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After-treatment sensors are used in the ECU feedback control to calibrate the engine operating parameters. Due to their contact with exhaust gases, especially NOx sensors are prone to soot deposition with a consequent decay of their performance. Several phenomena occur at the same time leading to sensor contamination: thermophoresis, unburnt hydrocarbons condensation and eddy diffusion of submicron particles. Conversely, soot combustion and shear forces may act in reducing soot deposition. This study proposes a predictive 3D-CFD model for the analysis of the development of soot deposition layer on the sensor surfaces. Alongside with the implementation of deposit and removal mechanisms, the effects on both thermal properties and shape of the surfaces are taken in account. The latter leads to obtain a more accurate and complete modelling of the phenomenon influencing the sensor overall performance.

The unsteady flow fields behind two different automobile outside side rear view mirrors were examined experimentally in order to obtain a comprehensive data base for the validation of the ongoing computational investigation effort to predict the aero-acoustic noise due to the outside rear view mirrors. This study is part of a larger scheme to predict the aero-acoustic noise due to various external components in vehicles. To aid with the characterization of this complex flow field, mean and unsteady surface pressure measurements were undertaken in the wake of two mirror models. Velocity measurements with particle image velocimetry were also conducted to develop the mean velocity field of the wake. Two full-scale mirror models with distinctive geometrical features were investigated.

ORVR (Onboard Refueling Vapor Recovery) canisters trap vapors during normal operations of a vehicle's engine, and during refueling. This study evaluates the relative risks involved should a canister rupture in a crash. A canister impactor was developed to simulate real-world impacts and to evaluate the canisters' rupture characteristics. Numerous performance aspects of canisters were evaluated: the energy required to rupture a canister; the spread of carbon particles following rupture; the ease of ignition of vapor-laden particles; the vapor concentration in the area of ruptured, vapor-laden canisters; and the potential of crashes to rupture and ignite canisters. Results from these five items were combined into a risk analysis.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time.