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Automatic characterization of ignition processes with machine learning clustering techniques

Blurock, Edward LU (2006) In International Journal of Chemical Kinetics 38(10). p.621-633
Abstract
Machine learning clustering techniques are used to characterize and, after the training phase, to identify phases within an ignition process. Forth e ethanol mechanism used in this paper, four physically identifiable phases were found and characterized: the initiation phase, preignition phase, ignition phase, and the postignition phase. The clustering is done with respect to fuzzy logic predicates identifying the maxima, minima, and inflection points of the species profiles. The cluster descriptions characterize the phases found and are in human interpretable form. in addition, these descriptions are powerful enough to be used to predict the phase structure under new conditions. Cluster phases were calculated for the ethanol mechanism at... (More)
Machine learning clustering techniques are used to characterize and, after the training phase, to identify phases within an ignition process. Forth e ethanol mechanism used in this paper, four physically identifiable phases were found and characterized: the initiation phase, preignition phase, ignition phase, and the postignition phase. The clustering is done with respect to fuzzy logic predicates identifying the maxima, minima, and inflection points of the species profiles. The cluster descriptions characterize the phases found and are in human interpretable form. in addition, these descriptions are powerful enough to be used to predict the phase structure under new conditions. Cluster phases were calculated for the ethanol mechanism at an equivalence ratio of 0.5, a pressure of 3.3 bar, and the temperatures 1200, 1300, 1400, and 1500 K. The resulting cluster phase descriptions were then successfully used to predict the phase structure and ignition delay times for other temperatures in the range from 1200 to 1500 K. The effect of different fuzzy logic predicate profile descriptions is studied to emphasize that the boundaries of some phases, specifically that between the preignition and the ignition phase, are a matter of what the modeler considers important. The end of the ignition phase corresponds to the ignition delay time and was relatively independent of the predicate descriptions used to determine the phases. (c) 2006 Wiley Periodicals, Inc. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
International Journal of Chemical Kinetics
volume
38
issue
10
pages
621 - 633
publisher
John Wiley and Sons
external identifiers
  • wos:000240483000003
  • scopus:33749316368
ISSN
0538-8066
DOI
10.1002/kin.20191
language
English
LU publication?
yes
id
646dcd8d-a5c1-4d1f-8ce1-14fa0c31a640 (old id 394127)
date added to LUP
2016-04-01 11:36:52
date last changed
2021-06-30 04:35:11
@article{646dcd8d-a5c1-4d1f-8ce1-14fa0c31a640,
  abstract     = {Machine learning clustering techniques are used to characterize and, after the training phase, to identify phases within an ignition process. Forth e ethanol mechanism used in this paper, four physically identifiable phases were found and characterized: the initiation phase, preignition phase, ignition phase, and the postignition phase. The clustering is done with respect to fuzzy logic predicates identifying the maxima, minima, and inflection points of the species profiles. The cluster descriptions characterize the phases found and are in human interpretable form. in addition, these descriptions are powerful enough to be used to predict the phase structure under new conditions. Cluster phases were calculated for the ethanol mechanism at an equivalence ratio of 0.5, a pressure of 3.3 bar, and the temperatures 1200, 1300, 1400, and 1500 K. The resulting cluster phase descriptions were then successfully used to predict the phase structure and ignition delay times for other temperatures in the range from 1200 to 1500 K. The effect of different fuzzy logic predicate profile descriptions is studied to emphasize that the boundaries of some phases, specifically that between the preignition and the ignition phase, are a matter of what the modeler considers important. The end of the ignition phase corresponds to the ignition delay time and was relatively independent of the predicate descriptions used to determine the phases. (c) 2006 Wiley Periodicals, Inc.},
  author       = {Blurock, Edward},
  issn         = {0538-8066},
  language     = {eng},
  number       = {10},
  pages        = {621--633},
  publisher    = {John Wiley and Sons},
  series       = {International Journal of Chemical Kinetics},
  title        = {Automatic characterization of ignition processes with machine learning clustering techniques},
  url          = {http://dx.doi.org/10.1002/kin.20191},
  doi          = {10.1002/kin.20191},
  volume       = {38},
  year         = {2006},
}