Multi-objective Optimization of A-Class Catamaran Foils Adopting a Geometric Parameterization Based on RBF Mesh Morphing

Author(s): 
Marco Evangelos Biancolini (University of Tor Vergata), Ubaldo Cella (Design Methods), Alberto Clarich (ESTECO) and Francesco Franchini (Enginsoft)

CHALLENGE - The design of sailing boats appendages requires taking in consideration a large amount of design variables and diverse sailing conditions. The operative conditions of dagger boards depend on the equilibrium of the forces and moments acting on the system. This equilibrium has to be considered when designing modern fast foiling catamarans, where the appendages accomplish both the tasks of lifting up the boat and to make possible the upwind sailing by balancing the sail side force. In this scenario, the foil performing in all conditions has to be defined as a trade-off among contrasting needs.

SOLUTION - The multi-objective optimization, combined with experienced aerodynamic design, is the most efficient strategy to face these design challenges. The development of an optimization environment has been considered in this work to design the foils for an A-Class catamaran.The optimization procedure consists in combining two-phases CFD simulations of the foils, using the ANSYS Fluent solver, with the mesh morphing tool RBF Morph within the modeFRONTIER optimization workflow. The design variables control the foil planform and the front shape subjected to geometrical constraints. The objective functions are defined to improve the performances in upwind (navigation against the wind) and downwind (navigation with the wind) sailing conditions at two values of boat speed.  

BENEFITS - The optimization process led to a Pareto front on which a compromising design, that improved the performance by 7% in upwind conditions and by 7.9% in downwind, has been selected. Since the main objective of the work was to demonstrate the efficiency of the proposed approach to design, a very light mesh (less than one millions of hexahedral cells) was used in the optimization workflow.​

LOG IN TO DOWNLOAD