01866nas a2200169   4500008004100000245014800041210006900189300001200258490000700270520119600277100001701473700001901490700002101509700002101530700002201551856012301573       2021                        eng d00aAn efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques0 aefficient computational framework for naval shape design and opt  a211-2300 v143 a<p>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.</p>1 aDemo, Nicola1 aOrtali, Giulio1 aGustin, Gianluca1 aRozza, Gianluigi1 aLavini, Gianpiero  uhttps://math.sissa.it/publication/efficient-computational-framework-naval-shape-design-and-optimization-problems-means