A one-dimensional meanline prediction code suitable for performance prediction of small-to-medium size turbopumps (3–40 kW) is developed, which is able to predict both on and off-design performance of a turbopump given the loss of the diffusion system at the design point. This model is then calibrated against experimental data using an optimization technique. Due to the rapidity of code computations, a single-objective evolutionary algorithm is used for model calibration in order to obtain a wide exploration of the search space. Accuracy of the calibrated model is finally demonstrated by means of a comparison between predicted and actual pump performance.
JapikseD., Centrifugal pump design and performance (Wilder: Concepts ETI, Inc.1997).
2.
WhitfieldA.BainesN. C., Design of radial turbomachines (New York: Longman Scientific & Technical, John Wiley and Sons, Inc.1990).
3.
DixonS. L., Fluid mechanics and thermodynamics of turbomachineryFourth edition (Boston: Butterworth-Heinemann1998).
4.
JapikseD.BainesN. C., Introduction to turbomachinery (Norwich, VT: Concepts ETI, Inc.1994) and Oxford University Press, Oxford, England.
5.
RodgersC., Efficiency of centrifugal compressor impellers In AGARD Conference Proceedings, No. 282, Centrifugal Compressors, Flow Phenomena and Performance, Brussels, 7-9 May 1980, pp. 22-1–22-14.
JapikseD., Enhanced TEIS and secondary flow modeling for diverse compressors (Wilder, VT: Concepts NREC2001) TM 810.
10.
VeresJ. P., Centrifugal and axial pump design and off-design performance prediction NASA Technical Memorandum 106745, Presented at the 1994 Joint Subcommittee and User Group Meetings, Sunnyvale, 02/1995.
11.
PfleidererC., Turbomachines (New York: Springer-Verlag1952).
12.
StepanoffA. J., Centrifugal and axial flow pumps: Theory, design, and applications (New York: Wiley1948).
13.
DebK., Multi-objective optimization using evolutionary algorithms (New York: Wiley2001).
14.
ToffoloA.BeniniE., Genetic diversity as an objective in evolutionary algorithmsEvol. Comput.112 (2003): 151–167.
