Neural Network Based Identification of Fuel Injection Rate Profiles for Diesel Engines

Author(s):  
Eero Immonen ◽  
Mika Lauren ◽  
Lassi Roininen ◽  
Simo Sarkka
2004 ◽  
Vol 24 (17-18) ◽  
pp. 2715-2726 ◽  
Author(s):  
J.M. Desantes ◽  
J. Benajes ◽  
S. Molina ◽  
C.A. González

1989 ◽  
Author(s):  
Akio Takamura ◽  
Susumu Fukushima ◽  
Yukimitsu Omori ◽  
Takeyuki Kamimoto

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3265
Author(s):  
Ardhika Setiawan ◽  
Bambang Wahono ◽  
Ocktaeck Lim

Experimental research was conducted on a rapid compression and expansion machine (RCEM) that has characteristics similar to a gasoline compression ignition (GCI) engine, using two gasoline–biodiesel (GB) blends—10% and 20% volume—with fuel injection pressures varying from 800 to 1400 bar. Biodiesel content lower than GB10 will result in misfires at fuel injection pressures of 800 bar and 1000 bar due to long ignition delays; this is why GB10 was the lowest biodiesel blend used in this experiment. The engine compression ratio was set at 16, with 1000 µs of injection duration and 12.5 degree before top dead center (BTDC). The results show that the GB20 had a shorter ignition delay than the GB10, and that increasing the injection pressure expedited the autoignition. The rate of heat release for both fuel mixes increased with increasing fuel injection pressure, although there was a degradation of heat release rate for the GB20 at the 1400-bar fuel injection rate due to retarded in-cylinder peak pressure at 0.24 degree BTDC. As the ignition delay decreased, the brake thermal efficiency (BTE) decreased and the fuel consumption increased due to the lack of air–fuel mixture homogeneity caused by the short ignition delay. At the fuel injection rate of 800 bar, the GB10 showed the worst efficiency due to the late start of combustion at 3.5 degree after top dead center (ATDC).


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