Hostname: page-component-89b8bd64d-9prln Total loading time: 0 Render date: 2026-05-07T12:29:12.857Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1258 , December 2018 , pp. 1871 - 1883
Check for updates
Copyright
© Royal Aeronautical Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

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

References

1. Koutsothanasis, G. Marine Gas Turbine Performance Model for Rim Driven Propeller & More Electric Architectures, PhD Thesis, Cranfield University, 2010.Google Scholar
2. Koutsothanasis, G., Kalfas, A.I. and Doulgeris, G. Marine gas turbine performance model for more electric ships. ASME Turbo Expo 2011: Power for Land, Sea, and Air, ASME, 2011, GT2011-46101, pp 881–891.Google Scholar
3. Hempert, F. Rotorcraft engine cycle optimisation at mission level, PhD Thesis, Cranfield University, 2012.Google Scholar
4. Kritikos, K., Giordano, E., Kalfas, A.I. and Tantot, N. Prediction of certification noise levels generated by contra-rotating open rotor engines, ASME Turbo Expo 2012: Power for Land, Sea, and Air, ASME, 2012, GT2012-69232, pp 239–247.Google Scholar
5. Celis, C. Evaluation and Optimisation of Environmentally Friendly Aircraft Propulsion Systems, PhD Thesis, Cranfield University, 2010.Google Scholar
6. Di Lorenzo, G. Advanced low-carbon power plants – the TERA approach, PhD Thesis, Cranfield University, 2010.Google Scholar
7. Mohaghegh, S. Surrogate reservoir model, EGU General Assembly Conference, 2010, 12, p 234.Google Scholar
8. Bai, F., Lim, C.H., Jia, J., Santostefano, K., Simmons, C., Kasahara, H., Wu, W., Terada, N. and Jin, S. Directed differentiation of embryonic stem cells into cardiomyocytes by bacterial injection of defined transcription factors, Scientific Reports, 2015, 5, p 15014.Google Scholar
9. Macmillan, W.L. Development of a Module Type Computer Program for the Calculation of Gas Turbine Off Design Performance, PhD Thesis, Cranfield University, 1974.Google Scholar
10. Li, Y.G., Pilidis, P. and Newby, M.A. An adaptation approach for gas turbine design-point performance simulation, J Engineering for Gas Turbines and Power, 2006, 128, (4), pp 789–795.Google Scholar
11. Mucino, M., Li, Y.G., Ojile, J. and Newby, M.A. Advanced performance modelling of a single and double pressure once through steam generator. ASME Turbo Expo 2007: Power for Land, Sea, and Air, ASME, 2007, GT2007-27505, pp 663–673.Google Scholar
12. Goodger, E.M. and Ogaji, S.O.T. Fuels and combustion in heat engines, Cranfield Design + Print, 2011.Google Scholar
13. Young, J.B. and Wilcock, R.C. Modeling the air-cooled gas turbine: Part 2—coolant flows and losses, J Turbomach, 2002, 124, (2), pp 214–218.Google Scholar
14. Horlock, J.H., Watson, D. T. and Jones, T.V. Limitations on gas turbine performance imposed by large turbine cooling flows, Journal of Engineering for Gas Turbines and Power, 2001, 123, (3), p 487.Google Scholar
15. Kehlhofer, R., Hannemann, F., Rukes, B. and Stirnimann, F. Combined-Cycle Gas & Steam Turbine Power Plants, PennWell Books, 2009, Tulfa, US.Google Scholar
16. Ganapathy, V. Steam Generators and Waste Heat Boilers. CRC Press, 2014, Boca Raton, US.Google Scholar
17. Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, 2001, Chichester, UK.Google Scholar
18. Holliday, T., Lawrance, A.J. and Davis, T.P. Engine-mapping experiments: a two-stage regression approach, Technometrics, 2012, 40, (2), pp 120–126.Google Scholar
19. Forrester, A, Sobester, D.A. and Keane, D.A. Engineering Design via Surrogate Modelling. John Wiley & Sons, 2008, Chichester, UK.Google Scholar