An Adaptive Neuro-fuzzy Modelling of Diesel Spray Penetration Intelligent Systems and 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 and R.J.Howlett@Brighton.ac.uk

Format:

Conference/Event

Author/Creator:

Lee, S. H., author.

Conference Name:

7th International Conference on Engines for Automobile (2005-09-11 : Naples, Italy)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Naples, ITALY Consiglio Nazionale delle Ricerche 2005

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2005-24-064

Access Restriction:

Restricted for use by site license