Low-speed pre-ignition in gasoline, turbo-charged, direct injection engines: An analysis of engine testing data

2018 ◽  
Author(s):  
Andrew Harvey ◽  
Guillaume DeSercey ◽  
Morgan Heikal ◽  
Steven Begg ◽  
Richard Osborne
2021 ◽  
pp. 146808742110132
Author(s):  
XiaoHang Fang ◽  
Fengyu Zhong ◽  
Nick Papaioannou ◽  
Martin H Davy ◽  
Felix CP Leach

The understanding and prediction of NOx emissions formation mechanisms during engine transients are critical to the monitoring of real driving emissions. While many studies focus on the engine out NOx formation and treatment, few studies consider cyclic transient NOx emissions due to the low time resolution of conventional emission analysers. Increased computational power and substantial quantities of accessible engine testing data have made ANN a suitable tool for the prediction of transient NOx emissions. In this study, the transient predictive ability of artificial neural networks where a large number of engine testing data are available has been studied extensively. Significantly, the proposed transient model is trained from steady-state engine testing data. The trained data with 14 input features are provided with transient signals which are available from most engine testing facilities. With the help of a state-of-art high-speed NOx analyser, the predicted transient NOx emissions are compared with crank-angle resolved NOx measurements taken from a high-speed light duty diesel engine at test conditions both with and without EGR. The results show that the ANN model is capable of predicting transient NOx emissions without training from crank-angle resolved data. Significant differences are captured between the predicted transient and the slow-response NOx emissions (which are consistent with the cycle-resolved transient emissions measurements). A particular strength is found for increasing load steps where the instantaneous NOx emissions predicted by the ANN model are well matched to the fast-NOx analyser measurements. The results of this work indicate that ANN modelling could strongly contribute to the understanding of real driving emissions.


2017 ◽  
Author(s):  
Martia Shahsavan ◽  
John Hunter Mack

In turbulent non-premixed combustion applications, such as diesel and direct injection engines, the mixedness of the injected fuel with oxygen and the working fluid inside the combustion chamber is a crucial parameter since it can significantly affect the ignition behavior. In this study, a comprehensive method for investigating mixedness, defined by spatial variation and scalar dissipation, is implemented to assess the turbulent injection of hydrogen into mixture of oxygen with nitrogen, argon, and xenon. Evaluating both criteria reflects the mixture homogeneity as well as local gradients, which aids in discriminating scalar distributions with identical homogeneity and different patterns. The results indicate that replacing nitrogen with argon as the working fluid can provide more suitable ignition conditions for the hydrogen jet.


2013 ◽  
Vol 68 ◽  
pp. 505-511 ◽  
Author(s):  
Helmisyah Ahmad Jalaludin ◽  
Shahrir Abdullah ◽  
Mariyam Jameelah Ghazali ◽  
Bulan Abdullah ◽  
Nik Rosli Abdullah

MTZ worldwide ◽  
2018 ◽  
Vol 79 (7-8) ◽  
pp. 50-55 ◽  
Author(s):  
Felix Eitel ◽  
Jörg Schäfer ◽  
Achim Königstein ◽  
Christof Heeger

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