The design of gas turbine engines is a complex problem. This complexity has led to the adoption of a modular design approach, in which a conceptual design phase fixes the values for some global parameters and dimensions in order to facilitate the subdivision of the overall task into a number of simpler subproblems. This approach, while making a complex problem more tractable, necessarily has to rely on designer experience and simple evaluations to specify these process-intrinsic constraints at a point in the design process where very little knowledge about the final design exists. Later phases of the design process, using higher-fidelity tools but acting on a limited region of the design space, can only refine an already established design. While substantial improvements in performance have been possible with the current approach, further gains are becoming increasingly hard to achieve. A gas turbine is a complex multidisciplinary system: a more integrated design approach can facilitate a better exploitation of the trade-offs between different modules and disciplines, postponing the setting of these critical interface parameters (such as flow areas, radii, etc.) to a point where more information exists, reducing their impact on the final design. In the resulting large, possibly multimodal, highly constrained design space, and with a large number of objectives to be considered simultaneously, finding an optimal solution by simple trial-and-error can prove extremely difficult. A more intelligent search approach, in which a numerical optimizer takes the place of the human designer in seeking optimal designs, can enable the design space to be explored significantly more effectively, while also yielding a substantial reduction in development times thanks to the automation of the design process. This paper describes the development of a system for the integrated design and optimization of gas turbine engines, linking a metaheuristic optimizer to a geometry modeler and to evaluation tools with different levels of fidelity. In recognition of the substantial increase in design space size required by the integrated approach, an improved parameterization based on the concept of principal components’ analysis was implemented, allowing a rotation of the design space along its most significant directions and a reduction in its dimensionality, proving essential for a faster and more effective exploration of the design space.

1.
Giles
,
M. B.
, 1998, “
Some Thoughts on Exploiting CFD for Turbomachinery Design
,” University of Oxford Technical Report.
2.
Horlock
,
J. H.
, 1973,
Axial Flow Compressors
,
Robert E. Krieger Publishing
,
NY
.
3.
Wisler
,
D. C.
, 1998, “
The Technical and Economic Relevance of Understanding Blade Row Interaction Effects in Turbomachinery
,”
Blade Row Interference Effects in Axial Turbomachinery Stages
(von Karman Institute for Fluid Dynamics Lecture Series, Vol.
1
),
von Karman Institute for Fluid Dynamics
,
Brussels, Belgium
.
4.
Ballal
,
D. R.
, and
Zelina
,
J.
, 2004, “
Progress in Aeroengine Technology (1939-2003)
,”
J. Aircr.
0021-8669,
41
(
1
), pp.
43
50
.
5.
Sehra
,
A. K.
, and
Whitlow
,
W. J.
, 2004, “
Propulsion and Power for 21st Century Aviation
,”
Prog. Aerosp. Sci.
0376-0421,
40
, pp.
199
235
.
6.
Kroo
,
I.
, 2004, “
Innovations in Aeronautics-2004 AIAA Dryden Lecture
,” Paper No. AIAA 2004-0001.
7.
ACARE
, 2001, “
European Aeronautics: A Vision for 2020
,” ACARE Technical Report No. KI-34-01-827-EN-C.
8.
Jarrett
,
J. P.
,
Ghisu
,
T.
, and
Parks
,
G. T.
, 2009, “
On the Coupling of Designer Experience and Modularity in the Aerothermal Design of Turbomachinery
,”
ASME J. Turbomach.
0889-504X,
131
(
3
), p.
031018
.
9.
Balling
,
R. J.
, and
Sobieszczanski-Sobieski
,
J.
, 1994, “
Optimization of Coupled Systems: A Critical Overview of Approaches
,”
NASA
Technical Report No. 195019.
10.
Panchenko
,
Y.
,
Moustapha
,
H.
,
Mah
,
S.
,
Patel
,
K.
,
Dowhan
,
M. J.
, and
Hall
,
D.
, 2002, “
Preliminary Multi-Disciplinary Optimisation in Turbomachinery Design
,”
Symposium on “Reduction of Military Vehicle Acquisition Time and Cost Through Advanced Modeling and Visual Simulation”
, Paris, France.
11.
Dornberger
,
R.
,
Buche
,
D.
, and
Stoll
,
P.
, 2000, “
Multidisciplinary Optimization in Turbomachinery Design
,”
European Congress on Computational Methods in Applied Sciences and Engineering
, Barcelona, Spain.
12.
Jeschke
,
P.
,
Kurzke
,
J.
,
Shaber
,
R.
, and
Riegler
,
C.
, 2002, “
Preliminary Gas Turbine Design Using the Multidisciplinary Design System MOPEDS
,”
Proceedings of the ASME Turbo Expo-Land, Sea and Air
, Amsterdam, The Netherlands.
13.
Jones
,
S. M.
, 2007, “
An Introduction to Thermodynamic Performance Analysis of Aircraft Gas Turbine Engine Cycles Using the Numerical Propulsion System Simulation Code
,”
NASA
Technical Report No. TM-2007-214690.
14.
Lapworth
,
L.
, 2003, “
Challenges and Methodologies in the Design of Axial Flow Fans for High-Bypass-Ratio, Gas Turbine Engines Using Steady and Unsteady CFD
,”
Advances of CFD in Fluid Machinery Design
,
R. L.
Elder
,
A.
Tourlidakis
, and
M. K.
Yates
, eds.,
Professional Engineering Publishing
,
Bury St. Edmunds, UK
.
15.
Reddy
,
E. S.
,
Curtis
,
D. R.
,
Reddy
,
D. R.
, and
Malak
,
M. F.
, 1996, “
A Performance Enhancement Tool for a Multi-Stage Compressor
,”
ASME, SAE and ASEE Joint Propulsion Conference and Exhibit
, Lake Buena Vista, FL.
16.
Shahpar
,
S.
, 2004, “
Automatic Aerodynamic Design Optimisation of Turbomachinery Components—An Industrial Perspective
,”
Optimisation Methods & Tools for Multicriteria/Multidisciplinary Design
,
von Karman Institute for Fluid Dynamics
,
Belgium
, pp.
1
40
.
17.
Koller
,
U.
,
Monig
,
R.
,
Kusters
,
B.
, and
Shreiber
,
H. A.
, 2000, “
Development of Advanced Compressor Airfoils for Heavy-Duty Gas Turbines-Part I: Design and Optimisation
,”
ASME J. Turbomach.
0889-504X,
122
, pp.
397
405
.
18.
Wolpert
,
D. H.
, and
MacReady
,
W. G.
, 1997, “
No Free Lunch Theorems for Optimization
,”
IEEE Trans. Evol. Comput.
1089-778X,
1
, pp.
67
82
.
19.
Glover
,
F.
, and
Laguna
,
M.
, 1997,
Tabu Search
,
Kluwer Academic
,
Boston
.
20.
Harvey
,
S. A.
, 2002, “
The Design Optimisation of Turbomachinery Blade Rows
,” Ph.D. thesis, University of Cambridge, Cambridge, UK.
21.
Kipouros
,
T.
,
Jaeggi
,
D. M.
,
Dawes
,
W. N.
,
Parks
,
G. T.
,
Savill
,
A. M.
, and
Clarkson
,
P. J.
, 2008, “
Biobjective Design Optimisation for Axial Compressors Using Tabu Search
,”
AIAA J.
0001-1452,
46
(
3
), pp.
701
711
.
22.
Keskin
,
A.
, and
Bestle
,
D.
, 2006, “
Application of Multi-Objective Optimization to Axial Compressor Preliminary Design
,”
Aerosp. Sci. Technol.
1270-9638,
10
, pp.
581
589
.
23.
Bell
,
T. A.
,
Jarrett
,
J. P.
, and
Clarkson
,
P. J.
, 2008, “
Exploring the Effects of Removing Process-Intrinsic Constraints on Gas Turbine Design
,”
J. Propul. Power
0748-4658,
24
(
4
), pp.
751
762
.
24.
Cumpsty
,
N. A.
, 1989,
Compressor Aerodynamics
,
Longman
,
UK
.
25.
Wright
,
P. I.
, and
Miller
,
D. C.
, 1991, “
An Improved Compressor Performance Prediction Model
,”
Proceedings of the IMechE: Turbomachinery: Latest Developments in a Changing Scence
,
IMechE
,
Bury St. Edmunds, UK
, Paper No. C423/028.
26.
Jakipse
,
D.
, and
Platt
,
M. J.
, 2004, “
Optimization in Component Design and Redesign
,”
The 10th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery
.
27.
Ortiz-Dueñas
,
C.
,
Miller
,
R. J.
,
Hodson
,
H. P.
, and
Longley
,
J. P.
, 2007, “
Effect of Length on Compressor Inter-stage Duct Performance
,” ASME Paper No. GT 2007-27752.
28.
Jarrett
,
J. P.
,
Dawes
,
W. N.
, and
Clarkson
,
P. J.
, 2007, “
An Approach to Integrated Multi-Disciplinary Turbomachinery Design
,”
ASME J. Turbomach.
0889-504X,
129
(
3
), pp.
488
494
.
29.
Ghisu
,
T.
, 2009, “
Robust Aerodynamic Design of Compression Systems
,” Ph.D. thesis, University of Cambridge, Cambridge, UK.
30.
Britchford
,
K. M.
,
Manners
,
A. P.
,
McGuirk
,
J. J.
, and
Stevens
,
S. J.
, 1994, “
Measurements and Prediction of Flow in Annular S-Shaped Ducts
,”
Exp. Therm. Fluid Sci.
0894-1777,
9
, pp.
197
205
.
31.
Butz
,
L. A.
, 1979, “
Turbulent Flow in S-Shaped Ducts
,” MS thesis, Purdue University, West Lafayette, IN.
32.
Schlichting
,
H.
, and
Gersten
,
K.
, 1999,
Boundary Layer Theory
,
8th ed.
,
Springer
,
New York
.
33.
Naylor
,
E. M. J.
,
Ortiz-Dueñas
,
C.
,
Miller
,
R. J.
, and
Hodson
,
H. P.
, 2010, “
Optimization of Nonaxisymmetric Endwalls in Compressor S-Shaped Ducts
,”
ASME J. Turbomach.
0889-504X,
132
(
1
), p.
011011
.
34.
Wallin
,
F.
, and
Eriksson
,
L. E.
, 2006, “
Response Surface-Based Transition Duct Shape Optimization
,” ASME Paper No. GT2006-90978.
35.
Castillo
,
L.
,
Wang
,
X.
, and
George
,
W. K.
, 2004, “
Separation Criterion for Turbulent Boundary Layers Via Similarity Analysis
,”
ASME J. Fluids Eng.
0098-2202,
126
, pp.
297
304
.
36.
Norris
,
G.
, and
Dominy
,
R. G.
, 1997, “
The Influences of Blade Wakes on the Performance of Interturbine Diffusers
,”
Proc. Inst. Mech. Eng., Part A
0957-6509,
211
, pp.
235
242
.
37.
Sobol’
,
I. M.
, 1979, “
On the Systematic Search in a Hypercube
,”
SIAM (Soc. Ind. Appl. Math.) J. Numer. Anal.
0036-1429,
16
(
5
), pp.
790
793
.
38.
Jaeggi
,
D. M.
,
Parks
,
G. T.
,
Kipouros
,
T.
, and
Clarkson
,
P. J.
, 2008, “
The Development of a Multi-Objective Tabu Search Algorithm for Continuous Optimisation Problems
,”
Eur. J. Oper. Res.
0377-2217,
185
, pp.
1192
1212
.
39.
Hooke
,
R.
, and
Jeeves
,
T. A.
, 1961, “
‘Direct Search’ Solution of Numerical and statistical problems
,”
J. Assoc. Comput. Mach.
0004-5411,
2
(
8
), pp.
212
229
.
40.
Bäck
,
T.
,
Fogel
,
D. B.
, and
Michalewicz
,
Z.
, 1997,
Handbook of Evolutionary Computation
,
Oxford University Press
,
New York
.
41.
Kirby
,
M.
, 2002,
Geometric Data Analysis. An Empirical Approach to Dimensionality Reduction and the Study of Patterns
,
Wiley
,
New York
.
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