Dipl.-Ing. Ioannis Karypidis, BETA CAE Systems, GREECE

Ing. Federico Zaramella, BETA CAE Italy Srl, ITALY

A demanding simulation task for Brake Systems evaluation is the reduction of the Squealing phenomenon, at low frequencies. While studying the complex eigenmodes of the structure, engineers locate the squealing effect to unstable modes and try to evaluate how the contributing natural modes, and system components, contribute to the overall noise. Apart from the components design, temperature, applied pressure, friction, and the rotational velocity, also play an important role. For this reason, the problem is better addressed through a DOE study combining all the aforementioned variables.

For the implementation of this simulation case a complete process is needed, where, first, the CAD components are processed to produce several ready-to-run FE simulation experiments. Then, after the solver execution, the results need to be post-processed in a comparative mode to arrive to valuable conclusions. Performing this process manually would be a time-consuming task, making the need for automation important. This presentation highlights the three main steps of such a process which can be automated to offer significant time reduction:

- Model Build: The CAD geometry is cleaned up to match CAE standards. The mesh is generated automatically according to the simulation’s requirements with discipline dependent algorithms for the easy and efficient handling of the element size and high control over the element’s quality. Then, properties and materials are defined according to standard specifications as well as part interaction through contacts and joints.

- Experiments definition: In this step the Load Cases are setup for several DOE studies using inherent parameters for the variables in question. Through the use of an advanced DOE tool, experiments are created with combinations of the parameter ranges while ready to run solver models are extracted.

- Post-Processing: Analysis of all results simultaneously in an automated post-processing tool for the evaluation of unstable modes per experiment and the extraction of CCF (Component Contribution Factor) and CMCF (Component Modal Contribution Factor) indices.

On this process, a solution is suggested that utilizes the functionality of the ANSA and META pre- and post- processing tools, combined with a python interface that provides the user with full access to all of the tools’ functionality. In this solution, the full process can either run step-by-step by automating only the main points or be set up to a full batch mode execution, saving significant amount of time in both cases avoiding all manual tedious tasks. In addition, the ANSA part replacement capabilities enable the possibility to change some parts of the model (caliper, disk, pads, suspension arms...) without starting from scratch the process of the FE model creation.