FEMMINER: Data Mining Techniques for Car Simulation Results
More product variants, the increase in government regulations and the use of stochastic and multidisciplinary optimization cause a drastic increase of the number of numerical simulation performed in the car design process. The availability of this data creates the opportunity to compare thousands of simulation variants, to find optimal design variants (even from previous projects) and to evaluate their impact on the functionality of the car. The huge volume of data (large number of simulations and large data-sizes per simulation) and the diverse content of simulation data are major obstacles that can be overcome by data mining methods based on dimension reduction.
Aspects of the car design process in automotive industry
Our activities in data mining of simulation results focus on two aspects of the virtual product development process:
- In order to find an optimal design while taking specific constraints into account (like safety regulations), many model variants are analyzed in which small modifications in material parameters and geometries are introduced. This is an iterative and time consuming process. Post-processing software tools are able to display the model geometry and the results of the numerical simulation in an intuitive way, but engineering judgment is limited to the analysis of just a few (perhaps at most five) simulations at once.
- The other aspect of the car design process involves the reuse of information from previous projects or development phases. Geometrical shapes and geometrically distributed properties like stresses or deformations, defined on very fine meshes, are the essential information to evaluate. Unfortunately, this information is not accessible for direct data base queries because of its size and its missing common structure.
In both application cases, nonlinear dimension reduction methods are used that exploit the high correlation arising from the basic similarities in the models and problems to be solved. The major trends of the differences, which are hidden in the detailed results, are computed and simple parameters provide information about the contribution of each of these trends to the individual geometries or simulation results. A simple illustration of this idea can be given by considering several pictures of a car that has been rotated around one axis (see Figure 1). After application of a nonlinear dimension reduction to adjust the images consisting of pixel values via the so-called Multidimensional Scaling (MDS) method, we are able to extract dependency on the rotation angle and represent all images in a parametric way.