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Multi-objective Optimization of an America's Cup Class Yacht Bulb

Author: 
Matteo Ledri, Mauro Poian, Giorgio Contento
Year: 
2012

The analysis and optimization of underwater appendages of a racing yacht using Computational Fluid Dynamics (CFD) is a complex task that requires the integration of CAD systems, mesh generators and CFD codes.

The design of an IACC yacht must comply with the class rules which fix certain relationships between principal dimensions of the yacht. There are no particular restrictions for the bulb shape according to these rules. The weight of the bulb is kept fixed in order to maintain the boat’s total displacement (note that the bulb represents 80% of the vessel’s total displacement), and a length constraint is applied. The objectives of the optimization are to minimize the drag and to keep the center of gravity as low as possible, in order to decrease resistance and increase stability. Each design evaluation consists of three phases: modification of geometry, automatic grid generation and a CFD analysis. 

The flow simulations are carried out using CFX 5.7.1. The yacht is tested upright, in order to have a symmetric geometry, which allows the simulation of only half of the domain. Free surface effects are neglected as the bulb is deeply immersed. The modeFRONTIER workflow contains:

  • 20 input variables
  • 4 output variables
  • 2 objectives
  • 1 constraint
The scripts and macros to run the applications involved in the design analysis are also incorporated. The optimization algorithm used is MOGA-II (Multi-Objective Genetic Algorithm), which ran 20 generations, starting from an initial population (obtained using Sobol DOE) of 24 designs; a total number of 480 bulb configurations has been tested.
Three designs are chosen from the Pareto set: two extremes (minimum drag and lowest center of gravity) and the best trade-off based on the design requirements. 
 
References 
[1] D.Quagliarella, J.Periaux, C.Poloni, G.Winter, (1998), “Genetic Algorithms and Evolution Strategies in Engineering and Computer Science”, J.Wiley and Sons 
[2] K.Miettinen, (1999), “Nonlinear Multiobjective Optimization”, Kuwler Academic Publishers, ISBN 0-7923-8278-1 

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