samples are randomly divided into training sets and test sets for back-propagation (BP) neural network training and testing to obtain a neural network model. Specifically, the network input parameters are various structural design parameters, and the network output parameters are comprehensive evaluation indexes. For example, the network input parameters are the liner cone angle α, the liner wall thickness δ, and the liner inner arc radius R. The hidden layer nodes of the neural network can be set to 10, and the grid structure is 3-10-1. The structure diagram is as follows: Figure 4 shown

[0116] like Figure 5 As shown in , the error histogram is designed to intuitively present the distribution of errors in the training and evaluation process of the neural network. By dividing the error data into different intervals (i.e., the "bins" of the histogram) and counting the number of data points in each interval, the prediction results of the test set data obtained by the neural network are compared with the actual fitting results. Most of the data are in the range of -0.02 to 0.02, with a small error. The prediction accuracy of the neural network for the training samples fluctuates little, and the resulting linear regression graph is shown in Figure 6 shown. Figure 7 Indicates the changing trend of the mean square error (MSE) of the training set, validation set, test set and the overall sample as the number of training rounds increases during the training process, where the MSE parameter value is 0.00078. Figure 5-Figure 7 It can be seen that the trained neural network model has a high prediction accuracy for the comprehensive evaluation index of jet forming, and can more accurately reflect the relationship between the structural parameters of the liner and the jet forming performance.

[0117] S105, performing optimization based on the neural network model to obtain the optimal structural design scheme of the amorphous alloy charge liner.

[0118] In an exemplary embodiment, an optimization is performed based on a neural network model to obtain an optimal structural design scheme for an amorphous alloy liner, including: using the neural network model as an objective function of a genetic algorithm, and multiple structural design parameters as variables of the genetic algorithm, optimizing the structural design parameters of the amorphous alloy liner through the genetic algorithm, and determining the structural design parameter value corresponding to the maximum objective function value during the genetic algorithm iteration process as the optimal structural design scheme.

[0119] The trained neural network model is saved as a function to serve as the objective function in the genetic algorithm. The predicted value output by the neural network model is the objective function value of the genetic algorithm, that is, the comprehensive index of jet forming serves as the individual fitness in the genetic algorithm. The genetic algorithm is opened and input into this function. The number of structural design parameters is used as the genetic algorithm variable. The value range of each structural design parameter is set. The crossover probability and mutation probability are set, as well as the population size and number of iterations of the genetic algorithm are set. The initial values of the structural design parameters are set and the genetic algorithm is run. When the maximum number of iterations is reached, the optimization process is terminated. The combination of structural design parameters corresponding to the maximum fitness value is determined as the optimal structural design scheme for the liner. The liner is designed based on the optimal structural design scheme.

[0120] Specifically, the number of variables was set to 3, and the ranges of the liner's structural parameters were set to [55, 70], [0.7, 1.6], and [6, 9], respectively. The crossover probability was set to 0.5, the mutation probability was set to 0.01, the genetic algorithm population size was set to 25, and the number of genetic iterations was set to 100. When the maximum number of iterations was reached, the optimization process was terminated, and the parameter combination corresponding to the optimal fitness value was the ideal liner structural parameters. As the number of iterations increased, the optimal fitness value gradually increased. After exceeding 80 iterations, the fitness value stabilized and showed almost no significant fluctuations. The optimal fitness value obtained through the genetic algorithm was 2.186, and the optimal liner structural parameter combinations were: a liner cone angle of 61.29°, a wall thickness of 1.57 mm, and an inner arc radius of 7.15 mm. For the convenience of developing the actual product, the optimized parameters for the liner cone angle, wall thickness, and inner arc radius were approximately 61°, 1.6 mm, and 7.2 mm, respectively.

[0121] When applying the structural design method of the amorphous alloy liner provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0122] The above is a structural design method of an amorphous alloy liner provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding structural design device of an amorphous alloy liner, such as Figure 8shown.

[0123] Figure 8 This is a schematic diagram of a structural design device for an amorphous alloy liner provided by the present invention. The device 800 includes:

[0124] An acquisition module 801 is used to acquire multiple initial values of each structural design parameter of the amorphous alloy liner;

[0125] The design module 802 is used to perform orthogonal experimental design based on multiple initial values of each structural design parameter to obtain multiple structural design schemes of the amorphous alloy liner;

[0126] The simulation module 803 is used to perform numerical simulation on the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme;

[0127] A construction module 804 is configured to construct a neural network model based on a plurality of structural design schemes and at least one corresponding jet forming index;

[0128] The optimization module 805 is used to perform optimization based on the neural network model to obtain the optimal structural design scheme of the amorphous alloy liner.

[0129] The specific definitions of the amorphous alloy liner structural design device can be found in the definitions of the amorphous alloy liner structural design method described above and will not be repeated here. Each module in the aforementioned amorphous alloy liner structural design device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0130] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The structural design method of amorphous alloy liner is provided.

[0131] The present invention also provides Figure 9 The structural diagram of the computer equipment shown in FIG. Figure 9 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The structural design method of amorphous alloy liner is provided.

[0132] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0133] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A structural design method for an amorphous alloy liner, characterized in that: include: Obtaining multiple initial values of each structural design parameter of the amorphous alloy liner; Based on multiple initial values of each structural design parameter, orthogonal experimental design was performed to obtain multiple structural design schemes for the amorphous alloy liner. Numerical simulations were performed on the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme. constructing a neural network model according to the plurality of structural design schemes and the corresponding at least one jet forming index; Performing optimization according to the neural network model to obtain the optimal structural design scheme of the amorphous alloy liner; The constructing of a neural network model based on the multiple structural design schemes and the corresponding at least one jet forming index includes: normalizing the values of the jet forming index under each structural design scheme for each jet forming index; weighting the values of the normalized jet forming indexes under each structural design scheme according to the weight of each jet forming index to obtain a comprehensive evaluation index under each structural design scheme; and constructing a neural network model based on the multiple structural design schemes and the corresponding comprehensive evaluation indexes; The neural network model is constructed based on the multiple structural design schemes and the corresponding comprehensive evaluation indexes, including: for any structural design parameter, fitting the initial value of the structural design parameter under the multiple structural design schemes with the corresponding comprehensive evaluation index to obtain a fitting function under the structural design parameter; the fitting function represents the corresponding relationship between the structural design parameter and the comprehensive evaluation index; determining a training sample based on the fitting function under each of the structural design parameters; and training the neural network model based on the training sample; The jet forming index includes jet length, jet head velocity and jet total energy; the calculation method of the comprehensive evaluation index is: Among them, W represents the comprehensive evaluation index, W a represents the jet length, represents the weight of the jet length, W b represents the jet head velocity, represents the weight of the jet head velocity, W c represents the total energy of the jet, The weight representing the total energy of the jet.

2. The method according to claim 1, characterized in that The determining of the training samples by the fitting function under each of the structural design parameters includes: For any structural design parameter, multiple sample values of the structural design parameter and corresponding comprehensive evaluation indexes are obtained according to the fitting function under the structural design parameter; Determine multiple sample structural design schemes based on multiple sample values of each structural design parameter, and determine the average value of the comprehensive evaluation index corresponding to each sample value under each sample structural design scheme as the comprehensive evaluation index of each sample structural design scheme; The multiple sample structure design schemes and the corresponding comprehensive evaluation indexes are determined as the training samples.

3. The method according to claim 1, characterized in that The optimization process according to the neural network model is performed to obtain the optimal structural design scheme of the amorphous alloy liner, including: The neural network model is used as the objective function of the genetic algorithm, and multiple structural design parameters are used as variables of the genetic algorithm. The structural design parameters of the amorphous alloy charge liner are optimized through the genetic algorithm, and the structural design parameter value corresponding to the maximum objective function value during the iterative process of the genetic algorithm is determined as the optimal structural design scheme.

4. The method according to claim 1, wherein The numerical simulation of the amorphous alloy liner under each structural design scheme is performed to obtain at least one jet forming index of each structural design scheme, including: For any structural design scheme, in the shaped charge finite element model, boundary conditions are added to the air domain and the Euler grid is divided, and then the amorphous alloy liner is numerically simulated under the structural design scheme through a numerical simulation algorithm.

5. The method according to claim 1, wherein The material of the amorphous alloy liner is W skeleton / Zr-based amorphous alloy, and the amorphous alloy liner is a conical liner.

6. A structural design device for an amorphous alloy liner, characterized in that: include: An acquisition module, used to obtain multiple initial values of each structural design parameter of the amorphous alloy liner; The design module is used to perform orthogonal experimental design based on multiple initial values of each structural design parameter to obtain multiple structural design schemes for the amorphous alloy liner; A simulation module is used to numerically simulate the amorphous alloy liner under each structural design scheme to obtain at least one jet forming index for each structural design scheme; A construction module, configured to construct a neural network model according to the plurality of structural design schemes and the corresponding at least one jet forming index; The constructing of a neural network model based on the multiple structural design schemes and the corresponding at least one jet forming index includes: normalizing the values of the jet forming index under each structural design scheme for each jet forming index; weighting the values of the normalized jet forming indexes under each structural design scheme according to the weight of each jet forming index to obtain a comprehensive evaluation index under each structural design scheme; and constructing a neural network model based on the multiple structural design schemes and the corresponding comprehensive evaluation indexes; The neural network model is constructed based on the multiple structural design schemes and the corresponding comprehensive evaluation indexes, including: for any structural design parameter, fitting the initial value of the structural design parameter under the multiple structural design schemes with the corresponding comprehensive evaluation index to obtain a fitting function under the structural design parameter; the fitting function represents the corresponding relationship between the structural design parameter and the comprehensive evaluation index; determining a training sample based on the fitting function under each of the structural design parameters; and training the neural network model based on the training sample; The jet forming index includes jet length, jet head velocity and jet total energy; the calculation method of the comprehensive evaluation index is: Among them, W represents the comprehensive evaluation index, W a represents the jet length, represents the weight of the jet length, W b represents the jet head velocity, represents the weight of the jet head velocity, W c represents the total energy of the jet, represents the weight of the total energy of the jet; An optimization module is used to perform optimization according to the neural network model to obtain the optimal structural design scheme of the amorphous alloy liner.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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