Multi-fidelity optimization of super-cavitating hydrofoils

We present an effective multi-fidelity framework for shape optimization of super-cavitating hydrofoils using viscous solvers. We employ state-of-the-art machine learning tools such as multi-fidelity Gaussian process regression and Bayesian optimization to synthesize data obtained from multi-resoluti...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering 2018-04, Vol.332, p.63-85
Main Authors: Bonfiglio, L., Perdikaris, P., Brizzolara, S., Karniadakis, G.E.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian optimization
Cavitation
Configuration management
Design optimization
Gaussian process
Hydrofoils
Machine learning
Multi-fidelity modeling
Numerical analysis
Shape optimization
Solvers
Super-cavitating hydrofoils
Turbulent flow
Turbulent multi-phase flows
URANS
Workflow
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Summary:We present an effective multi-fidelity framework for shape optimization of super-cavitating hydrofoils using viscous solvers. We employ state-of-the-art machine learning tools such as multi-fidelity Gaussian process regression and Bayesian optimization to synthesize data obtained from multi-resolution simulations, and efficiently identify optimal configurations in the design space. We validate our simulation results against experimental data, and showcase the efficiency of the proposed work-flow in a realistic design problem involving the shape optimization of a three-dimensional super-cavitating hydrofoil parametrized by 17 design variables.
ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2017.12.009