A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls

TL;DR

This paper presents a multi-fidelity structural optimization framework for passenger ship hulls that combines parameter space reduction with non-intrusive POD, enabling efficient exploration of high-dimensional design spaces during early design phases.

Contribution

It introduces a novel integration of active subspaces with proper orthogonal decomposition in a multi-fidelity setting for ship hull optimization, addressing high-dimensional parameter spaces.

Findings

Effective reduction of input parameters and outputs in ship hull models.

Identification of new design options during preliminary phases.

Demonstrated framework's accuracy and efficiency on real ship models.

Abstract

Nowadays, the shipbuilding industry is facing a radical change towards solutions with a smaller environmental impact. This can be achieved with low emissions engines, optimized shape designs with lower wave resistance and noise generation, and by reducing the metal raw materials used during the manufacturing. This work focuses on the last aspect by presenting a complete structural optimization pipeline for modern passenger ship hulls which exploits advanced model order reduction techniques to reduce the dimensionality of both input parameters and outputs of interest. We introduce a novel approach which incorporates parameter space reduction through active subspaces into the proper orthogonal decomposition with interpolation method. This is done in a multi-fidelity setting. We test the whole framework on a simplified model of a midship section and on the full model of a passenger ship, controlled by 20 and 16 parameters, respectively. We present a comprehensive error analysis and show the capabilities and usefulness of the methods especially during the preliminary design phase, finding new unconsidered designs while handling high dimensional parameterizations.