Side Impact Analysis

ŠKODA AUTO is the largest car manufacturer in the Czech Republic and part of the Volkswagen Group since 1991. The company uses highly advanced simulation tools in its R&D departments to meet all design goals, where passenger safety is one of the top priorities.

Challenge

Although side collisions occur less likely than front or rear crashes, it is still a very common type of car accident, especially in cities. Moreover, at the same speed, these can cause more severe injuries to passengers inside the vehicle. Thus, it is crucial for design engineers to know the most important aspects of side crash behavior. Finite element method computations are used to analyze the impact of a car collision on passengers. Computations are performed repeatedly with variations in design parameters to see their effect on forces acting on passengers. However, the total number of FEM computations is limited since these can be a lengthy process even with powerful computers. Side crash is a complex problem depending on a large number of parameters and it was not possible to investigate all of them in this study. In the first step, the ŠKODA AUTO experts simplified the problem and selected 11 parameters which should be important or interesting to analyse. In the second step, a preliminary analysis applied on these 11 preselected parameters to assess their importance. Then within just 65 computations, the preliminary analysis of Uptimai software was able to propose a reduction of this list further to 6 most influential parameters.

Fig. 1. Side collision testing, photo by Euro NCAP

Solution

In the beginning, we coupled the Uptimai tool with the simulation software PAM-Crash using the sampling based approach. Then, the UptimAI tool began to set combinations of inputs for the PAM-Crash, automatically varying design parameters of the car. The Uptimai algorithm starts to analyze the task using a very effective logical scheme. From the results of only 40 FEM simulations, the program is able to identify the sensitivities of input variables. This is about 50% of simulations required by the second-order polynomial chaos, an advanced statistical method used today.

The Uptimai sensitivity analysis allows to see not only the effect of inputs on the overall variance of the results but also how they affect the mean value of the probabilistic result from the so-called preliminary surrogate model. As mentioned earlier, the main goal of the analysis so far was to perform the sensitivity analysis with the lowest possible use of resources. To complement data with FEM results required to build the preliminary surrogate model, additional PAM-Crash calls were made. In the end, 65 samples were used in total for both observed outputs - rib force and rib deformation. This is a significantly lower number than for design-of-experiment methods commonly used in engineering. E.g. Box-Behnken experimental design would require almost three times more FEM computations for this task with 11 input variables.

Fig. 2. Sensitivity analysis of rib force and rib deformation

The information learned from the sensitivity analysis was further visualized in the Uptimai histograms, showing clearly the effect of each input variable on the probability distribution of results. The so-called influencer plot is the graphic representation of changes in results caused by the selected parameter. Thus, engineers had a straightforward illustration of changes in the mean value and the variance of results.

Fig. 3. Example of an input parameter influencing only one output

Another piece of information gained from the model was the probability of inputs to interact with the others. It was only possible due to the novel methodology used in the Uptimai software, its smart scheme and principles of model residuals handling. Based on all these data, the number of input parameters was reduced from 11 to 6. In the following stage of the project, this allowed to obtain the detailed surrogate models of both outputs for only 255 FEM simulations. The model of acting forces was the more demanding, the model of deformations used 99 simulations and these were already solved for the first output.

Benefits

• The low number of required FEM simulations. Reducing the total number of simulations to 50%.
• Identification of influential parameters. Fast recognition of variables with high impact on results.
• Visualisation of effects of variables. Better handling of analysis results with illustrative plots.