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An Adaptive Neuro-fuzzy Modelling of Diesel Spray Penetration
Technical Paper
2005-24-064
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The aim of this study was to demonstrate the effectiveness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of diesel spray penetration length in an internal combustion engine. The technique involved extraction of necessary representative features from a collection of raw image data. An ANFIS was used to train the fuzzy inference system (FIS) and model the penetration length under different engine operating parameters, for example: in-cylinder pressure and temperature. The data obtained experimentally from the engine test rig was pre-processed using curve-fitting and averaging techniques. The devised mapping was compared with the experimental results and reasonable prediction was achieved. The results indicate that ANFIS can be used for modelling in-cylinder fuel spray behaviour as well as other operating parameters, potentially achieving very satisfactory results.
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Authors
- S. H. Lee - Intelligent Systems & Signal Processing Laboratories, Engineering Research Centre, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, UK. Email: S.H.Lee@Brighton.ac.uk , S.D.Walters@Brighton.ac.uk & R.J.Howlett@Brighton.ac.uk
- S. D. Walters - Intelligent Systems & Signal Processing Laboratories, Engineering Research Centre, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, UK. Email: S.H.Lee@Brighton.ac.uk , S.D.Walters@Brighton.ac.uk & R.J.Howlett@Brighton.ac.uk
- R. J. Howlett - Intelligent Systems & Signal Processing Laboratories, Engineering Research Centre, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, UK. Email: S.H.Lee@Brighton.ac.uk , S.D.Walters@Brighton.ac.uk & R.J.Howlett@Brighton.ac.uk
Topic
Citation
Lee, S., Walters, S., and Howlett, R., "An Adaptive Neuro-fuzzy Modelling of Diesel Spray Penetration," SAE Technical Paper 2005-24-064, 2005, https://doi.org/10.4271/2005-24-064.Also In
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