A method for determining cell parameters in honeycomb wall barrier simulation modeling

Abstract

The present invention relates to the technical field of mathematical modeling, and in particular, to a method for determining cell parameters for simulation modeling of honeycomb barriers. Equivalent cell expansion is performed according to the unit size of the physical barrier to determine the proportional relationship between the span and thickness of the equivalent cell; meshing is performed in the non-planar thickness direction of the equivalent cell; in a simulation environment, a cell wall discrete model is constructed using thin plate units; based on the proportional relationship, the number of cells in the coplanar lateral direction, the number of cells in the coplanar longitudinal direction and the cell wall thickness of the cell wall discrete model are optimized with the goal of minimizing the peak load during the collision, minimizing the deformation distance of the cell wall discrete model, and maximizing the absorbed energy of the cell wall discrete model. The present invention improves the simulation efficiency of the model while accurately simulating the deformation process in the physical barrier collision.

Description

Technical Field

[0001] The present invention relates to the technical field of mathematical modeling, and more particularly, to a method for determining cell parameters of a honeycomb barrier simulation model. Background Art

[0002] The purpose of automotive crash safety tests is to replicate the conditions of a two-vehicle collision, and a barrier is used to simulate the deformation and mechanical properties of the front of a vehicle. Because its stiffness is similar to the average stiffness of the front end of a vehicle, honeycomb is often used to simulate the characteristics of the vehicle colliding with the test vehicle and serves as the main scale and measuring tool for vehicle collision intensity.

[0003] To reduce design costs and improve efficiency, computer simulation of the automotive collision process has become an important method and means for automotive crash safety design and improvement. Therefore, how to ensure the accuracy and precision of the simulation plays a crucial role in engineering applications. The accuracy and precision of automotive collision simulation depend not only on the algorithms of the finite element software itself but also, to a large extent, on the accuracy of the simulation model, and the accuracy of the barrier model is a very important aspect among them.

[0004] When constructing a simulation model of honeycomb aluminum, various methods such as an equivalent material model, a solid element model, and a shell element model can be used. Although the equivalent material model is computationally efficient, the material model is complex, resulting in limited simulation accuracy; although the solid element model has high analysis accuracy, it has deficiencies in simulating local deformation, and the problem of hourglass energy is significant; the shell element model can accurately reproduce the true geometric shape of honeycomb aluminum and effectively capture its complex deformation characteristics, but the simulation accuracy still needs to be improved. Therefore, selecting an appropriate modeling method to accurately simulate the material properties of honeycomb aluminum and taking into account computational efficiency while improving simulation accuracy has a profound impact on automotive crash safety testing. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for determining cell parameters of a honeycomb barrier simulation model, which can improve the simulation efficiency of the model while accurately simulating the deformation process during the collision of a physical barrier.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] The present invention provides a method for determining cell parameters of a honeycomb barrier simulation model, including:

[0008] Performing equivalent cell expansion according to the size of the physical barrier unit to obtain an equivalent honeycomb model, and determining the proportional relationship between the span and thickness of the equivalent cells in the equivalent honeycomb model;

[0009] Performing mesh division on the equivalent honeycomb model;

[0010] In a simulation environment, constructing a cell wall discrete model using thin plate elements;

[0011] According to the proportional relationship, to minimize the peak load F during the collision process p , to minimize the deformation distance D of the cellular wall discrete model, and to maximize the energy absorption W1 of the cellular wall discrete model as the objective, the number of cells N in the coplanar transverse direction of the cellular wall discrete model W , the number of cells N in the coplanar longitudinal direction L and the cellular wall thickness t2 are optimized.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0013] The present invention deeply optimizes the equivalent cell expansion method to reduce errors, improve simulation accuracy while maintaining computational efficiency, and further achieve the optimal parameter design of aluminum honeycomb, which has great practical significance and application value for the simulation research of aluminum honeycomb. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 is a schematic diagram of the aluminum honeycomb structure provided by the embodiment of the present invention;

[0016] Figure 2 is a schematic diagram of the cell of the hexagonal honeycomb structure provided by the embodiment of the present invention;

[0017] Figure 3 is a schematic flow chart of a method for determining the cell parameters of a honeycomb wall barrier simulation model provided by the embodiment of the present invention;

[0018] Figure 4 is a schematic diagram of the double rigid walls provided by the embodiment of the present invention;

[0019] Figure 5 is a schematic diagram of the area equivalent constraint condition provided by the embodiment of the present invention;

[0020] Figure 6 is a comparison diagram of the honeycomb deformation modes before and after optimization provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, descriptions of well-known functions and structures are omitted below for clarity and conciseness.

[0022] The embodiments of the present invention are applicable to the scenario of optimizing the parameters of a honeycomb model (i.e., a cell wall discrete model) during the simulation modeling of honeycomb aluminum using the equivalent cell expansion method in a vehicle collision scenario.

[0023] To facilitate the introduction of the method provided by the present invention, the honeycomb aluminum structure is first introduced.

[0024] Honeycomb is an anisotropic material manufactured based on bionics. As Figure 1 shown, the dimension in the L direction is called the coplanar longitudinal length, the dimension in the W direction is called the coplanar transverse length, and the dimension in the T direction is called the out-of-plane thickness. Loading along the L or W direction is called coplanar loading, and loading along the T direction is called out-of-plane loading. As a buffer energy-absorbing material, the out-of-plane load-bearing capacity of aluminum honeycomb is much better than its coplanar load-bearing capacity. Therefore, the energy absorption of the honeycomb structure during collision is usually based on out-of-plane compression.

[0025] See Figure 2 , the cell parameters of the hexagonal honeycomb structure include cell length l, cell span h, cell thickness t, and the included angle θ between cell walls. The thickness of the cell bonding surface is double-layer thickness, which is 2t.

[0026] See Figure 3 , the method provided by the embodiments of the present invention includes:

[0027] S110. Perform equivalent cell expansion according to the size of the physical barrier unit to obtain an equivalent honeycomb model, and determine the proportional relationship between the span and thickness of the equivalent cells in the equivalent honeycomb model.

[0028] In a specific embodiment, cells with h / l = 1 and are selected as the research objects. Of course, cells with other sizes can also be selected for research.

[0029] The energy absorption characteristics of the honeycomb structure are directly related to the t / l value. For a regular hexagonal honeycomb structure ( t=l ), without considering the influence of the seams on the volume, to ensure the consistency of the energy absorption characteristics, when selecting the cell size of the honeycomb modeling unit cell, the t / l number should be kept consistent or controlled within a certain range, that is, ensure t / hThe numbers are kept consistent or controlled within a certain range. After equivalent cell expansion, an equivalent honeycomb model composed of multiple equivalent cells is obtained.

[0030] S120. Mesh the equivalent honeycomb model.

[0031] Optionally, mesh the three-dimensional space of the equivalent honeycomb model. The following takes the T direction as an example for illustration.

[0032] The mesh element size of the cell in the T direction directly determines the plastic folding wavelength λ of the pore wall, and this wavelength is usually approximately equal to the cell size h. Therefore, the mesh element size in the T direction has an important influence on the deformation mode of the aluminum honeycomb. The calculation of the critical time step (i.e., the simulation time step) depends on the type of element used. The critical time step of the shell element is calculated by the Courant criterion:

[0033] ; Formula (1)

[0034] Among them, is the critical time step, is the minimum side length, and c is the stress propagation speed. The Courant criterion defines the relationship between the time step and the space step, ensuring the stability and convergence in the numerical simulation process.

[0035] For the modeling of the present invention based on shell elements, the stress propagation speed c is:

[0036] ; Formula (2)

[0037] Among them, E is the Young's modulus, is the Poisson's ratio, is the material mass density.

[0038] The smaller the mesh element size in the T direction, the closer the buckling wavelength of the pore wall in the simulation is to the real wavelength in reality. However, the mesh element size directly determines the simulation time step of the cell wall discrete model. The most commonly used simulation time step in vehicle collision simulation is 1 ms.

[0039] Since the strain gradient at the connection between the pore walls and the pore wall edges is much higher than that in the central part of the pore wall. Smaller mesh elements should be used at positions with large strain gradients to obtain accurate results, and larger mesh elements can be used at positions with small strain gradients. Therefore, the cell wall discrete model is meshed using a graded mesh, that is, the mesh at the edge of the aluminum honeycomb pore wall is a square unit, and its side length is determined by the required model time step, while the mesh elements in the central part of the pore wall use rectangular units with a high aspect ratio.

[0040] S130. In the simulation environment, use thin plate elements to construct the cell wall discrete model.

[0041] Optionally, first use a display dynamics solution software package (such as DYNA3D) to numerically verify the structure before and after cell expansion.

[0042] Use Belytschko-Tsay thin plate elements to construct a discrete model of the cell wall. The discrete model of the cell wall and boundary conditions are as Figure 4 shown. The cell substrate is aluminum A3003, with a density ρ = 2100 kg / m 3 , elastic modulus E = 70 GPa, Poisson's ratio ν = 0.33, yield strength σ yｓ = 190 MPa, and ultimate tensile strength 800 MPa. Since the cell wall adhesives have sufficient strength during low-speed impact and will not cause tearing between double cell walls, the cell wall adhesives are not considered in the modeling. The equivalent cells all adopt a double rigid wall mode, with a fixed rigid wall at the bottom as a rigid boundary and a moving rigid wall at the top, loaded at a constant speed of 5 m / s. To prevent cell penetration, an automatic single-sided contact algorithm is selected with a friction factor of 0.2.

[0043] S140. According to the proportional relationship, with the goal of minimizing the peak load F during the collision process p , minimizing the deformation distance D of the discrete model of the cell wall, and maximizing the energy absorption W1 of the discrete model of the cell wall, optimize the number of cells N in the coplanar transverse direction, the number of cells N in the coplanar longitudinal direction W of the discrete model of the cell wall, and the equivalent cell wall thickness t2. L

[0044] If only the thickness of the pore wall is modified accordingly after expanding the honeycomb pore type to maintain the strength of the discrete model of the cell wall in the T direction, without optimizing other parameters (such as materials, failure, etc.) in the discrete model of the cell wall, the discrete model of the cell wall will lose the deformation mode of gradual collapse. Therefore, in order to maintain the strength of the honeycomb in the T direction and the deformation mode of gradual collapse after expanding the pore type, it is necessary to continue to optimize the compression simulation of the discrete model of the cell wall in the T direction.

[0045] When the moving distance of the moving rigid wall is fixed, the smaller the deformation distance of the honeycomb, the smaller the buckling wavelength of the pore wall of the aluminum honeycomb, the more concentrated the deformation of the aluminum honeycomb in the T direction, and the more in line with the actual collision compression situation. At the same time, when the aluminum honeycomb is used as an energy absorption element, in order to minimize the kinetic energy of the impact object to the greatest extent, the peak load of the honeycomb structure should be minimized and the energy absorption increased during the collision process.

[0046] Based on the above analysis, the optimization objectives include: minimizing the deformation distance D of the discrete model of the cell wall in the T-direction compression simulation, minimizing the peak load F during the collision process p , and maximizing the energy absorption W1 of the discrete model of the cell wall.

[0047] Figure 5 It is a schematic diagram of the area equivalent constraint condition provided by the embodiment of the present invention. The thick black hexagon is the equivalent cell, with a span of and a thickness of t2. The un-bolded black hexagon is the cell before equivalence, with a span of h1 and a thickness of t1. Let the number of cells in the W direction and the L direction be N W and N L respectively. The total length of the model in the W direction is d W and the total length of the model in the L direction is d L . θ is the inner angle of the cell wall. For a regular hexagon, h=l , and ignoring the thickness of the intercellular glue, from the geometric relationship, it can be known that the equivalent cell needs to meet the following conditions in the W and L directions respectively:

[0048] ; Formula (3)

[0049] ; Formula (4)

[0050] The optimal value of the span of the equivalent cell is determined by the optimal combination of N W and N L . Its overall goal is to make the difference in the bearing area before and after equivalence meet the error limit ε requirement when is as large as possible. Judge whether the difference in the bearing area caused by N W and N L before and after equivalence meets the error limit ε requirement. If not, reset N W and N L until the difference in the bearing area meets the error limit ε requirement. In the design of the wall barrier, different stiffness regions are usually used to optimize its performance to adapt to different impact conditions and protection requirements. In the present invention, the spans of the cells in different stiffness regions (hereinafter referred to as regions) that meet the conditions are 8, 10, 12, 14, and 16 respectively.

[0051] For a regular hexagon, its error limit ε is:

[0052] ; Formula (5)

[0053] ; Formula (6)

[0054] Among them, represents the span of the equivalent cell in the iter th iteration, represents the error limit corresponding to the difference in the bearing area in the iter th iteration, represents the iter + 1The error limit corresponding to the bearing area difference in the next iteration.

[0055] According to the proportional relationship between the span and thickness of the equivalent cell, when the side length of the cell is determined, the cell wall thickness of the i-th region cell after equivalence is initially obtained as:

[0056] ; Formula (7)

[0057] where is the cell span of the i-th region cell before equivalence, is the cell span of the i-th region cell after equivalence, is the cell wall thickness of the i-th region cell before equivalence.

[0058] In this embodiment, the number of cells N in the coplanar transverse direction of the cell wall discrete model W , the number of cells N in the coplanar longitudinal direction L and the cell wall thickness t2 are optimized. The cell optimization analysis is carried out for 5 different regions. The objective function is shown in Formula (8).

[0059] 1; Formula (8)

[0060] The optimization conditions in the optimization process are as follows:

[0061] For the following 5 optional cell honeycomb structures: , The value range of is:

[0062] 0.04 ≤ ≤ 0.52( = 8)

[0063] 0.04 ≤ ≤ 0.52( = 10)

[0064] 0.04 ≤ ≤ 0.52( = 12); Formula (9)

[0065] 0.04 ≤ ≤ 0.52( = 14)

[0066] 0.04 ≤ ≤ 0.52( = 16)

[0067] For the aforementioned 5 cell honeycomb structures: The value range of is:

[0068] ; Formula (10)

[0069] In summary, the mathematical model of this optimization problem can be constructed as follows:

[0070] ; Formula (11)

[0071] Optionally, since the variables of the objective function f are , after minimizing the objective function, we first obtain , and then according to Formula (7) and each , we calculate and obtain .

[0072] Calculate the number of cells in the coplanar transverse direction and the number of cells in the coplanar longitudinal direction for the i-th regional cell according to the following formula , and the number of cells in the coplanar longitudinal direction :

[0073] ; Formula (12)

[0074] ; Formula (13)

[0075] Optionally, adopt the memory algorithm of the crow population. According to the proportional relationship, with the goal of minimizing the peak load F p during the collision process, minimizing the deformation distance D of the cell wall discrete model, and maximizing the energy absorption W1 of the cell wall discrete model, the cell wall thickness and the cell span of the i-th cell honeycomb structure are obtained.

[0076] The memory algorithm of the crow population provided in this embodiment includes the following operations:

[0077] The specific steps are as follows:

[0078] Step 1: Initialize the algorithm parameters. The parameters include: the number of crow populations n, the flight distance fl, the perception probability Ap, the number of iterations iter, and the maximum number of iterations itermax.

[0079] Step 2: Initialize the crow positions and crow memories. n crows are randomly placed in the d-dimensional search space. Each crow represents a feasible solution to the problem, and d is the dimension of the decision variable;

[0080] ; Formula (14)

[0081] In Formula (14), Crows is the initialized crow position, is the position of the n-th crow in the d-dimensional search space (the d-th decision variable space).

[0082] ; Formula (15)

[0083] In Formula (15), Memory is the initialized memory of the crow, which represents the matrix of the best feasible solution after initialization; is the memory of the nth crow in the d-dimensional search space (the dth decision variable space), which represents the best value of the nth feasible solution in the dth decision variable space at the current iteration number.

[0084] For this problem, only the cell span h and the cell thickness t are solved, so d is 2, and Formulas (14) and (15) can be changed to:

[0085] ; Formula (16)

[0086] ; Formula (17)

[0087] Step 3: Evaluate the fitness of the position of each crow according to Formula (8). For this problem, as can be seen from Formula (11), the position of the crow corresponding to the smaller solution value of Formula (8) has better fitness.

[0088] Step 4: Generate a new position for each crow.

[0089] ; Formula (18)

[0090] In Formula (18), is the position of crow i in the iter-th iteration, is the new position of crow i in the (iter + 1)-th iteration, is the flight distance of crow i in the iter-th iteration, is the memory of crow i in the iter-th iteration, r i is a random number within [0, 1], is the perception probability of crow i in the iter-th iteration, represents a random position.

[0091] Step 5: Check the feasibility of the new position. If the fitness of the fitness function corresponding to the new position of the crow is higher, the crow will update its position to the crow memory matrix; otherwise, the crow memory matrix will not be updated.

[0092] Step 6: Evaluate the fitness of the new position according to Formula (18) and update the memory;

[0093] ; Formula (19)

[0094] In Formula (19), is the new memory of crow i in the (iter + 1)-th iteration, is the memory of crow i in the iter-th iteration, is the position of crow i in the (iter + 1)-th iteration, is the objective function formula (8).

[0095] Step 7: Test the termination criterion. Repeat Steps 4 to 6 until the maximum number of iterations itermax or other constraint conditions are reached. When the termination criterion is met, consider the optimal position of the crow memory corresponding to the minimum value of the objective function as the solution to the problem.

[0096] The logic of the memory algorithm for the entire crow population is to compare the fitness function values corresponding to the crow positions in the t-th iteration with the fitness function values corresponding to the crow positions in the (t - 1)-th iteration, place the crow positions corresponding to the higher fitness function values into the crow memory matrix, and then compare the newly generated crow positions with the crow memory to determine whether to update the crow memory matrix again. After repeated iterations, the best crow positions are stored in the crow memory matrix.

[0097] According to the crow search algorithm, the minimum value of the objective function under the iteration stop condition and the corresponding variable cell wall thickness can be output and cell element span . At this time, according to formula (7), the cell wall thicknesses of the 5 different region cells can be calculated ; according to formulas (12) and (13), the number of cells in the W direction and the L direction are calculated respectively, which are N W and N L .

[0098] Figure 6 is a comparison diagram of the honeycomb deformation modes before and after optimization provided by the embodiment of the present invention. The left side is the cell wall discrete model before optimization, and the right side is the cell wall discrete model after optimization. After optimization, the deformation region of the model in the compression direction is mainly concentrated at the top, which is closer to the deformation region of the wall barrier physical object. In contrast, for the model before optimization, in addition to the deformation at the top, there is also corresponding deformation at the bottom, which does not conform to the actual deformation region.

[0099] The following uses a specific embodiment to detail the method provided by the present invention.

[0100] The optimization objective is: to minimize the objective function, see formula (8), to minimize the peak load F p , to minimize the deformation distance D, and to maximize the absorbed energy W1.

[0101] The variables are: the number of cells N W in the W direction and N L in the L direction, and the cell thickness is t2.

[0102] d W and d L are known and satisfy Formula (12) and Formula (13) in the W direction and the L direction.

[0103] For multiple stiffness regions: h1 = 8, 10, 12, 14, 16. Taking h1 = 8 as an example, there are the following formulas:

[0104] ; Formula (20)

[0105] The constraint conditions when h1 = 8 are as follows:

[0106] ; Formula (21)

[0107] Then, the optimal solution is found through the memory algorithm of the crow population.

[0108] It should be understood that various forms of the process shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present invention can be achieved. No limitation is made herein.

[0109] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining cell parameters in honeycomb barrier simulation modeling, characterized in that, including: Performing equivalent cell expansion according to the size of the physical barrier unit to obtain an equivalent honeycomb model, and determining the proportional relationship between the span and thickness of the equivalent cells in the equivalent honeycomb model; Performing mesh division on the equivalent honeycomb model; In a simulation environment, constructing a cell wall discrete model using thin plate elements; the cell element substrate of the cell wall discrete model is aluminum, and the boundary conditions of the cell wall discrete model include density, elastic modulus, Poisson's ratio, yield strength, and ultimate tensile strength; all equivalent cells adopt a double rigid wall mode, with the bottom fixed rigid wall as the rigid boundary and the top as the moving rigid wall, loaded at a constant speed. To prevent cell penetration, an automatic single-sided contact algorithm is selected with a friction factor of 0.2; According to the proportional relationship, with the goal of minimizing the peak load F during the collision process p , minimizing the deformation distance D of the cell wall discrete model, and maximizing the energy absorption W1 of the cell wall discrete model, optimize the number of cells N in the coplanar transverse direction of the cell wall discrete model W , the number of cells N in the coplanar longitudinal direction L and the cell wall thickness t2, including: Based on the above proportional relationship, with the aim of minimizing the peak load Fp during the collision process, minimizing the deformation distance D of the cell wall discrete model, and maximizing the energy absorption W1 of the cell wall discrete model, the cell wall thickness of the i-th regional cell before equivalence is obtained and the cell element span of the i-th regional cell after equivalence ; Calculate the cell wall thickness of the i-th regional cell according to the following formula :[[]]END]] ； Among them, is the cell span of the ith regional cell before equivalence, is the cell wall thickness of the ith regional cell after equivalence, is the cell wall thickness of the structure of the ith regional cell before equivalence, is the cell span of the ith regional cell after equivalence; Calculate the number of cells in the coplanar transverse direction for the i-th regional cell according to the following formula N Wi , and the number of cells in the coplanar longitudinal direction N Li : ； ； Among them, is the included angle within the cell wall, d W and d L are the lengths of the cell wall discrete model in the coplanar transverse direction and the coplanar longitudinal direction, respectively.

2. The method for determining the cell parameters of the honeycomb wall barrier simulation model according to claim 1, wherein Performing mesh division on the equivalent honeycomb model, including: Performing mesh division using a gradient mesh in the different-plane thickness direction of the equivalent cell; The gradient mesh includes: the meshes at the edges of the aluminum honeycomb cell walls are square elements, and the meshes in the center of the cell walls are rectangular elements.

3. The method for determining the cell parameters of the honeycomb wall barrier simulation model according to claim 2, wherein Before constructing the cell wall discrete model using thin plate elements, it also includes: Using an explicit dynamics solution software package to perform numerical verification on the structure before and after cell expansion.

4. The method for determining the cell parameters of the honeycomb wall barrier simulation model according to claim 3, wherein The parameters and boundary conditions of the cell wall discrete model include: The cell base material is aluminum A3003, with a density ρ = 2100 kg / m 3 , elastic modulus E = 70 GPa, Poisson's ratio ν = 0.33, yield strength σ yｓ = 190 MPa, and ultimate tensile strength 800 MPa.

5. The method for determining the cell parameters of the honeycomb wall barrier simulation model according to claim 4, characterized in that, The constraint conditions during the optimization process include: 0.04≤ ≤0.52； ； Among them, is the cell wall thickness of the i-th regional cell structure before equivalence, is the cell span of the i-th regional cell honeycomb structure before equivalence, is the inner angle of the cell wall, d W and d L are the lengths of the cell wall discrete model in the coplanar transverse direction and the coplanar longitudinal direction respectively, is the cell span of the i-th regional cell after equivalence.

6. The method for determining the cell parameters of the honeycomb wall barrier simulation model according to claim 1, characterized in that Based on the said proportional relationship, with the goal of minimizing the peak load Fp during the collision process, minimizing the deformation distance D of the cellular wall discrete model, and maximizing the energy absorption W1 of the cellular wall discrete model, the wall thickness of the i-th cellular honeycomb structure is obtained and the cellular span , including: Adopt a memory algorithm for the crow population. According to the proportional relationship, minimize the peak load F during the collision process p , with the goal of minimizing the deformation distance D of the cell wall discrete model and maximizing the energy absorption W1 of the cell wall discrete model, to obtain the cell wall thickness of the i-th type of cell honeycomb structure and the cell span .

7. The method for determining the cell parameters of the honeycomb wall barrier simulation model according to claim 6, wherein The cell span of the i-th cell honeycomb structure is determined in the following manner: The number of cells N in the coplanar transverse direction of the cell wall discrete model W , and the number of cells N in the coplanar longitudinal direction L are optimized and combined so that the difference in load-bearing area before and after equivalence meets the error limit requirements, and are 8, 10, 12, 14, and 16 respectively.

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