Inhaltszusammenfassung

The development of vehicles is increasingly based on computer simulations. CAE simulations can be used to validate and optimize vibration behaviour, acoustics, handling and durability properties even in early design stages when prototypes are not yet available. To do that, two essential types of input data are needed: Model data: construction parameters like the geometry, masses, mass inertias, characteristic curves for springs, dampers and tyre models; Dynamical load data: system loads cause...The development of vehicles is increasingly based on computer simulations. CAE simulations can be used to validate and optimize vibration behaviour, acoustics, handling and durability properties even in early design stages when prototypes are not yet available. To do that, two essential types of input data are needed: Model data: construction parameters like the geometry, masses, mass inertias, characteristic curves for springs, dampers and tyre models; Dynamical load data: system loads caused by the road profile. To perform simulations for new vehicles in absence of prototype measurements a realistic system input is essential. A possibility to obtain an invariant system input is to identify a synthetic road profile by appropriate mathematical algorithms like iterative learning schemes. In this work methods originally developed for passenger car engineering are adopted to agricultural machinery. For a John Deere forage harvester (reference vehicle) wheel forces and accelerations have been measured on a test track. Using multi body simulation a 'digital road excitation' is identified which is then applied as load input for the new harvester version. The results are verified by measurements. For forage harvesters the tyre dynamics is of special importance. Thus it has been analysed which kind of tyre model and which accuracy for the tyre parameters is sufficient for this type of analysis. Entnommen aus TEMA» weiterlesen» einklappen

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Klassifikation

DDC Sachgruppe:
Ingenieurwissenschaften