Knock intensity behaves as a random process which may be characterized using simple scalar metrics such as the mean and variance, or (more commonly) by the probability of knock events. However, such measures discard much of the information present in the signal. Several researchers have therefore sought to obtain a more complete characterization of the process by fitting parametric log-normal or gamma distribution models to knock intensity distributions. The present study extends this work both in terms of the range of engine operating conditions considered and in terms of the evaluation of the goodness of fit between two different models and the experimental data. In particular, new and arguably more application-appropriate measures of the goodness of fit provide a clearer assessment of the performance of the models, and a like-for-like comparison of log-normal and gamma distribution model forms demonstrates that the log-normal model better characterizes the experimental data used in this study.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Characterization of knock intensity distributions: Part 2: parametric models


    Contributors:


    Publication date :

    2013-12-01


    Size :

    11 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Characterization of knock intensity distributions: part 2: parametric models

    Spelina,J.M. / Jones,J.C. / Frey,J. et al. | Automotive engineering | 2013


    Characterization of knock intensity distributions: Part 1: statistical independence and scalar measures

    Spelina, Jill M / Peyton Jones, James C / Frey, Jesse | SAGE Publications | 2014



    Characterization of knock intensity distributions: part 1: statistical independence and scalar measures

    Spelina,J.M. / Jones,J.C. / Frey,J. et al. | Automotive engineering | 2014