Predictive Modeling of Dynamic Processes

Abstract

Simulation of recoverable foams is usually based on hyperelasticity. Since foams are always strain-rate dependent, the viscosity of the material has to be considered additionally in the material model. One disadvantage of a viscous description is the time-consuming parameter identification associated with the determination of the damping constants. An alternative is given by tabulated formulations where stress-strain relations based on uniaxial static and dynamic tensile tests at different strain rates are used directly as input. This approach is implemented in the material law no. 83 (Fu-Chang-Foam) in LS-DYNA, see [1] and [2]. We briefly show the theoretical background and the algorithmic setup of the tabulated FuChang model and demonstrate the applicability of the model to non-uniaxial loading. Major problems occur in the simulation of unloading processes. These difficulties are due to the identification of unloading by the product of strain and strain rate as implemented in material law no. 83 so far. If the strain rate oscillates strongly, a unique identification of loading and unloading is no longer possible. Therefore, an extension of the model with elastic damage is presented that is capable of identifying unloading in a natural way, i.e. by a decrease of the stored hyperelastic energy of the system. With our model, hysteresis effects can be simulated and energy is dissipated. The model is formulated in a user-friendly way by a tabulated description of damage. Stefan Kolling Giessen University of Applied Sciences, Laboratory of Mechanics, Wiesenstr. 14, 35390 Giessen, Germany, e-mail: stefan.kolling@mmew.fh-giessen.de Andre Werner Northeastern University, Department of Structural Engineering, Boston, MA 02115, USA e-mail: A W e r n e r@web.de Tobias Erhart Dynamore GmbH, Industriestr. 2, D70565 Stuttgart, Germany e-mail: tobias.erhart@dynamore.de Paul A. Du Bois Consulting Engineer, Freiligrathstr. 6, 63071 Offenbach, Germany e-mail: paul.dubois@gmx.net S. Hiermaier (ed.), Predictive Modeling of Dynamic Processes, 9 DOI 10.1007/978-1-4419-0727-1 1, c © Springer Science+Business Media, LLC 2009 10 Stefan Kolling, Andre Werner, Tobias Erhart and Paul A. Du Bois