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Design optimisation of separate-jet exhausts for the next generation of civil aero-engines

Published online by Cambridge University Press:  19 September 2018

I. Goulos*
Affiliation:
Cranfield UniversityPropulsion Engineering CentreCranfield, BedfordUK
J. Otter
Affiliation:
Cranfield UniversityPropulsion Engineering CentreCranfield, BedfordUK
T. Stankowski
Affiliation:
Cranfield UniversityPropulsion Engineering CentreCranfield, BedfordUK
D. Macmanus
Affiliation:
Cranfield UniversityPropulsion Engineering CentreCranfield, BedfordUK
N. Grech
Affiliation:
Installation AerodynamicsRolls-Royce plcDerby, UK
C. Sheaf
Affiliation:
Installation AerodynamicsRolls-Royce plcDerby, UK

Abstract

The next generation of civil large aero-engines will employ greater bypass ratios compared with contemporary architectures. This results in higher exchange rates between exhaust performance and specific fuel consumption (SFC). Concurrently, the aerodynamic design of the exhaust is expected to play a key role in the success of future turbofans. This paper presents the development of a computational framework for the aerodynamic design of separate-jet exhaust systems for civil aero-engines. A mathematical approach is synthesised based on class-shape transformation (CST) functions for the parametric geometry definition of gas-turbine exhaust components such as annular ducts and nozzles. This geometry formulation is coupled with an automated viscous and compressible flow solution method and a cost-effective design space exploration (DSE) approach. The framework is deployed to optimise the performance of a separate-jet exhaust for very-high-bypass ratio (VHBR) turbofan engine. The optimisations carried out suggest the potential to increase the engine’s net propulsive force compared with a baseline architecture, through optimum exhaust re-design. The proposed method is able to identify and alleviate adverse flow-features that may deteriorate the aerodynamic behaviour of the exhaust system.

Type
Research Article
Copyright
© Rolls-Royce plc. 2018 

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References

REFERENCES

1. Epstein, A.H. Aeropropulsion for commercial aviation in the twenty-first century and research directions needed, AIAA J, 2014, 52, (5), pp 901911.Google Scholar
2. Walsh, P. and Fletcher, P. Gas Turbine Performance , 2004, Blackwell Publishing, New Jersey, US.Google Scholar
3. Guha, A. Optimum fan pressure ratio for bypass engines with separate or mixed exhaust streams, AIAA J of Propulsion and Power, 17 (5), September–October 2001, pp 1117–1122.Google Scholar
4. Stankowski, T., MacManus, D.G., Sheaf, C.T. and Christie, R. Aerodynamics of aero-engine installation, Proceedings of the Institution of Mechanical Engineers, Part G: J of Aerospace Engineering, 2016, 230, (14), pp 26732692.Google Scholar
5. Stankowski, T., MacManus, D.G., Robinson, M., and Sheaf, C.T. Aerodynamic effects of propulsion integration for high bypass ratio engines, J of Aircr, 2017, 54, (6), pp 2270–2284.Google Scholar
6. MIDA, S.G. Guide to In-Flight Thrust Measurement of Turbojets and Fan Engines, Advisory Group for Aerospace Research and Development, AGARD-AG-237, 7 Rue Ancelle 92200 Newilly, Sur Seine, France, January 1979.Google Scholar
7. AGARD, Aerodynamics of Power Plan Installation, Advisory Group for Aerospace Research and Development, AGARD-CP-301, 7 Rue Ancelle 92200 Newilly, Sur Seine, France, May 1981.Google Scholar
8. Covert, E.E., James, C.R., Kimsey, W.M., Rickey, G.K., and Rooney, E., Thrust and Drag: Its Prediction and Verification (Progress in Astronautics and Aeronautics Series), 1985, American Institute of Aeronautics & Astronautics, Reston, Virginia.Google Scholar
9. Rolls-Royce, The Jet Engine, 5th ed., Wiley-Blackwell, Hoboken, New Jersey, USA, 1966.Google Scholar
10. Dusa, D., Lahti, D., and Berry, D., Investigation of subsonic nacelle performance improvement concept, 18th Joint Propulsion Conference, Cleveland, Ohio, USA, June 21–23 1982.Google Scholar
11. Wilson, E.A., Adler, D., and Bar-Yoseph, P. Nozzle performance modeling, AIAA J, 2002, 40, (7), pp 13311338.Google Scholar
12. Bussman, W. and White, J. Explicit equation for flow through American Society of Mechanical Engineers Nozzle Meters, AIAA J., 1998, 36, (9), pp 17441746.Google Scholar
13. Decher, R. and Tegeler, D.C. High accuracy force accounting procedures for turbo powered simulator testing, 11th AIAA Joint Propulsion Conference, AIAA-1975-1324, Anaheim, California, USA, 1975.Google Scholar
14. Sloan, B., Wang, J., Spence, S., Raghunathan, S., and Riordan, D. Aerodynamic performance of a bypass engine with fan nozzle exit area change by warped Chevrons, IMechE J. of Aerospace Engineering, 2010, 224, (6), pp 731743.Google Scholar
15. Malecki, R.E. and Lord, K. Aerodynamic performance of exhaust nozzles derived from CFD simulation, 31st AIAA Joint Propulsion Conference, AIAA-1995-2623, San Diego, California, USA, 1995.Google Scholar
16. Zhang, Y., Chen, H., Zhang, M., Zhang, M., Li, Z., and Fu, S. Performance prediction of conical nozzle using Navier–Stokes computation, AIAA J. of Propulsion and Power, 2015, 31, (1), pp 192203.Google Scholar
17. Zhang, Y., Chen, H., Fu, S., Zhang, M., and Zhang, M. Drag prediction method of powered-on civil aircraft based on thrust-drag bookkeping, Chinese J. of Aeronautics, 2015, 28, (4), pp 10231033.Google Scholar
18. Mikkelsen, K.L., Myren, D.J., Dahl, D.G., and Christiansen, M.D., Initial subscale performance measurements of the AIAA dual separate flow reference (DSFR) nozzle, 51st AIAA/SAE/ASEE Joint Propulsion Conference, No. AIAA Paper No. 2015-3883, Orlando, Florida, USA, 2015.Google Scholar
19. Domel, N.D. Perspectives on propulsion CFD for nozzle applications relevant to the AIAA propulsion aerodynamics workshop, 51st AIAA/SAE/ASEE Joint Propulsion Conference, No. AIAA Paper 2015-3778, Orlando, Florida, USA, 2015.Google Scholar
20. Li, Z., Che, H., and Zhang, M., NSAWET results of the dual separate flow reference nozzle from AIAA PAW02, 51st AIAA/SAE/ASEE Joint Propulsion Conference, No. AIAA Paper No. 2015-3779, Orlando, Florida, USA, 2015.Google Scholar
21. Keith, B.D., Uenishi, K., and Dietrich, D.A. CFD-based three-dimensional turbofan exhaust nozzle analysis system, AIAA J. of Propulsion and Power, 1993, 9, (6), pp 840846.Google Scholar
22. Anderson, W.K., Thomas, J.L., and Whitfield, D.L. Multigrid acceleration of the flux-split Euler equations, AIAA J., 1988, 26, (6), pp 649654.Google Scholar
23. Clemen, C., Albrecht, P., and Herzog, S., Systematic Optimisation of a Turbofan Bypass Duct System, ASME Turbo Expo 2012: Turbine Technical Conference and Exposition, No. GT2012-68276, Copenhagen, Denmark, 11–15 June 2012.Google Scholar
24. Lapworth, B.L. HYDRA-CFD: a framework for collaborative CFD development, International Conference on Scientific and Engineering Computation (IC-SEC), Singapore, 30 June–2 July 2004.Google Scholar
25. Sobol, I.M. On the systematic search in a hypercube, Siam J. Numer. Anal., 1979, 16, (5), pp 790793.Google Scholar
26. Wright, G.B. Radial Basis Function Interpolation: Numerical and Analytical Development, Ph.D. Thesis, Department of Applied Mathematics, University of Colorado, USA, 2003.Google Scholar
27. Sasaki, D., Obayashi, S., and Nakahashi, K. Navier–Stokes optimization of supersonic wings with four objectives using evolutionary algorithm, J. of Aircr, 2002, 39, (4), pp 621629.Google Scholar
28. Macmillan, W.L. Development of a Module Type Computer Program for the Calculation of Gas Turbine Off Design Performance, Ph.D. Thesis, Department of Power and Propulsion, Cranfield University, 1974.Google Scholar
29. Kulfan, B.M. Recent extensions and applications of the ‘CST’ universal parametric geometry representation method, Aeronautical J., 2010, 114, (1153), pp 157176.Google Scholar
30. Zhu, F. and Qin, N. Intuitive class/shape function parameterization for airfoils, AIAA J., 2014, 52, (1), pp 1725.Google Scholar
31. Ansys Inc., 275 Technology Drive, Canonsburg, PA 15317, ANSYS ICEM CFD Tutorial Manual.Google Scholar
32. Ansys Inc., 275 Technology Drive, Canonsburg, PA 15317, ANSYS FLUENT User’s Guide.Google Scholar
33. Lorenzen, T. and Anderson, V. Design of Experiments, A No-Name Approach, 1993, Marcel Dekker Inc., New York, New York.Google Scholar
34. Bayraktar, H. and Turalioglu, F. A Kriging-based approach for locating a sampling site in the assessment of air quality, Stochastic Environmental Research and Risk Assessment, 2005, 19, pp 301305.Google Scholar
35. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, EEE Transactions on Evolutionary Computation, 2002, 6, (2), pp 182197.Google Scholar
36. Deb, K., Thiele, L., Laumanns, M., and Zitzler, E., Scalable multi-objective optimization test problems, Proceedings of the IEEE World Congress on Computational Intelligence, Honolulu, HI, USA, 8 2002.Google Scholar
37. Fan, J. and Gijbels, I. Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66, CRC Press, Boca Raton, Florida, USA, March 1996.Google Scholar
38. Bremner, F.J., Gotts, S.J., and Denham, D.L. Hinton diagrams: viewing connection strengths in neural networks, Behavior Research Methods, Instruments, & Computers, 1994, 26, (2), pp 215–218.Google Scholar
39. Goulos, I., Stankowski, T., Otter, J., MacManus, D., Grech, N., and Sheaf, C., Aerodynamic design of separate-jet exhausts for future civil aero-engine, Part 1: parametric geometry definition and CFD approach, ASME J. Eng. Gas Turbines and Power, 138, (8), 2016, pp 081201.Google Scholar
40. Goulos, I., Otter, J., Stankowski, T., MacManus, D., Grech, N., and Sheaf, C. Aerodynamic Design of separate-jet exhausts for future civil aero-engines, Part 2: surrogate modeling and optimization, ASME J. Eng. Gas Turbines and Power, 2016, 138, (8), pp 081202.Google Scholar
41. Kulfan, B.M. Universal parametric geometry representation method, J. of Aircr, 2008, 45, (1), pp 142158.Google Scholar
42. Pachidis, V., Pilidis, P., Marinai, L., and Templalexis, I. Towards a full two dimensional gas turbine performance simulator, Aeronautical J., 2007, 111, (1121), pp 433442.Google Scholar
43. Otter, J., Goulos, I., MacManus, D., and Slaby, M., Aerodynamic analysis of civil aero-engine exhaust systems using computational fluid dynamics, AIAA J. of Propulsion and Power, 2018, 34, (5), pp 1152–1165.Google Scholar
44. Wilcox, D.C. Comparison of two-equation turbulence models for boundary layers with pressure gradient, AIAA J., 1993, 31, (8), pp 14141421.Google Scholar
45. ANSYS, I., ANSYS FLUENT Theory Guide: Release 16.2, ANSYS, Canonsburg, Pennsylvania, 2013.Google Scholar
46. Sutherland, W. The viscosity of gases and molecular forces, Philosophical Magazine, 1893, 36, pp 507531.Google Scholar
47. Lee, Y.-S., Ma, Y., and Jegadesh, G. Rolling-ball method and contour marching approach to identifying critical regions for complex surface machining, Computers in Industry, 2000, 41, (2), pp 163180.Google Scholar
48. Olsson, A., Sandberg, G., and Dahlblom, O., On Latin hypercube sampling for structural reliability analysis, Structural Safety, 25, (1), 2003, pp 47–68.Google Scholar
49. Costa, J.P., Pronzato, L., and Thierry, E., A comparison between kriging and radial basis function networks for nonlinear prediction, Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP 99), Antalya, Turkey, June, 1999.Google Scholar
50. Simpson, T.W., Mauery, T.M., Korte, J.J., and Mistree, F. Kriging models for global approximation in simulation-based multidisciplinary design optimization, AIAA J., 2001, 39, (12), pp 12.Google Scholar
51. Song, W. and Keane, A.J. Surrogate-based aerodynamic shape optimization of a civil aircraft engine nacelle, AIAA J., 2007, 45, (10), pp 25652574.Google Scholar
52. Robinson, M., MacManus, D.G., Heidebrecht, A., and Grech, N., An optimization method for nacelle design, 55th AIAA Aerospace Sciences Meeting, Grapevine, Texas, 2017.Google Scholar
53. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Vol. 2, 1995, pp 1137–1143.Google Scholar
54. Robinson, M., MacManus, D.G., Richards, K., and Sheaf, C., Short and slim nacelle design for ultra-high BPR aero-engines, 55th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, Grapevine, Texas, 2017.Google Scholar
55. Liu, X. and Reynolds, A.C. Gradient-based multi-objective optimization with applications to water-flooding optimization, Computational Geosciences, 2015, 20, (3), pp 677693.Google Scholar
56. Gunston, B., Jane’s Aero-engines, Jane’s Information Group, 1996.Google Scholar
57. Hotelling, H., New light in the correlation coefficient and its transforms, J. of the Royal Statistical Society, 15, (2), 193–232, 1953.Google Scholar
58. Bonett, D.G. and Wright, T.A. Sample size requirements for estimating Pearson, Kendall and Spearman correlations, Psychometrika, 2000, 65, (1), pp 2328.Google Scholar
59. Diaz-Gomez, P.A. and Hougen, D.F. Initial population for genetic algorithms: a metrics approach, International Conference of Genetic and Evolutionary Methods, Las Vegas, Nevada, USA, June 2007, pp 43–49.Google Scholar