The study is focused on the use of mesh morphing to explore different trims of yachts sails. In particular, four trims of the fore and aft sail of a model-scale sailing yacht were modelled leading to 16 configurations in total. Sail pressure distributions were validated with wind-tunnel measurements for all the 16 configurations, and full verification and validation was performed for one of these conditions. The 16 configurations were modelled with two different approaches: generating a new mesh for each trim condition (standard method) and using a morphed version of the baseline condition. This second novel method, based on the use of radial basis functions to morph the mesh, allows the computational time of exploring different geometries with computational fluid dynamics to be significantly decreased. Good agreement is observed between the pressure distributions computed with new meshes and morphed meshes. In order to show an example of trim optimisation, a metamodel approach is defined for the estimation of the response surface using radial basis function interpolation in the parameter space. Thanks to the continuum nature of morphing approach, the optimal trim angles for the given flow condition could be verified using new full computational fluid dynamic simulations. The original full factorial map of 16 points was replaced with a new map of 9 points with an optimal space filling approach to understand the faithfulness of a reduced metamodel. In both cases optimal point is evaluated using a fine Design Of Experiment table built using the metamodel (41 levels for each parameter). The maximum thrust is achieved at the same trim for both metamodels.Proposed method can be easily extended to a wide number of parameters. Such flexibility is demonstrated in the present paper showing the sensitivity of results with respect to apparent wind angle and heeling angle.

The aerodynamics of a sailing yacht with different sail trims are presented, derived from simulations performed using Computational Fluid Dynamics. A Reynolds-averaged Navier-Stokes approach was used to model sixteen sail trims first tested in a wind tunnel, where the pressure distributions on the sails were measured. An original approach was employed by using two successive simulations: the first one on a large domain to model the blockage due to the wind tunnel walls and the sails model, and a second one on a smaller domain to model the flow around the sails model. A verification and validation of the computed aerodynamic forces and pressure distributions was performed. The computed pressure distribution is shown to agree well with the measured pressures. The sail surface pressure was correlated with the increase of turbulent viscosity in the laminar separation bubble, the flow reattachment and the trailing edge separation. The drive force distribution on both sails showed that the fore part of the genoa (fore sail) provides the majority of the drive force and that the effect of the aft sail is mostly to produce an upwash effect on the genoa. An aerodynamic model based on potential flow theory and a viscous correction is proposed. This model, with one free parameter to be determined, is shown to fit the results better than the usual form drag and induced drag only, even if no friction drag is explicitly considered.

SUMMARYA verification and validation procedure for yacht sail aerodynamics is presented. Guidelines and an example of application are provided. The grid uncertainty for the aerodynamic lift, drag and pressure distributions for the sails is computed. The pressures are validated against experimental measurements, showing that the validation procedure may allow the identification of modelling errors. Lift, drag and L2 norm of the pressures were computed with uncertainties of the order of 1%. Convergence uncertainty and round‐off uncertainty are several orders of magnitude smaller than the grid uncertainty. The uncertainty due to the dimension of the computational domain is computed for a flat plate at incidence and is found to be significant compared with the other uncertainties. Finally, it is shown how the probability that the ranking between different geometries is correct can be estimated knowing the uncertainty in the computation of the value used to rank. Copyright © 2013 John Wiley & Sons, Ltd.