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Preliminary design for turbine housing and shroud segments

Published online by Cambridge University Press:  21 December 2017

C. Savaria ,
P. Phutthavong ,
H. Moustapha  and
F. Garnier
C. Savaria
Affiliation:
École de Technologie Supérieure (ETS), Montréal, Québec, Canada
P. Phutthavong
Affiliation:
Pratt and Whitney Canada (P&WC), Longueuil Québec, Canada
H. Moustapha
Affiliation:
École de Technologie Supérieure (ETS), Montréal, Québec, Canada
F. Garnier*
Affiliation:
École de Technologie Supérieure (ETS), Montréal, Québec, Canada
*
[email protected]
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Abstract

At the preliminary design phase of a gas turbine, time is crucial in capturing new business opportunities. In order to minimise the design time, the concept of Preliminary Multi-Disciplinary Optimisation (PMDO) was used to create parametric models, geometry and cooling flow correlations towards a new design process for turbine housing and shroud segments. First, dedicated parametric models were created because of their reusability and versatility. Their ease of use compared to non-parameterised models allows more design iterations and reduces set-up and design time. A user interface was developed to interact with the parametric models and improve the design time. Second, geometry correlations were created to minimise the number of parameters used in turbine housing and shroud segment design. Third, a correlation study was conducted to minimise the number of engine parameters required in cooling flow predictions. The parametric models, the geometry correlations, and the user interface resulted in a time saving of 50% and an increase in accuracy of 56% compared to the existing design system. For the cooling flow correlations, the number of engine parameters was reduced by a factor of 6 to create a simplified prediction model and hence a faster shroud segment selection process.

Keywords

Gas turbinepreliminary designmulti-disciplinary optimisationturbine housing
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1248 , February 2018 , pp. 260 - 282
Copyright
Copyright © Royal Aeronautical Society 2017 

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References

REFERENCES

1. Brophy, F., Mah, S. and Turcotte, J. Preliminary multi-disciplinary optimization (PMDO), an example at engine level, Lecture Series on Strategies for Optimization and Automated Design of Gas Turbine Engines, 2009, Berlin, Germany. AVT-167, p 14.Google Scholar
2. Panchenko, Y., Patel, K., Moustapha, H., Dowhan, M.J., Mah, S. and Hall, D. Preliminary multi-disciplinary optimization in turbomachinery design, In Proceedings of RTO/AVT symposium on “Reduction of Military Vehicle Acquisition Time and Cost through Advanced Modelling and Virtual Simulation”, 2002, Paris, France, Vol. 57, p. 22. RTO-MP-089.Google Scholar
3. Lattime, S. B. and Steinetz, B. M. High-pressure-turbine clearance control systems: current practices and future directions, J Propulsion and Power, 2004, 20, (2), pp 302–311.CrossRefGoogle Scholar
4. Hennecke, D.K. Active and passive tip clearance control. VKI Lecture Series, 1985, p. 34.Google Scholar
5. General Electric Company. 1992. Shroud Cooling Assembly for Gas Turbine Engine. US Patent 5169287.Google Scholar
6. Samareh, Jamshid A. A survey of shape parameterization techniques, NASA Conference Publication, 1999, Citeseer, pp 333-344.Google Scholar
7. Ouellet, Y., Garnier, F., Roy, F. and Moustapha, H. A preliminary design system for turbine discs, In Proceedings of the ASME Turbo Expo 2014 Conference, 2014, Dusseldorf, Germany, Vol. 2B, p 10.CrossRefGoogle Scholar
8. Mesbah, N., Seifert, S., Chatelois, B., Garnier, F. and Moustapha, H. Parameterization modelling of a gas turbine coverplate, J Energy and Power Engineering, 2014, 8, p 1378–1385.Google Scholar
9. Chatterjee, S. and Simonoff, J.S. Multiple linear regression, Handbook of Regression Analysis, 2013, John Wiley & Sons, pp 1–21.Google Scholar
10. Chatterjee, S. and Simonoff, J.S. Model building, Handbook of Regression Analysis, 2013, John Wiley & Sons, pp 23–49.Google Scholar
11. Taeger, D. and Kuhnt, S. Tests in regression analysis Statistical Hypothesis Testing with SAS and R, 2014, John Wiley & Sons, pp 237–245.CrossRefGoogle Scholar
12. Kreyszig, E. Mathematical statistics, Advanced Engineering Mathematics, 2010, John Wiley & Sons, pp 1049–1062.Google Scholar
13. Lesik, S. Simple linear regression, Applied Statistical Inference with MINITAB®, 2009, CRC Press, pp 177–180.CrossRefGoogle Scholar
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