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

Abstract

This contribution describes the implementation of a data--driven shape optimization pipeline in a naval architecture application. We adopt reduced order models (ROMs) 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 (FFD). 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 (DMD) 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 (POD-GPR) technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

References in corpus (2)

• Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
• Enhancing CFD predictions in shape design problems by model and parameter space reduction

Cited by in corpus (5)

• Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
• Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
• A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems
• Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
• Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation