scholarly journals The Characteristics on the Engine Performance for Variation of Fuel Injection Timing in DI Diesel Engine Using Biodiesel Fuel

2012 ◽  
Vol 16 (3) ◽  
pp. 16-21 ◽  
Author(s):  
S.H. Jang
Keyword(s):  
Diesel Engine ◽  
Fuel Injection ◽  
Engine Performance ◽  
Biodiesel Fuel ◽  
Injection Timing ◽  
Fuel Injection Timing ◽  
Di Diesel Engine

Predicting the Engine Performance and Exhaust Emissions of a Diesel Engine Fueled With Hazelnut Oil Methyl Ester: The Performance Comparison of Response Surface Methodology and LSSVM

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Nadir Yilmaz ◽  
Erol Ileri ◽  
Alpaslan Atmanlı ◽  
A. Deniz Karaoglan ◽  
Umut Okkan ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Diesel Engine ◽  
Methyl Ester ◽  
Response Surface ◽  
Fuel Injection ◽  
Engine Performance ◽  
Experimental Results ◽  
Hazelnut Oil ◽  
Injection Timing ◽  
Fuel Injection Timing

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.

Diesel engine performance mapping using a parametrized mixing time model

2017 ◽  
Vol 19 (2) ◽  
pp. 202-213 ◽  
Author(s):  
Michal Pasternak ◽  
Fabian Mauss ◽  
Christian Klauer ◽  
Andrea Matrisciano
Keyword(s):  
Diesel Engine ◽  
Fuel Injection ◽  
Mixing Time ◽  
Combustion Process ◽  
Engine Performance ◽  
Injection Timing ◽  
Engine Control ◽  
Reactor Model ◽  
Time Model ◽  
Tabulated Chemistry

A numerical platform is presented for diesel engine performance mapping. The platform employs a zero-dimensional stochastic reactor model for the simulation of engine in-cylinder processes. n-Heptane is used as diesel surrogate for the modeling of fuel oxidation and emission formation. The overall simulation process is carried out in an automated manner using a genetic algorithm. The probability density function formulation of the stochastic reactor model enables an insight into the locality of turbulence–chemistry interactions that characterize the combustion process in diesel engines. The interactions are accounted for by the modeling of representative mixing time. The mixing time is parametrized with known engine operating parameters such as load, speed and fuel injection strategy. The detailed chemistry consideration and mixing time parametrization enable the extrapolation of engine performance parameters beyond the operating points used for model training. The results show that the model responds correctly to the changes of engine control parameters such as fuel injection timing and exhaust gas recirculation rate. It is demonstrated that the method developed can be applied to the prediction of engine load–speed maps for exhaust NOx, indicated mean effective pressure and fuel consumption. The maps can be derived from the limited experimental data available for model calibration. Significant speedup of the simulations process can be achieved using tabulated chemistry. Overall, the method presented can be considered as a bridge between the experimental works and the development of mean value engine models for engine control applications.

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