Effect of Fuel Injection Timing on the Injection, Combustion, and Performance Characteristics of a Direct-Injection (DI) Diesel Engine Fueled with Canola Oil Methyl Ester−Diesel Fuel Blends

2010 ◽  
Vol 24 (5) ◽  
pp. 3199-3213 ◽  
Author(s):  
Metin Gumus ◽  
Cenk Sayin ◽  
Mustafa Canakci
Author(s):  
K Anand ◽  
R P Sharma ◽  
P S Mehta

Suitability of vegetable oil as an alternative to diesel fuel in compression ignition engines has become attractive, and research in this area has gained momentum because of concerns on energy security, high oil prices, and increased emphasis on clean environment. The experimental work reported here has been carried out on a turbocharged direct-injection multicylinder truck diesel engine using diesel fuel and jatropha methyl ester (JME)-diesel blends. The results of the experimental investigation indicate that an increase in JME quantity in the blend slightly advances the dynamic fuel injection timing and lowers the ignition delay compared with the diesel fuel. A maximum rise in peak pressure limited to 6.5 per cent is observed for fuel blends up to 40 per cent JME for part-load (up to about 50 per cent load) operations. However, for a higher-JME blend, the peak pressures decrease at higher loads remained within 4.5 per cent. With increasing proportion of JME in the blend, the peak pressure occurrence slightly advances and the maximum rate of pressure rise, combustion duration, and exhaust gas temperature decrease by 9 per cent, 15 per cent and 17 per cent respectively. Although the changes in brake thermal efficiencies for 20 per cent and 40 per cent JME blends compared with diesel fuel remain insignificant, the 60 per cent JME blend showed about 2.7 per cent improvement in the brake thermal efficiency. In general, it is observed that the overall performance and combustion characteristics of the engine do not alter significantly for 20 per cent and 40 per cent JME blends but show an improvement over diesel performance when fuelled with 60 per cent JME blend.


2004 ◽  
Vol 126 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Renshan Liu ◽  
Chao Zhang

A numerical study of NOx reduction for a Direct Injection (DI) Diesel engine with complex geometry, which includes intake/exhaust ports and moving valves, was carried out using the commercial computational fluid dynamics software KIVA-3v. The numerical simulations were conducted to investigate the effects of engine operating and geometrical parameters, including fuel injection timing, fuel injection duration, and piston bowl depth, on the NOx formation and the thermal efficiency of the DI Diesel engine. The tradeoff relationships between the reduction in NOx and the decrease in thermal efficiency were established.


2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Nadir Yilmaz ◽  
Erol Ileri ◽  
Alpaslan Atmanlı ◽  
A. Deniz Karaoglan ◽  
Umut Okkan ◽  
...  

An experimental investigation was conducted to evaluate the suitability of hazelnut oil methyl ester (HOME) for engine performance and exhaust emissions responses of a turbocharged direct injection (TDI) diesel engine. HOME was tested at full load with various engine speeds by changing fuel injection timing (12, 15, and 18 deg CA) in a TDI diesel engine. Response surface methodology (RSM) and least-squares support vector machine (LSSVM) were used for modeling the relations between the engine performance and exhaust emission parameters, which are the measured responses and factors such as fuel injection timing (t) and engine speed (n) parameters as the controllable input variables. For this purpose, RSM and LSSVM models from experimental results were constructed for each response, namely, brake power, brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), oxides of nitrogen (NOx), carbon dioxide (CO2), carbon monoxide (CO), and smoke opacity (N), which are affected by the factors t and n. The results of RSM and LSSVM were compared with the observed experimental results. These results showed that RSM and LSSVM were effective modeling methods with high accuracy for these types of cases. Also, the prediction performance of LSSVM was slightly better than that of RSM.


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