Robust Design Optimization of a Compressor Rotor Using Recursive Cokriging Based Multi-Fidelity Uncertainty Quantification and Multi-Fidelity Optimization

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

This work focuses on the application of multi-fidelity methods for the robust design optimization of engine components. The robust design optimization approach yields geometric designs that have high efficiencies and are less sensitive to uncertainties from manufacturing and wear. However, the uncertainty quantification techniques required to evaluate the robustness are computationally expensive, which limits their use in robust optimization. Multi-fidelity methods offer a promising solution to reduce the computational cost while maintaining accuracy in both uncertainty quantification and optimization. A Kriging and a multi-fidelity recursive Cokriging framework are developed, implemented, and applied to a test function. In addition, a multi-fidelity super efficient global optimization algorithm is developed. The optimizer is surrogate model-based and can handle constraints. The developed methods are then applied to a compressor test case of a high pressure compressor blade row with 9 uncertainty and 24 design parameters of the geometry. The 2.5% quantile of the stage efficiency is used as a robustness measure and it is therefore optimized. Design bounds and performance constraints are applied. In addition, various uncertainty quantification techniques are analyzed. A multi-fidelity uncertainty quantification approach is developed that combines simplified coarse-grid low-fidelity results with high-fidelity results to reduce the computational cost while maintaining high accuracy. Uncertainty quantification techniques of three fidelity levels are then developed and used for the multi-fidelity approach in the design space. The robust design optimization of the compressor is performed and the optimal designs obtained from the multi-fidelity approach show superior performance compared to existing robust design optima in the literature.

Details

Original languageEnglish
Article number061009
Number of pages12
JournalJournal of turbomachinery
Volume147
Issue number6
Early online date22 Nov 2024
Publication statusE-pub ahead of print - 22 Nov 2024
Peer-reviewedYes

External IDs

ORCID /0000-0002-6433-4929/work/173054397
unpaywall 10.1115/1.4067076
Scopus 85216206157

Keywords

ASJC Scopus subject areas

Keywords

  • and turbine aerodynamic design, compressor, computational fluid dynamics (CFD), multi-fidelity, optimization, robust optimization, turbomachinery blading design, uncertainty quantification