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In order to comply with the stringent future emission mandates of automotive diesel engines it is essential to deploy a suitable combination of after treatment devices like diesel oxidation catalyst (DOC), diesel particulate filter (DPF) and DeNox converter (Lean NOx Trap (LNT) or Selective Catalytic reduction (SCR) system). Since arriving at a suitable strategy through experiments will involve deploying a lot of resources, development of well-tuned simulation models that can reduce time and cost is important. In the first phase of this study experiments were conducted on a single cylinder light duty diesel engine fitted with a diesel oxidation catalyst (DOC) at thirteen steady state mode points identified in the NEDC (New European Driving cycle) cycle. Inlet and exit pressures and temperatures, exhaust emission concentrations and catalyst bed temperature were measured. A one dimensional simulation model was developed in the commercial software AVL BOOST.

Fuel-air mixing is the main parameter, which affects formation of NOx and PM during CI combustion. Hence better understanding of air-flow characteristics inside the combustion chamber of a diesel engine became very important. In this study, in-cylinder air-flow characteristics of four-valve diesel engine were investigated using time-resolved high-speed tomographic Particle Imaging Velocimetry (PIV). For visualization of air-flow pattern, fine graphite particles were used for flow seeding. To investigate the effect of different operating parameters, experiments were performed at different engine speeds (1200 rpm and 1500 rpm), intake air temperatures (room temperature and 50°C) and intake port configurations (swirl port, tangential port and combined port). Intake air temperature was controlled by a closed loop temperature controller and intake ports were deactivated by using a customized aluminum gasket.

Experiments were conducted on a turbocharged three cylinder automotive common rail diesel engine with port injection of butanol. This dual fuel engine was run with neat butanol and blends of water and butanol (up to 20% water by mass). Experiments were performed at a constant speed of 1800 rpm and a brake mean effective pressure of 11.8 bar (full load) at varying butanol to diesel energy share values while diesel was either injected as a single pulse or as twin pulses (Main plus Post). Open engine controllers were used for varying the injection parameters of diesel and butanol. Water butanol blends improved the brake thermal efficiency by a small extent because of better combustion phasing as compared to butanol without water. When the butanol to diesel energy share was high, auto-ignition of butanol occurred before the injection of diesel. This lowered the ignition delay of diesel and hence elevated the smoke level.

Today, homogenous charge compression ignition (HCCI) engines are becoming very popular because of their potential to reduce soot and nitric oxides (NOx) emissions simultaneously. But, their performance and emission characteristics are very much dependent upon fuel injection strategy and parameters. However, they also have many challenges viz., improper combustion phasing, high rate of pressure rise and narrow operating range. Therefore, addressing them is very essential before making them a commercial success. This study focuses on evaluating the effect of fuel injection strategy and parameters on the performance and emission characteristics of a HCCI engine by computational fluid dynamics (CFD) analysis. In this study, a four-stroke engine operating in the HCCI mode is considered and the CFD analysis is carried out by using the CONVERGE.

Automotive crash data analysis and reconstruction is vital for ensuring automotive safety. The objective of vehicle crash reconstruction is to determine the vehicle's motion before, during, and after the crash, as well as the impact on occupants in terms of injuries. Simulation approaches, such as PC CrashTM, have been developed to understand pre-crash and post-crash vehicle motion, rather than the crash phase behavior. Over the past few decades, crash phase simulations have utilized vehicle finite element models. While multibody simulation tools are suitable for crash simulations, they often require detailed crash test data to accurately capture vehicle behavior, which is not always readily available. This paper proposes a solution to this limitation by incorporating crash test data from databases, such as NHTSA, Global NCAP, consumer rating reports, and videos, along with a multibody-based approach, to conduct crash phase simulations.