Global optimization design method, device and medium for gradient aluminum foam impact suppression structure

Abstract

This invention discloses a global optimization design method, device, and medium for a gradient aluminum foam impact suppression structure. The method includes: constructing a dynamic crushing process model of the gradient aluminum foam structure under high-speed impact from a mass block, based on plastic shock wave theory and the relationship between the design parameters of the gradient aluminum foam structure and the impact parameters during the dynamic crushing process; constructing evaluation indices for assessing the buffering effect based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient aluminum foam structure; constructing a global optimization model of the gradient aluminum foam impact suppression structure based on the constraints of the evaluation indices and the design parameters; determining adaptive crossover probability and mutation probability based on the number of generations and the fitness values ​​of individuals in the population; and solving the global optimization model using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters of the gradient aluminum foam impact suppression structure.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of material structure design technology, and in particular to a global optimization design method, device and medium for a gradient aluminum foam impact suppression structure. Background Technology

[0002] With advancements in fabrication processes, the relative density of foamed metals can be designed by controlling the internal cell distribution of materials, resulting in gradient foamed metal materials, particularly gradient aluminum foam. However, there is a lack of theoretical models for the dynamic crushing process of gradient foamed metal structures, as well as analysis of the impact of gradient aluminum foam impact suppression structure design parameters on buffering effects. Furthermore, there is a lack of global optimization mathematical models for gradient aluminum foam impact suppression structures that comprehensively consider various optimization objectives, and research on optimizing foamed metal materials according to impact suppression structure design requirements. Summary of the Invention

[0003] In view of this, the present invention aims to provide a global optimization design method, device and medium for gradient aluminum foam impact suppression structure; it can model the dynamic crushing process of gradient aluminum foam structure under high-speed impact of mass block, and analyze the influence of design parameters on evaluation index, thereby performing global optimization design of gradient aluminum foam impact suppression structure to obtain the optimal buffer structure under constraint conditions.

[0004] The technical solution of this invention is implemented as follows:

[0005] In a first aspect, embodiments of the present invention provide a global optimization design method for a gradient aluminum foam impact suppression structure, the method comprising:

[0006] Based on the plastic shock wave theory, a dynamic crushing process model of a gradient foam metal structure under high-speed impact of a mass block is constructed according to the relationship between the design parameters of the gradient foam metal structure and the impact parameters during the dynamic crushing process.

[0007] Evaluation indicators for assessing the buffering effect are constructed based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure.

[0008] A global optimization model for the gradient aluminum foam impact suppression structure is constructed based on the constraints of the evaluation index and the design parameters.

[0009] The adaptive crossover probability and mutation probability are determined based on the number of generations and the fitness values ​​of individuals in the population;

[0010] The global optimization model is solved using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters for the gradient aluminum foam impact suppression structure.

[0011] Secondly, embodiments of the present invention provide a global optimization design device for a gradient aluminum foam impact suppression structure, the device comprising: a first construction part, a second construction part, a third construction part, a determination part, and an optimal parameter acquisition part; wherein...

[0012] The first construction part is configured to construct a dynamic crushing process model of a gradient foam metal structure under high-speed impact of a mass block based on the plastic shock wave theory and the relationship between the design parameters of the gradient foam metal structure and the impact parameters in the dynamic crushing process.

[0013] The second construction part is configured to construct evaluation indicators for evaluating the buffering effect based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure;

[0014] The third construction part is configured to construct a global optimization model of the gradient aluminum foam impact suppression structure based on the constraints of the evaluation index and the design parameters.

[0015] The determining part is configured to determine the adaptive crossover probability and mutation probability based on the number of generations and the fitness values ​​of individuals in the population;

[0016] The optimal parameter acquisition section is configured to solve the global optimization model using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters of the gradient aluminum foam impact suppression structure.

[0017] Thirdly, embodiments of the present invention provide a computing device, the computing device comprising: a communication interface, a memory, and a processor; the various components are coupled together via a bus system; wherein...

[0018] The communication interface is used for receiving and sending signals during the process of sending and receiving information with other external network elements;

[0019] The memory is used to store computer programs that can run on the processor;

[0020] The processor is configured to execute the steps of the global optimization design method for the gradient aluminum foam impact suppression structure described in the first aspect when running the computer program.

[0021] Fourthly, embodiments of the present invention provide a computer storage medium storing a global optimization design program for a gradient aluminum foam impact suppression structure. When the global optimization design program for the gradient aluminum foam impact suppression structure is executed by at least one processor, it implements the steps of the global optimization design method for the gradient aluminum foam impact suppression structure described in the first aspect.

[0022] This invention provides a global optimization design method, device, and medium for a gradient aluminum foam impact suppression structure. Based on plastic shock wave theory, it models the dynamic crushing process of a gradient aluminum foam structure under high-speed impact from a mass block to parameterize the gradient aluminum foam, providing the relationship between key impact parameters and the design parameters of the gradient aluminum foam itself. This allows for an accurate description of the deformation and stress-strain state of the gradient aluminum foam under high-speed impact loads. Then, based on the number of generations and the fitness values ​​of individuals in the population, it determines the adaptive crossover and mutation probabilities to improve the genetic algorithm. The improved genetic algorithm is then used to globally optimize the gradient aluminum foam impact suppression structure, obtaining the optimal buffer structure under constraints, thereby accelerating the convergence speed of the genetic algorithm towards the optimal solution. Attached Figure Description

[0023] Figure 1 A schematic diagram of a global optimization design method for a gradient aluminum foam impact suppression structure provided in an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of the dynamic crushing process of a gradient foam metal structure provided in an embodiment of the present invention;

[0025] Figure 3 A schematic diagram showing the changes in total energy absorption, specific energy absorption, stroke efficiency, and peak impact force as a function of the average relative density of graded aluminum foam, provided in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the solution process of the genetic algorithm improved by adaptive crossover probability and mutation probability provided in the embodiments of the present invention;

[0027] Figure 5 A schematic diagram illustrating the results of optimal individual fitness, average population fitness, and number of iteration steps provided in an embodiment of the present invention;

[0028] Figure 6 A schematic diagram of the global optimization design device for the gradient aluminum foam impact suppression structure provided in this embodiment of the invention;

[0029] Figure 7 This is a schematic diagram of the hardware structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0031] See Figure 1 This illustrates a global optimization design method for a gradient aluminum foam impact suppression structure provided by an embodiment of the present invention. The method may include:

[0032] S101: Based on the plastic shock wave theory, a dynamic crushing process model of a gradient foam metal structure under high-speed impact of a mass block is constructed according to the relationship between the design parameters of the gradient foam metal structure and the impact parameters during the dynamic crushing process.

[0033] S102: Construct evaluation indicators for evaluating the buffering effect based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure;

[0034] S103: Construct a global optimization model for the gradient aluminum foam impact suppression structure based on the constraints of the evaluation index and the design parameters;

[0035] S104: Determine the adaptive crossover probability and mutation probability based on the number of generations and the fitness values ​​of individuals in the population;

[0036] S105: Solve the global optimization model using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters of the gradient aluminum foam impact suppression structure.

[0037] pass Figure 1 The technical solution shown is based on the plastic shock wave theory to model the dynamic crushing process of a gradient aluminum foam structure under high-speed impact from a mass block, thereby parameterizing the gradient aluminum foam. It gives the relationship between the main impact parameters and the design parameters of the gradient aluminum foam itself, thus accurately describing the deformation state of the gradient aluminum foam under high-speed impact load and the stress-strain state inside the structure. Then, based on the number of generations and the fitness value of individuals in the population, the adaptive crossover probability and mutation probability are determined to improve the genetic algorithm. The improved genetic algorithm is then used to perform global optimization design of the gradient aluminum foam impact suppression structure, obtaining the optimal buffer structure under constraints, thereby accelerating the convergence speed of the genetic algorithm to the optimal solution.

[0038] for Figure 1 In some possible implementations of the technical solution shown, the model of the dynamic crushing process of the gradient foam metal structure under high-speed impact of a mass block, based on the plastic shock wave theory and the relationship between the design parameters of the gradient foam metal structure and the impact parameters during the dynamic crushing process, includes:

[0039] Determine the design parameters of the gradient foam metal structure;

[0040] Based on the theory of plastic shock waves, the impact parameters in the dynamic crushing process are defined.

[0041] The relationship between each impact parameter and impact time is established based on the design parameters of the gradient foam metal structure.

[0042] In some examples of the above implementation, determining the design parameters of the gradient foam metal structure includes:

[0043] The design parameters of the gradient foam metal structure are defined as follows: structural length L0, cross-sectional area A0, and density of the gradient foam metal material ρ(x)ρ0; where ρ0 is the density of the matrix material, ρ(x) is the relative density gradient distribution of the gradient foam metal along the Lagrangian direction of the structure, and the expression for the relative density gradient distribution of the gradient foam metal material is given. ρ1 represents the average relative density of the impact-suppressing structure, γ represents the density gradient of the foam metal material, and γ > 0, x represents the Lagrange coordinates in the X-direction of the impact direction.

[0044] In some examples, the impact parameters defined in the dynamic crushing process based on plastic shock wave theory include:

[0045] The dynamic crushing process is defined as follows: a mass block of mass M impacts the gradient foam metal structure fixed at one end with an initial velocity v0, and during the impact, a plastic shock wave propagating towards the support end is generated only at the impact end.

[0046] The gradient foam metal structure is divided into a wavefront portion and a waveback portion based on the shock wave front surface; wherein, the wavefront portion is the part of the gradient foam metal structure located in front of the shock wave front surface along the impact direction; and the waveback portion is the part of the gradient foam metal structure located behind the shock wave front surface along the impact direction.

[0047] The impact parameters of the wavefront portion are defined as follows: wavefront stress σ zA (t), wavefront strain ε zA (t), wavefront particle velocity v zA (t);

[0048] The impact parameters of the wave-back portion are defined as follows: wave-back stress σ zB (t), wave back strain ε zB (t), the velocity of the particle after the wave v zB (t) and wavefront position φ z (t); where the wave-back particle velocity v zB (t) represents the velocity v(t) of the mass block.

[0049] Based on the above example, specifically taking linear gradient aluminum foam as an example, its dynamic crushing model under high-speed impact of a mass block is as follows: Figure 2 As shown. See also Figure 2In the upper figure, a mass block of mass M impacts a gradient foam metal structure fixed at one end with an initial velocity v0. The original length of this gradient foam metal structure model is L0, the cross-sectional area is A0, and the density of the gradient foam metal material is ρ(x)ρ0, where ρ0 is the density of the matrix material, and ρ(x) is the relative density gradient distribution of the gradient foam metal along the Lagrangian direction of the structure. The expression for the relative density gradient distribution of the gradient foam metal material is as follows: ρ1 represents the average relative density of the impact-suppressing structure, γ represents the density gradient of the foam metal material, and γ > 0, x represents the Lagrange coordinates in the X-direction of the impact direction.

[0050] Based on the Dynamic-Rigid-Plastic Hardening (DR-PH) model, linear gradient foam metal is considered as a rate-independent rigid-plastic hardening material. Its constitutive equation can be expressed as σ = σ(ε), where stress σ and strain ε are positively oriented in the compression direction. The unloading process is assumed to be rigid, and X represents the Lagrangian coordinates in the impact direction. Under dynamic impact, the linear gradient foam metal material will undergo local deformation, with the crushing zone propagating from the impact end to the support end. The dynamic crushing process of the gradient foam metal structure is similar to that of the constant-density foam metal structure. During impact, a plastic shock wave propagating towards the support end is generated only at the impact end, and its related physical quantities are denoted by the subscript z. At time t, let the position of the shock wave front be φ. z (t), the shock wave velocity is

[0051] See also Figure 2 In the figure below, the shock wave front divides the linear gradient foam metal structure into two parts. Along the impact direction X, the part of the structure in front of the wave front that has not yet been crushed and deformed is defined as the wavefront, and its relevant physical quantities are represented by the subscript A. The part of the structure behind the wave front that has been compacted is defined as the waveback, and its relevant physical quantities are represented by the subscript B. The mass block M and the waveback part of the gradient foam metal structure move towards the support end simultaneously at the same velocity, denoted as v(t). There are stress and strain discontinuities on the shock wave front. Then, at time t, the wavefront stress, wavefront strain, and wavefront particle velocity in front of the wave front are respectively:

[0052] {σ zA (t),ε zA (t),v zA (t)}={σ zA (t),0,0}

[0053] The wavefront stress, wavefront strain, and wavefront particle velocity are as follows:

[0054] {σ zB (t),ε zB (t),v zB(t)}={σ zB (t),ε zB (t),v(t)}.

[0055] Based on the above example, establishing the relationship between each impact parameter and impact time according to the design parameters of the gradient foam metal structure includes:

[0056] According to the laws of conservation of mass and momentum across the wavefront, the expression for the wave-back stress is:

[0057]

[0058] According to the DR-PH model of gradient foam metal materials, the wavefront stress is:

[0059]

[0060] Among them, dynamic initial crushing pressure For continuously gradient closed-cell aluminum foam, α = 170 MPa, m = 1.86;

[0061] Based on the first expression for the wave-after stress, the expression for the wave-after strain is obtained as follows:

[0062]

[0063] Wherein, the dynamic strain hardening parameter D=βρ(φ z ) n For continuous gradient closed-cell aluminum foam, β = 323 MPa and n = 2.84.

[0064] Taking the mass block M and the rear portion of the shock wave from the gradient foam metal structure as the research object, the expression for the velocity of the mass block can be obtained from Newton's second law as follows:

[0065]

[0066] Integrating the above equation over the impact time, the expression for the position of the wavefront can be obtained as follows:

[0067]

[0068] Taking t=0 as the initial time, we have v(0)=v0, φ z Substituting (0) = 0 into the expression for wave-back stress, we get:

[0069]

[0070] Based on the above equation, the initial conditions σ for post-impact stress and post-impact strain are obtained. zB (0),εzB (0);

[0071] The dynamic crushing process ends when the shock wave reaches the support end or the mass block stops moving, and the time when the dynamic crushing process ends is defined as t. * ;

[0072] Based on the expressions for the back stress, the back strain, the mass block velocity, the wavefront position, and the initial conditions for the back stress and strain, a numerical solution for the dynamic crushing response of a linear gradient foam metal structure is obtained using the fourth-order Runge-Kutta method.

[0073] For the above example, it should be noted that since the expressions for the wave-after stress, the wave-after strain, the mass block velocity, and the wavefront position are all implicit functions of time, the solution of the implicit functions can usually be obtained by combining the corresponding initial conditions and using the fourth-order Runge-Kutta method to obtain the numerical solution of the response result.

[0074] for Figure 1 In some possible implementations of the technical solution shown, the step of constructing evaluation indicators for assessing the buffering effect based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure includes:

[0075] Based on the dynamic crushing process model of linear gradient foam metal structure, total energy absorption, specific energy absorption, stroke efficiency, and peak impact force are selected as the evaluation indicators.

[0076] In detail, regarding the above implementation method, in the embodiments of the present invention, the total absorbed energy, specific absorbed energy, stroke efficiency, and peak impact force are defined as follows:

[0077] 1) Total energy absorbed E d During the crushing deformation process of a linear gradient foam metal structure under impact load, the total absorbed energy E d Defined as Substituting the aforementioned expression for the velocity of the mass block, we get:

[0078] According to the principle of energy conservation, the total absorbed energy E can be obtained by calculating the difference in kinetic energy of the entire system before and after the impact. d The mathematical expression is as follows:

[0079]

[0080] 2) Specific Energy Absorption (SEA) is used to represent the impact energy absorbed per unit mass of an impact-damping structure during the entire impact process. According to the definition of specific energy absorption, it can be known that...

[0081] 3) Stroke efficiency SE: Based on the one-dimensional shock wave model of the dynamic compression process of foamed metal, the effective compression stroke is the distance traveled by the mass block M when the plastic shock wave propagates to the fixed end in the impact suppression structure. Therefore, the stroke efficiency SE is... The impact velocity v(t) is an implicit function of time, so the numerical solution of the stroke efficiency SE can be obtained by using the fourth-order Runge-Kutta method.

[0082] 4) Peak impact force P max In the collapse process of a linear gradient foam metal structure, taking the undeformed region of the structure as the research object, the force P at the support end can be obtained from Newton's third law as follows: Clearly, during the impact crushing process of the linear gradient closed-cell aluminum foam model, the force at the support end remains unchanged. Therefore, the mathematical expression for the peak impact force can be obtained as follows: P max =max(P).

[0083] for Figure 1 The technical solution shown, in some possible implementations, involves constructing a global optimization model for the gradient aluminum foam impact suppression structure based on the constraints of the evaluation indicators and the design parameters, including:

[0084] Based on the relationship between the evaluation indicators and the design parameters, with the optimization objectives of maximizing total energy absorption, maximizing specific energy absorption, maximizing stroke efficiency, and minimizing peak impact force, corresponding initial objective functions are designed for each evaluation indicator.

[0085] The initial objective function is normalized according to the weights corresponding to each evaluation index to obtain a single objective function.

[0086] The global optimization model is constructed based on the design parameters, corresponding constraints, and the single objective function.

[0087] In detail, the theoretical model of the plastic shock wave in the dynamic crushing process of the linear gradient foam metal structure can be simulated through mathematical analysis. The specific design parameters of the linear gradient foam aluminum impact suppression structure in the simulation are as follows:

[0088] Assume the gradient aluminum foam impact suppression structure is a cylindrical structure with a length envelope of L0 = 60-75 mm and a cross-sectional radius envelope of r0 = 20-30 mm. For practical processing considerations, the variation step for both length and cross-sectional radius is taken as 0.01 mm. Ignoring additives, the matrix material of the gradient aluminum foam is assumed to be pure aluminum with a matrix material density of ρ0 = 2700 kg / m³. The average relative density range of the gradient aluminum foam is ρ1 = 0.2-0.3 with a variation step of 0.001, and the density gradient range is γ = 0-1 with a variation step of 0.1.

[0089] Based on the actual impact velocity and structural mass during the operation of the explosive bolt, the mass of the block is assumed to be M = 0.45 kg, and the initial impact velocity is v0 = 52 m / s. The simulation time step for the dynamic crushing process is 0.001 ms. The simulation ends when the plastic shock wave propagates completely or the block stops moving.

[0090] Based on the above design parameters, the dynamic crushing process of the gradient aluminum foam impact suppression structure was simulated. In this embodiment of the invention, the average relative density ρ1 of the gradient aluminum foam material is taken as an example. When the length L0, the cross-sectional radius r0, and the density gradient γ of the gradient aluminum foam are determined, and only the average relative density ρ1 is changed, the design parameters are set as shown in Table 1 below:

[0091] Table 1

[0092] <![CDATA[Structural length L0 / mm]]> <![CDATA[Cross-sectional radius r0 / mm]]> density gradient γ <![CDATA[Average relative density ρ1 range]]> 70 30 0.5 0.2-0.3

[0093] Based on the design parameters shown in Table 1 above, the total absorbed energy E is obtained through the above simulation process. d Specific energy absorption (SEA), stroke efficiency (SE), and peak impact force (P) max The curves showing the variation of the average relative density ρ1 with the influence of the curves are as follows: Figure 3 As shown in (a), (b), (c), and (d).

[0094] Furthermore, simulations can be performed based on other design parameters to obtain the impact of average relative density, gradient density, structural length, and cross-sectional radius on evaluation indicators. Specifically, the total energy absorbed, E... d With increasing average relative density of gradient aluminum foam, the length and cross-sectional radius of the impact-suppressing structure initially increase until the impact energy of the exploding bolt is completely absorbed; however, they decrease slowly with increasing density gradient, E dmax =608.4 J. The specific energy absorption (SEA) decreases with increasing average relative density of the gradient aluminum foam material, density gradient, impact suppression structure length, and cross-sectional radius. The stroke efficiency (SE) decreases continuously with increasing average relative density of the linear gradient aluminum foam material, impact suppression structure length, and cross-sectional radius; the rate of decrease in stroke efficiency remains essentially constant with increasing structure length; the rate of decrease gradually increases with increasing cross-sectional radius; the rate of decrease in stroke efficiency decreases slowly with increasing average relative density of the gradient aluminum foam material; and the stroke efficiency of the linear gradient aluminum foam impact suppression structure continuously increases with increasing density gradient. Peak impact force P max The impact force increases with the increase of the average relative density, density gradient, and cross-sectional radius of the linear gradient aluminum foam material; however, the length of the impact-suppressing structure does not affect the peak impact force.

[0095] Furthermore, the design parameters for gradient aluminum foam impact suppression structures are divided into two categories: one is the geometric parameters of the structure, including the length L0 and cross-sectional radius r0 of the impact suppression structure; the other is the material parameters, including the average relative density ρ1 and density gradient γ of the gradient aluminum foam material. The specific value ranges of the constraints for each design parameter are as follows:

[0096] 60mm < L0 < 75mm

[0097] 20mm < r0 < 30mm

[0098] 0.2 < ρ1 < 0.3

[0099] 0 < γ < 1

[0100] Next, for the four evaluation indicators mentioned above, optimization objective functions can be defined respectively. In this embodiment of the invention, the objective functions can be represented as four optimization objectives: maximizing total energy absorption, maximizing specific energy absorption, maximizing stroke efficiency, and minimizing peak impact force. To ensure that the other three optimization objectives are also maximized, the negative of the peak impact force function can be used as the corresponding optimization objective function. Based on this, the four optimization objective functions are defined as follows:

[0101] maxf1(L0,r0,ρ1,γ)=maxE d (L0,r0,ρ1,γ)

[0102] maxf2(L0,r0,ρ1,γ)=maxSEA(L0,r0,ρ1,γ),

[0103] maxf3(L0,r0,ρ1,γ)=maxSE(L0,r0,ρ1,γ),

[0104] maxf4(L0,r0,ρ1,γ)=-minP max (L0,r0,ρ1,γ).

[0105] For the four objective functions mentioned above, taking the aforementioned optimization objective as an example, and combining the selection of design parameters and their constraints, the corresponding global optimization mathematical model for the gradient aluminum foam impact suppression structure is obtained as follows:

[0106]

[0107] However, during the global optimization process based on the above global optimization model, the units of the objective functions are different and cannot be directly converted. Therefore, it is necessary to normalize the four evaluation indicators of the impact suppression structure: total energy absorption, specific energy absorption, stroke efficiency, and peak impact force, that is, to unify the dimensions of each objective function. The normalization process first obtains the maximum and minimum values ​​of each objective function. Substituting the objective function values ​​from the optimization process into the following formula yields the corresponding normalized functions for each objective function:

[0108]

[0109] Among them, f i (x) represents the value of the i-th objective function; minf i (x) represents the minimum value of the i-th objective function; maxf i (x) represents the maximum value of the i-th objective function. Specifically:

[0110] 1) Total energy absorbed E d The normalized function of the total absorbed energy varies with the length of the impact-suppressing structure, the cross-sectional radius, the average relative density of the linear gradient aluminum foam, and the density gradient as follows: The maximum total absorbed energy is equal to the total impact energy generated during the actuation of the explosive bolt, which is 608.4 J; as the structural length increases, the total absorbed energy of the impact-suppressing structure continuously increases until it reaches a maximum value and then remains constant; as the cross-sectional radius increases, the total absorbed energy of the impact-suppressing structure continuously increases until it reaches a maximum value and then remains constant; as the average relative density increases, the total absorbed energy of the impact-suppressing structure continuously increases until it reaches a maximum value and then remains constant; as the density gradient increases, the total absorbed energy of the impact-suppressing structure slowly decreases.

[0111] Clearly, the total energy absorption is minimized when the structural length, cross-sectional radius, and average relative density are at their minimum values ​​within the constraints, and the density gradient γ = 1. Therefore, the maximum and minimum values ​​of the total energy absorption function can be obtained as follows:

[0112]

[0113] Therefore, the normalized function of the total energy absorption function can be obtained as follows:

[0114] 2) Specific Energy Absorption (SEA): The normalized function of specific energy absorption varies with the length of the impact-suppressing structure, the cross-sectional radius, the average relative density of the linear gradient aluminum foam, and the density gradient as follows: As the structural length increases, the specific energy absorption of the impact-suppressing structure continuously decreases; as the cross-sectional radius increases, the specific energy absorption of the impact-suppressing structure continuously decreases; as the average relative density increases, the specific energy absorption of the impact-suppressing structure continuously decreases; as the density gradient increases, the specific energy absorption of the impact-suppressing structure slowly decreases.

[0115] Clearly, the specific energy absorption is maximized when the structural length, cross-sectional radius, and average relative density are at their minimum values ​​within the constraints, and the density gradient γ = 0; conversely, the specific energy absorption is minimized when the structural length, cross-sectional radius, and average relative density are at their maximum values ​​within the constraints, and the density gradient γ = 1. Therefore, the maximum and minimum values ​​of the specific energy absorption function can be obtained as follows:

[0116]

[0117] Therefore, the normalized function of the specific energy absorption function can be obtained as follows:

[0118] 3) Stroke efficiency SE: The normalized function of stroke efficiency varies with the length of the impact-suppressing structure, the cross-sectional radius, the average relative density of the linear gradient aluminum foam, and the density gradient as follows: As the structural length increases, the stroke efficiency of the impact-suppressing structure continuously decreases; as the cross-sectional radius increases, the stroke efficiency of the impact-suppressing structure continuously decreases; as the average relative density increases, the stroke efficiency of the impact-suppressing structure continuously decreases; as the density gradient increases, the stroke efficiency of the impact-suppressing structure continuously increases.

[0119] Clearly, the stroke efficiency is maximized when the structural length, cross-sectional radius, and average relative density are at their minimum values ​​within the constraints, and the density gradient γ = 1; conversely, the stroke efficiency is minimized when the structural length, cross-sectional radius, and average relative density are at their maximum values ​​within the constraints, and the density gradient γ = 0. Therefore, the maximum and minimum values ​​of the stroke efficiency function can be obtained as follows:

[0120]

[0121] Therefore, the normalized function of the stroke efficiency function can be obtained as follows:

[0122] 4) Peak impact force P max The normalization function of the peak impact force varies with the length of the impact-suppressing structure, the cross-sectional radius, the average relative density of the linear gradient aluminum foam, and the density gradient as follows: As the structural length increases, the peak impact force of the impact-suppressing structure remains basically unchanged; as the cross-sectional radius increases, the peak impact force of the impact-suppressing structure continuously increases; as the average relative density increases, the peak impact force of the impact-suppressing structure continuously increases; as the density gradient increases, the peak impact force of the impact-suppressing structure continuously increases.

[0123] Clearly, the peak impact force is maximized when the structural length is the minimum within the constraints, the cross-sectional radius and average relative density are the maximum within the constraints, and the density gradient γ = 1; conversely, the peak impact force is minimized when the structural length, cross-sectional radius, and average relative density are the minimum within the constraints, and the density gradient γ = 0. Therefore, the maximum and minimum values ​​of the peak impact force function can be obtained as follows:

[0124]

[0125] Therefore, the normalized function of the peak impact force function can be obtained as follows:

[0126] To optimize the gradient aluminum foam impact suppression structure, considering all four objective functions, the total energy absorption and peak impact force are chosen to be equally important, and both are slightly more important than specific energy absorption, while the stroke efficiency is significantly more important. Based on this weighting principle, the weights of each objective function are as follows: the weight coefficient of the total energy absorption function is 0.3908, the weight coefficient of the specific energy absorption function is 0.1509, the weight coefficient of the stroke efficiency function is 0.0675, and the weight coefficient of the peak impact force function is 0.3908.

[0127] The total energy absorption function, specific energy absorption function, stroke efficiency function, and peak impact force function are transformed into a single objective function for subsequent optimization. The expression of the single objective function is as follows:

[0128]

[0129] Based on the single objective function and the constraints of each design parameter, the global optimization model is obtained.

[0130] for Figure 1 In some possible implementations of the technical solution shown, the determination of adaptive crossover probability and mutation probability based on the number of generations and the fitness values ​​of individuals in the population includes:

[0131] Calculate the first operator p based on the evolutionary algebra according to the following formula. c1 and the second operator p based on the fitness value of individuals in the population c2 ;

[0132]

[0133]

[0134] The adaptive crossover probability is obtained based on the first operator and the second operator, using the following formula:

[0135] p c =p c1 p c2 ;

[0136] Calculate the third operator p based on the evolutionary algebra according to the following formula. m1 and the fourth operator p based on the fitness value of individuals in the population m2 ;

[0137]

[0138]

[0139] The adaptive mutation probability is obtained based on the third operator and the fourth operator, using the following formula:

[0140] p m =p m1 p m2 .

[0141] Regarding the above implementation, it's important to note that the crossover and mutation steps are crucial for the genetic algorithm to find the global optimum. Crossover is the main step in exploring the unknown space within the constraints of the independent variables, while mutation ensures population diversity and guarantees the genetic algorithm's global search capability. Therefore, the values ​​of crossover and mutation probabilities significantly impact the optimization results. Generally, a higher crossover probability and a lower mutation probability are preferable. However, on the one hand, for individuals with relatively high fitness in the population, crossover and mutation are likely to decrease their fitness; on the other hand, at lower generations, a lower mutation probability is detrimental to maintaining population diversity, while at higher generations, the average fitness of the population is higher, requiring an increased crossover probability to accelerate the convergence speed of the genetic algorithm towards the optimal solution.

[0142] Therefore, in the above implementation, for the crossover probability, the first operator p based on the evolutionary generation... c1 and the second operator p based on the fitness value of individuals in the population c2 The following are examples:

[0143]

[0144]

[0145] Among them, maxp c ,minp c Let represent the maximum and minimum crossover probabilities, respectively; T represents the final generation; T0 represents the initial generation, and T0 = 1; t represents the current generation; k1 and k2 represent operator constants, and k1 ∈ (0, 1], k2 ∈ [1, ∞); fit max This represents the maximum fitness of an individual in the current population; fit min This represents the minimum fitness value of an individual in the current population; fit avg This represents the average fitness of individuals in the current population.

[0146] Considering that T0 is much smaller than T, therefore p c1 It can be simplified to:

[0147] The mutation probability also consists of two parts, based on the third operator p of the evolutionary generations. m1 and the fourth operator p based on the fitness value of individuals in the population m2 They are respectively:

[0148]

[0149]

[0150] Among them, maxp m ,minp m k1 and k2 represent the maximum and minimum mutation probabilities, respectively; k3 and k4 represent the internal constants of the operator, and k3∈(0,1], k4∈[1,∞);

[0151] Considering that T0 is much smaller than T, therefore p m1 It can be simplified to

[0152] Clearly, as the number of generations increases, p c1 continuously increasing, p m1 By continuously decreasing p, the convergence speed towards the optimal solution is accelerated when the population is relatively good in the later stages of the genetic algorithm, while the probability of individual gene mutations leading to poor fitness is reduced. Simultaneously, for individuals with relatively high fitness in the population, p... c2 p m2 The size of the population decreases significantly, which helps to retain the best individuals in the population and speeds up the convergence. However, even the best individuals in the population have a certain probability of crossover and mutation, which helps to maintain the diversity of the population and avoid getting trapped in local optima.

[0153] Based on the aforementioned implementation method, in some examples, the global optimization model is solved using a genetic algorithm improved by the adaptive crossover probability and mutation probability, as detailed below. Figure 4 As shown, in Figure 4 First, a population is established and initialized according to the design parameters. Then, the fitness value of individuals in the population is calculated according to the objective function, and the optimal individual is selected. If the optimal individual meets the termination criterion, it is taken as the optimal solution and determined as the optimal design parameters. If the termination criterion is not met, the individuals in the population are subjected to natural selection, and then crossover and mutation are performed in sequence according to the aforementioned adaptive crossover probability and mutation probability to generate an updated population. The fitness value is then calculated again until the optimal solution is obtained, thus obtaining the optimal design parameters.

[0154] Regarding the aforementioned technical solutions, their implementation methods, and examples, the embodiments of the present invention verify their technical effects through the following simulation experiments, under the following simulation experimental conditions:

[0155] The results for the optimal individual fitness, average population fitness, and number of iterations of the improved genetic algorithm are as follows: Figure 5 (a) and Figure 5 As shown in (b), the calculation process converges.

[0156] Furthermore, the optimal solution combination obtained through the aforementioned technical solution is: structural length L0 = 74.80 mm, cross-sectional radius r0 = 30.00 mm, average relative density of gradient aluminum foam ρ1 = 0.2, and density gradient γ = 0. The optimal individual fitness is 0.7861, and the average fitness of the population at the end of evolution is 0.7434. Substituting these values ​​into the dynamic crushing model of gradient aluminum foam, the total energy absorbed by the structure is E. d =566.4J, specific energy absorption is SEA=4959.7J / kg, stroke efficiency is SE=0.2886, peak impact force is P max =20.09kN.

[0157] Based on the same inventive concept as the aforementioned technical solution, see [link to inventive concept]. Figure 6 This illustration shows a global optimization design device 60 for a gradient aluminum foam impact suppression structure provided by an embodiment of the present invention. The device 60 includes: a first construction part 601, a second construction part 602, a third construction part 603, a determination part 604, and an optimal parameter acquisition part 605; wherein,

[0158] The first construction part 601 is configured to construct a dynamic crushing process model of a gradient foam metal structure under high-speed impact of a mass block based on the plastic shock wave theory and the relationship between the design parameters of the gradient foam metal structure and the impact parameters in the dynamic crushing process.

[0159] The second construction part 602 is configured to construct evaluation indicators for evaluating the buffering effect based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure;

[0160] The third construction part 603 is configured to construct a global optimization model of the gradient aluminum foam impact suppression structure based on the constraints of the evaluation index and the design parameters.

[0161] The determining part 604 is configured to determine the adaptive crossover probability and mutation probability based on the number of generations and the fitness values ​​of individuals in the population;

[0162] The optimal parameter acquisition section 605 is configured to solve the global optimization model using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters of the gradient aluminum foam impact suppression structure.

[0163] It should be noted that for the specific implementation of the functions configured in each "part" of the above-mentioned device, please refer to the aforementioned... Figure 1 The implementation methods and examples of the corresponding steps in the global optimization design method of the gradient aluminum foam impact suppression structure shown are not elaborated here.

[0164] Understandably, in this embodiment, "part" can be a part of a circuit, a part of a processor, a part of a program or software, etc., or it can be a unit, a module, or a non-modular one.

[0165] Furthermore, in this embodiment, the components can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional module.

[0166] If the integrated unit is implemented as a software functional module and not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the method described in this embodiment. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] Therefore, this embodiment provides a computer storage medium storing a global optimization design program for a gradient aluminum foam impact suppression structure. When the global optimization design program for the gradient aluminum foam impact suppression structure is executed by at least one processor, it implements the steps of the global optimization design method for the gradient aluminum foam impact suppression structure described in the above technical solution.

[0168] Based on the global optimization design device 60 for the aforementioned gradient aluminum foam impact suppression structure and the computer storage medium, see [link to relevant documentation]. Figure 7This illustration shows the specific hardware structure of a computing device 70, provided by an embodiment of the present invention, capable of implementing the aforementioned gradient aluminum foam impact suppression structure through a globally optimized design apparatus 60. The computing device 70 can be a wireless device, mobile or cellular phone (including so-called smartphones), personal digital assistant (PDA), video game console (including video display, mobile video game device, mobile video conferencing unit), laptop computer, desktop computer, set-top box, tablet computing device, e-book reader, fixed or mobile media player, etc. The computing device 70 includes: a communication interface 701, a memory 702, and a processor 703; the various components are coupled together through a bus system 704. It is understood that the bus system 704 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 704 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 7 The general designated all buses as Bus System 704. Among them,

[0169] The communication interface 701 is used for receiving and sending signals during the process of sending and receiving information with other external network elements;

[0170] The memory 702 is used to store computer programs that can run on the processor 703;

[0171] The processor 703 is used to execute the steps of the global optimization design method for the gradient aluminum foam impact suppression structure described in the above technical solution when running the computer program.

[0172] It is understood that the memory 702 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 702 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0173] The processor 703 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 703 or by instructions in software form. The processor 703 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 702, and the processor 703 reads the information in memory 702 and, in conjunction with its hardware, completes the steps of the above method.

[0174] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0175] For software implementation, the techniques described herein can be achieved through modules (e.g., procedures, functions, etc.) that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or externally.

[0176] It is understood that the exemplary technical solutions of the global optimization design device 60 and computing device 70 for the aforementioned gradient aluminum foam impact suppression structure belong to the same concept as the aforementioned global optimization design method for the gradient aluminum foam impact suppression structure. Therefore, all details not described in detail above regarding the technical solutions of the global optimization design device 60 and computing device 70 for the gradient aluminum foam impact suppression structure can be found in the description of the aforementioned global optimization design method for the gradient aluminum foam impact suppression structure. This embodiment of the invention will not elaborate further on these details.

[0177] It should be noted that the technical solutions described in the embodiments of the present invention can be combined arbitrarily without conflict.

[0178] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A global optimization design method for a gradient aluminum foam impact suppression structure, characterized in that, The method includes: Based on the plastic shock wave theory, a dynamic crushing process model of a gradient foam metal structure under high-speed impact of a mass block is constructed according to the relationship between the design parameters of the gradient foam metal structure and the impact parameters during the dynamic crushing process. Evaluation indicators for assessing the buffering effect are constructed based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure. A global optimization model for the gradient aluminum foam impact suppression structure is constructed based on the constraints of the evaluation index and the design parameters. The adaptive crossover probability and mutation probability are determined based on the number of generations and the fitness values ​​of individuals in the population; The global optimization model is solved using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters of the gradient aluminum foam impact suppression structure. The global optimization model for constructing the gradient aluminum foam impact suppression structure based on the constraints of the evaluation index and the design parameters includes: Based on the relationship between the evaluation indicators and the design parameters, with the optimization objectives of maximizing total energy absorption, maximizing specific energy absorption, maximizing stroke efficiency, and minimizing peak impact force, corresponding initial objective functions are designed for each evaluation indicator. The initial objective function is normalized according to the weights corresponding to each evaluation index to obtain a single objective function. The global optimization model is constructed based on the design parameters, corresponding constraints, and the single objective function. The determination of adaptive crossover probability and mutation probability based on evolutionary generations and the fitness values ​​of individuals in the population includes: Calculate the first operator based on the evolutionary algebra according to the following formula. and the second operator based on the fitness value of individuals in the population ; The adaptive crossover probability is obtained based on the first operator and the second operator, using the following formula: ； Calculate the third operator based on the evolutionary algebra according to the following formula. and the fourth operator based on the fitness value of individuals in the population ; The adaptive mutation probability is obtained based on the third operator and the fourth operator, using the following formula: 。 2. The method according to claim 1, characterized in that, The aforementioned model, based on plastic shock wave theory, constructs a dynamic crushing process model of a gradient foam metal structure under high-speed impact from a mass block, according to the relationship between the design parameters of the gradient foam metal structure and the impact parameters during the dynamic crushing process. This model includes: Determine the design parameters of the gradient foam metal structure; Based on the theory of plastic shock waves, the impact parameters in the dynamic crushing process are defined. The relationship between each impact parameter and impact time is established based on the design parameters of the gradient foam metal structure.

3. The method according to claim 2, characterized in that, Determining the design parameters of the gradient foam metal structure includes: The design parameters for the gradient foam metal structure include: structural length. Cross-sectional area is The density of gradient foam metal material is ;in, Density of the matrix material The relative density gradient distribution of the gradient foam metal along the structural Lagrangian direction is given by the expression for the relative density gradient distribution of the gradient foam metal material. , This represents the average relative density of the impact-suppressing structure. This represents the density gradient of the foamed metal material, and , Indicates the direction of impact The Lagrange coordinates of the direction.

4. The method according to claim 2, characterized in that, The impact parameters defined in the dynamic crushing process based on the plastic shock wave theory include: The dynamic crushing process is defined as follows: a mass block of mass M moves at an initial velocity... The impact occurs on the gradient foam metal structure fixed at one end, and during the impact, a plastic shock wave propagates towards the support end only at the impact end. The gradient foam metal structure is divided into a wavefront portion and a waveback portion based on the shock wave front surface; wherein, the wavefront portion is the part of the gradient foam metal structure located in front of the shock wave front surface along the impact direction; and the waveback portion is the part of the gradient foam metal structure located behind the shock wave front surface along the impact direction. The impact parameters of the wavefront portion are defined as follows: wavefront stress Wavefront strain Wavefront particle velocity ; The impact parameters of the wave-back portion are defined as follows: wave-back stress. Post-wave strain Post-wave particle velocity and wavefront position Wherein, the velocity of the wave-after particles That is, the speed of motion of the mass block. .

5. The method according to claim 4, characterized in that, The step of establishing the relationship between each impact parameter and impact time based on the design parameters of the gradient foam metal structure includes: According to the laws of conservation of mass and momentum across the wavefront, the expression for the wave-back stress is: According to the DR-PH model of gradient foam metal materials, the wavefront stress is: Among them, dynamic initial crushing pressure For continuously gradient closed-cell aluminum foam materials, =170MPa =1.86; Based on the first expression for the wave-after stress, the expression for the wave-after strain is obtained as follows: Among them, dynamic strain hardening parameters For continuously gradient closed-cell aluminum foam materials, =323MPa =2.84; With mass block M Taking the shock wave trailing portion of the gradient foam metal structure as the research object, the expression for the velocity of the mass block can be obtained from Newton's second law: Integrating the above equation over the impact time, the expression for the position of the wavefront can be obtained as follows: Will t If 0 is taken as the initial time, then we have , Substituting into the expression for wave-back stress, we get: The initial conditions for post-impact stress and post-impact strain are obtained based on the above equation. ; The dynamic crushing process ends when the shock wave reaches the support end or the mass block stops moving, and the moment when the dynamic crushing process ends is defined as... ; Based on the expressions for the back stress, the back strain, the mass block velocity, the wavefront position, and the initial conditions for the back stress and strain, a numerical solution for the dynamic crushing response of a linear gradient foam metal structure is obtained using the fourth-order Runge-Kutta method.

6. The method according to claim 1, characterized in that, The evaluation index for assessing the buffering effect, constructed based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure, includes: Based on the dynamic crushing process model of linear gradient foam metal structure, total energy absorption, specific energy absorption, stroke efficiency, and peak impact force are selected as the evaluation indicators.

7. A globally optimized design device for a gradient aluminum foam impact suppression structure, characterized in that, The device includes: a first construction part, a second construction part, a third construction part, a determining part, and an optimal parameter acquisition part; wherein, The first construction part is configured to construct a dynamic crushing process model of a gradient foam metal structure under high-speed impact of a mass block based on the plastic shock wave theory and the relationship between the design parameters of the gradient foam metal structure and the impact parameters in the dynamic crushing process. The second construction part is configured to construct evaluation indicators for evaluating the buffering effect based on the impact parameters in the dynamic crushing process model and the design parameters of the gradient foam metal structure; The third construction part is configured to construct a global optimization model of the gradient aluminum foam impact suppression structure based on the constraints of the evaluation index and the design parameters. The determining part is configured to determine the adaptive crossover probability and mutation probability based on the number of generations and the fitness values ​​of individuals in the population; The optimal parameter acquisition section is configured to solve the global optimization model using a genetic algorithm improved by the adaptive crossover probability and mutation probability to obtain the optimal design parameters of the gradient aluminum foam impact suppression structure. The third construction part is configured as follows: Based on the relationship between the evaluation indicators and the design parameters, with the optimization objectives of maximizing total energy absorption, maximizing specific energy absorption, maximizing stroke efficiency, and minimizing peak impact force, corresponding initial objective functions are designed for each evaluation indicator. The initial objective function is normalized according to the weights corresponding to each evaluation index to obtain a single objective function. The global optimization model is constructed based on the design parameters, corresponding constraints, and the single objective function. The determined portion is configured as follows: Calculate the first operator based on the evolutionary algebra according to the following formula. and the second operator based on the fitness value of individuals in the population ; The adaptive crossover probability is obtained based on the first operator and the second operator, using the following formula: ； Calculate the third operator based on the evolutionary algebra according to the following formula. and the fourth operator based on the fitness value of individuals in the population ; The adaptive mutation probability is obtained based on the third operator and the fourth operator, using the following formula: 。 8. A computer storage medium, characterized in that, The computer storage medium stores a global optimization design program for a gradient aluminum foam impact suppression structure. When the global optimization design program for the gradient aluminum foam impact suppression structure is executed by at least one processor, it implements the steps of the global optimization design method for the gradient aluminum foam impact suppression structure according to any one of claims 1 to 6.

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