Automated Post-processing of Multi-Model Optimization Data

Due to more compressed timelines in the product development process there is an increasing demand regarding CAE simulations. The increase of HPC resources in combination with a massive parallelization more complex simulations could be performed in a proper timeline. Numerical optimization (in combination with CAE simulation) is such a complex process because of the resource requirements in combination with an iterative solution scheme. In order to handle different simulation disciplines so called Multi Model Optimization (MMO) and Multi Disciplinary Optimization (MDO) optimizations are getting more important. Unfortunately these calculations will lead to large result data and demanding hard- and software requirements. Taking this into account there are two major aspects which need to be addressed in the future.

First of all the analyst needs to get as much information out of the simulation (optimization) in order to physically understand the CAE model. For this task optimization is a well suited utility as the numerical processes could handle a large amount of design variables and different constraints efficiently. But what needs to be taken into account is a proper visualization of this data.

Secondly it will be still important to keep the timelines in the development process even with a complex optimization task. To reach this target it is necessary to force the optimizer to converge in a few iterations or to react on changing circumstances in the development process.

In this paper an automated approach BMW is currently applying will be described which is addressing the two above points for linear CAE simulations.

 

The Author

Markus Schemat
Dr. Daniel Heiserer
BMW