Head finite element model verification and evaluation method for automobile crash safety

Abstract

The invention relates to the technical field of head model verification, and discloses an automobile crash safety-oriented head finite element model verification and evaluation method, which comprises the following steps: a model adaptability benchmarking step: carrying out benchmarking on head indexes and fineness of a to-be-verified head finite element model, and screening the head finite element model meeting the standard; a biological fidelity verification step: performing analogue simulation verification according to working conditions from brain / facial skull mechanical response under an impact load and brain tissue pressure and kinematics response dimensions under a rotating load to obtain a simulation result; and a head finite element model evaluation step: performing biological fidelity score assignment on the head finite element model based on a simulation result, and performing evaluation based on a score assignment result. According to the method, through systematic and multi-dimensional verification indexes and in combination with a quantitative evaluation system, the evaluation accuracy, integrity and applicability of the head finite element model are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of head model verification, and particularly relates to a method for verifying and evaluating a head finite element model for vehicle crash safety. Background Art

[0002] Human finite element models have high biological fidelity and can truly reflect human injuries. They are the best digital equipment for virtual vehicle safety assessment. In recent years, there has been an endless stream of human models. However, the quality of evaluation tools for human digital models varies, and it is impossible to comprehensively evaluate the biological fidelity of models and promote updates and iterations.

[0003] Currently, there is a lack of systematic verification methods, especially for the verification of head finite element models. In vehicle crash safety, head injuries are extremely harmful to human injuries and cause a very high mortality rate. Therefore, head finite element models play a crucial role in evaluating and reducing the harm of human injuries. Secondly, existing technologies often only evaluate the biological fidelity of head models from a single dimension for brain injuries, such as only focusing on the mechanical response of the skull or the pressure change of brain tissue, ignoring the interaction and comprehensive influence between the two in multi-directional collisions.

[0004] The existing technologies mainly include imperfect verification methods, as well as problems such as ignoring the influence of rotational loads and single-dimensional evaluation, which cannot comprehensively and accurately evaluate the risk of brain injuries in different collision situations, and thus cannot verify and evaluate the biological fidelity of head models. Therefore, there is currently no systematic verification method and technical requirement for the finite element model of Chinese 50th percentile males in this patented technology to establish a multi-dimensional and comprehensive evaluation system for the Chinese male head finite element model with multi-dimensional analysis. Summary of the Invention

[0005] The present invention aims to provide a method for verifying and evaluating a head finite element model for vehicle crash safety, so as to comprehensively improve the evaluation accuracy, integrity, and applicability of the head finite element model through systematic and multi-dimensional verification indicators in combination with a quantitative evaluation system.

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

[0007] A method for verifying and evaluating a head finite element model for vehicle crash safety includes:

[0008] A model adaptability benchmarking step of benchmarking the head indicators and fineness of the head finite element model to be verified, and screening out the head finite element models that meet the standards;

[0009] A biological fidelity verification step of performing simulation verification on different working conditions from the dimensions of the mechanical response of the brain / facial skull under impact load and the pressure and kinematic response of brain tissue under rotational load to obtain simulation results;

[0010] The evaluation steps of the head finite element model are to assign a bio - fidelity score to the head finite element model based on the simulation results and evaluate based on the scoring results.

[0011] The principle and advantages of this solution are as follows: In practical applications, in the model adaptability benchmarking step, the head indicators and fineness of the head finite element model to be verified are benchmarked to screen out the head finite element models that meet the standards, ensuring the basic quality of the head finite element model to be verified and providing a reliable starting point for subsequent verification. In the bio - fidelity verification step, simulation verification is carried out for different working conditions from the dimensions of the mechanical response of the brain / facial skull under impact load and the pressure and kinematic response of brain tissue under rotational load. The mechanical response of the brain / facial skull under impact load simulates the mechanical behavior of the head when directly impacted during a collision, facilitating the evaluation of whether the model can accurately reflect the stress and strain distributions of the brain and skull; the pressure and kinematic response of brain tissue under rotational load simulates the shear force, displacement, and pressure changes of brain tissue caused by the rotational movement of the head, facilitating the verification of the model's performance under complex working conditions. Based on the simulation results of different working conditions, a comprehensive evaluation of the model's bio - fidelity is carried out. A quantitative scoring system is used to assign a score to the bio - fidelity of the model, and the model is comprehensively evaluated according to the scoring results. The scoring system covers key mechanical response indicators (such as stress, strain, acceleration, etc.), thus providing a scientific basis for the applicability of the model. The introduction of the bio - fidelity scoring mechanism converts qualitative descriptions into quantitative indicators, making the comparison between different head finite element models more intuitive and scientific. Through the three - step process of "model adaptability benchmarking - bio - fidelity verification - model evaluation", this application forms a complete closed - loop verification system, which not only improves the standardization of the verification process but also ensures the quality control of the model at different stages.

[0012] Preferably, as an improvement, the bio - fidelity scoring includes: comparing the simulation result curve with the benchmark channel, obtaining the CORA score of each simulation result curve through the CORA scoring model. If the working condition includes multiple result curves, the average value of the CORA scores of all simulation result curves is processed.

[0013] Technical effect: Facilitating to ensure that the comparison between different head finite element models is more fair and reliable.

[0014] Preferably, as an improvement, the CORA scoring model is:

[0015] CORA = G 1 *C 1 +G 2 *(G V *V + G P *P + G G *G)

[0016] Among them, G 1 , G 2 , G V , G P and G G are weighting factors, C 1 is the corridor score, V is the progress score, P is the phase shift score, and G is the size score.

[0017] Technical effect: It converts subjective judgment into objective numerical values, facilitating the quantification of evaluation criteria, thereby improving the scientificity and consistency of the evaluation process.

[0018] Preferably, as an improvement, the calculation model of the corridor score is:

[0019]

[0020] Among them, C i is the score at each time step, and n is the number of time steps within the evaluation interval.

[0021] Technical effect: The corridor score can measure whether the simulation curve is always within the allowable fluctuation range of the experimental data, facilitating ensuring that the overall trend of the simulation results is consistent with the experimental data, improving the judgment ability of the overall trend of the simulation results, and avoiding ignoring the global matching due to local detail problems.

[0022] Preferably, as an improvement, the calculation model of the progress score is:

[0023] V = K V

[0024] Among them, K is the maximum cross-correlation value.

[0025] Technical effect: The progress score can measure the time step consistency between the simulation curve and the experimental curve, that is, whether they reach the same eigenvalue at the same time point, facilitating ensuring that the simulation model can accurately capture the time characteristics of the experimental data, improving the evaluation accuracy of the dynamic response characteristics, and for collision safety analysis involving time dependence, ensuring the time accuracy of the simulation results.

[0026] Preferably, as an improvement, the calculation model of the phase shift score is:

[0027]

[0028] Among them, δ is the time shift corresponding to the maximum cross-correlation K.

[0029] Technical effect: The phase shift score can measure the time delay or advance between the simulation curve and the experimental curve, detect whether there is a systematic deviation in time in the simulation model, verify whether the model can correctly reflect the occurrence order of physical phenomena, facilitate improving the evaluation ability of time-correlated features, and is particularly important in periodic or oscillatory behaviors such as brain tissue shear force analysis.

[0030] Preferably, as an improvement, the calculation model of the size score is:

[0031]

[0032] where A sim is the area between the simulation curve and the time axis, and A ref is the area between the reference curve and the time axis.

[0033] Technical effect: The size score can measure the matching degree of the simulation curve and the experimental curve in amplitude, ensure that the simulation model can accurately predict the numerical size of physical quantities, facilitate improving the evaluation ability of numerical accuracy, and is for ensuring the reliability of research results that require accurate prediction of physical quantities such as intracranial pressure distribution.

[0034] Preferably, as an improvement, the evaluation includes: obtaining the correlation assignment of the simulation data and the experimental results under the CORA score based on a preset rule, obtaining the dynamic weight values of each working condition, and performing a dynamic score according to the dynamic weight values of each working condition and the correlation assignment. The dynamic score model is:

[0035] Q = ∑w m s m

[0036] where w m is the weight factor of working condition m, and s m is the correlation assignment;

[0037] Obtain the biofidelity evaluation result of the head finite element head model according to the dynamic score mapping.

[0038] Technical effect: By converting the matching degree of the simulation data and the experimental results into specific values, the scientificity and consistency of the evaluation process are ensured.

[0039] Preferably, as an improvement, the working conditions include: forward head linear impact, top head linear impact, forward head linear / rotational impact, forward / lateral / backward head linear / rotational impact.

[0040] Technical effect: The above working condition simulations cover the most common head force scenarios in vehicle collisions, significantly improving the reliability of the model under complex working conditions.

[0041] Preferably, as an improvement, the simulation results include: head linear acceleration, rotational angular velocity, stress distribution, intracranial pressure, and force distribution.

[0042] Technical effect: Through the above results, it is convenient to comprehensively reflect the dynamic response of the head during a collision, finely depict the mechanical behavior of the brain tissue and skull, and comprehensively evaluate the head force distribution. Brief Description of the Drawings

[0043] Figure 1 is a schematic flow chart of a method for validating and evaluating a head finite element model for vehicle crash safety;

[0044] Figure 2 is a schematic flow chart of the model adaptability benchmarking in an embodiment of the present invention;

[0045] Figure 3 is a schematic diagram a of the head measurement items in an embodiment of the present invention;

[0046] Figure 4 is a schematic diagram b of the head measurement items in an embodiment of the present invention;

[0047] Figure 5 is a schematic diagram c of the head measurement items in an embodiment of the present invention;

[0048] Figure 6 is a schematic flow chart of the dynamic analysis in an embodiment of the present invention;

[0049] Figure 7 is a schematic diagram a of the preset benchmarking channels in an embodiment of the present invention;

[0050] Figure 8 is a schematic diagram b of the preset benchmarking channels in an embodiment of the present invention;

[0051] Figure 9 is a schematic diagram c of the preset benchmarking channels in an embodiment of the present invention. Detailed Description of the Invention

[0052] The following is a further detailed description through specific embodiments:

[0053] The reference numerals in the drawings of the specification include: head width 1, head length 2, morphological facial length 3, interpupillary distance 4, head circumference 5, head sagittal arc 6, intertragal arc 7, head height 8.

[0054] The embodiment is basically as shown in the attached Figure 1 A method for validating and evaluating a head finite element model for vehicle crash safety includes a model adaptability benchmarking step, a biofidelity verification step, and a head finite element model evaluation step.

[0055] As Figure 2As shown, in the model adaptability benchmarking step, the head indicators and fineness of the head finite element model to be verified are benchmarked, and the head finite element models that meet the standards are screened. Specifically, in this embodiment, taking the verification and evaluation of the head finite element model of Chinese 50th percentile males as an example, benchmarking the head index measurement methods of Chinese adult males aged 18 to 70 at the 50th percentile in the national standard, measure the head width 1, head length 2, morphological facial length 3, interpupillary distance 4, head circumference 5, head sagittal arc 6, intertragal arc 7 (head coronal arc), head height 8, etc. of the head finite element model to be verified. The measurement method is as Figures 3 to 5 shown. Calculate the relative error between the measurement results and the national standard. When the error range meets the admission threshold, it meets the admission standard. The preferred admission threshold in this embodiment is within 10%; otherwise, the difference between the head finite element model to be verified and the national standard of Chinese 50th percentile males is too large and it is not applicable to this verification system. At the same time, check the fineness of the head finite element model, and check the organizational structure such as Figure 1 shown, including facial bones, cranial bones, brain tissues, etc., to facilitate the subsequent dynamic verification. If the model is missing, it is not applicable to this verification system.

[0056] The biological fidelity verification step, as Figure 6 shown, through dynamic analysis, conduct simulation verification for each working condition from the dimensions of the mechanical response of the brain / facial cranial bones under impact load and the pressure and kinematic response of the brain tissue under rotational load to obtain the simulation results. Specifically, the working conditions include forward and backward head linear impact, top head linear impact, forward head linear / rotational impact, forward / lateral / backward head linear / rotational impact.

[0057] Specifically, the mechanical responses of the brain / facial skull under impact loads include: linear impact of the head (ball impactor), linear impact of the face (disc impactor), linear impact of the face (rod impactor), and linear impact of the face (rod impactor); linear impact of the head (ball impactor) is: the load loading direction is the top, the assessment indicator is the load-deflection curve of the skull, and the boundary condition is to use a rigid ball impactor with a radius of 48mm and a mass of 1.213kg to impact the parietal bone area of ​​the head model downward at an initial velocity of 8.0m / s, and restrict the movement of the nodes on the lower surface of the skull in the Z direction; select the highest node on the skin surface of the top of the skull to set the sensor output, output the node Z-direction displacement curve, output the contact force curve between the rigid ball impactor and the head from the simulation result file, and obtain the force-deformation curve of the skull based on the two curves. The linear impact on the face (disc impactor) is as follows: the load loading part is the face; the assessment index is the load-time curve of the facial bones; the boundary condition is to constrain the six degrees of freedom of the occipital area of ​​the head model, and a rod impactor with a radius of 12.5mm and a mass of 32kg is used to hit the nose area of ​​the head model along the X direction at an initial velocity of 7.0m / s; the contact force curve between the impactor and the head is output from the simulation result file. The linear impact on the face (disc impactor) is as follows: the load loading part is the nose; the assessment index is the load-deflection curve of the facial bones; the boundary condition is to use a disk impactor with a radius of 75mm and a mass of 13kg to hit the facial area of ​​the head model along the X direction at an initial velocity of 6.7m / s; select the node closest to the skin surface of the impactor and the head model, output the displacement time history curve of the node in the direction of the initial velocity of the impactor, and output the contact force time history curve between the impactor and the head model through the simulation result file. Based on the two curves, the force-displacement curve of the impactor is obtained. The linear impact of the face (rod impactor) is as follows: the load loading site is the forehead and maxilla; the assessment index is the load-deflection curve of the facial bones; the six degrees of freedom of the occipital area of ​​the head model are constrained, and a rod impactor with a radius of 10mm and a mass of 14.5kg is used to impact the forehead and maxilla areas of the head model along the X direction at initial velocities of 3.6m / s and 3.0m / s respectively; the node closest to the impactor and the skin surface of the head model is selected, and the displacement time history curve of the node in the direction of the initial velocity of the impactor is output, and the contact force time history curve between the impactor and the head model is output through the simulation result file. The force-displacement curve of the impactor is obtained based on the two curves.Brain tissue pressure and kinematic responses under rotational loads include head linear impact, head linear / rotational impact (loading forced motion), head linear / rotational impact (loading forced motion); head linear impact is: the load loading direction is front-rear; the evaluation index is the intracranial pressure curve of the brain tissue; the boundary condition is to apply forced motion to the skull, and the loading speed direction forms a 45° angle with the Frankfurt plane of the head; in the brain tissue, select the solid elements in five regions: frontal lobe, parietal lobe, occipital lobe (one on each side of left and right symmetry), and posterior cranial fossa, and set the sensor to output the intracranial pressure time history curve. Refer to SAE J211-1, and it is recommended to use SAE1000 to filter the curve. Head linear / rotational impact (loading forced motion) is: the load loading direction is forward; the evaluation indexes are the acceleration and intracranial pressure curve of the brain tissue; the boundary condition is to change the material of the skull to a rigid material and apply an angular acceleration of 7600 rad / s around the Y-axis to the skull. 2 and a linear acceleration of 1000 m / s along the X-axis 2 ; in the brain tissue, select three nodes at the frontal lobe, basal ganglia (where the amygdala is located), and occipital lobe positions and the elements in five regions: frontal lobe, parietal lobe, third ventricle, lateral ventricle, and occipital lobe, and set the sensors to output the acceleration history curve and intracranial pressure time history curve respectively. It is recommended to use SAE1000 to filter the acceleration and intracranial pressure curves. Head linear / rotational impact (loading forced motion) is: the load loading directions are forward, lateral, and backward; the evaluation index is the brain tissue displacement curve; the boundary condition includes three loading conditions, respectively simulating the rotation of the head model along the impact directions of the front, side, and back. Change the material of the skull to a rigid material, and in the frontal impact condition, apply a linear acceleration of 200 m / s along the X-axis to the skull 2 and a rotational acceleration of 1800 rad / s around the Y-axis 2 ; in the lateral impact condition, apply a linear acceleration of 330 m / s along the Y-axis to the skull 2 and a rotational acceleration of 7400 rad / s around the X-axis 2 ; in the rear impact condition, apply a linear acceleration of 400 m / s along the X-axis to the skull 2 and a rotational acceleration of 2300 rad / s around the Y-axis 2

[0058] Perform simulation calculations under different working conditions in the simulation software. According to different working conditions, respectively simulate scenarios such as head linear impact, rotational impact, and facial collision trolley motion. After each simulation, record the simulation output data to obtain the simulation results. The simulation results include head linear acceleration, rotational angular velocity, stress distribution, intracranial pressure, and force distribution.

[0059] Evaluation steps of the head finite element model: Assign a bio - fidelity score to the head finite element model based on the simulation results, and evaluate based on the scoring results. Specifically, compare the obtained simulation result curve with the preset benchmark channel, and calculate the CORA score of each simulation result curve. When there are multiple result curves in a working condition, average the CORA scores of all simulation result curves in this working condition to obtain the score of the head finite element model under this working condition. The closer the CORA score is to 1, the higher the bio - fidelity of the head finite element model. The head finite element model used for virtual evaluation needs to ensure that the CORA scores in all working conditions exceed 0.6, and the CORA scores in one - third or more of the working conditions exceed 0.7. A single simulation result curve, as Figure 7 shown, based on the simulation results of the verification working condition of the head finite element model, the contact force curve between the impactor and the head needs to be compared with the Figure 7 test response curve in it. The simulation curve needs to ensure that the deviation value of the resultant force at each moment relative to the upper / lower limit does not exceed 100% of the channel width. At this time, the score of this simulation curve is the percentage value that needs to fall within the channel. Multiple simulation result curves, as Figures 8 to 9 shown, first process the multiple curves of the test to obtain an average curve. Based on the simulation results of the verification working condition of the head finite element model, the force - displacement curve of the impactor hitting the frontal region and the force - displacement curve of the impactor hitting the maxilla region need to be compared with the Figure 8 , Figure 9 average curve of the test response curves in it respectively, calculate the CORA score and take the average value.

[0060] The CORA scoring model is as follows:

[0061] CORA = G 1 *C 1 +G 2 *(G V *V + G P *P + G G *G)

[0062] where G 1 , G 2 , G V , G P and G G are weighting factors, C 1 is the corridor score, V is the progress score, P is the phase - shift score, and G is the size score. Specifically, G 1 , G 2 , G V , G P and G G are the weighting factors of the corridor score, cross - correlation score, progress score, phase - shift score, and size score respectively.

[0063] The calculation model of the corridor score is as follows:

[0064]

[0065] Among them, C i is the score for each time step, and n is the number of time steps within the evaluation interval. Four curves are determined based on the inner and outer corridor widths input by the user. The inner and outer corridors are defined around the reference curve. If the simulation curve is within the inner corridor, C i the score result is 1; if the simulation curve is outside the outer corridor, C i the result is 0; otherwise, interpolation processing of the score is performed.

[0066] The calculation model of the progress score is as follows:

[0067] V = K V

[0068] Among them, K is the maximum cross-correlation value.

[0069] The phase shift score P is controlled by the parameters D_MIN and D_MAX and is calculated at the maximum cross-correlation K and the corresponding time shift δ. D_MIN represents the minimum allowable phase shift, which is used to limit the range of time shifts allowed when calculating the phase shift score, and its value range is between [0, 1]. D_MAX represents the maximum allowable phase shift, which is also used to limit the time shift range to ensure that the calculated phase shift score is within a reasonable range, and its value range is between [0, 1]. In the CORA score model of this embodiment, the preferred default values of D_MIN and D_MAX are 0.01 and 0.12 respectively.

[0070] The calculation model of the phase shift score is as follows:

[0071]

[0072] The size score G is obtained by calculating the square of the area between the curve and the time axis. The calculation model of the size score is as follows:

[0073]

[0074] Among them, A sim is the area between the simulation curve and the time axis, and A ref is the area between the reference curve and the time axis.

[0075] The evaluation includes: obtaining the correlation assignment between the simulation data and the experimental results under the CORA score based on a preset rule, that is, multiple sub-scores measure the matching degree of the simulation data with the experimental results in different dimensions, and the CORA value reflecting the biological fidelity of the simulation model, obtaining the dynamic weight values of each working condition, and performing a dynamic score according to the dynamic weight values and the correlation assignment of each working condition. The biological fidelity evaluation result of the head finite element head model is mapped according to the dynamic score. The fidelity evaluation result includes high, general, and qualified. The dynamic scoring model is:

[0076] Q = ∑w m s m

[0077] where w m is the weight factor of working condition m, and s m is the correlation assignment; in this embodiment, the preset rule is: if CORA≥0.85, assign 100 points; if 0.7≤CORA<0.85, assign 60 points; if CORA<0.7, assign 0 points; assign scores in sequence to obtain S 1 、S 2 、S 3 、S 4 、S 5 、S 6 、S 7 。s m needs to be >0.6, otherwise the head model is unavailable, that is, unqualified.

[0078] According to the relevant regulations and the serious damage of skull fractures to the human body, the weight factors of the seven working conditions in this embodiment are preferably: 0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1 in sequence. If Q≥85, the biological fidelity of the head finite element head model of Chinese 50th percentile male is high; if 85>Q>60, the biological fidelity of the head finite element head model of Chinese 50th percentile male is relatively high; if 60≥Q>18, the biological fidelity of the head finite element head model of Chinese 50th percentile male is average; if 18≥Q, the biological fidelity of the head finite element head model of Chinese 50th percentile male is qualified.

[0079] The above are only embodiments of the present invention. Specific technical solutions and / or common knowledge such as characteristics well known in the art are not described in detail herein. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.

Claims

1. A head finite element model verification and evaluation method for automobile collision safety, characterized in that: include: The model adaptability benchmarking step is to benchmark the head indicators and precision of the head finite element model to be verified, and select the head finite element model that meets the standards; The bio-fidelity verification step is to conduct simulation verification from the dimensions of brain / facial skull mechanical response under impact load and brain tissue pressure and kinematic response under rotation load to obtain simulation results; The head finite element model evaluation step is to assign a biofidelity score to the head finite element model based on the simulation results, and to perform an evaluation based on the assigned score results.

2. The head finite element model verification and evaluation method for automobile collision safety according to claim 1, characterized in that: The biofidelity scoring includes: comparing the simulation result curve with the benchmark channel, obtaining the CORA score of each simulation result curve through the CORA scoring model, and if the working condition includes multiple result curves, processing the average CORA score of all simulation result curves.

3. The head finite element model verification and evaluation method for automobile collision safety according to claim 2 is characterized in that: The CORA scoring model is: CORA=G1*C1+G2*(G V *V+G P *P+G G *G) Among them, G1, G2, G V , G P and G G is the weight factor, C1 is the corridor score, V is the progression score, P is the phase shift score, and G is the size score.

4. The head finite element model verification and evaluation method for automobile collision safety according to claim 3 is characterized in that: The calculation model of the corridor score is: Among them, C i is the score for each time step, and n is the number of time steps in the evaluation interval.

5. The head finite element model verification and evaluation method for automobile collision safety according to claim 3, characterized in that: The calculation model of the progress score is: V=K V Where K is the maximum cross-correlation value.

6. The head finite element model verification and evaluation method for automobile collision safety according to claim 3, characterized in that: The calculation model of the phase shift score is: Where δ is the time offset corresponding to the maximum cross-correlation K.

7. The head finite element model verification and evaluation method for automobile collision safety according to claim 3, characterized in that: The calculation model of the size score is: Among them, A sim is the area between the simulation curve and the time axis, A ref is the area between the reference curve and the time axis.

8. The head finite element model verification and evaluation method for automobile collision safety according to claim 1, characterized in that: The evaluation includes: obtaining the correlation value between the simulation data and the experimental results under the CORA score based on the preset rules, obtaining the dynamic weight value of each working condition, and performing dynamic scoring according to the dynamic weight value and correlation value of each working condition. The dynamic scoring model is: Q=∑w m s m Among them, w m is the weight factor of working condition m, s m Assign values ​​to relevance; The biofidelity evaluation results of the head finite element head model were obtained based on the dynamic score mapping.

9. The head finite element model verification and evaluation method for automobile collision safety according to claim 8, characterized in that: The working conditions include: front-to-back linear impact on the head, top linear impact on the head, forward linear / rotational impact on the head, and forward / lateral / backward linear / rotational impact on the head.

10. The head finite element model verification and evaluation method for automobile collision safety according to claim 9, characterized in that: The simulation results include: head linear acceleration, rotational angular velocity, stress distribution, intracranial pressure and force distribution.

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