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Application of computational method in studying soot formation and its characteristics has become more preferable in today’s automotive field. Current developments of computer programs with higher precision mathematical models enable simulation results to become closer to the real engine combustion phenomena. In the present study, investigation on soot has been performed using various soot models with different levels of complexity, from simple two-step Hiroyasu-NSC soot model to the detailed-kinetic soot model. Detailed soot models, Particulate Mimic (PM) which is based on methods of moment and Particulate Size Mimic (PSM) which is based on sectional method, are applied in this study. Result of soot mass from Hiroyasu-NSC model provides 120% error compare to experimental result, while both detailed models provide an acceptable error of 7%.

Recent fluctuation in oil prices has generated interest in fuel-efficient vehicles, especially their aerodynamic profile. The literature indicates that turbulent wakes that form at the rear end of the vehicle contribute to vehicle drag in a major way. Minor studies have addressed the effects of rear-end wall angle to the drag force through effecting the wake behind the vehicle; however, this study assesses the reduction of drag using angular side walls. A previous simulation of external airflow over Ahmed's body was investigated, utilizing the k-ω SST models. Different angles of side walls were analyzed, and a maximum 36.85% reduction in drag coefficient was achieved using an angular rear side wall. The turbulent model was validated and the effectiveness of angular rear side walls thus proven. The study then simulated the flow for a road vehicle model to investigate the real world effect of angular rear side walls.

Compressed natural gas (CNG) has been widely used as alternatives to gasoline and diesel in automotive engines. It is a very promising alternative fuel due to many reasons including adaptability to those engines, low in cost, and low emission levels. Unfortunately, when converting to CNG, engines usually suffer from reduced power and limited engine speed. These are due to volumetric loss and slower flame speed. Direct injection (DI) can mitigate these problems by injecting CNG after the intake valve closes, thus increasing volumetric efficiency. In addition, the high pressure gas jet can enhance the turbulence in the cylinder which is beneficial to the mixing and burning. However, conversion to direct fuel injection (DFI) requires a costly modification to the cylinder head to accommodate the direct injector and also can involve piston crown adjustment. This paper discusses a new alternative to converting to DFI using a device called Spark Plug Fuel Injector (SPFI).

This paper describes the application and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in terms of indicated power and emissions in the appearance of CO and NO emissions level. A series of numerical computations by means of computational fluid dynamics (CFD) code were carried out based on the statistics-based design of experiment method. The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN).

This paper describes the applicable and capability of neural network as an artificial intelligence tool to determine the performance and emissions in a compressed natural gas direct injection (CNG-DI) engine. A feed-forward back-propagation artificial neural network (BPANN) approach is explored to predict the combustion performance in the term of indicated power and emissions in the appearance of CO and NO emissions level. A series of numerical computations by mean of computational fluid dynamics (CFD) code were carried out based on the statistics-based design of experiment method. The data for combustion process under various engine operating parameters at the fixed speed at 1000 rpm were obtained to train the developed artificial neural network (ANN).