Automatic characterization of ignition processes with machine learning clustering techniques
(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:
https://lup.lub.lu.se/record/394127
- author
- Blurock, Edward LU
- organization
- publishing date
- 2006
- type
- Contribution to journal
- publication status
- published
- subject
- in
- International Journal of Chemical Kinetics
- volume
- 38
- issue
- 10
- pages
- 621 - 633
- publisher
- John Wiley & Sons Inc.
- 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
- 2022-01-26 07:37:34
@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 & Sons Inc.}},
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}},
}