![]() ![]() Finally, the wheel strains were calculated by an ABAQUS simulation, which received the tire/wheel load data from the simulation in MSC.ADAMS and CDTire. The wheel/tire/inner drum contact was simulated by means of CDTire as it works parallel to MSC.ADAMS, one single co-simulation could perform the tire dynamics and the test facility dynamics. In the proposed virtualization, the multibody dynamics of the test facility was implemented in MSC.ADAMS. Thus, the scenario of this test can be modeled in three levels: the multibody dynamics of the test facility, the wheel/tire/inner drum contact, and the analysis of the flexible wheel. During this test, tire and wheel specimens run inside an inner drum while standardized vertical and horizontal loads are applied. This test is the state-of-art and the standard requirement for the validation of vehicle wheels. This paper presents a new approach to the virtualization of the scenario of the biaxial wheel fatigue test. A further improvement is analysed to extend the methodology to wheel-to-tyre interactions during fatigue tests allowing the indirect structure global stress estimation. Components to assembly experimental modal analysis direct correlation is performed to identify the representative modes of the phenomena, while polynomial chaos expansion-based meta models developed upon experimental observations allow to extend the obtained results to component and assembly characteristics. ![]() The stiffening effect is related to components and assembly characteristics, such as masses, natural frequencies, generalised tolerances and uncertainties of elasto-plastic material properties and manufacturing process parameters. The methodology is applied to predict the local stiffening effect between the rim and the disk induced by the residual stress variation during the fitting procedure. The principal results are obtained using consolidated techniques as modal analysis and statistical approaches to estimate useful, but difficult to directly measure, system quantities. In this paper, enrichments of automotive steel wheels design procedure are presented. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. ![]()
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