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Advanced Modelling Tecnique Applied to Improve Dummy Model for Passive Safety Virtual Tests
EAEC-07/VD03-2

Authors

Domenico Macrì - Politecnico di Torino
Giovanni Belingardi - Politecnico di Torino Giorgio Chiandussi - Politecnico di Torino
Alex Mugnai - TASS – TNO Automotive Safety Solutions

Abstract

Keywords: passive safety, virtual test, crashworthiness

Nowadays, numerical simulations have become an essential part of the vehicle design process, since they are a powerful tool to speed up the process, to save money (virtual tests cost hundred times less than a physical test on a prototype) and time (time to market has been and will be more and more reduced) and to obtain, as a consequence, a competitive advantage (Shephard et al, 2004). In the passive safety field, numerical simulations have helped the designers to improve occupant protection with the development of many safety features. Despite good results have been reached in the protection of the occupant head and upper torso under several load conditions, the protection of the lower extremities is an important task for research activities since lower extremity injuries are the responsible of large social costs due to medical treatments, insurance and working days off (Backaitis, 1996; Crandall et al., 1996; Dischinger et al., 2005; Luchter, 1995; Philippens et al., 1997; Scarboro et al., 2005).

In order to achieve a robust, accurate and CPU effective model for lower leg simulation (Wismans et al, 2000), a new HybridIII 50%ile crash test dummy model has been developed in MADYMO. The so called facet modelling technique has been used which takes advantage of finite elements for a better geometry representation in combination with a rigid body model. Thanks to their CPU time efficiency, facet models are suitable for stochastic models and for optimization and parameter variation studies (Avalle et al., 2007; Belingardi et al., 2005; Ibba et al., 2005). A systematic validation has been performed using objective rating techniques and an extended set of experimental data. Rating is used in order to show the increased predictivity of the new facet model.

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