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An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

TitleAn efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
Publication TypeJournal Article
Year of Publication2021
AuthorsDemo, N, Ortali, G, Gustin, G, Rozza, G, Lavini, G
JournalBolletino dell Unione Matematica Italiana
Volume14
Pagination211-230
Abstract

This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

DOI10.1007/s40574-020-00263-4

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