Car Box Beam Deformation

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

During the development of a new car model, it was necessary to increase the amount of energy absorbed during a crash incident. Thus, engineers had to learn and understand how can the geometry of certain body parts affect the amount of energy needed for deformation of these parts. To validate the new know-how before its use for car design, results of performed simulations had to be compared against the real testing. However, test results were prone to correct settings and adjustment of the testbed. The task had 27 input parameters in total. Standard approaches of design exploration would require at least 4000 runs of the PAM-Crash simulation software, making the study unfeasible. On the other hand, when reduced, the study would fail to give complete information about the behavior of the car part and relations between inputs and the absorbed energy.

Solution

In the beginning, we coupled the UptimAI tool with the simulation software PAM-Crash, with no need to interfere with its code or features. Then, the UptimAI tool begun to set combinations of inputs for the PAM-Crash. It used the UptimAI algorithm to process simulation results into the metamodel of the tested part under deformation.

There were geometrical input parameters in the task meant to be adjusted to increase the performance of the part. Once the metamodel was ready, UptimAI identified ranges of selected parameters raising the probability of an increase in the energy absorbed by the deformed part. At the same time, these new ranges were designed to decrease the variance of results as much as possible. This approach diminishes the influence of uncertainties in the rest of the inputs.

UptimAI sensitivity analysis and UptimAI histograms helped to identify input parameters with a major influence on both, the variance and the mean value of the amount of energy absorbed during part deformation. It was revealed that the variance of results is strongly affected by barrier rotation – the inclination angle between the testbed impactor and the tested part. Thus, one of the optimization goals was the minimization of the influence of this particular parameter.

Another guidance to understanding the problem of the deformed box beam was given by the UptimAI increment plot. It showed shapes of increment functions depicting dependencies and relations of input parameters and the value of absorbed energy. To reduce the influence of uncertainties on the testbed, UptimAI recommended setting ranges of inputs with the lowest gradient of corresponding increment functions. One example – material quality check of used sheet metals is one of the ways how to diminish the influence of the impactor rotation.

The UptimAI software also found input parameters connected with others through higher-order interactions. Then, engineers used the UptimAI preliminary optimization tool to identify ranges of inputs resulting in increased absorbed energy and also ranges to be avoided. A good example of such output is the recommendation to choose distinct thicknesses of two sheet metals welded together to form the tested part. There was a larger amount of absorbed energy in comparison to the case with both sheet metals being thicker. Additionally, the results of an older study recommending non-equidistant positions of spot welds were confirmed.

Benefits

• The absorbed impact energy of optimized geometries increased by 10% on average, the lowest value (absorbed energy due to the most adverse combination of inputs) increased by 18%.
• Decrease of result sensitivity to testing conditions – the variance of the results decreased by 63%.
• Significant reduction of the computational time – necessary number of simulation runs (280) was reduced to 7% of the original estimate.