LS-OPT 3.3 released
LS-OPT 3.3
Robustness
LS-OPT papers dealing with robustness analysis
New Developments in LS-OPT - Robustness Studies
N. Stander (Livermore Software Technology Corporation - LSTC)
BioRID-II Dummy Model Development -- Stochastic Investigations
Stahlschmidt S., Keding B., Witowski K., H. Müllerschön H., Franz U., DYNAmore GmbH, Stuttgart, Germany, Conference Proceedings, 5th DYNAmore User Forum, Ulm, Germany, 2006. --- Whiplash injuries frequently occur in low speed rear crashes. Many consumer and insurance organizations use the BioRID-II dummy as test device, to assess the risk of whiplash injuries in car accidents. An LS-DYNA model of the BioRID-II dummy has been developed by DYNAmore GmbH in cooperation with the German Automotive Industry....
Two-Stage Stochastic and Deterministic Optimization
Rzesnitzek, T. DaimlerChrysler AG Müllerschön, H. DYNAmore, Günther, F. DaimlerChrysler AG, Wozniak, F. DaimlerChrysler AG
Robust Design for Crash at DaimlerChrysler Commercial Vehicles CAE
F. Günther (DaimlerChrysler AG); H. Müllerschön (DYNAmore GmbH); W. Roux (LSTC)
Robustness Investigation of a Numerical Simulation of the ECE-R14 with particular regard to correlation analysis
K. Hessenberger, R. Henniger (DaimlerChrysler), H. Müllerschön (DYNAmore) The stability of seats, seat-belts and seat-belt anchorages of vehicles are very important for the passengers’ safety during an accident. Therefore, dedicated safety tests have to be passed in order to ensure a correct functionality of those parts. These tests are defined in the European regulation ECE-R14 in detail, whereas crashing loads are substituted by appropriate pulling forces on the seat-belts. The complete configuration is designed, simulated and tested with certain (physical) parameters (geometry, materials, testing conditions, ...) which are assumed to be constant. In reality, most of these quantities may vary and take other values with appointed probabilities. Generally, different values for the design parameters will cause a variation in the simulation or testing result, which will also occur with a certain probability. Thus the results of arbitrary separate investigations are commonly not meaningful enough because their probability of occurrence is unknown. The determination of this probability is part of a robustness investigation. In this paper the Monte Carlo Analysis as a very simple method is applied to a FEA model observing ECE-R14. Additionally, the identification of each parameter’s importance for the simulation or testing result is a main topic. This is done by means of a linear correlation analysis, which is described in section 3 in detail.
