CN114550851A - Brittle material constitutive model parameter optimization method and system - Google Patents

Abstract

The invention discloses a brittle material constitutive model parameter optimization method and system, and relates to the field of brittle material mechanical behavior simulation, wherein the method comprises the following steps: adopting elastomers in different threat forms to penetrate brittle materials with different thicknesses, and constructing a residual penetration test penetration simulation model and a dynamic compression test simulation model based on a dynamic compression test of a Hopkinson pressure bar; calculating an optimized parameter value according to the value interval of the optimized parameter and the chaotic variable of the optimized parameter; calculating a simulation error according to the optimized parameter value, the simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength; carrying out multiple iterations based on the simulation error to obtain a final optimization parameter; the final optimized parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used to describe the physical response of brittle materials under different conditions. The method can improve the efficiency of parameter optimization, so that the JH-2 constitutive model can efficiently describe the physical response of the brittle material under different conditions.

Description

Brittle material constitutive model parameter optimization method and system

Technical Field

The invention relates to the field of brittle material mechanical behavior simulation, in particular to a brittle material constitutive model parameter optimization method and system.

Background

With the rapid development of computational science, the application of the numerical simulation technology in the fields of material research and structural design is increasingly wide, in order to accurately describe the mechanical response and failure behavior of a material under specific conditions, a constitutive model of the material needs to be defined in simulation software, and the accuracy of parameters of the constitutive model is a main factor for determining the calculation precision of simulation. For brittle materials such as ceramics, glass, amorphous materials and the like, the JH-2 constitutive structure can accurately describe the physical response of the materials under different conditions, however, the JH-2 constitutive parameters are more, partial parameters cannot be directly obtained, the precision of the parameters tested or fitted by an indirect method is lower, a large amount of numerical simulation debugging is generally required, and the application and development of the JH-2 constitutive structure are limited.

The JH-2 constitutive complete damage strength equation (formula 1) and the damage equation (formula 2-3) are important components of the JH-2 constitutive, and mainly relate to damage-containing parameters B, M, D1 and D2. Wherein

Normalized equivalent strength, P, of a material under certain hydrostatic pressure conditions^*For normalizing the hydrostatic pressure, C is the strain rate sensitivity coefficient,

is the normalized strain rate under the corresponding loading condition; d is the cumulative damage parameter,. DELTA.. epsilon^PIn order to be equivalent to the plastic strain,

is failure strain; t is^*Is the normalized maximum tensile hydrostatic pressure.

In the damage-containing parameters, B is a complete damage intensity coefficient, M is a complete damage intensity index, D1 is a damage equation coefficient, D2 is a damage equation index, 4 parameters have no practical physical significance, and the parameters can be accurately obtained by a high-efficiency experimental test method in the prior art; meanwhile, the JH-2 constitutive structure is sensitive to the grid size of the model, and constitutive parameters used by simulation models with different grid sizes need to be corrected in order to realize the same simulation result; therefore, under the condition of determining the simulation model, parameter debugging is an effective acquisition method for the damage-containing parameters B, M, D1 and D2.

The traditional parameter optimization method is characterized in that parameter values are manually adjusted one by one, coupling relations among parameters are omitted, and the obtained parameters are only suitable for one type of model and have large use limitation; meanwhile, parameter debugging is often based on personal experience, blindness and low efficiency. Therefore, JH-2 constitutive model cannot effectively describe the physical response of the brittle material under different conditions.

Disclosure of Invention

Based on the above, the embodiment of the invention provides a brittle material constitutive model parameter optimization method and system, which improve the efficiency of parameter optimization, so that a JH-2 constitutive model can efficiently describe the physical response of a brittle material under different conditions.

In order to achieve the purpose, the invention provides the following scheme:

a brittle material constitutive model parameter optimization method comprises the following steps:

adopting elastomers in different threat forms to penetrate brittle materials with different thicknesses, and constructing a residual penetration test penetration simulation model and a dynamic compression test simulation model based on a dynamic compression test of a Hopkinson pressure bar;

determining the value interval of each optimization parameter under the kth iteration; the optimization parameters comprise a complete damage intensity coefficient, a complete damage intensity index, a damage equation coefficient and a damage equation index;

determining a chaotic variable of each optimized parameter under the kth iteration;

determining an optimized parameter value under the kth iteration according to the value interval of each optimized parameter under the kth iteration and the chaotic variable of each optimized parameter under the kth iteration;

calculating a simulation error under the kth iteration according to the optimized parameter value under the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength;

judging whether the simulation error is smaller than a set error value or not to obtain a first judgment result;

if the first judgment result is yes, determining the optimization parameter value under the k iteration as a final optimization parameter; the final optimization parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used for describing the physical response of the brittle material under different conditions;

if the first judgment result is negative, calculating the parameter error of each optimized parameter in the kth iteration according to the simulation error in the kth iteration, updating the chaotic variable of the optimized parameter and the value range of the optimized parameter according to the parameter error of each optimized parameter in the kth iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the first k-1 iterations, and then performing the next iteration.

Optionally, the calculating a parameter error of each optimized parameter in the kth iteration according to the simulation error in the kth iteration, updating the chaotic variable of the optimized parameter and the value range of the optimized parameter according to the parameter error of each optimized parameter in the kth iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the previous k-1 iterations, and performing the next iteration specifically includes:

for any optimization parameter in the k iteration, judging whether the parameter error of the optimization parameter is smaller than the maximum parameter error corresponding to the optimization parameter value in the optimal solution set after the previous k-1 iterations, and obtaining a second judgment result;

if the second judgment result is yes, replacing the optimized parameter value with the maximum parameter error in the optimal solution set after the first k-1 iterations with the optimized parameter under the kth iteration to obtain the optimal solution set after the first k iterations, updating the chaotic variable and the iteration times of the optimized parameter, and returning to the step of determining the chaotic variable of each optimized parameter under the kth iteration;

if the second judgment result is negative, judging whether the optimal solution set after the first K-1 iterations is not updated for K times continuously to obtain a third judgment result;

if the third judgment result is yes, updating the value interval and the iteration times of the optimized parameters, and returning to the step of determining the value interval of each optimized parameter under the kth iteration;

if the third judgment result is negative, after updating the chaos variable and the iteration times of the optimized parameters, returning to the step of determining the chaos variable of each optimized parameter under the kth iteration.

Optionally, the calculating a simulation error in the kth iteration according to the optimized parameter value in the kth iteration, the penetration test simulation model for the residual penetration depth, the dynamic compression test simulation model, the actually measured residual penetration depth, and the actually measured dynamic compression strength specifically includes:

inputting the optimized parameter value under the k iteration into the penetration test simulation model of the residual penetration depth test to obtain the simulation calculation residual penetration depth under the k iteration;

inputting the optimized parameter value under the k iteration into the dynamic compression test simulation model to obtain the simulation calculation dynamic compression strength under the k iteration;

and calculating the simulation error under the kth iteration according to the simulation calculation residual penetration depth under the kth iteration, the simulation calculation dynamic compression strength under the kth iteration, the actual measurement residual penetration depth and the actual measurement dynamic compression strength.

Optionally, the simulation error includes a first error, a second error and a third error;

the calculation formula of the first error is as follows:

wherein, Error₁Representing a first error; sd₁The simulation calculation residual penetration depth obtained by inputting the optimized parameter value into the first residual penetration depth test penetration simulation model is represented; the first residual penetration test penetration simulation model is a residual penetration test penetration simulation model constructed when a first set type of elastomer is adopted to penetrate a brittle material with a first set thickness; td₁The actual measurement residual penetration depth is shown when the actual structure corresponding to the first residual penetration depth test penetration simulation model is adopted for testing; pd₁The power penetration depth of the projectile body corresponding to the first residual penetration depth test penetration simulation model on the residual penetration depth test supporting back plate is represented; delta of₁Representing a set error value when simulation calculation is carried out by adopting a first residual penetration test penetration simulation model;

the calculation formula of the second error is as follows:

wherein, Error₂Representing a second error; sd₂The optimized parameter values are input into a second residual penetration test penetration simulation model to obtain simulation calculation residual penetration; the second residual penetration test penetration simulation model is a residual penetration test penetration simulation model constructed when a second set type of elastomer is adopted to penetrate a brittle material with a second set thickness; td₂The actual residual penetration depth is measured when the actual structure corresponding to the second residual penetration depth test penetration simulation model is used for testing; pd₂Penetration simulation representing second residual penetration testThe force penetration depth of the bullet body corresponding to the model on the residual penetration depth test supporting back plate is measured; delta of₂Representing a set error value when simulation calculation is carried out by adopting a second residual penetration test penetration simulation model;

the calculation formula of the third error is as follows:

wherein, Error_SHPBIndicating a third error; sigma_SHPBRepresenting the simulation calculation dynamic compression strength obtained by inputting the optimized parameter values into a dynamic compression test simulation model; sigma_cRepresenting the measured dynamic compressive strength; delta_SHPBIndicating the set target offset value corresponding to the dynamic compressive strength.

Optionally, the calculation formula of the parameter error is as follows:

wherein, Error_BRepresenting parameter errors corresponding to the complete damage intensity coefficients; error_MRepresenting the parameter error corresponding to the complete damage intensity index; error_D1Expressing parameter errors corresponding to the damage equation coefficients; error_D2And expressing the parameter error corresponding to the damage equation index.

Optionally, the update formula of the chaotic variable of the optimized parameter is as follows:

μ∈(-1,1)

μ≠0；

wherein mu represents a chaotic variable of the optimization parameter; mu.s_kChaotic variable, mu, representing the optimized parameter at the kth iteration_k+1Expressing a chaotic variable of an optimized parameter under the k +1 th iteration; when k is 1, the values of the chaotic variables corresponding to different optimization parameters are different.

Optionally, the calculation formula of the optimized parameter value is as follows:

wherein x is_iAn optimization parameter value representing an optimization parameter i; mu.s_iA chaotic variable representing an optimization parameter i; [ x ] of_max,x_min]Represents the value range, x, of the optimization parameter i_maxRepresents the maximum value, x, of the optimization parameter i_minRepresenting the minimum value of the optimization parameter i; i e (B, M, D1, D2), B represents the complete damage strength index, M represents the complete damage strength index, D1 represents the damage equation coefficient, and D2 represents the damage equation index.

Optionally, the value of K is not less than 3.

The invention also provides a brittle material constitutive model parameter optimization system, which comprises:

the simulation model building module is used for adopting the bullets in different threat forms to penetrate brittle materials with different thicknesses, and building a residual penetration test penetration simulation model and a dynamic compression test simulation model based on a dynamic compression test of a Hopkinson pressure bar;

a value interval determination module for determining the value interval of each optimized parameter under the kth iteration; the optimization parameters comprise a complete damage intensity coefficient, a complete damage intensity index, a damage equation coefficient and a damage equation index;

the chaotic variable determining module is used for determining the chaotic variable of each optimized parameter under the kth iteration;

the optimization parameter value determining module is used for determining the optimization parameter value under the kth iteration according to the value interval of each optimization parameter under the kth iteration and the chaotic variable of each optimization parameter under the kth iteration;

the simulation error calculation module is used for calculating a simulation error under the kth iteration according to the optimized parameter value under the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength;

the error judgment module is used for judging whether the simulation error is smaller than a set error value or not to obtain a first judgment result;

a final parameter determining module, configured to determine an optimized parameter value under the kth iteration as a final optimized parameter if the first determination result is yes; the final optimization parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used for describing the physical response of the brittle material under different conditions;

and the updating iteration module is used for calculating the parameter error of each optimized parameter under the k iteration according to the simulation error under the k iteration if the first judgment result is negative, updating the chaotic variable of the optimized parameter and the value interval of the optimized parameter according to the parameter error of each optimized parameter under the k iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the previous k-1 iterations, and then performing the next iteration.

Compared with the prior art, the invention has the beneficial effects that:

the embodiment of the invention provides a brittle material constitutive model parameter optimization method and system, which are characterized in that elastomers in different threat forms are adopted to penetrate brittle materials with different thicknesses, and a residual penetration test penetration simulation model and a dynamic compression test simulation model are constructed based on a dynamic compression test of a Hopkinson pressure bar; calculating an optimized parameter value according to the value interval of the optimized parameter and the chaotic variable of the optimized parameter; calculating a simulation error according to the optimized parameter value, the simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength; carrying out multiple iterations based on the simulation error to obtain a final optimization parameter; the final optimized parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used to describe the physical response of brittle materials under different conditions. The method determines final optimization parameters based on the thought of the chaotic optimization algorithm, and can realize global search due to low requirement and strong adaptability of the chaotic optimization algorithm on a target function, and the characteristics of randomness and ergodicity can quickly reduce the optimization interval, greatly reduce the calculation time and increase the probability of finding a global minimum value, so that the method can improve the efficiency of parameter optimization, and the JH-2 constitutive model can efficiently describe the physical response of the brittle material under different conditions.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a schematic diagram showing the influence of JH-2 containing damage parameters on the DOP and SHPB simulation results; fig. 1(a) is a schematic diagram of an influence rule of the parameter B, M on the DOP simulation result, fig. 1(b) is a schematic diagram of an influence rule of the parameters D1 and D2 on the DOP simulation result, and fig. 1(c) is a schematic diagram of an influence rule of the parameter B, M on the SHPB simulation result; FIG. 1(D) is a schematic diagram showing the influence of parameters B, D1 and D2 on the SHPB simulation result;

FIG. 2 is a flowchart of a brittle material constitutive model parameter optimization method according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a simulation model used in a parameter optimization process; wherein FIG. 3(a) is a schematic structural view of a typical Hopkinson pressure bar; FIG. 3(b) is a schematic diagram of a DOP simulation model of 7.62mm API penetration of 3mm ceramic; FIG. 3(c) is a schematic diagram of a DOP simulation model of 12.7mm API penetration of 8mm ceramic;

FIG. 4 is a schematic diagram of a specific implementation process of a brittle material constitutive model parameter optimization method according to an embodiment of the present invention;

FIG. 5 is a schematic diagram showing the comparison between DOP predicted results and actual measured results using optimized parameter simulation;

fig. 6 is a structural diagram of a brittle material constitutive model parameter optimization system according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

Aiming at the optimization of JH-2 constitutive parameters, the traditional parameter optimization method is to manually adjust parameter values one by one, and the traditional parameter optimization method has large limitation and low efficiency. The method is to set the function value as a target function and the independent variable as an optimization variable, and the global optimal solution of the function is obtained by optimizing the independent variable. The chaotic optimization algorithm has low requirement on the target function and strong adaptability, can realize global search, and has the characteristics of randomness and ergodicity, so that the optimization interval can be quickly shortened, the calculation time is greatly reduced, and the probability of finding the global minimum value is increased. The algorithm has obvious advantages in the problems of high nonlinearity, large calculation amount and complex model. The parameter optimization of the brittle material constitutive model is realized based on the chaos optimization algorithm, so that the JH-2 constitutive model is determined according to the final optimized parameters, the physical response of the brittle material under different conditions is described by adopting the JH-2 constitutive model, and the method has important application value.

Firstly, introducing a chaos optimization algorithm, wherein the specific optimization steps are as follows:

(1) a chaotic variable is initialized. For a system with n parameters to be optimized, n numbers with slight differences are selected as initial values of chaotic variables mu.

(2) And mapping the chaotic variable to a parameter optimization interval. For example, for a system with an optimization interval [ a, b ], an infinite folding mapping equation is used to generate a chaotic variable (output range is (-1,1)), and the parameter mapping relationship is as follows:

(3) and setting the number of solutions in the initial optimal solution set as N, and taking the first N points generated by the chaotic variables as the initial optimal solution set.

(4) Let x_kFor the parameter value to be optimized generated after the kth iteration, the point corresponding to the maximum value of the objective function in the optimal solution set is X_jThe objective function is f (x), if f (x) exists_k)＜f(X_j) Then X in the optimal solution set is added_jIs replaced by x_kAnd after iteration is carried out for a plurality of times according to the requirement of the problem, if the optimal solution in the optimal solution set does not change after continuous K times of iteration, starting the next round of optimization, and generating a new optimal solution set.

(5) And (4) taking the boundary (the maximum value and the minimum value of each independent variable) of the optimal solution set as a new search optimization interval, mapping the chaotic variable to the new interval, and turning to the step (4) for further optimization.

(6) And after the termination condition is met, terminating the search, and returning the optimal solution in the optimal solution set as the global optimal solution of the system.

Taking alumina ceramics as an example, numerical simulations of dynamic compression test (SHPB) and residual penetration test (DOP) under different conditions of complete damage strength coefficient B, complete damage strength index M, damage equation coefficient D1 and damage equation index D2 are carried out to obtain the influence rule of the alumina ceramics. The law of the influence of the damage-containing parameters on the residual penetration depth and the dynamic compressive strength is shown in fig. 1.

FIG. 1(a) shows the effect of the optimization parameter B, M on the residual penetration simulation, wherein the residual penetration calculation results show continuous smooth curved surfaces; as the value of M increases, the residual penetration depth exhibits a near linear increase; as the B value increases, the residual penetration decreases, and the degree of decrease becomes more severe. Overall, as the value of B increases and the value of M decreases, the residual penetration depth shows a continuous decreasing trend; by combining the complete damage strength equation, the parameter B, M has an isoline for the residual penetration simulation result, and the same simulation result has multiple sets of B, M corresponding relations under the fixed simulation structure.

FIG. 1(B) shows the effect of parameters D1 and D2 on the simulation of residual penetration depth, wherein as the B value increases and the M value decreases, the total damage strength of the material at a specific hydrostatic pressure increases, and the resistance to penetration of the elastomer increases, which is shown as a decrease in residual penetration depth. Under the determined B, M condition, the change of the parameters D1 and D2 has less influence on the residual penetration depth, i.e. the material is not sensitive to damage parameters during penetration.

FIG. 1(c) shows the rule of the effect of the parameter B, M on the simulation result of the dynamic compression test, in which the dynamic compression strength of the extracted material is increased when the D1 is increased under different conditions of D1 and D2; as the B value increases, the difference in dynamic compressive strength from the change in D1 gradually decreases. The dynamic compression strength set obtained by different B, M calculations presents a continuous smooth curved surface and has strong regularity, wherein the parameter B has a large influence on the dynamic compression strength, and the parameter M has a limited influence.

Fig. 1(D) shows the rule of the influence of the parameters D1 and D2 on the dynamic compression test simulation result, the complete damage strength of the material under the same net water pressure increases with the increase of the B value and the decrease of the M value, the regulation and control effects of the parameters D1 and D2 on the complete strength and the complete damage strength in the process of accumulated damage are weakened, and the change of the dynamic compression strength of the material is shown to be insignificant. Under the same B, M condition, the dynamic compression strength values obtained by different D1 and D2 calculation form a continuous smooth curved surface, and show stronger regularity; wherein the parameter D1 is increased, the dynamic compression strength of the material tends to be flat after being increased sharply, the parameter D2 is increased, and the dynamic compression strength of the material is reduced nearly linearly. The parameter D1 has a more significant effect on the dynamic compressive strength simulation results.

In conclusion, in the simulation calculation of the dynamic compression test, parameters B, D1 and D2 have large influence on the calculation result, and the influence of M is small; and DOP test simulation calculation is only sensitive to the parameter B, M, so that the material parameter B, M can be debugged and checked through DOP test data, the material parameters D1 and D2 are corrected through SHPB test data, and two DOP simulation models with different structures can be created at the same time to participate in optimization due to the fact that the parameter B, M has a contour line.

Fig. 2 is a flowchart of a brittle material constitutive model parameter optimization method provided in an embodiment of the present invention. Referring to fig. 2, the brittle material constitutive model parameter optimization method includes:

step 101: and (3) penetrating brittle materials with different thicknesses by adopting the elastomers in different threat forms, and constructing a residual penetration test penetration simulation model and a dynamic compression test simulation model based on a dynamic compression test of the Hopkinson pressure bar.

Specifically, creating residual penetration test (DOP) penetration simulation models of different structures according to actual test conditions, such as different ceramic thicknesses, different threat forms and the like; creating a dynamic compression test (SHPB) simulation model; the size of the model mesh is reasonably designed, and when the optimized parameters are used in the DOP or SHPB simulation model of the same type, the size of the model mesh is consistent with that of the model participating in optimization. The shape, size and material of the bullet may vary from projectile to projectile, and the projectile in this embodiment is not limited to one, and may be a armor-piercing combustion projectile, a common pistol projectile, a rifle projectile, a spherical projectile, an ovoid projectile, a long-rod projectile, a fragment simulation projectile, a gun projectile, a cannonball, or the like.

Step 102: determining the value interval of each optimization parameter under the kth iteration; the optimization parameters include a complete damage intensity coefficient, a complete damage intensity index, a damage equation coefficient, and a damage equation index.

Specifically, according to literature research or empirical estimation, the optimal value ranges of the complete damage intensity coefficient B, the complete damage intensity index M, the damage equation coefficient D1 and the damage equation index D2 are set as [ B [ ]_min,B_max]、[M_min,M_max]、[D1_min,D1_max]、[D2_min,D2_max]. The optimization efficiency can be improved by properly reducing the initial parameter range, but the optimization failure can also be caused, and the parameter range needs to be adjusted according to the actual situation because the JH-2 constitutive parameter has a matching relation with the grid size.

Step 103: and determining the chaotic variable of each optimized parameter under the k iteration.

Developing a corresponding test according to the created simulation model, and taking a test result as a target value of simulation calculation; selecting 4 nonzero numbers mu with small difference in the value range of the chaotic mapping equation_B,μ_M,μ_D1,μ_D2As a mixtureInitial values of chaos variables.

Step 104: and determining the optimized parameter value under the kth iteration according to the value interval of each optimized parameter under the kth iteration and the chaotic variable of each optimized parameter under the kth iteration. Specifically, the chaotic variable of each optimized parameter under the k-th iteration is mapped to the value interval of the corresponding optimized parameter, and the optimized parameter value of the shape and shadow is generated. In order to improve the optimization efficiency and quickly reduce the optimization search interval, the mapping equation can use wireless folding mapping.

Step 105: and calculating the simulation error under the kth iteration according to the optimized parameter value under the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength.

Step 106: and judging whether the simulation error is smaller than a set error value or not to obtain a first judgment result. If the first determination result is yes, go to step 107; if the first determination result is negative, go to step 108.

Step 107: determining the optimized parameter value under the k iteration as a final optimized parameter; the final optimization parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used for describing the physical response of the brittle material under different conditions.

Step 108: and calculating the parameter error of each optimized parameter in the kth iteration according to the simulation error in the kth iteration, updating the chaotic variable of the optimized parameter and the value interval of the optimized parameter according to the parameter error of each optimized parameter in the kth iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the previous k-1 iterations, and then performing the next iteration.

Wherein, step 108 specifically includes:

and for any optimization parameter in the k iteration, judging whether the parameter error of the optimization parameter is smaller than the maximum parameter error corresponding to the optimization parameter value in the optimal solution set after the previous k-1 iterations, and obtaining a second judgment result.

If the second judgment result is yes, the optimal value after the first k-1 iterations is obtainedAnd replacing the optimized parameter value with the maximum parameter error in the solution set with the optimized parameter under the k iteration to obtain the optimal solution set after the previous k iterations, updating the chaos variable and the iteration times of the optimized parameter, and returning to the step 103. The updating formula of the chaotic variable of the optimized parameters is as follows:

wherein, mu_kChaotic variable, mu, representing the optimized parameter at the kth iteration_k+1Chaotic variable, mu, representing optimized parameters under the k +1 th iteration_kRepresenting a chaotic variable.

And if the second judgment result is negative, judging whether the optimal solution set after the first K-1 iterations is not updated for K times continuously to obtain a third judgment result. Wherein the value of K is not less than 3.

If the third judgment result is yes, the value interval and the iteration times of the optimization parameters are updated, and then the step 102 is returned; if the third judgment result is negative, after updating the chaos variable and the iteration times of the optimized parameter, returning to the determining step 103.

Wherein, step 105 specifically includes:

and inputting the optimized parameter value under the k iteration into the penetration test simulation model of the residual penetration depth test to obtain the simulation calculation residual penetration depth under the k iteration. In order to improve the penetration structure discrimination and obtain a better optimization effect, two residual penetration test penetration simulation models (a first residual penetration test penetration simulation model and a second residual penetration test penetration simulation model) are constructed, and the two residual penetration test penetration simulation models adopt two different threat forms (namely two types of bullets).

And inputting the optimized parameter value under the k iteration into the dynamic compression test simulation model to obtain the simulation calculation dynamic compression strength under the k iteration.

And calculating the simulation error under the kth iteration according to the simulation calculation residual penetration depth under the kth iteration, the simulation calculation dynamic compression strength under the kth iteration, the actual measurement residual penetration depth and the actual measurement dynamic compression strength.

The simulated errors include a first error, a second error, and a third error.

The calculation formula of the first error is as follows:

wherein, Error₁Representing a first error; sd₁The simulation calculation residual penetration depth obtained by inputting the optimized parameter value into the first residual penetration depth test penetration simulation model is represented; the first residual penetration test penetration simulation model is a residual penetration test penetration simulation model constructed when a first set type of elastomer is adopted to penetrate a brittle material with a first set thickness; td₁The actual residual penetration depth is measured when an actual structure (a structure formed by placing a supporting back plate after a brittle material with a first set thickness) corresponding to a first residual penetration depth test penetration simulation model is used for testing; pd₁The power penetration of the projectile body corresponding to the first penetration test penetration simulation model on the residual penetration test supporting back plate (the common supporting back plate is made of 2024Al, 603 steel and the like); delta₁And the set error value is represented when simulation calculation is carried out by adopting a first residual penetration test penetration simulation model.

In practical application, the projectile body of the first set type adopts a armor piercing bomb with the caliber of 7.62mm, the brittle material with the first set thickness is the brittle material with the thickness of 3mm, and a calculation formula of a first error corresponding to the caliber of 7.62mm is as follows:

wherein, Error_7.62A first error corresponding to a 7.62mm caliber is shown; sd_7.62The simulation calculation residual penetration depth obtained by inputting the optimized parameter value into a first residual penetration depth test penetration simulation model corresponding to the caliber of 7.62mm is represented; the first residual penetration test penetration simulation model corresponding to the caliber of 7.62mm is constructed when a penetration combustion bomb with the caliber of 7.62mm is adopted to penetrate a brittle material with the thickness of 3mmA model; td_7.62The actual residual penetration depth is measured when the actual structure (the structure formed by placing a supporting back plate behind a brittle material with the thickness of 3mm) corresponding to the first residual penetration depth test penetration simulation model with the caliber of 7.62mm is adopted for testing; pd_7.62The power penetration depth of a armor-piercing combustion bomb with the caliber of 7.62mm on a residual penetration depth test support back plate made of 2024Al is represented; delta_7.62The set error value when simulation calculation is carried out by using a first residual penetration test penetration simulation model corresponding to the caliber of 7.62mm is shown.

The calculation formula of the second error is as follows:

wherein, Error₂Representing a second error; sd₂The optimized parameter values are input into a second residual penetration test penetration simulation model to obtain simulation calculation residual penetration; the second residual penetration test penetration simulation model is a residual penetration test penetration simulation model constructed when a second set-type projectile body (different threat form from the first residual penetration test penetration simulation model) is adopted to penetrate a brittle material with a second set thickness (different target plate structure from the first residual penetration test penetration simulation model); td₂The actual residual penetration depth is measured when the actual structure (the structure formed by placing the supporting back plate after the brittle material with the second set thickness) corresponding to the second residual penetration depth test penetration simulation model is used for testing; pd₂The power penetration of the projectile body corresponding to the second residual penetration test penetration simulation model on the residual penetration test supporting back plate (the common supporting back plate is made of 2024Al, 603 steel and the like); delta₂And the set error value is represented when simulation calculation is carried out by adopting a second residual penetration test penetration simulation model.

In practical application, the projectile body of the first set type adopts a armor piercing bomb with the caliber of 12.7mm, the brittle material with the first set thickness is the brittle material with the thickness of 10mm, and a calculation formula of a second error corresponding to the caliber of 12.7mm is as follows:

wherein, Error_12.7A second error corresponding to the aperture of 12.7mm is represented; sd_12.7The optimized parameter value is input into a second residual penetration depth test penetration simulation model corresponding to the caliber of 12.7mm to obtain simulation calculation residual penetration depth; the second residual penetration test penetration simulation model corresponding to the caliber of 12.7mm is a residual penetration test penetration simulation model constructed when a penetration combustion bomb with the caliber of 12.7mm is adopted to penetrate a brittle material with the thickness of 10 mm; td_12.7The actual residual penetration depth is measured when an actual structure (a structure formed by placing a supporting back plate behind a brittle material with the thickness of 10 mm) corresponding to a second residual penetration depth test penetration simulation model with the caliber of 12.7mm is used for testing; pd_12.7The power penetration depth of a armor-piercing combustion bomb with the caliber of 12.7mm on a residual penetration depth test support back plate made of 2024Al is represented; delta_12.7The set error value when simulation calculation is carried out by using a first residual penetration test penetration simulation model corresponding to the caliber of 7.62mm is shown.

The calculation formula of the third error is as follows:

wherein, Error_SHPBRepresenting a third error; sigma_SHPBRepresenting the simulation calculation dynamic compression strength obtained by inputting the optimized parameter value into the dynamic compression test simulation model; sigma_cRepresenting the measured dynamic compressive strength; delta_SHPBIndicating the set target offset value corresponding to the dynamic compressive strength.

In step 108, the calculation formula of the parameter error is:

wherein, Error_BError of parameter corresponding to coefficient of intensity of total damageA difference; error_MRepresenting the parameter error corresponding to the complete damage intensity index; error_D1Expressing parameter errors corresponding to the damage equation coefficients; error_D2And expressing the parameter error corresponding to the damage equation index. When Error₁Using Error_7.62，Error₂Using Error_12.7The calculation formula of the parameter error is as follows:

during initial iteration, an initial value interval and an initial chaotic variable of an optimization parameter need to be set. Specifically, the value interval of the complete damage strength coefficient under the 1 st iteration is [0.088,1]]The value interval of the complete damage strength index is [0.2,1]]The value interval of the damage equation coefficient is [0.001,0.5]]The value interval of the damage equation index is [ -0.325,1.85 [)]. The values of the chaotic variables corresponding to different optimization parameters in the 1 st iteration are different, namely, mu needs to be satisfied_B≠μ_M≠μ_D1≠μ_D2，μ_BIs a chaotic variable of the complete damage intensity coefficient, mu, at iteration 1_MIs a chaotic variable of the complete damage intensity index, mu, at iteration 1_D1Is the chaotic variable of the damage equation coefficient under 1 st iteration, mu_D2Is the chaos variable of the damage equation index under the 1 st iteration. In practical applications, set μ_B＝0.15，μ_M＝0.21，μ_D1＝0.28，μ_D2＝0.17。

Wherein, the calculation formula of the optimized parameter value in step 104 is:

wherein x is_iAn optimization parameter value representing an optimization parameter i; mu.s_iA chaotic variable representing an optimization parameter i; [ x ] of_max,x_min]Represents the value range, x, of the optimization parameter i_maxRepresents the maximum value, x, of the optimization parameter i_minRepresenting the minimum value of the optimization parameter i; i e (B, M, D1, D2), B represents the complete damage strength index, M represents the complete damage strength index, D1 represents the damage equation coefficient, and D2 represents the damage equation index. For example, an optimized parameter value for the full lesion strength factor is calculated,

the determination method of the optimized parameter values of other optimized parameters is the same.

In practical application, the concrete implementation process of the brittle material constitutive model parameter optimization method of the above embodiment is as follows.

Aiming at damage parameters B, M, D1 and D2 contained in the sapphire ceramic, an SHPB simulation model, a 3mm ceramic DOP simulation model of 7.62mm penetration armor-piercing bomb (API) and a 10mm ceramic DOP simulation model of 12.7mm penetration API are created. The JH-2 constitutive parameters of sapphire ceramics are shown in Table 1, except for the damage parameters.

TABLE 1 sapphire ceramics JH-2 constitutive parameters

The diameter of a Hopkinson loading rod system in an SHPB model is 16mm, the length of an impact rod is 200mm, and the rod system is made of 55CrSi alloy steel; the size of the ceramic sample is phi 6 multiplied by 6mm, and phi 6 multiplied by 1mmH62 brass is arranged at the front end of the incident rod to be used as a waveform shaper; the H62 brass adopts JC constitutive structure, the loading rod system adopts a linear elastic constitutive model, the ceramic sample adopts a JH-2 constitutive model, and the grid size of the ceramic sample is about 0.3 mm; to simplify the calculation process, a model 1/4 was created, and a typical hopkinson strut structure is shown in fig. 3 (a).

In DOP numerical simulation models of 7.62mm of API penetration 3mm ceramic and 12.7mm of API penetration 8mm ceramic, the ceramic back cushion 40mm2024 aluminum is used as a supporting back plate; in a 7.62mm API penetration model, a 0.5mm hexahedral mesh is adopted for the elastomer and the ceramic; in a 12.7mm API penetration model, a 1.0mm hexahedral mesh is adopted for the elastomer and the ceramic; the aluminum back plates in the two models are both 0.5mm hexahedral meshes. To reduce the computation time, the simulation uses the 1/4 model, as shown in fig. 3(b), 3 (c).

And generating parameters to be optimized in a parameter value range by adopting an infinite folding chaotic mapping formula for global optimization. The infinite folding chaotic mapping formula is

The specific implementation process of the brittle material constitutive model parameter optimization method is shown in fig. 4, and referring to fig. 4, the specific optimization process is as follows:

(1) setting an optimization target and initializing chaotic variables. Developing a corresponding test according to the created simulation model, and taking a test result as a target value of simulation calculation; selecting 4 nonzero numbers mu with slight difference in (-1,1) interval_B＝0.15，μ_M＝0.21，μ_D1＝0.28，μ_D20.17 is taken as the initial value of the chaotic variable.

(2) And setting an optimized parameter interval. According to literature research, parameters B, M, D1 and D2 respectively have values in the ranges of [0.088,1], [0.2,1], [0.001,0.5], [ -0.325,1.85] for typical armor ceramics.

(3) Each chaotic variable is represented by formula

Mapping to a parameter value interval, generating a parameter value to be optimized, and bringing the generated B, M, D1 and D2 values into a simulation model to carry out calculation; and generating a new chaos optimization variable through the infinite folding chaos mapping formula.

(4) And extracting a simulation calculation result and calculating a simulation error. The first error of 7.62mm API penetration 3mm ceramic DOP simulation is calculated as

Sd_7.62Residual penetration, Td, was calculated for the simulation_7.62For the test, the residual penetration depth (measured result is 16.3mm), Pd_7.62The penetration depth of the bomb with the penetration force of 7.62mm on the 2024Al plate (Pd)_7.62＝40mm)，Δ_7.62For calculating the result target errorDifference, set Δ_7.62＝5％。

The second error of 12.7mm API penetration 8mm ceramic DOP simulation is calculated as

Wherein Sd_12.7Residual penetration, Td, was calculated for the simulation_12.7For testing the residual penetration depth (measured result is 5mm), Pd_12.7The penetration depth of the 12.7mm penetration bomb on the 2024Al plate is strong (Pd)_12.7＝66mm)，Δ_12.7To calculate the resulting target error, Δ is set_12.7＝5％。

The third error of the SHPB simulation is calculated as

In the formula sigma_SHPBCalculating dynamic compressive Strength, σ, for the simulation_cMeasured as dynamic compressive strength (2926MPa), Delta_SHPBTo calculate the resulting target deviation.

(5) And calculating parameter errors according to the simulation errors. The parameter B has obvious influence on all the 3 models, and the error of the parameter B is set to be the sum of the simulation errors of the 3 models; the parameter M only has obvious influence on the DOP simulation result, and the error of the parameter M is set to be the sum of simulation errors of 2 DOP simulation models; the parameters D1 and D2 have larger influence on the SHPB simulation result and have insignificant influence on the DOP simulation result, the error is set to be equal to the SHPB simulation error, and the calculation formula of the parameter error is as follows

(6) The number of solutions in the initial optimal solution set is not less than 4, the number of solutions in the initial optimal solution set is set to be N-4, and the first 4 points generated by the chaotic variables are used as the initial optimal solution set. Let B be_kThe value of the parameter B to be optimized generated after the kth iteration and the parameter error f (B) in the B optimal solution set_j) The point corresponding to the maximum value is B_j，B_kCorresponding to a parameter Error of Error_BIf there is Error_B＜f(B_j) Then the optimal solution set is setIn the process of B_jIs replaced by B_k. Similarly, according to the above principle, parameter error f (M) in the set is collected according to the corresponding M optimal solutions_j) And D the parameter error f (D) in the optimal solution set_j) (including f (D1)_j) And f (D2)_j) Completes the updating of the parameters M and the optimal point sets D1, D2.

(7) Setting the parameter K to be 3, after iteration is carried out for a plurality of times, if the optimal solution in each parameter optimal solution set is not changed in continuous 3 times of iteration, taking the boundary (the maximum value and the minimum value of each independent variable) of the optimal solution set as a new search optimization interval, and starting the next round of optimization (returning to the step (2)); and (4) if the new optimization starting condition is not met, returning to the step (3), and generating a new parameter to be optimized to continue calculation.

(8) When the simulated Error simultaneously satisfies the set target Error, i.e. Error_7.62、Error_12.7、Error_SHPBIf the sum is less than 1, ending the optimization, and outputting optimization results B, M, D1 and D2; and if the target error requirement cannot be met after 6 rounds of optimization, outputting the parameter with the minimum error in the optimal points of the parameters as a final optimization result.

In the above steps, the parameter N is not less than 4, and the value of the parameter K is not less than 3, so that the problem that the optimal interval is converged in advance, which results in failure to obtain the global optimal solution, can be avoided.

Through the optimization of the procedures, the damage-containing parameters of the sapphire ceramic, namely B0.65685, M0.63435, D1 0.12103 and D2 2.06676, are obtained; the optimized parameters are substituted into a DOP simulation model of 7.62mm API penetration 3mm ceramic, the simulation calculation result is 15.84mm, and compared with the actual measurement value of 16.3mm, the error is 1.15%; the optimized parameters are substituted into a 12.7mm API penetration 8mm ceramic DOP simulation model, the simulation calculation result is 12.4mm, and compared with the actual measurement value of 12.3mm, the error is 0.15%; and (3) bringing the optimized parameters into an SHPB simulation model, wherein the simulation result is 2911MPa, and compared with the actual measurement result of 2926MPa, the error is 0.51%.

In order to distinguish test data used in fitting, 7.62mm API penetration 3.5mm sapphire DOP test results are predicted by using JH-2 constitutive parameters obtained through optimization, a simulation model is created according to actual test conditions, 2024Al with the thickness of 40mm is used as a supporting back plate, the target distance is 10m, and the actual measurement bullet speed is 801 m/s. Through simulation calculation, the penetration depth of the penetration residue is 12.41mm, the penetration depth of the penetration residue of the aluminum back plate is measured to be 12.23mm after test verification is carried out, and the error of the simulation calculation is 0.45 percent, as shown in fig. 5. The method is effective, the calculation precision of the optimized parameters is high, and the use requirements are met.

The brittle material constitutive model parameter optimization method of the embodiment is based on a chaotic global optimization algorithm, combines two residual penetration test simulation models with different structures and a dynamic compression test simulation model, develops global optimization of a JH-2 constitutive model containing damage parameters B, M, D1 and D2, comprehensively considers a multivariate coupling relation between the damage-containing parameters and grid sensitivity of a JH-2 constitutive model, improves parameter debugging efficiency and model compatibility of the constitutive parameters, and reduces parameter simulation errors, so that the JH-2 constitutive model can efficiently and accurately describe physical responses of brittle materials under different conditions.

The invention also provides a brittle material constitutive model parameter optimization system, referring to fig. 6, the system comprises:

the simulation model building module 201 is used for adopting the projectiles with different threat forms to penetrate the brittle materials with different thicknesses, and building a residual penetration test penetration simulation model and a dynamic compression test simulation model based on the dynamic compression test of the Hopkinson pressure bar.

A value interval determination module 202, configured to determine a value interval of each optimized parameter in the kth iteration; the optimization parameters include a complete damage intensity coefficient, a complete damage intensity index, a damage equation coefficient, and a damage equation index.

The chaotic variable determining module 203 is used for determining the chaotic variable of each optimized parameter under the kth iteration;

and the optimization parameter value determining module is used for determining the optimization parameter value under the kth iteration according to the value interval of each optimization parameter under the kth iteration and the chaotic variable of each optimization parameter under the kth iteration.

And the simulation error calculation module 204 is configured to calculate a simulation error in the kth iteration according to the optimized parameter value in the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the actually measured residual penetration depth, and the actually measured dynamic compression strength.

The error determining module 205 is configured to determine whether the simulation error is smaller than a set error value, so as to obtain a first determination result.

A final parameter determining module 206, configured to determine, if the first determination result is yes, an optimized parameter value under the kth iteration as a final optimized parameter; the final optimization parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used for describing the physical response of the brittle material under different conditions.

And the updating iteration module 207 is configured to, if the first determination result is negative, calculate a parameter error of each optimized parameter in the kth iteration according to the simulation error in the kth iteration, update the chaotic variable of the optimized parameter and the value interval of the optimized parameter according to the parameter error of each optimized parameter in the kth iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the previous k-1 iterations, and perform the next iteration.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (9)

1. A brittle material constitutive model parameter optimization method is characterized by comprising the following steps:

adopting elastomers in different threat forms to penetrate brittle materials with different thicknesses, and constructing a residual penetration test penetration simulation model and a dynamic compression test simulation model based on a dynamic compression test of a Hopkinson pressure bar;

determining the value interval of each optimization parameter under the kth iteration; the optimization parameters comprise a complete damage intensity coefficient, a complete damage intensity index, a damage equation coefficient and a damage equation index;

determining a chaotic variable of each optimized parameter under the kth iteration;

determining an optimized parameter value under the kth iteration according to the value interval of each optimized parameter under the kth iteration and the chaotic variable of each optimized parameter under the kth iteration;

calculating a simulation error under the kth iteration according to the optimized parameter value under the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength;

judging whether the simulation error is smaller than a set error value or not to obtain a first judgment result;

if the first judgment result is yes, determining the optimization parameter value under the k iteration as a final optimization parameter; the final optimization parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used for describing the physical response of the brittle material under different conditions;

if the first judgment result is negative, calculating the parameter error of each optimized parameter in the kth iteration according to the simulation error in the kth iteration, updating the chaotic variable of the optimized parameter and the value range of the optimized parameter according to the parameter error of each optimized parameter in the kth iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the first k-1 iterations, and then performing the next iteration.

2. The brittle material constitutive model parameter optimization method according to claim 1, wherein the parameter error of each optimized parameter in the kth iteration is calculated according to the simulation error in the kth iteration, the chaotic variable of the optimized parameter and the value interval of the optimized parameter are updated according to the parameter error of each optimized parameter in the kth iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the previous k-1 iterations, and the next iteration is performed, specifically comprising:

for any optimization parameter in the k iteration, judging whether the parameter error of the optimization parameter is smaller than the maximum parameter error corresponding to the optimization parameter value in the optimal solution set after the previous k-1 iterations, and obtaining a second judgment result;

if the second judgment result is yes, replacing the optimized parameter value with the maximum parameter error in the optimal solution set after the first k-1 iterations with the optimized parameter under the kth iteration to obtain the optimal solution set after the first k iterations, updating the chaotic variable and the iteration times of the optimized parameter, and returning to the step of determining the chaotic variable of each optimized parameter under the kth iteration;

if the second judgment result is negative, judging whether the optimal solution set after the first K-1 iterations is not updated for K times continuously to obtain a third judgment result;

if the third judgment result is yes, updating the value interval and the iteration times of the optimized parameters, and returning to the step of determining the value interval of each optimized parameter under the kth iteration;

if the third judgment result is negative, after updating the chaos variable and the iteration times of the optimized parameters, returning to the step of determining the chaos variable of each optimized parameter under the kth iteration.

3. The brittle material constitutive model parameter optimization method according to claim 1, wherein the calculating of the simulation error in the kth iteration according to the optimized parameter value in the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the measured residual penetration depth and the measured dynamic compression strength specifically comprises:

inputting the optimized parameter value under the k iteration into the penetration test simulation model of the residual penetration depth test to obtain the simulation calculation residual penetration depth under the k iteration;

inputting the optimized parameter value under the k iteration into the dynamic compression test simulation model to obtain the simulation calculation dynamic compression strength under the k iteration;

and calculating the simulation error under the kth iteration according to the simulation calculation residual penetration depth under the kth iteration, the simulation calculation dynamic compression strength under the kth iteration, the actual measurement residual penetration depth and the actual measurement dynamic compression strength.

4. The brittle material constitutive model parameter optimization method according to claim 3, wherein the simulation error comprises a first error, a second error and a third error;

the calculation formula of the first error is as follows:

wherein, Error₁Representing a first error; sd₁The simulation calculation residual penetration depth obtained by inputting the optimized parameter value into the first residual penetration depth test penetration simulation model is represented; the first residual penetration test penetration simulation model is a residual penetration test penetration simulation model constructed when a first set type of elastomer is adopted to penetrate a brittle material with a first set thickness; td₁The actual measurement residual penetration depth is shown when the actual structure corresponding to the first residual penetration depth test penetration simulation model is adopted for testing; pd₁The power penetration depth of the projectile body corresponding to the first residual penetration depth test penetration simulation model on the residual penetration depth test supporting back plate is represented; delta₁Representing a set error value when simulation calculation is carried out by adopting a first residual penetration test penetration simulation model;

the calculation formula of the second error is as follows:

wherein, Error₂Representing a second error;Sd₂the optimized parameter values are input into a second residual penetration test penetration simulation model to obtain simulation calculation residual penetration; the second residual penetration test penetration simulation model is a residual penetration test penetration simulation model constructed when a second set type of elastomer is adopted to penetrate a brittle material with a second set thickness; td₂The actual residual penetration depth is measured when the actual structure corresponding to the second residual penetration depth test penetration simulation model is used for testing; pd₂The power penetration depth of the projectile body corresponding to the second residual penetration depth test penetration simulation model on the residual penetration depth test supporting back plate is represented; delta₂Representing a set error value when simulation calculation is carried out by adopting a second residual penetration test penetration simulation model;

the calculation formula of the third error is as follows:

wherein, Error_SHPBIndicating a third error; sigma_SHPBRepresenting the simulation calculation dynamic compression strength obtained by inputting the optimized parameter values into a dynamic compression test simulation model; sigma_cRepresenting the measured dynamic compressive strength; delta_SHPBIndicating the set target offset value corresponding to the dynamic compressive strength.

5. The brittle material constitutive model parameter optimization method according to claim 4, wherein the calculation formula of the parameter error is as follows:

wherein, Error_BRepresenting parameter errors corresponding to the complete damage intensity coefficients; error_MRepresenting the parameter error corresponding to the complete damage intensity index; error_D1Expressing parameter errors corresponding to the damage equation coefficients; error_D2And expressing the parameter error corresponding to the damage equation index.

6. The brittle material constitutive model parameter optimization method according to claim 1, wherein the updating formula of the chaotic variable of the optimized parameter is as follows:

μ∈(-1,1)

μ≠0；

wherein mu represents a chaotic variable of the optimization parameter; mu.s_kChaotic variable, mu, representing the optimized parameter at the kth iteration_k+1Expressing a chaotic variable of an optimized parameter under the k +1 th iteration; when k is 1, the values of the chaotic variables corresponding to different optimization parameters are different.

7. The brittle material constitutive model parameter optimization method according to claim 1, wherein the calculation formula of the optimized parameter value is as follows:

wherein x is_iAn optimization parameter value representing an optimization parameter i; mu.s_iA chaotic variable representing an optimization parameter i; [ x ] of_max,x_min]Represents the value range, x, of the optimization parameter i_maxRepresents the maximum value, x, of the optimization parameter i_minRepresenting the minimum value of the optimization parameter i; i e (B, M, D1, D2), B represents the complete damage strength index, M represents the complete damage strength index, D1 represents the damage equation coefficient, and D2 represents the damage equation index.

8. The brittle material constitutive model parameter optimization method according to claim 2, wherein a value of K is not less than 3.

9. A brittle material constitutive model parameter optimization system, comprising:

the simulation model building module is used for adopting the bullets in different threat forms to penetrate the brittle materials with different thicknesses, and building a residual penetration test penetration simulation model and a dynamic compression test simulation model based on the dynamic compression test of the Hopkinson pressure bar;

a value interval determination module for determining the value interval of each optimized parameter under the kth iteration; the optimization parameters comprise a complete damage intensity coefficient, a complete damage intensity index, a damage equation coefficient and a damage equation index;

the chaotic variable determining module is used for determining the chaotic variable of each optimized parameter under the kth iteration;

the optimization parameter value determining module is used for determining the optimization parameter value under the kth iteration according to the value interval of each optimization parameter under the kth iteration and the chaotic variable of each optimization parameter under the kth iteration;

the simulation error calculation module is used for calculating the simulation error under the kth iteration according to the optimized parameter value under the kth iteration, the residual penetration depth test penetration simulation model, the dynamic compression test simulation model, the actually-measured residual penetration depth and the actually-measured dynamic compression strength;

the error judgment module is used for judging whether the simulation error is smaller than a set error value or not to obtain a first judgment result;

a final parameter determining module, configured to determine an optimized parameter value under the kth iteration as a final optimized parameter if the first determination result is yes; the final optimization parameters are used for determining a JH-2 constitutive model; the JH-2 constitutive model is used for describing the physical response of the brittle material under different conditions;

and the updating iteration module is used for calculating the parameter error of each optimized parameter under the k iteration according to the simulation error under the k iteration if the first judgment result is negative, updating the chaotic variable of the optimized parameter and the value interval of the optimized parameter according to the parameter error of each optimized parameter under the k iteration and the maximum parameter error corresponding to the optimized parameter value in the optimal solution set after the previous k-1 iterations, and then performing the next iteration.

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