A Simulation Based Design Optimization method for marine propellers using a two-fidelity levels meta- model for global design space exploration and optimization is presented. Response surfaces are built using the co-Kriging approximation, i.e. a multi-output Gaussian process that combines large low- fidelity dataset with few, costly, high-fidelity data. The method is applied for the CFD-based shape optimization of the E779A propeller using, as fidelity levels, two different physical models for the propeller performances prediction, namely a Boundary Element Method (low-fidelity) and a RANSE solver (high-fidelity). Results demonstrate the feasibility of multi-objective, constrained, design proce- dures, like those involving marine propellers, using these multi-fidelity response surfaces. At the same time, the need of good correlations between low- and high- fidelity data feeding the surrogate models is highlighted as a requisite for robust and reliable predictions using these approximated methods.

A Two-Fidelity Level Approach for Marine Propeller Design

Stefano Gaggero;Giuliano Vernengo;Diego Villa
2021-01-01

Abstract

A Simulation Based Design Optimization method for marine propellers using a two-fidelity levels meta- model for global design space exploration and optimization is presented. Response surfaces are built using the co-Kriging approximation, i.e. a multi-output Gaussian process that combines large low- fidelity dataset with few, costly, high-fidelity data. The method is applied for the CFD-based shape optimization of the E779A propeller using, as fidelity levels, two different physical models for the propeller performances prediction, namely a Boundary Element Method (low-fidelity) and a RANSE solver (high-fidelity). Results demonstrate the feasibility of multi-objective, constrained, design proce- dures, like those involving marine propellers, using these multi-fidelity response surfaces. At the same time, the need of good correlations between low- and high- fidelity data feeding the surrogate models is highlighted as a requisite for robust and reliable predictions using these approximated methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1048013
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