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Advanced gas turbine performance modelling using response surface methods

Published online by Cambridge University Press:  26 October 2018

V. Seetharama-Yadiyal ,
G.D. Brighenti  and
P.K. Zachos
V. Seetharama-Yadiyal
Affiliation:
Propulsion Engineering CentreCranfield UniversityCranfieldUK
G.D. Brighenti
Affiliation:
Propulsion Engineering CentreCranfield UniversityCranfieldUK
P.K. Zachos*
Affiliation:
Propulsion Engineering CentreCranfield UniversityCranfieldUK
*
p.zachos@cranfield.ac.uk
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Abstract

Surrogate models are widely used for dataset correlation. A popular application very frequently shown in public literature is in the field of engineering design where a large number of design parameters are correlated with performance indices of a complex system based on existing numerical or experimental information. Such an approach allows the identification of the key design parameters and their impact on the system’s performance. The generated surrogate model can become part of wider computational platforms and enable optimisation of the complex system without the need to run expensive simulations.

In this paper, a number of design point simulations for a combined gas-steam cycle are used to generate a response surface. The generated response surface correlates a range of cycle’s key design parameters with its thermal efficiency while it also enables identification of the optimum overall pressure ratio and the high pressure level of the raised steam across a range of recuperator effectiveness, pinch temperature difference across the heat recovery steam generator and the pressure at the condenser. The accuracy of a range of surrogate models to capture the design space is evaluated using root mean square statistical metrics.

Keywords

Surrogate modellingResponse surfaceCombined cycle gas turbines
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1258 , December 2018 , pp. 1871 - 1883
Copyright
© Royal Aeronautical Society 2018 

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Footnotes

A version of this paper was presented at the ISABE 2017 Conference, 3-8 September 2017, Manchester, UK.

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