TWOFOLD ADAPTIVE DESIGN SPACE REDUCTION FOR CONSTRAINED BAYESIAN OPTIMIZATION OF TRANSONIC COMPRESSOR BLADES

Lisa Pretsch, Ilya Arsenyev, Elena Raponi, Fabian Duddeck

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

As turbomachinery designs become more complex, shape optimization tasks involve computationally expensive simulations and many constraints and design variables. Bayesian optimization (BO) is a class of adaptive surrogate-based methods for global optimization. It can efficiently utilize a small budget of high-fidelity evaluations and handle a large number of constraints. However, it suffers from a hampered convergence rate for problems with a large number of design variables. Adaptive design space reductions via principal component analysis (PCA) and trust region (TR) approaches have been shown to improve the scalability in different phases of the optimization. Extending existing methods, we implement the ability to benefit from parallel high-fidelity evaluations and to handle constraints. We then sequentially combine our PCA-BO and TR-BO in a hybrid method to profit from their respective complexity reduction strategies. We assess the performance of our hybrid algorithm by comparing it to vanilla BO on two problems: a 40D analytical test function and a 55D aerodynamic compressor blade design. The empirical results show that PCA-BO enhances the convergence rate in the initial optimization phase, while TR-BO allows for further improvements in the later iterations. Moreover, the algorithm computation time is more than halved. For the compressor blade case, our approach yields an equally good design as vanilla BO after only 20 instead of 96 iterations. The proposed approach has the potential to extend the good performance of BO to even higher-dimensional constrained problems, including multi-stage turbomachinery optimizations.

OriginalspracheEnglisch
TitelTurbomachinery - Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery
Herausgeber (Verlag)American Society of Mechanical Engineers (ASME)
ISBN (elektronisch)9780791888087
DOIs
PublikationsstatusVeröffentlicht - 2024
Veranstaltung69th ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition, GT 2024 - London, Großbritannien/Vereinigtes Königreich
Dauer: 24 Juni 202428 Juni 2024

Publikationsreihe

NameProceedings of the ASME Turbo Expo
Band12D

Konferenz

Konferenz69th ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition, GT 2024
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtLondon
Zeitraum24/06/2428/06/24

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