Hybrid uncertainty analysis and optimisation based on probability box for bus powertrain mounting system

Abstract

In engineering practice, the bus powertrain mounting system (BPMS) may have both epistemic and aleatory uncertainty under the influence of manufacturing, measurement, and assembly errors. The hybrid uncertainty in BPMS may result in over-design or insufficient design. Therefore, the probability box (p-box) model, which can handle both aleatory and epistemic variables, is introduced into the uncertainty analysis of BPMS. Considering the elastic connection between the compressor and powertrain, a 12-degree-of-freedom dynamic model is constructed to calculate the inherent characteristic of BPMS. A rejection sampling method based on the fast envelope function (RSMBFEF) is proposed to propagate the hybrid uncertainties. Then double-loop Monte Carlo method is used to be compared with RSMBFEF. To reduce the number of uncertainty analyses, a two-step uncertainty optimisation method is proposed. Finally, the proposed method's efficacy and accuracy are verified through a numerical case. The applicability of the p-box model is illustrated by comparing it with the BPMS model with only pure aleatory or pure interval variables.