A statistical approach to the analysis of the surge phenomenon
R. Bontempo,
M. Cardone,
M. Manna and
G. Vorraro
Energy, 2017, vol. 124, issue C, 502-509
Abstract:
The paper presents an innovative data processing methodology for the analysis of the surge phenomenon occurring in a compressor. Since the dynamic of the surge cycle does not have a deterministic character, its proper description can only be obtained through a statistical approach. To this aim, the temporally resolved traces of the pressure and mass flow rate signals are processed through a phase averaged decomposition technique. Furthermore, the shape of the oscillating surge cycle is detected and quantified by introducing the joint probability density function of the aforementioned signals which are reported in the pressure ratio versus mass flow rate plane. This probabilistic approach offers two significant advantages over the conventional deterministic approach, namely the possibility to quantify the time of residence of all individual unstable states in a statistical sense, as well as the possibility to carry out a proper code-to-experiments or experiments-to-experiments comparison of such an unstable phenomenon.
Keywords: Surge; Compressor; Turbocharger; Turbocharger test rig (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217302001
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:124:y:2017:i:c:p:502-509
DOI: 10.1016/j.energy.2017.02.026
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().