Simulation-Driven Design Optimization of a Destroyer-Type Vessel via Multi-Fidelity Supervised Active Learning

Author:

Spinosa Emanuele1ORCID,Pellegrini Riccardo1ORCID,Posa Antonio1ORCID,Broglia Riccardo1ORCID,De Biase Mario2,Serani Andrea1ORCID

Affiliation:

1. CNR-INM, National Research Council-Institute of Marine Engineering, 00128 Rome, Italy

2. CEIMM, Italian Navy Hydrodynamic Research Center, 00128 Rome, Italy

Abstract

The paper presents the use of a supervised active learning approach for the solution of a simulation-driven design optimization (SDDO) problem, pertaining to the resistance reduction of a destroyer-type vessel in calm water. The optimization is formulated as a single-objective, single-point problem with both geometrical and operational constraints. The latter also considers seakeeping performance at multiple conditions. A surrogate model is used, based on stochastic radial basis functions with lower confidence bounding, as a supervised active learning approach. Furthermore, a multi-fidelity formulation, leveraging on unsteady Reynolds-averaged Navier–Stokes equations and potential flow solvers, is used in order to reduce the computational cost of the SDDO procedure. Exploring a five-dimensional design space based on free-form deformation under limited computational resources, the optimal configuration achieves a resistance reduction of about 3% at the escape speed and about 6.4% on average over the operational speed range.

Funder

Italian Navy

Italian Ministry of University and Research

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

Reference43 articles.

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