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.File | Dimensione | Formato | |
---|---|---|---|
P_IDC12_210.pdf
accesso aperto
Descrizione: Contributo in atti di convegno
Tipologia:
Documento in Post-print
Dimensione
3.39 MB
Formato
Adobe PDF
|
3.39 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.