Performing these measurements quickly, reliably and with high accuracy presents a yet unsolved challenge. Especially when working in or close to complex terrain, such as forests, hillsides or urban landscapes, no available system can properly perform such measurements. Stemmler, Simon, Werner, Christoph S.
This allows us to handle multiple returns from objects which are only separated by a 15 cm gap. The measurement range can reach several 10 m, depending on the water quality.
These often exhibit a complex internal architecture including interfaces between the discrete material phases. Particularly in highly nonlinear crash simulations of complex structures, model fidelity is a key aspect in the trade-off considerations between accurate predictive capabilities of a modeling technique and the resources needed for model development as well as computationally producing and processing solutions.
The present study investigates various model simplification strategies for Finite Element FE -simulations of automotive crash structural concepts composed of fiber-reinforced plastics FRP -metal hybrid material systems in view of the conflicts invoked by strict model efficiency requirements.
This particularly implies the modeling resolution of the FRP phase and different modeling techniques for the adhesive interface between the material kmc davos trading and the respective implications on the predictive qualities of the simulation results. All results are compared to hardware experiments of hybrid generic structural components under crash loading conducted by the authors. The results show, that the model fidelity in the FRP phase does generally have a strong correlation with the predictive capabilities of the overall simulation.
Comparing the simulation and the experimental data of generic components shows that explicitly modeling the cohesive behavior at the adhesive interface is not only more complex, it is also not necessarily bound to produce more accurate results than a simple modeling approach using rigid tie connections.