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In an effort to support design and testing activities at product development lifecycle of the engine, proper duty cycle is required. However, to collect data and develop accurate duty cycles, there are not any vehicles equipped with prototype engines at customers. Therefore, in this paper, discrete duty cycle development methodology is studied to generate trailer truck engine usage profile which represents driving conditions in Turkey for engines in development phase. Cycles are generated using several vehicles equipped with prototype engines and professional drivers that can mimic customer usage. Methodology is based on defining real-world customer driving profile, discretizing real-world drives into separate events, collecting vehicle data from each discrete drive, determining the weight of events by conducting customer surveys and creating a representative reference usage profile with data analysis.

This paper deals with the experimental and numerical investigation of a 2.0 litre single cylinder Heavy Duty Diesel Engine fuelled by natural gas and diesel oil in Dual Fuel mode. Due to the gaseous nature of the main fuel and to the high compression ratio of the diesel engine, reduced emissions can be obtained. An experimental study has been carried out at three different load level (25%, 50% and 75% of full engine load). Basing on experimental data, the authors recreated a 45° mesh sector of the engine cylinder and performed CFD simulations for the cases at 50% and 75% load levels. Numerical simulations were carried out on the 3D code Ansys FORTE. The aim of this work is to study combustion phenomena and, in particular, the interaction between natural gas and diesel oil, respectively represented by methane and n-dodecane. A reduced kinetic scheme for methane auto-ignition was implemented while for n-dodecane two set of reactions were utilised.

In order to meet increasingly stringent emissions standards and lower the fuel consumption of heavy-duty (HD) vehicles, significant efforts have been made to develop high efficiency and clean diesel engines and aftertreatment systems. However, a trade-off between the actual engine efficiency and nitrogen oxides (NOx) emission remains to minimize the operational costs. In addition, the conversion efficiency of the diesel aftertreatment system decreases rapidly with lower exhaust gas temperatures (EGT), which occurs at low load operations. Thus, it is necessary to investigate the optimum combustion and engine control strategies that can lower the vehicle’s running costs by maintaining low engine-out NOx emissions while increasing the conversion efficiency of the NOx aftertreament system through higher EGTs.

The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low.

An experimental study has been carried out with the end goal of minimizing engine-out methane emissions with Premixed Micro Pilot Combustion (PMPC) in a natural gas-diesel Dual-Fuel™ engine. The test engine used is a heavy-duty single cylinder engine with high pressure common rail diesel injection as well as port fuel injection of natural gas. Multiple variables were examined, including injection timings, exhaust gas recirculation (EGR) percentages, and rail pressure for diesel, conventional Dual-Fuel, and PMPC Dual-Fuel combustion modes. The responses investigated were pressure rise rate, engine-out emissions, heat release and indicated specific fuel consumption. PMPC reduces methane slip when compared to conventional Dual-Fuel and improves emissions and fuel efficiency at the expense of higher cylinder pressure.

Worldwide, there is the demand to reduce harmful emissions from non-road vehicles to fulfill European Stage V+ and VI (2022, 2024) emission legislation. The rules require significant reductions in nitrogen oxides (NOx), methane (CH4) and formaldehyde (CH2O) emissions from non-road vehicles. Compressed natural gas (CNG) engines with appropriate exhaust aftertreatment systems such as three-way catalytic converter (TWC) can meet these regulations. An issue remains for reducing emissions during the engine cold start where the CNG engine and TWC yet do not reach their optimum operating conditions. The resulting complexity of engine and catalyst calibration can be efficiently supported by numerical models. Hence, it is required to develop accurate simulation models which can predict cold start emissions. This work presents a real-time engine model for transient engine-out emission prediction using tabulated chemistry for CNG.

In more or less all aspects of life and in all sectors, there is a generalized global demand to reduce greenhouse gas (GHG) emissions, leading to the tightening and expansion of existing emissions regulations. Currently, non-road engines manufacturers are facing updates such as, among others, US Tier 5 (2028), European Stage V (2019/2020), and China Non-Road Stage IV (in phases between 2023 and 2026). For on-road applications, updates of Euro VII (2025), China VI (2021), and California Low NOx Program (2024) are planned. These new laws demand significant reductions in nitrogen oxides (NOx) and particulate matter (PM) emissions from heavy-duty vehicles. When equipped with an appropriate exhaust aftertreatment system, natural gas engines are a promising technology to meet the new emission standards.