Based on the vehicle front crash finite element analysis, it shows that there is a large acceleration, so it needs further optimization. In order to improve the performance of vehicle collision, eight parts were selected which have large impact for the result, its thickness as design variables to the right of the B-pillar acceleration peak of optimization goal; 17 sample points were selected by Latin hypercube sampling method. Many structure parameters are optimized using sequential quadratic program (SQP) based on the surrogate model. The results show that the improved RSM has high accuracy; the right B-pillar acceleration reduced approximately 22.8%, reached the expected objective and was more conducive to the occupant safety.
Automobile has been used as an indispensable means of transport more and more common in the family, bringing convenience for people, at the same time a large number of traffic accidents have become a serious social problem. The automobile traffic accident methods are as follows: frontal collision, side collision, rear-end collision and rolling etc. According to the statistics of automobile accident statistics, the proportion of different forms of frontal crash accounts for about 40% of all the collision accidents [
In the process of automobile design, the use of the finite element has a great help. According to the results of the finite element analysis, we can know that the application of improved response surface method obtains the optimal thickness value of each component. Through the recalculation of the model, the simulation results are obtained, and the results are compared with that before optimization.
Latin hypercube sampling method can be used to conduct a comprehensive sampling distribution, and then from the range of values of each layer. The sampling method is a type of design of experiment and widely used in the simulation experiment, often in a large design space, relatively evenly fill test interval, and all the factors containing the same number of partitions [
No matter what kind of function relationship between variables and objectives in practical engineering problems, the polynomial model can always be used. In the nonlinear design space, the function relation between the design variables x and the response y can be expressed:
In the equations,
Each design point is composed of n independent variable, and the quadratic model can be constructed with
At the sample point
The square sum of the error at the selected
By using the least square method to make error sum of squaresin type (2-4) is the smallest, only need to:
Can obtained polynomial coefficients a:
Type (2-6),
In the response surface model is the most widely used quadratic polynomial model, reduce the structure calculation, ignoring the cross terms [
Type (2-8):
The current experimental sites are
Using least square method, according to the type (2-5) get the unbiased estimation of (2-10):
Let a plug in type (2-9),
The corresponding surface model accuracy can be tested by the following two aspects:
1) Response surface sample points fitting state decision coefficient
Among them, p is the number of sample points, k is degrees of freedom, the values is the adjust parameter minus 1,
2) In the design space, randomly generated a certain amount of test sample points, to examine its relative error, the relative error expression is as follows:
Type (2-13), y and
Eventually the final vehicle grid is divided as shown in
The acceleration curve of the vehicle B column is shown in
deceleration curve, the black curve is the left side of B pillar deceleration curve, it can be seen from two acceleration curve that the maximum acceleration of right B-pillar is 39.9 g and left is 37.2 g. The maximum acceleration is slightly larger, it needs further optimization studies.
This paper is optimized according to the large acceleration of vehicle front crash simulation. Thicknesses of the front and middle parts directly influence the result of the collision, so set the thickness values of front and middle vehicle components as optimization variables, the right B-pillar acceleration peak value as the optimization objective function. Eight parts were selected to the result of larger impact, respectively floor, the front bumper beam, engine compartment outside cover panel, engine compartment inner cover plate, front rails, front panel, fender, front wheel cover plate, the location as shown in
The thickness of each component is
fitting expression for the acceleration peak value of each component of the design variables and the target value is obtained by the analysis of 17 sample points and the improved response surface method:
The extrapolation accuracy was tested in the nonlinear test sample points by improved response surface model, the 17 sample points of relative error formula applied 2 - 6 calculation. As shown in
The thicknesses of front and middle parts affect the structure crashworthiness and the B pillar peak acceleration directly [
Type (3-2):
The optimization variable is obtained using sequential quadratic programming algorithm, as follows:
The thicknesses of components were changed in Hyper Mesh software, new model was calculated again by LS-DYNA.
The acceleration curves comparison is shown in
We can know that the right peak acceleration of B pillar is 30.8 g, reduced about 22.8% in
In vehicle crash simulation test, the front and middle vehicle body structure affects crashworthiness directly.
In order to improve the performance of vehicle collision, eight parts were selected which have large impact for the result, its thickness as design variables to the right of the B-pillar acceleration peak of optimization goal; the application of improved response surface method can obtain the optimization thickness value of different parts. The right side of the B pillar acceleration decreased about 22.8%, compared with before optimization; so that the result has reached the expected purpose.
Chunke Liu,Jianxing Li,Xiaojun Xu, (2016) Application of Improved RSM in the Optimization of Automotive Frontal Crashworthiness. Journal of Transportation Technologies,06,155-161. doi: 10.4236/jtts.2016.63015