Robust design using Bayesian Monte Carlo
Robust design using Bayesian Monte Carlo
In this paper, we propose an efficient strategy for robust design based on Bayesian Monte Barlo simulation. Robust design is formulated as a multiobjective problem to allow explicit trade-off between the mean performance and variability. The proposed method is applied to a compressor blade design in the presence of maufacturing uncertainty. Process capability data are utilized in conjunction with a parametric geometry model for manufacturing uncertainty quantification. High-fidelity computational fluid dynamics simulations are used to evaluate the aerodynamic performance of the compressor blade. A probabilistic analysis for estimating the effect of manufacturing variations on the aerodynamic performance of the blade is performed and a case for the application of robust design is established. The proposed approach is applied to robust design of compressor blades and a selected design from the final Pareto set is compared with an optimal design obtained by minimizing the nominal performance. The selected robust blade has substantial improvement in robustness against manufacturing variations in comparison with the deterministic optimal blade. Significant savings in computational effort using the proposed method are also illustrated.
multiobjective robust design, Bayesian Monte Carlo, manufacturing uncertainty, process
capability, compressor blade
1497-1517
Kumar, Apurva
9152d989-0e22-4bba-a1f2-93aa080b8766
Nair, Prasanth B
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, Andy J
26d7fa33-5415-4910-89d8-fb3620413def
Shahpar, Shahrokh
eacd16f8-2e5f-4559-8d84-0c9c904d49a1
12 March 2008
Kumar, Apurva
9152d989-0e22-4bba-a1f2-93aa080b8766
Nair, Prasanth B
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, Andy J
26d7fa33-5415-4910-89d8-fb3620413def
Shahpar, Shahrokh
eacd16f8-2e5f-4559-8d84-0c9c904d49a1
Kumar, Apurva, Nair, Prasanth B, Keane, Andy J and Shahpar, Shahrokh
(2008)
Robust design using Bayesian Monte Carlo.
International Journal for Numerical Methods in Engineering, 73 (11), .
(doi:10.1002/nme.2126).
Abstract
In this paper, we propose an efficient strategy for robust design based on Bayesian Monte Barlo simulation. Robust design is formulated as a multiobjective problem to allow explicit trade-off between the mean performance and variability. The proposed method is applied to a compressor blade design in the presence of maufacturing uncertainty. Process capability data are utilized in conjunction with a parametric geometry model for manufacturing uncertainty quantification. High-fidelity computational fluid dynamics simulations are used to evaluate the aerodynamic performance of the compressor blade. A probabilistic analysis for estimating the effect of manufacturing variations on the aerodynamic performance of the blade is performed and a case for the application of robust design is established. The proposed approach is applied to robust design of compressor blades and a selected design from the final Pareto set is compared with an optimal design obtained by minimizing the nominal performance. The selected robust blade has substantial improvement in robustness against manufacturing variations in comparison with the deterministic optimal blade. Significant savings in computational effort using the proposed method are also illustrated.
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e-pub ahead of print date: 30 July 2007
Published date: 12 March 2008
Keywords:
multiobjective robust design, Bayesian Monte Carlo, manufacturing uncertainty, process
capability, compressor blade
Identifiers
Local EPrints ID: 59254
URI: http://eprints.soton.ac.uk/id/eprint/59254
ISSN: 0029-5981
PURE UUID: b984147c-8e09-4732-870d-76a89b227d38
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Date deposited: 01 Sep 2008
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
Apurva Kumar
Author:
Prasanth B Nair
Author:
Shahrokh Shahpar
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