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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    Title :

    An Adaptive Neuro-fuzzy Modelling of Diesel Spray Penetration


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    7th International Conference on Engines for Automobile ; 2005



    Publication date :

    2005-09-11




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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