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A computational study was conducted in order to characterize the heat transfers in a sedan vehicle underbody and the exhaust system. A steady-state analysis with consideration for both the radiation and conjugate heat transfers was undertaken using the High-Reynolds formulation of the k-epsilon turbulence model with standard wall function and the DO model for the radiation heat transfer. All three mechanisms of heat transfer, i.e., convection, conduction, and radiation, were included in the analysis. The convective heat transfer due to turbulent fluid motion was modeled with the assumption of constant turbulent Prandtl number; and heat conduction was solved directly for both fluid and solid.

In this study, the orifice inlet rounding radii of four diesel nozzles with different hydro erosive grinding time were measured based on the x-ray CT scan technology provided by Shanghai Synchrotron Radiation Facility (SSRF), and a wide parametrical study of the spray macroscopic behavior in the first 18 mm from the nozzle tip have been carried out with high speed camera. And then the influence of orifice inlet rounding radius on the spray behavior in the near-nozzle field was thoroughly investigated. The investigation results show that: the mean values of orifice inlet rounding radii of different nozzles are measured to be on the order of 21.5-56.8 μm. Although the spray tip penetrations of different nozzles tend to increase with the hydro erosive grinding time through statistical analyzing method, the variations of penetration from nozzles are less than 15% according to different hydro erosive grinding timing.

The study of heavy vehicles rollover prediction, especially in algorithm-based heavy vehicles active safety control for improving road handling, is a challenging task for the heavy vehicle industry. Due to the high fatality rate caused by vehicle rollover, how to precisely and effectively predict the rollover of heavy vehicles became a hot topic in both academia and industry. Because of the strong non-linear characteristics of Human-Vehicle-Road interaction and the uncertainty of modeling, the traditional deterministic method cannot predict the rollover hazard of heavy vehicles accurately. To deal with the above issues, this paper applies a probability method of uncertainty to the design of a dynamic rollover prediction algorithm for heavy vehicles and proposes a novel algorithm for predicting the rollover hazard based on the combined empirical model of reliability index and failure probability.