Honeycomb barrier cell element simulation modeling method

Abstract

The invention relates to the technical field of vehicles, and discloses a cellular barrier cell element simulation modeling method. The method comprises the steps of establishing a discrete Y-shaped section cell body unit model based on geometric symmetry of a honeycomb structure, and eliminating interference of an air residual effect on a deformation rule in traditional modeling by decomposing the honeycomb structure into discrete cell body units; fitting a stress-strain curve of the honeycomb material based on the Y-shaped section cell body unit model, and realizing coupling characterization of strain hardening, rate sensitivity and temperature softening effect through reverse calibration of constitutive parameters of the material. According to the method, a cellular topological structure is reconstructed through discretization of cell elements, non-physical deformation propagation is inhibited, mechanical response characteristics in the dynamic compression process of a cellular material are precisely restored, the problem of deformation mode distortion caused by structure simplification in a traditional simulation model is solved, and the dynamic compression performance of the cellular material is improved. And a high-precision basic model support is provided for digital modeling of the automobile collision honeycomb counterguard.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a honeycomb barrier cell simulation modeling method. Background Art

[0002] In the field of automobile collision safety testing, honeycomb aluminum materials are widely used in barrier model construction to simulate the collision energy absorption process because their mechanical properties are highly compatible with the front-end structure of the vehicle. However, traditional modeling methods have significant bottlenecks: although the equivalent material model simplifies the calculation, it is difficult to capture the nonlinear deformation details of the honeycomb porous structure; the solid unit model causes distortion of local buckling behavior due to excessive homogenization; and although the shell unit model can restore the geometric features, it is limited by boundary effects such as bonding failure and air entrapment, and it is difficult to accurately reproduce the progressive collapse law of the honeycomb when it is crushed. Especially when the honeycomb is subjected to dynamic impact, the existing model is prone to non-physical bottom deformation, which is significantly different from the regular folding mode dominated by the top of the real object, which seriously restricts the credibility of the simulation results.

[0003] On the other hand, the calibration accuracy of the material constitutive parameters directly affects the effectiveness of the simulation. Traditional methods rely on local test data or empirical formulas, lacking the ability to globally map complex dynamic responses, resulting in insufficient matching between the stress-strain curve and the test results. Especially under the strain rate sensitivity and temperature coupling effects, the parameter inversion process is prone to fall into the local optimal solution, making it difficult to establish a strong correlation between material properties and macroscopic mechanical behavior. How to build a honeycomb simulation model that takes into account both geometric details and physical realism while ensuring computational efficiency, and realize intelligent optimization of constitutive parameters, has become a key problem in improving the level of collision safety design. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a honeycomb barrier cell simulation modeling method. By establishing a discrete Y-section cell unit model, the problem of air entrapment is eliminated and the accuracy of the stress-strain curve is improved.

[0005] The present invention provides a honeycomb barrier cell simulation modeling method, comprising: A discrete Y-section cell model is established based on the geometric symmetry of the honeycomb structure. Fitting the stress-strain curve of the honeycomb material by the discrete Y-section cell unit model; The discrete Y-shaped cross-section cell unit model is established based on the geometric symmetry of the honeycomb structure, including: Determining a minimum Y-shaped cell unit based on the honeycomb structure; Determining the discrete Y-shaped cross-section cell unit model based on the minimum Y-shaped cell unit; Wherein, the minimum Y-shaped cell unit is formed by bonding two flange wall groups with an angle of 120°; The method of fitting the stress-strain curve of the honeycomb material by using the discrete Y-section cell unit model comprises: Determining the parameter space range of the constitutive parameters of the minimum Y-shaped cell unit material; Generate multiple sets of parameter sampling sets within the parameter space and perform dynamic compression simulation to obtain corresponding simulation stress and strain data; Training a parameter inversion model based on a mapping relationship between the simulation stress-strain data and target test data; The test curve is reversely calibrated through the parameter inversion model, the optimized constitutive parameters are output and a fitting stress-strain curve is generated.

[0006] Optionally, the minimum Y-shaped cell unit includes a first flange wall, a second flange wall and a third flange wall, the first flange wall and the second flange wall are single-layer thickness shell units, and the third flange wall is a double-layer thickness shell unit.

[0007] Optionally, the third flange wall surface represents the adhesive connection of two flange walls based on the single-thickness shell element with double thickness.

[0008] Optionally, the third flange wall surface represents the adhesive connection between the two flange walls based on the contact between the two single-thickness shell elements.

[0009] Optionally, the third flange wall surface represents the adhesive connection of the two flange walls based on the two single-thickness shell elements and an adhesive layer located between the two single-thickness shell elements.

[0010] Optionally, the material constitutive parameters satisfy the following fluid stress equation: ; in, is the yield stress, is the hardening modulus, is the hardening index, is the strain rate sensitivity coefficient, is the temperature softening index, is the flow stress, is the plastic strain, is the strain rate, Reference strain rate, is the melting temperature, is the current temperature.

[0011] Optionally, the parameter inversion model is a neural network model, whose input is a characteristic vector of stress-strain data and whose output is a material constitutive parameter vector. Optionally, The generation of the parameter sampling set includes dynamically adjusting the sampling density in the parameter space according to the prediction error feedback of the parameter inversion model, so that the distribution of the sampling points matches the parameter sensitivity; The reverse calibration includes an iterative optimization process. When the matching degree between the fitted stress-strain curve and the target test curve does not meet the preset conditions, the parameter sampling set is re-expanded and the parameter inversion model is updated to generate a new fitting curve until the matching conditions are met.

[0012] The present invention has the following technical effects: The honeycomb barrier cell simulation modeling method provided by the present invention suppresses the non-physical deformation problems caused by the residual air effect and the weakening of the bonding interface strength in the traditional model from the structural root through the construction mechanism of the discretized Y-section cell unit. The traditional scheme does not take into account the air flow characteristics inside the honeycomb cell and the dynamic failure behavior of the adhesive, which leads to abnormal buckling in the bottom area in the simulation, and there is a significant deviation from the regular crushing mode dominated by the top in the actual impact test. This method uses the discretization modeling of the geometrically symmetrical Y-shaped cell unit to accurately characterize the load transfer path between adjacent honeycomb walls, eliminate the interference of residual air on the deformation law, and make the simulation model strictly follow the real physical characteristics of top priority folding and bottom stability during dynamic compression, which significantly improves the spatial consistency of the deformation mode.

[0013] Through the differentiated design of flange wall layer thickness and the diversified modeling strategy of bonding connection, a balance is achieved between the restoration of interface mechanical properties and computational efficiency. The hybrid configuration of single-layer and double-layer shell units not only avoids the bottom virtual displacement caused by the hourglass effect in the traditional solid unit model through local stiffness regulation, but also ensures that the bonding interface does not experience unexpected strength attenuation during compression. Combined with the global optimization capability of the parameter inversion model, the neural network is used to decouple the coupling relationship of multi-dimensional constitutive parameters, effectively solving the problem of insufficient adaptability of traditional calibration methods in wide strain rate and temperature gradient scenarios, so that the morphological characteristics of the simulation curves in the elastic yield, plastic flow and densification stages are highly matched with the experimental results. In addition, the adaptive sampling mechanism of the parameter space significantly improves the calibration efficiency by dynamically focusing on the high-sensitivity parameter area, providing standardized process support for the rapid modeling of different batches of honeycomb materials. The multi-path implementation scheme of adhesive connection can flexibly adapt to different simulation accuracy requirements through strategies such as contact algorithm, equivalent stiffness characterization and material property definition. While ensuring the authenticity of interface separation behavior, it reduces the computing resource consumption caused by iterative correction of adhesive failure in traditional schemes, and provides a solution that takes into account both robustness and practicality for the prediction of honeycomb barrier performance under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic flow chart of a honeycomb barrier cell simulation modeling method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a minimum Y-shaped cell unit structure provided by an embodiment of the present invention; Figure 3 A schematic diagram of a constraint direction provided by an embodiment of the present invention; Figure 4 A schematic diagram of a minimum Y-shaped cell unit structure provided by an embodiment of the present invention; Figure 5 A schematic diagram of another minimum Y-shaped cell unit structure provided by an embodiment of the present invention; Figure 6 A schematic diagram of another minimum Y-shaped cell unit structure provided by an embodiment of the present invention; Figure 7 A deformation mode comparison diagram provided by an embodiment of the present invention; Figure 8 A schematic diagram for comparing average compressive stress provided by an embodiment of the present invention; Fig. 9 It is a schematic diagram of the deformation mode of a real honeycomb; Fig.10 This is a schematic diagram of traditional modeling simulation results; Fig.11 It is a schematic diagram of the modeling simulation results of the present invention.

[0016] Reference numerals 11. First flange wall surface; 12. Second flange wall surface; 13. Third flange wall surface; 14. Adhesive layer. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0018] In view of the defects of traditional honeycomb barrier modeling methods in deformation mode distortion, computational efficiency and accuracy, the core of the present invention is to propose a collaborative optimization method based on discretized cell reconstruction and parameter inverse calibration. By decomposing the honeycomb structure into geometrically symmetrical Y-section cell units, the non-physical deformation caused by the residual air effect in the continuum model is eliminated; at the same time, an intelligent mapping relationship between the material constitutive parameters and the global mechanical response is established, and the parameter inversion model is driven by dynamic compression simulation data to break through the adaptability bottleneck of traditional calibration methods in wide strain rate scenarios. The following is a detailed description of the technical implementation path of the present invention in combination with the accompanying drawings and embodiments, specifically including: three core steps of discretized cell topology construction, multi-mode modeling of bonding interfaces, and inverse optimization of constitutive parameters, as well as experimental verification results.

[0019] Figure 1 A schematic flow chart of a honeycomb barrier cell simulation modeling method provided in an embodiment of the present invention, the method comprising: S1. A discrete Y-shaped cross-section cell model is established based on the geometric symmetry of the honeycomb structure. S2. Fit the stress-strain curve of honeycomb material through a discrete Y-section cell unit model.

[0020] The core of the honeycomb barrier cell simulation modeling method is to reconstruct the geometric and mechanical properties of the honeycomb structure through a discretized modeling strategy. Traditional modeling methods use continuum units or homogenization treatments, which makes it difficult to accurately characterize the porous topological characteristics of honeycomb aluminum, resulting in the deformation path in the compression simulation deviating from the physical experimental law. To solve this problem, this method decomposes the entire honeycomb block into discrete Y-section cell units based on the geometric symmetry of the honeycomb structure. The spatial arrangement law of each cell unit is determined by geometric symmetry analysis, and the connection relationship between adjacent cells is defined by a local coordinate system, eliminating non-physical deformations caused by air residue or simplified bonding interfaces in traditional models. Discrete modeling converts complex honeycomb structures into an expandable set of standardized units through topological decomposition, retains the true geometric shape of the honeycomb wall, and constructs a mechanical response network between units, so that the load transfer path during compression is closer to the actual material behavior.

[0021] In a specific implementation, this embodiment can construct a discretized model based on a finite element pre-processing tool and use an explicit dynamics solver to perform dynamic compression simulation. Figure 2 A schematic diagram of a minimum Y-shaped cell unit structure provided by an embodiment of the present invention, Figure 3 A schematic diagram of a constraint direction provided by an embodiment of the present invention. According to the geometric symmetry characteristics of the honeycomb structure, select Figure 2 The smallest Y-shaped cell unit (UC unit) shown in the figure is used as the basic modeling unit. After the local coordinate system is assigned to the surface of each UC unit, Figure 3The constraint directions shown impose normal displacement constraints x and tangential coupling constraints yz on the three surface nodes, eliminating the effects of element edge curvature on the deformation mode.

[0022] When fitting the stress-strain curve of honeycomb materials based on a discretized model, it is necessary to establish a mapping relationship between the material constitutive parameters and the global mechanical response. Traditional parameter calibration relies on empirical formulas or local test data, and it is difficult to simultaneously capture the coupling relationship between strain hardening, rate sensitivity, and temperature softening effects. This method optimizes the material constitutive parameters through a reverse calibration process, combines dynamic compression simulation to generate stress-strain data of multiple sets of parameter sampling sets, and constructs a parameter-response database covering a wide range of working conditions. The synergistic effect of discrete modeling and parameter inversion enables the simulation model to reproduce the deformation law of top preferential buckling during dynamic compression, and predict the stress platform characteristics under different strain rates, significantly improving the consistency of simulation results with physical experiments.

[0023] In some embodiments, a discrete Y-shaped cross-section cell unit model is established based on the geometric symmetry of the honeycomb structure, including: Determine the minimum Y-shaped cell unit based on the honeycomb structure; Determine the discrete Y-shaped cross-section cell unit model based on the minimum Y-shaped cell unit; Among them, the smallest Y-shaped cell unit is composed of two flange wall groups with an angle of 120° bonded together.

[0024] The determination of the minimum Y-shaped cell unit is the basis of discretization modeling. The periodic symmetry characteristics of the honeycomb structure determine the spatial arrangement of its microscopic units. Through the micro-geometric analysis of honeycomb aluminum, it is found that it is composed of multiple periodically arranged hexagonal cells, and each hexagonal cell can be decomposed into six Y-shaped cross-sectional units. The Y-shaped cross-sectional unit is composed of two flange wall groups, and the angle between the flange walls is determined by the honeycomb symmetry. With one vertex of the hexagonal cell as the center of symmetry, adjacent flange wall groups extend and intersect at a specific angle to form the basic configuration of the Y-shaped section. The connection method of the flange wall group directly affects the overall stiffness and deformation mode of the honeycomb structure. During the modeling process, the bonding connection between the flange walls is realized through geometric constraints and contact algorithms to ensure that the load transfer between adjacent units is consistent with the interface behavior of the real material.

[0025] The expansion of the minimum Y-shaped cell unit is completed through replication and spatial transformation. Based on the periodic characteristics of the honeycomb, the single Y-shaped unit is arrayed along the spatial extension direction of the honeycomb block, and the connection angle and spacing between the units are adjusted to match the geometric topology of the physical honeycomb. The connection relationship between the units of the discretized model is defined by shared nodes or contact pairs to avoid stiffness distortion caused by unit continuity in traditional overall modeling. The discretization strategy suppresses the bottom virtual displacement phenomenon in the compression simulation by regulating the local unit stiffness, so that the deformation is strictly limited to the top area, which is consistent with the crush law observed in the experiment.

[0026] In some embodiments, fitting a stress-strain curve of a honeycomb material by a discrete Y-section cell model comprises: Determine the parameter space range of the minimum Y-shaped cell unit material constitutive parameters; Generate multiple sets of parameter sampling sets within the parameter space and perform dynamic compression simulation to obtain corresponding simulation stress and strain data; The parameter inversion model is trained based on the mapping relationship between the simulated stress-strain data and the target test data; The test curve is reversely calibrated through the parameter inversion model, the optimized constitutive parameters are output and the fitting stress-strain curve is generated.

[0027] The reverse calibration of the constitutive parameters of honeycomb materials needs to be achieved through the coordinated optimization of parameter space sampling and dynamic compression simulation. The determination of the parameter space range needs to comprehensively consider the manufacturing process and mechanical properties of honeycomb aluminum, covering the physically reasonable range of key parameters such as yield stress, hardening modulus, and strain rate sensitivity coefficient. The generation of the parameter sampling set adopts Latin hypercube design to ensure that the sampling points are evenly distributed in the multidimensional space and avoid the omission of local parameter combinations. After each set of parameters is input into the finite element model, the stress-strain data is obtained through dynamic compression simulation. The simulation conditions must be consistent with the loading rate and boundary constraints of the physical test to ensure data comparability. The simulation data set is preprocessed to extract feature vectors, including key indicators such as elastic modulus, yield platform length, and densification strain, to construct a parameter-response mapping relationship library.

[0028] The training of the parameter inversion model is based on a deep neural network architecture. The input layer corresponds to the eigenvector of the stress-strain curve, and the output layer is the constitutive parameter vector. Among them, the eigenvector of the input layer can include the slope of the elastic segment, the mean stress of the plastic platform, and the densification strain rate, which are extracted through segmented sampling. The network optimizes the weights through the back-propagation algorithm, and the loss function takes into account both the absolute error of the parameters and the similarity of the curve morphology. The trained model can parse the optimal constitutive parameter combination from the test curve, breaking through the reliance of traditional calibration methods on empirical formulas. During the reverse calibration process, if the matching degree between the fitting curve and the test data is insufficient, it is necessary to expand the parameter sampling set and retrain the model, and gradually reduce the uncertainty of the parameter space through iterative optimization. The process realizes parameter update and simulation task scheduling through automated scripts, significantly reducing the cost of manual intervention, and providing a standardized process for the rapid modeling of different batches of honeycomb materials.

[0029] Continue reading Figure 2 In some embodiments, the minimum Y-shaped cell unit includes a first flange wall 11, a second flange wall 12 and a third flange wall 13, the first flange wall 11 and the second flange wall 12 are single-layer thickness shell units, and the third flange wall 13 is a double-layer thickness shell unit.

[0030] The thickness design of the flange wall directly affects the mechanical properties and computational efficiency of the model. The traditional solid unit model cannot represent the stiffness difference of the bonding connection due to the homogeneous thickness, resulting in weakened interface strength and hourglass effect. The first flange wall 11 and the second flange wall 12 use single-layer thickness shell elements to simulate the actual thickness of the honeycomb aluminum foil; the third flange wall 13 uses double-layer thickness shell elements to equivalently represent the bonding connection of adjacent aluminum foils. The double-layer thickness shell element is realized by geometric superposition, and its equivalent stiffness is consistent with the real bonding interface, avoiding the iterative convergence problem of the contact algorithm. The single-layer shell unit area buckles first to form the initial folding belt at the top; the double-layer shell unit area delays the buckling propagation due to the enhanced stiffness, presenting a step-by-step progressive deformation. The mixed thickness design optimizes the overall mechanical response through local stiffness regulation, suppresses abnormal deformation at the bottom, and reduces the number of interface contact pairs and the computational complexity.

[0031] The simulation results of the mixed thickness model show that the deformation process strictly follows the physical law of top priority crushing, and the bottom area remains stable due to stiffness reinforcement. This design significantly improves the calculation efficiency while maintaining high accuracy, providing a feasible solution for large-scale honeycomb barrier modeling in engineering scenarios.

[0032] Figure 4 A schematic diagram of a minimum Y-shaped cell unit structure provided by an embodiment of the present invention. In some embodiments, the third flange wall 13 is based on a double-thickness single-layer shell unit to represent the bonding connection between the two flange walls. The single-layer thickness of the shell unit is indicated as h, so Figure 4The thickness of the double-thickness single-layer shell element is 2h.

[0033] The bonding connection of the third flange wall 13 is realized by a double-layer thickness shell unit. This design equates the bonding interface of adjacent honeycomb aluminum foils to a single shell unit structure. Traditional contact algorithms require the definition of complex contact pairs and failure criteria, which can easily lead to computational convergence difficulties. This method uses the principle of geometric superposition to merge the bonding areas of two single-layer shell units into a double-layer thickness shell unit, whose equivalent bending stiffness is consistent with the bending resistance of the real bonding interface. The thickness parameters of the double-layer shell unit are determined according to the bonding process of the honeycomb aluminum foil to ensure that the stiffness distribution of the interface area matches the mechanical behavior of the physical material. In the dynamic compression simulation, the double-layer shell unit area forms a local stiffness reinforcement zone due to the increase in thickness, which inhibits the unexpected deformation diffusion caused by premature failure of the bonding interface. This equivalent modeling method significantly improves the simulation efficiency by reducing the number of contact pairs and the need for iterative calculations, while avoiding the model reconstruction cost caused by bonding failure correction in the traditional contact algorithm.

[0034] The geometric continuity of the double-thickness shell element ensures the integrity of the load transfer path. During the compression process, the double-layer shell element of the third flange wall 13 acts as a mechanical transition area, evenly dispersing the stress waves generated by the top buckling to the adjacent elements, avoiding mesh distortion caused by local stress concentration. The simulation results show that the design can accurately reproduce the progressive failure behavior of honeycomb materials at the bonding interface, making the deformation mode of the overall model highly consistent with the stacking crush characteristics observed in physical tests.

[0035] Figure 5 A schematic diagram of another minimum Y-shaped cell unit structure provided by an embodiment of the present invention. In some embodiments, the third flange wall 13 is based on two single-layer shell unit contact characteristics of the bonding connection of the two flange walls.

[0036] The bonding connection of the third flange wall 13 is achieved through the contact algorithm of two single-layer shell elements, which retains the real geometric shape of the honeycomb aluminum foil. The two single-layer shell elements are arranged in parallel along the bonding interface, and the contact surface defines the bonding behavior through the master and slave nodes. The master node bears the normal contact force and tangential friction stress, and the slave node moves synchronously with the master node through the constraint equation to ensure the continuity of the interface load transfer. The bonding strength threshold is set in the contact algorithm. When the interface stress exceeds the threshold, the failure separation is triggered to simulate the peeling behavior of the real bonding layer. The design avoids the interface strength distortion problem caused by stiffness averaging in the traditional equivalent thickness model by refining the contact definition.

[0037] The parameter setting of the contact algorithm needs to match the bonding process parameters of the honeycomb aluminum foil. The normal contact stiffness is calibrated according to the compression modulus of the bonding layer, and the tangential friction coefficient is determined by the interface shear test data. In the dynamic compression simulation, the failure threshold of the contact pair controls the separation timing of the bonding interface, so that the deformation process presents a staged buckling and expansion feature. While retaining the interface mechanical details, this scheme reduces the global calculation amount by localizing the definition of the contact pair, and is suitable for the simulation of small and medium-sized honeycomb models with strict accuracy requirements on interface failure behavior.

[0038] Figure 6 A schematic diagram of another minimum Y-shaped cell unit structure provided by an embodiment of the present invention. In some embodiments, the third flange wall 13 represents the bonding connection of the two flange walls based on two single-layer shell elements and an adhesive layer 14 located between the two single-layer shell elements.

[0039] The bonding connection of the third flange wall 13 is achieved through two single-layer shell elements and an intermediate bonding layer 14. This scheme uses independent unit layers to accurately characterize the mechanical properties of the interface. The bonding layer 14 is composed of thin-layer solid elements, and its thickness is consistent with the actual adhesive coating process. The bonding layer unit is connected to the upper and lower single-layer shell elements through shared nodes to form a "sandwich" structure. For example, a 0.05mm thick solid unit can be inserted between the single-layer shell elements, and the elastic-plastic constitutive model of the adhesive is used to define the mechanical behavior of the interface, and the separation process is characterized by the normal / tangential stress function. The material properties of the bonding layer are defined according to the tensile-shear coupling response of the adhesive, and the normal strength and tangential peeling threshold are calibrated by the interface test data. Through the introduction of independent bonding layer units, this design can accurately simulate the progressive failure process of the bonding interface under complex loads, including normal peeling, shear slip and mixed mode fracture.

[0040] The mechanical response of the bonding layer unit directly affects the deformation mode of the overall model. In the dynamic compression simulation, the accumulation of plastic strain in the bonding layer 14 triggers interface failure, resulting in the gradual separation of the upper and lower shell units. This failure mechanism is consistent with the evolution process of the bonding layer 14 from elastic deformation to fracture in the physical experiment, which enables the simulation model to capture the stress relaxation characteristics of honeycomb materials in the late stage of crushing. Through the refined modeling of the bonding layer unit, this scheme provides a high-fidelity simulation tool for studying the impact of interface failure on the overall mechanical behavior of honeycombs, which is suitable for accurate prediction of honeycomb performance under extreme working conditions in fields such as aerospace.

[0041] Figure 7 A deformation mode comparison diagram provided by an embodiment of the present invention. Figure 7 As shown in the figure, the simulation deformation modes of the three models (double-thickness shell element, contact algorithm, and bonding layer solid element) are consistent with the actual physical test results, which verifies the accuracy of the top preferential buckling characteristics of the present invention. Figure 4The corresponding embodiment, model 2 is Figure 5 The corresponding embodiment, model 3 is Figure 6 Corresponding embodiment. Wherein, the double thickness shell element model (corresponding to Figure 4 The deformation propagation path of the embodiment ) is the most regular, indicating that its bonding interface stiffness enhancement strategy effectively suppresses asymmetric folding.

[0042] Figure 8 A schematic diagram for comparing average compressive stress provided by an embodiment of the present invention. Figure 8 The average collision strength of the three models was further compared with the test results. Model 1 is Figure 4 The corresponding embodiment, model 2 is Figure 5 The corresponding embodiment, model 3 is Figure 6 Corresponding embodiment. Double thickness shell element model (corresponding to Figure 4 The error between the collision strength curve of the embodiment of the present invention and the test value in the plastic platform stage is less than 5%, which is significantly better than the traditional modeling method (error>15%), proving that the present invention can accurately characterize the dynamic energy absorption characteristics of honeycomb materials through discretized cell reconstruction and parameter inversion optimization.

[0043] Three models (i.e. Figure 4 , 5 ,6) The calculation time comparison is shown in Table 1. Model 1 only takes 10 minutes. Figure 4 The corresponding model, Model 2 is Figure 5 The corresponding model, Model 3 is Figure 6 The corresponding model.

[0044] Table 1-Comparison of processing time of three models

[0045] In some embodiments, the material constitutive parameters satisfy the following fluid stress equation: ; in, is the yield stress, is the hardening modulus, is the hardening index, is the strain rate sensitivity coefficient, is the temperature softening index, is the flow stress (elastic + plastic component), is the plastic strain (true strain), is the strain rate, Reference strain rate, is the melting temperature in degrees Kelvin. The temperature calculation is assumed to be carried out under adiabatic conditions. is the current temperature, . is the specific heat per unit volume, is the initial temperature (unit: Kelvin), is the heat per unit volume under adiabatic conditions. , , , , The value ranges are 100~500, 40~800, 0.01~0.6, 0.01~1, and 1~2 respectively.

[0046] The material constitutive equation needs to simultaneously characterize the strain hardening, rate sensitivity and temperature softening effects of honeycomb aluminum. The mathematical form of the equation needs to satisfy the nonlinear characteristics of the stress-strain relationship in dynamic compression simulation, and its core items include strain hardening items, rate enhancement items and temperature softening items. The strain hardening item describes the stress growth caused by the accumulation of plastic strain through a power function, and the hardening exponent controls the rising slope of the curve. The rate enhancement item uses a logarithmic function to associate the strain rate with the stress increase, reflecting the improvement of the material's ability to resist deformation at high loading rates. The temperature softening item simulates the material strength attenuation caused by temperature increase through an exponential function, and the softening exponent determines the attenuation rate. The physical meaning of each parameter in the equation needs to correspond to the micromechanical behavior of honeycomb aluminum, for example, the yield stress corresponds to the initial plastic deformation threshold, and the hardening modulus reflects the resistance to dislocation movement.

[0047] The calibration of constitutive parameters needs to be combined with test data from multiple working conditions. Strain hardening and rate sensitivity characteristics are obtained through constant strain rate compression tests, and temperature softening effects are calibrated through variable temperature tests. In the simulation model, the combination of equation parameters needs to ensure the morphological consistency of the stress-strain curve under different loading conditions. For example, the stress platform under high strain rate needs to match the steep rise characteristics of the test curve by adjusting the rate sensitivity coefficient, while the stress drop caused by the increase in temperature needs to be reproduced by optimizing the softening index. The multiple coupling mechanisms of the equation enable it to adapt to the simulation needs of a wide range of working conditions, providing a reliable theoretical framework for the joint analysis of transient impact and thermal-mechanical coupling effects in vehicle collision scenarios.

[0048] In some embodiments, the parameter inversion model is a neural network model, whose input is a characteristic vector of stress-strain data and whose output is a material constitutive parameter vector. The architecture design of the neural network model needs to take into account the accuracy and computational efficiency of parameter inversion. The input layer receives the preprocessed stress-strain curve feature vector, including key morphological indicators such as the slope of the elastic segment, the length of the plastic platform, and the densification strain rate. The hidden layer adopts a fully connected structure, and realizes high-dimensional mapping of features through nonlinear activation functions. The output layer corresponds to the constitutive parameter vector, and the value of each node is directly related to physical quantities such as yield stress and hardening modulus. The training data of the network comes from the dynamic compression simulation results of the parameter sampling set. The data set must cover the mechanical response range of honeycomb materials under typical working conditions. During the training process, the design of the loss function needs to balance the absolute error of the parameters and the similarity of the curve morphology. For example, the dynamic time warping algorithm is introduced to quantify the morphological differences between the curves to avoid the local optimal trap caused by a single mean square error.

[0049] The inversion capability of the neural network depends on the effective characterization of the eigenvector. The feature extraction of the stress-strain curve needs to be achieved through sliding window segmented sampling and principal component analysis to eliminate noise interference and retain the key morphological information of the curve. The trained network can parse the implicit material parameter combination from the fluctuation characteristics of the test curve, such as inferring the hardening exponent through the steepness of the plastic platform, or judging the temperature softening effect through the hysteresis of the densification strain. This model breaks through the reliance of traditional inversion methods on prior formulas, and is particularly good at dealing with nonlinear problems of multi-parameter coupling, such as stress relaxation characteristics under the synergistic effect of strain rate and temperature. Simulation verification shows that the constitutive parameters of the neural network inversion can make the fitting curve highly consistent with the test data over a wide range of working conditions, providing reliable support for the modeling of honeycomb materials under complex load conditions.

[0050] In specific implementation, the initial constitutive parameters can be obtained in the following two ways: 1) Based on the finite element software and the JOHNSON-COOK model fitting function, input the material yield strength, ultimate strength and necking strain to automatically generate parameters; 2) Using parameter optimization tools, the optimal parameter combination is iteratively solved by the least square method through experimental stress-strain data. The above parameters are used as the initial sampling set for neural network training.

[0051] In some embodiments, The generation of parameter sampling sets includes dynamically adjusting the sampling density in the parameter space according to the prediction error feedback of the parameter inversion model, so that the distribution of sampling points matches the parameter sensitivity; The specific adjustment rules are: If the prediction error of the parameter inversion model exceeds the preset threshold (e.g. 10%), the original parameter sampling interval is reduced by 50% and re-sampled; If the model accuracy meets the requirements but the calculation time is too long, expand the sampling interval to twice the original interval.

[0052] Example: Taking the calibration of the yield stress parameter A as an example, the initial sampling range is 100-500 MPa with an interval of 100 MPa (values ​​are 100, 200, 300, 400, 500).

[0053] If the fitting error exceeds 10% after the first inversion, the interval is reduced to 50 MPa and resampled (with values ​​of 100, 150, 200, …, 500); If the error is less than 5% but a single simulation takes more than 24 hours, the interval is expanded to 200 MPa (with values ​​of 100, 300, 500).

[0054] The reverse calibration includes an iterative optimization process. When the matching degree between the fitted stress-strain curve and the target test curve does not meet the preset conditions, the parameter sampling set is re-expanded and the parameter inversion model is updated to generate a new fitting curve until the matching conditions are met.

[0055] The dynamic adjustment mechanism of the parameter sampling set achieves iterative optimization through feedback control. The range of the initial parameter space is determined according to the material process and historical test data, and the Latin hypercube design is used to generate uniformly distributed sampling points. The matching degree between the simulation curve corresponding to each set of parameters and the target test curve is quantified by the similarity score, and the scoring result is fed back to the sampling controller. The controller identifies the parameter sensitive area according to the score distribution, and improves the sampling density of the highly sensitive area in subsequent iterations, while reducing the redundant sampling of the less sensitive area. For example, if a small change in a parameter subspace causes a significant fluctuation in the matching degree, it is judged that the sensitivity of the area is high, and the number of sampling points needs to be increased through adaptive mesh refinement.

[0056] The iterative optimization process gradually converges to the optimal parameter solution through closed-loop feedback. After each iteration, the newly added sampling data is used to update the parameter inversion model to improve its prediction accuracy in highly sensitive areas. If the matching degree of the fitting curve output by the current inversion model does not reach the threshold, the parameter space expansion mechanism is triggered, such as expanding the parameter boundary along the sensitive direction or introducing a new coupling dimension. This process continues until the matching degree meets the preset conditions or the number of iterations is exhausted. The dynamic adjustment strategy significantly improves the calibration efficiency by balancing "exploration" (sampling of unknown areas) and "utilization" (refinement of highly sensitive areas). Engineering applications have shown that this mechanism can significantly reduce the total number of simulations for parameter calibration while ensuring the global optimality of the inversion parameters, providing core technical support for the rapid consistency evaluation of multiple batches of honeycomb materials.

[0057] In specific implementation, the test can use commercial 3003 aluminum honeycomb samples with specifications of 250mm×250mm×150mm and wall thickness of 19mm. Use a 72kg indenter to impact the test at a speed of 5.24m / s, and the compressive stress is calculated by σ=F / A, where F is the impact force and A is the cross-sectional area outside the plane. Fig. 9 This is a schematic diagram of the actual honeycomb deformation mode. Fig.10 This is a schematic diagram of traditional modeling simulation results. Fig.11 Schematic diagram of the modeling simulation results of the present invention. Figure 9-11 As shown, the actual deformation mode is highly consistent with the simulation result of the present invention in the top preferential buckling characteristics, while the traditional model has non-physical deformation at the bottom.

[0058] The embodiments of the present application may also be a computer-readable storage medium, which includes computer program instructions, and the computer program instructions enable a computer to execute the steps of the method for controlling the braking consistency of a main vehicle and a trailer in a tractor provided in any embodiment of the present application.

[0059] The computer program instructions may be written in any combination of one or more programming languages ​​to form program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user computing device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0060] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0061] It should be noted that the terms used in this application are only for describing specific embodiments, rather than limiting the scope of this application. As shown in the specification and claims of this application, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0062] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and its core ideas of this application. The above are only preferred implementation methods of this application. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the concept and technical solution of the invention to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A honeycomb barrier cell simulation modeling method, characterized in that: include: A discrete Y-section cell model is established based on the geometric symmetry of the honeycomb structure. Fitting the stress-strain curve of the honeycomb material by the discrete Y-shaped cross-section cell unit model; The discrete Y-shaped cross-section cell unit model is established based on the geometric symmetry of the honeycomb structure, including: Determining a minimum Y-shaped cell unit based on the honeycomb structure; Determining the discrete Y-shaped cross-section cell unit model based on the minimum Y-shaped cell unit; Wherein, the minimum Y-shaped cell unit is formed by bonding two flange wall groups with an angle of 120°; The method of fitting the stress-strain curve of the honeycomb material by using the discrete Y-section cell unit model comprises: Determining the parameter space range of the constitutive parameters of the minimum Y-shaped cell unit material; Generate multiple sets of parameter sampling sets within the parameter space and perform dynamic compression simulation to obtain corresponding simulation stress and strain data; Training a parameter inversion model based on a mapping relationship between the simulation stress-strain data and target test data; The test curve is reversely calibrated through the parameter inversion model, the optimized constitutive parameters are output and a fitting stress-strain curve is generated.

2. The method according to claim 1, characterized in that: The minimum Y-shaped cell unit includes a first flange wall surface, a second flange wall surface and a third flange wall surface. The first flange wall surface and the second flange wall surface are single-layer thickness shell units, and the third flange wall surface is a double-layer thickness shell unit.

3. The method according to claim 2, characterized in that The third flange wall represents the adhesive connection of two flange walls based on the single-thickness shell element with double thickness.

4. The method according to claim 2, characterized in that: The third flange wall surface represents the adhesive connection between the two flange walls based on the contact between the two single-thickness shell elements.

5. The method according to claim 2, characterized in that: The third flange wall surface represents the adhesive connection of the two flange walls based on the two single-thickness shell elements and an adhesive layer located between the two single-thickness shell elements.

6. The method according to claim 1, characterized in that The material constitutive parameters satisfy the following fluid stress equation: ； in, is the yield stress, is the hardening modulus, is the hardening index, is the strain rate sensitivity coefficient, is the temperature softening index, is the flow stress, is the plastic strain, is the strain rate, Reference strain rate, is the melting temperature, is the current temperature.

7. The method according to claim 6, characterized in that The parameter inversion model is a neural network model, the input of which is the characteristic vector of stress-strain data, and the output is the material constitutive parameter vector.

8. The method according to claim 1, characterized in that The generation of the parameter sampling set includes dynamically adjusting the sampling density in the parameter space according to the prediction error feedback of the parameter inversion model, so that the distribution of the sampling points matches the parameter sensitivity; The reverse calibration includes an iterative optimization process. When the matching degree between the fitted stress-strain curve and the target test curve does not meet the preset conditions, the parameter sampling set is re-expanded and the parameter inversion model is updated to generate a new fitting curve until the matching conditions are met.

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