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Rear underrun protection device is crucial for rear impact and rear under-running of the passenger vehicles to the heavy duty trucks. Rear underrun protection device design should obey the safety regulative rules and successfully pass several test conditions. The objective and scope of this paper is the constrained optimization of the design of a rear underrun protection device (RUPD) beam of heavy duty trucks for impact loading using correlated CAE and test methodologies. In order to minimize the design iteration phase of the heavy duty truck RUPD, an effective, real-life testing correlated, finite element model have been constructed via RADIOSS software. Later on, Pareto Optimization has been applied to the finite element model, by constructing designed experiments. The best solution has been selected in terms of cost, manufacturing and performance. Finally, real-life verification testing has been applied for the correlation of the optimum solution.

In the earth-moving industry manymachines work in typical loading cycles that are repeated periodically. For a statistic examination of the overall load configuration and the dynamic fatigue of these machines, it is necessary to develop an adaptive algorithm for the separation of the individual cycles. This article presents methods for an automatic detection of the cycle borders. Adaptive algorithms are constructed for a reliable separation at different points during the loading cycle. Additionally, each cycle can be divided into different operating phases by extending the algorithms to a tool for the identification of each single phase. To avoid problems during the cycle detection, the data are checked for outliers and sensor faults first. To guarantee a meaningful statistical analysis, the separated cycles have to be tested for incorrect or atypical characteristics. Therefore, statistical classification numbers are calculated and compared for each cycle.

Current commercial vehicles' engines are complex systems with multiple degrees of freedom. In conjunction with current emissions regulations manufacturers are forced to combine highly developed engines with complex aftertreatment systems. A comprehensive simulation model including the engine and aftertreatment system has been set up in order to study and optimize the overall system. The model uses a phenomenological spray combustion model to predict fuel consumption and NO emissions. In addition physical models for the material temperatures and the reaction kinetics were generated for the aftertreatment system. Steady state and transient measurements were used to calibrate the engine as well as the aftertreatment model. The aim for a system-level optimization was a reduction of fuel consumption while meeting emission standards.

For the estimation of life expectancy and dynamic fatigue of a machine, the overall load configuration of the typical application is of major importance. Regarding commercial vehicles, the load spectrum differs with the variation of machine parameters which requires costly measurements for analysis of damage. This article presents robust methods for the computation of characteristic values for the damage to a certain component. The methods are based on a hypermodel, which represents the relation between different machine configurations and the resulting characteristic values. Therefore, fewer typical machine configurations have to be measured. The statistical models of load and damage are made using the Rainflow counting algorithm and an extended version of Miner's Law. After the condensation into characteristic damage values, hypermodels for the relationship between these scalar values and the machine parameters are developed using Neural Networks.