On any race yacht, having the ability to maximise boat speed is key to obtain race winning performances. To achieve this the sail or wing must be set at its optimum profile. To find the best wingsail profile the trend recently has been towards more computationally expense approaches, but can we use less intensive methods to contribute to the design and optimisation process when time and resource may be limited? With an extensive number of different flying shapes, a computationally efficient approach at accurately finding optimum wingsail profiles for any given wind speed and direction is required. Using a two-dimensional section of the wingsail, lift and drag characteristics were found using Reynolds Averaged Navier-Stokes (RANS) simulations within Star-CCM+. A modified lifting line (LL) model was programmed in Python which used the two-dimensional characteristics to give fast and accurate predictions of drive force and heeling moment for a twisted inflow. The LL code was verified using experimental data, and showed that with analytical corrections, accurate predictions of lift and induced drag could be obtained. 3D RANS simulations confirmed that the LL model with correct tuning of the root vortices could predict driving forces and heeling moments within 1% and 5% respectively for a typical range of angle of attacks (AoA) and wing shapes. LL predictions took ~8 seconds on a laptop compared to ~6 hours for 3D RANS simulations running on a High-Performance Computing cluster. A machine learning algorithm using Kernel ridge multivariate regression was trained to produce a surrogate model of the wingsail giving accurate predictions within 1% of the LL results. Using the surrogate model, performance predictions could be obtained in ~0.001 seconds showcasing the large computational savings. This method permitted an exhaustive search of different wingsail profiles, giving information on parameter trends such as AoA, camber, and twist. This provides a tool that could be adopted in a velocity prediction program (VPP) and used by sailors or designers to aid in the setup and trimming of wingsails for maximum performance.
Wingsail Profile Optimisation Using Computationally Efficient Methods
Birch-Tomlinson, William, Turnock, Stephen, and Martyn Prince. "Wingsail Profile Optimisation Using Computationally Efficient Methods" Paper presented at the SNAME 24th Chesapeake Sailing Yacht Symposium, Annapolis, Maryland, USA, June 2022. doi: https://doi.org/10.5957/CSYS-2022-007
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