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This contribution describes the implementation of a data--driven shapeoptimization pipeline in a naval architecture application. We adopt reducedorder models (ROMs) in order to improve the efficiency of the overalloptimization, keeping a modular and equation-free nature to target theindustrial demand. We applied the above mentioned pipeline to a realisticcruise ship in order to reduce the total drag. We begin by defining the designspace, generated by deforming an initial shape in a parametric way using freeform deformation (FFD). The evaluation of the performance of each new hull isdetermined by simulating the flux via finite volume discretization of atwo-phase (water and air) fluid. Since the fluid dynamics model can result veryexpensive -- especially dealing with complex industrial geometries -- wepropose also a dynamic mode decomposition (DMD) enhancement to reduce thecomputational cost of a single numerical simulation. The real--time computationis finally achieved by means of proper orthogonal decomposition with Gaussianprocess regression (POD-GPR) technique. Thanks to the quick approximation, agenetic optimization algorithm becomes feasible to converge towards the optimalshape.

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