Gaussian-Process based optimization methods have become very popular in recent years for the global optimization of complex systems with high computational costs. These methods rely on the sequential construction of a statistical surrogate model, using a training set of computed objective function values, which is refined according to a prescribed infilling strategy. However, this sequential optimization procedure can stop prematurely if the objective function cannot be computed at a proposed point. Such a situation can occur when the search space encompasses design points corresponding to an unphysical configuration, an ill-posed problem, or a non-computable problem due to the limitation of numerical solvers. To avoid such a premature stop in the optimization procedure, we propose to use a classification model to learn non-computable areas and to adapt the infilling strategy accordingly. Specifically, the proposed method splits the training set into two subsets composed of computable and non-computable points. A surrogate model for the objective function is built using the training set of computable points, only, whereas a probabilistic classification model is built using the union of the computable and non-computable training sets. The classifier is then incorporated in the surrogate-based optimization procedure to avoid proposing new points in the noncomputable domain while improving the classification uncertainty if needed. The method has the advantage to automatically adapt both the surrogate of the objective function and the classifier during the iterative optimization process. Therefore, non-computable areas do not need to be a priori known. The proposed method is applied to several analytical problems presenting different types of difficulty, and to the optimization of a fully nonlinear fluid-structure interaction system. The latter problem concerns the drag minimization of a flexible hydrofoil with cavitation constraints. The efficiency of the proposed method compared favorably to a reference evolutionary algorithm, except for situations where the feasible domain is a small portion of the design space.

a b s t r a c tThe present paper presents a numerical investigation on the potential of wind-assisted propulsion for merchant ships. In particular, a KVLCC2M hull was equipped with a set of wingsails inspired from those used in the 34th America's Cup. The combined thrust due to the propeller and the wingsails required to achieve a given cruising speed was computed by solving the equations of motion. For every wind direction, the wingsail trim was optimised with a genetic algorithm in order to minimise the thrust of the propeller. The aerodynamic forces and moments due to the hull and the wingsails were computed with Reynolds-averaged Navier-Stokes simulations, while the hydrodynamic forces on the hull and rudder were computed by adapting formulations developed for manoeuvrability applications. It was found that the aerodynamic efficiency of the wingsails is critical in order to gain a meaningful thrust contribution. The propeller thrust was decreased by about 10% when sailing crosswind, and the maximum benefit was achieved by sailing at low speed in strong wind conditions. The oil saving was found to be particularly sensitive to the wingsail aspect ratio, suggesting that an efficient wingsail should employ several tall wingsails rather than a few short and larger wingsails.

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International audienceThis paper investigates the use of Gaussian processes to solve sail trimming optimization problems. The Gaussian process, used to model the dependence of the performance with the trimming parameters, is constructed from a limited number of performance estimations at carefully selected trimming points, potentially enabling the optimization of complex sail systems with multiple trimming parameters. The proposed approach is tested on a two-parameter trimming for a scaled IMOCA mainsail in upwind sailing conditions. We focus on the robustness of the proposed approach and study especially the sensitivity of the results to noise and model error in the point estimations of the performance. In particular, we contrast the optimization performed on a real physical model set in a wind tunnel with a fully non-linear numerical fluid structure interaction model of the same experiments. For this problem with a limited number of trimming parameters , the numerical optimization was affordable and found to require a comparable amount of performance estimation as for the experimental case. The results reveal a satisfactory agreement for the numerical and experimental optimal trimming parameters, considering the inherent sources of errors and uncertainties in both numerical and experimental approaches. Sensitivity analyses have been eventually performed in the numerical optimization problem to determine the dominant source of uncertainties and characterize the robustness of the optima