Issue |
MATEC Web Conf.
Volume 252, 2019
III International Conference of Computational Methods in Engineering Science (CMES’18)
|
|
---|---|---|
Article Number | 01008 | |
Number of page(s) | 6 | |
Section | Analysis of Engineering Processes | |
DOI | https://doi.org/10.1051/matecconf/201925201008 | |
Published online | 14 January 2019 |
Cyclic heat release variability in a spark ignition engine under exhaust gas recirculation
1
Department of Mathematical Sciences, Indiana University, 402 North Blackford Street, Indianapolis, Indiana 46202, USA
2
Department of Process Control, AGH University of Science and Technology, Mickiewicza 30, PL-30-059 Krakow, Poland
3
Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, PL-20-618 Lublin, Poland
4
Engineering Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
* Corresponding author: g.litak@pollub.pl
We have studied cyclic heat release variability in a spark-ignition engine under exhaust gas recirculation (EGR), using nitrogen to simulate EGR. Five EGR levels are examined. We used wavelet analysis to identify the dominant modes of fluctuation and how these modes vary in time. It is found that at a low EGR level, the heat release variations exhibit high-frequency intermittent oscillations. As the EGR level increases, the high-frequency oscillations tend to become more persistent, occurring continuously over many cycles. When the EGR level is sufficiently high, intermittent oscillations are observed at both high and low frequencies. In addition, persistent low-frequency fluctuations are present at the high EGR level. We have fitted theoretical probability models to the empirical heat release distributions. Depending on the EGR level, a three-parameter probability density function such as the generalized logistic distribution, a four-parameter distribution such as Johnson SB, or the five-parameter Wakeby distribution is found to provide a good fit. The goodness of fit of the theoretical distributions is assessed by the Kolmogorov-Smirnov (KS) test statistics. A good understanding of cyclic variability is essential to develop effective control strategies for efficient combustion.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.