IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v69y2001i2p101-117.html
   My bibliography  Save this article

Clustering of chaotic dynamics of a lean gas-turbine combustor

Author

Listed:
  • Fichera, A.
  • Losenno, C.
  • Pagano, A.

Abstract

This work deals with the dynamic behaviour of a lean premixed gas turbine combustor. The study aims to achieve a classification of experimental burner dynamic behaviour and is based on the geometrical properties of the attractors of the system variables. Several experiments were performed varying the flame stoichiometric ratio [lambda] and the pilot fuel percentage PFP. The dynamics of the experimental time series of the flame front heat release were described by using vectors collecting information on the topological distribution of the attractors. Therefore, unsupervised Kohonen associative memories were trained to create clusters of operating conditions characterised by similar dynamical behaviours. Kohonen associative memories were able to divide the experimental operating conditions into different clusters according to the different values of the flame stoichiometric ratio. The results of the clustering underline the possibility of being able to define an algorithm for combustion-instability pattern recognition that takes into account the highly non-linear effects which govern combustion processes.

Suggested Citation

  • Fichera, A. & Losenno, C. & Pagano, A., 2001. "Clustering of chaotic dynamics of a lean gas-turbine combustor," Applied Energy, Elsevier, vol. 69(2), pages 101-117, June.
    • Handle: RePEc:eee:appene:v:69:y:2001:i:2:p:101-117
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(00)00067-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Junghui & Hsu, Tong-Yang & Chen, Chih-Chien & Cheng, Yi-Cheng, 2010. "Monitoring combustion systems using HMM probabilistic reasoning in dynamic flame images," Applied Energy, Elsevier, vol. 87(7), pages 2169-2179, July.
    2. Fichera, A. & Pagano, A., 2006. "Application of neural dynamic optimization to combustion-instability control," Applied Energy, Elsevier, vol. 83(3), pages 253-264, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:69:y:2001:i:2:p:101-117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.