Robust design of compressor blades against manufacturing variations
Robust design of compressor blades against manufacturing variations
The aim of this paper is to develop and illustrate an efficient methodology to design blades with robust aerodynamic performance in the presence of manufacturing uncertainties. A novel geometry parametrization technique is developed to represent manufacturing variations due to tolerancing. A Gaussian Stochastic Process Model is trained using DOE techniques in conjunction with a high fidellity CFD solver. Bayesian Monte Carlo Simulation is then employed to obtain the statistics of the performance at each design point. A multiobjective optimizer s used to search the design space for robust designs. The multiobjective formulation allows explicit trade-off between the mean and variance of the performance. A design, selected from the robust design set is compared with a deterministic optimal design. The results demonstrate an effective method to obtain compressor blade designs which have reduced sensitivity to manufacturing variations with significant savings in computational effort.
Robust design, multiobjective optimization, surrogate modeling, compressor blades, manufacturing tolerances
The American Society of Mechanical Engineers
Kumar, A.
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Keane, A.J.
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Nair, P.B.
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Shahpar, S.
68625741-8304-4df1-96c6-08ee71dda686
2006
Kumar, A.
d5d62c56-87f6-4ae1-ac67-2fb4bf8db422
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Shahpar, S.
68625741-8304-4df1-96c6-08ee71dda686
Kumar, A., Keane, A.J., Nair, P.B. and Shahpar, S.
(2006)
Robust design of compressor blades against manufacturing variations.
In Proceedings of the 2006 ASME International Design Engineering Technical Conferences & Computers and Information In Engineering Conference (IDETC/CIE 2006).
The American Society of Mechanical Engineers.
14 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The aim of this paper is to develop and illustrate an efficient methodology to design blades with robust aerodynamic performance in the presence of manufacturing uncertainties. A novel geometry parametrization technique is developed to represent manufacturing variations due to tolerancing. A Gaussian Stochastic Process Model is trained using DOE techniques in conjunction with a high fidellity CFD solver. Bayesian Monte Carlo Simulation is then employed to obtain the statistics of the performance at each design point. A multiobjective optimizer s used to search the design space for robust designs. The multiobjective formulation allows explicit trade-off between the mean and variance of the performance. A design, selected from the robust design set is compared with a deterministic optimal design. The results demonstrate an effective method to obtain compressor blade designs which have reduced sensitivity to manufacturing variations with significant savings in computational effort.
Text
kuma_06a.pdf
- Accepted Manuscript
More information
Published date: 2006
Venue - Dates:
Proceedings of the 2006 ASME International Design Engineering Technical Conferences & Computers and Information In Engineering Conference (IDETC/CIE 2006), Pennsylvania, USA, 2006-09-10 - 2006-09-13
Keywords:
Robust design, multiobjective optimization, surrogate modeling, compressor blades, manufacturing tolerances
Identifiers
Local EPrints ID: 41995
URI: http://eprints.soton.ac.uk/id/eprint/41995
PURE UUID: 458713d1-7fe7-4d19-a750-b2e3d0cb4107
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Date deposited: 25 Oct 2006
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
A. Kumar
Author:
P.B. Nair
Author:
S. Shahpar
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