Methodologies for predicting the part-load performance of aero-derivative gas turbines

F. Haglind and B. Elmegaard

Energy, 2009, vol. 34, issue 10, 1484-1492

Abstract: Prediction of the part-load performance of gas turbines is advantageous in various applications. Sometimes reasonable part-load performance is sufficient, while in other cases complete agreement with the performance of an existing machine is desirable. This paper is aimed at providing some guidance on methodologies for predicting part-load performance of aero-derivative gas turbines. Two different design models – one simple and one more complex – are created. Subsequently, for each of these models, the part-load performance is predicted using component maps and turbine constants, respectively. Comparisons with manufacturer data are made. With respect to the design models, the simple model, featuring a compressor, combustor and turbines, results in equally good performance prediction in terms of thermal efficiency and exhaust temperature as does a more complex model. As for part-load predictions, the results suggest that the mass flow and pressure ratio characteristics can be well predicted with both methods. The thermal efficiency and exhaust temperature, however, are not well predicted below 60–70% load when using turbine constants and assuming constant efficiencies for turbomachinery.

Keywords: Aero-derivative gas turbine; Performance; Part-load; Map; Turbine constant (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054420900259X
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:34:y:2009:i:10:p:1484-1492

DOI: 10.1016/j.energy.2009.06.042

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 ().