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Two-stroke diesel engines could be a promising solution for reducing carbon dioxide (CO2) emissions from light-duty vehicles. The main objective of this study was to assess the potential of two-stroke engines in achieving a substantial reduction in CO2 emissions compared to four-stroke diesel baselines. As part of this study 1-D models were developed for loop scavenged two-stroke and opposed piston two-stroke diesel engine concepts. Based on the engine models and an in-house vehicle model, projections were made for the CO2 emissions for a representative light-duty vehicle over the New European Driving Cycle and the Worldwide Harmonized Light Vehicles Test Procedure. The loop scavenged two-stroke engine had about 5-6% lower CO2 emissions over the two driving cycles compared to a state of the art four-stroke diesel engine, while the opposed piston diesel engine had about 13-15% potential benefit.

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.

Internal combustion engines (ICEs) exhaust emissions, particularly nitrogen oxides (NOx), have become a growing environmental and health concern. The biggest challenge for contemporary ICE industry is the development of clean ICEs, and the use of advanced design tools like Computational Fluid Dynamics (CFD) simulation is paramount to achieve this goal. In particular, the development of aftertreatment systems like Selective Catalyst Reduction (SCR) is a key step to reduce NOx emissions, and accurate and efficient CFD models are essential for its design and optimization. In this work, we propose a novel 3D-CFD methodology, which uses a Machine Learning (ML) approach as a surrogate model for the SCR catalyst chemistry, which aims to enhance accuracy of the simulations with a moderate computational cost. The ML approach is trained on a dataset generated from a set of 1D-CFD simulations of a single channel of an SCR catalyst.