This paper investigates the use of meta-models for optimizing sails trimming. A Gaussian process is used to robustly approximate the dependence of the performance with the trimming parameters to be optimized. The Gaussian process construction uses a limited number of performance observations at carefully selected trimming points, potentially enabling the optimization of complex sail systems with multiple trimming parameters. We test the optimization procedure on the (two parameters) trimming of a scaled IMOCA mainsail in upwind conditions. To assess the robustness of the Gaussian process approach, in particular its sensitivity to error and noise in the performance estimation, we contrast the direct optimization of the physical system with the optimization of its numerical model. For the physical system, the optimization procedure was fed with wind tunnel measurements, while the numerical modeling relied on a fully non-linear Fluid-Structure Interaction solver. The results show a correct agreement of the optimized trimming parameters for the physical and numerical models, despite the inherent errors in the numerical model and the measurement uncertainties. In addition, the number of performance estimations was found to be affordable and comparable in the two cases, demonstrating the effectiveness of the approach.

While plain vanilla OpenFOAM(OF) has strong capabilities with regards to quite a few typical CFD-tasks, some problems actually require additional solvers and numerical methods for efficient computation of high-quality results. One of the fields requiring these additions is the computation of large-scale free-surface flows as found e.g. in naval architecture. This holds especially for the flow around typical modern yacht hulls, often planing, sometimes with surfacepiercing appendages. Particular challenges include, but are not limited to, breaking waves, sharpness of interface, numerical ventilation (aka streaking) and a wide range of flow phenomenon scales. A new OF-based application including newly implemented discretisation schemes, gradient computation and rigid body motion computation is described. The new code is validated against published experimental data; the effect on accuracy, computational time and solver stability is shown by comparison to standard OF-solvers (interFoam / interDyMFoam) and Star-CCM+. The code’s capabilities to simulate complex ”real-world” flows are shown on a well-known racing yacht design.

Sailing is a sport and activity that takes a long time both to learn and to master, as much of its competence-based knowledge is acquired through experience. Experiencebased learning is very important, time-intensive, and the factors for success are often tacit and hidden. Should these success factors become explicit and salient, learning would occur faster and produce obvious competitive advantages. This research was conducted by embedding on-going research results into two competitive sailing teams racing in different classes, one offshore keelboat racing with a crew of eight, and a one-design Star-class racing yacht with a crew of two. The data collection consisted of observations, interviews, and video recordings. The results were also verified with the crews to catch biases in the analysis process. A jibe, a specific but common maneuver was analyzed from the perspective of Common Ground within Joint Activity. Maneuvering a competitive offshore sail racer or a previously Olympic Star-class yacht are tasks that fulfill the requirements for Joint Activity. A high level of Common Ground is required for the effective coordination needed in order to perform at a high level and maintain the safety of the crew and equipment. Breakdowns in the coordination of maneuvers were observed, although they must be recorded on video for higher analysis reliability. To achieve greater validity, more and different maneuvers should be considered within the analysis. By better understanding the factors for success, sail racing teams can more quickly gain competence and thus competitive advantages. The research analyzes the teamwork found in sailing from the perspective of Joint Activity and Common Ground and provides insight into how to achieve performance improvements more efficiently.