The turbomachine industry is increasingly interested in developing automated design procedures that are able to summarize current design experience, to take into account manufacturing limitations and to define new rules for improving machine performance. In this paper, a strategy for the parametric analysis and optimization of transonic centrifugal impellers was developed, using the technique of the design of experiments coupled with a three dimensional fluid-dynamic solver. The geometrical parameterization was conducted using Bezier curves and a few geometrical parameters, which were chosen after a screening analysis in order to determine the most significant ones. The range of variation of the parameters was defined taking into account the manufacturing requirements. The analysis of the influence of such parameters on the main impeller performance was subdivided into two steps: first, the effect of the parameters acting on the blade shape was investigated and an optimum configuration was chosen, then the influence of three functional parameters was analyzed, fixing the already optimized variables. The whole strategy aimed at an industrial design approach, and attention was focused on the time required in the design process. From the present analysis it was possible not only to define an optimum geometry, but also to understand the influence of the input parameters on the main machine performance.

1.
Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
2.
Burgreen
,
G. W.
, and
Baysal
,
O.
,
1996
, “
Three-Dimensional Aerodynamic Shape Optimization Using Discrete Sensitivity Analysis
,”
AIAA J.
,
34
, pp.
1761
1770
.
3.
Trigg
,
M. A.
,
Tubby
,
G. R.
, and
Sheard
,
A. G.
,
1999
, “
Automatic Genetic Optimization Approach to Two-Dimensional Blade Profile Design for Steam Turbines
,”
ASME J. Turbomach.
,
121
, pp.
11
17
.
4.
Obayashi, S., 1998, “Pareto Genetic Algorithm for Aerodynamic Design Using the Navier-Stokes Equations,” Genetic Algorithms in Engineering and Computer Science, John Wiley and Sons, New York, pp. 245–266.
5.
Pierret
,
S.
, and
Van den Braembussche
,
R. A.
,
1999
, “
Turbomachinery Blade Design Using a Navier-Stokes Solver and Artificial Neural Network
,”
ASME J. Turbomach.
,
121
, pp.
326
332
.
6.
Shahpar, S., 2000, “A Comparative Study of Optimization Methods for Aerodynamic Design of Turbomachinery Blades,” ASME Paper No. 2000-GT-0523.
7.
Manna, M., and Tuccillo, R., 2000, “The Combined Use of Navier-Stokes Solvers and Optimization Methods for Decelerating Cascade Design,” ASME Paper 2000-GT-0524.
8.
Glas, W., and Jaberg, H., 2001, “Multi-Objective Evolutionary Algorithm for the Optimization of Swept Pump Impellers,” Proc., Fourth European Conference on Turbomachinery, Paper No. ATI-CST-038/01.
9.
Wahba, W. A., and Tourlidakis, A., 2001, “A Genetic Algorithm to the Design of Blade Profiles for Centrifugal Pump Impellers,” AIAA Paper No. 2001–2582.
10.
Ashimara, K., and Goto, A., 2001, “Turbomachinery Blade Design Using 3-D Inverse Design Method, CFD and Optimization Algorithm,” ASME Paper 2001-GT-0358.
11.
Cosentino, R., Alsalihi, Z., and Van den Braembussche, R. A., 2001, “Expert System for Radial Impeller Optimization,” Proc., Fourth European Conference on Turbomachinery, Paper No. ATI-CST-039/01.
12.
Bonaiuti, D., and Pediroda, V., 2001, “Aerodynamic Optimization of an Industrial Centrifugal Compressor Impeller Using Genetic Algorithms,” Proc., Eurogen 2001.
13.
Benini, E., and Tourlidakis, A., 2001, “Design Optimization of Vaned Diffusers of Centrifugal Compressors Using Genetic Algorithms,” AIAA Paper No. 2001–2583.
14.
Arnone
,
A.
,
Liou
,
M. S.
, and
Povinelli
,
L. A.
,
1992
, “
Navier-Stokes Solution of Transonic Cascade Flow Using Non-Periodic C-Type Grids
,”
J. Propul. Power
,
8
, pp.
410
417
.
You do not currently have access to this content.