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

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.

Automated summary

This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. Reduced order models are adopted to improve efficiency and a dynamic mode decomposition enhancement is used to reduce computational costs. Real-time computation is achieved through proper orthogonal decomposition with Gaussian process regression technique, enabling convergence towards the optimal shape using a genetic optimization algorithm.