A Study on Combustion of High Pressure Fuel Injection for Direct Injection Diesel Engine

1988 ◽  
Author(s):  
T. Kakegawa ◽  
T. Suzuki ◽  
K. Tsujimura ◽  
M. Shimoda
2000 ◽  
Vol 123 (3) ◽  
pp. 413-424 ◽  
Author(s):  
M. J. van Nieuwstadt ◽  
I. V. Kolmanovsky

Modern direct injection engines feature high pressure fuel injection systems that are required to control the fuel quantity very accurately. Due to limited manufacturing accuracy these systems can benefit from an on-line adaptation scheme that compensates for injector variability. Since cylinder imbalance affects many measurable signals, different sensors and algorithms can be used to equalize torque production by the cylinders. This paper compares several adaptation schemes that use different sensors. The algorithms are evaluated on a cylinder-by-cylinder simulation model of a direct injection high speed diesel engine. A proof of stability and experimental results are reported as well.


Author(s):  
P S Mehta ◽  
A K Gupta

A mathematical model for predicting spray–swirl interaction in a direct injection diesel engine combustion chamber is developed using centre-line velocity vector/continuum approach. The model has three-dimensional features in fuel spray motion. The present model responds to the various air swirl, fuel injection and cylinder charge conditions. The predicted results are compared with the analytical and experimental data available from various sources in the two-dimensional case. Very good agreement is achieved over a wide range of data. The three-dimensional predictions are directly possible without any alteration in the computation scheme.


2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
J. Mohammadhassani ◽  
Sh. Khalilarya ◽  
M. Solimanpur ◽  
A. Dadvand

In the present study, artificial neural network is used to model the relationship between NOxemissions and operating parameters of a direct injection diesel engine. To provide data for training and testing the network, a 6-inline-cylinder, four-stroke, diesel test engine is used and tested for various engine speeds, mass fuel injection rates, and intake air temperatures. 80% of a total of 144 obtained experimental data is employed for training process. In addition, 10% of the data (randomly selected) is used for network validation and the remaining data is employed for testing the accuracy of the network. The mean square error function is used for evaluating the performance of the network. The results show that the artificial neural network can efficiently be used to predict NOxemissions from the tested engine with about 10% error.


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