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Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

Part of: ISABE 2017

Published online by Cambridge University Press:  18 September 2017

Elias Tsoutsanis ,
Yi-Guang Li ,
Pericles Pilidis  and
Mike Newby
Elias Tsoutsanis*
Affiliation:
School of Engineering, Emirates Aviation University, Dubai, UAE Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK
Yi-Guang Li
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, UK
Pericles Pilidis
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedford, UK
Mike Newby
Affiliation:
Manx Utilities, Isle of Man, UK
*
[email protected], [email protected]
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Abstract

One of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.

Keywords

gas turbine performanceinverse modellingengine model tuningperformance adaptationoff-design performance
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1245 , November 2017 , pp. 1758 - 1777
Copyright
Copyright © Royal Aeronautical Society 2017 

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Footnotes

This paper was presented at the ISABE 2017 Conference, 3-8 September 2017, Manchester, UK.

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