Improving Performance and Operability for Wind-Propelled Ships using Machine- learning-driven Active Flow Control

Research Project, 2024 – 2025

Motor sailing ships is argued to be a key component to reach the IMO targets on reduced greenhouse gas emissions. Installations of different types of sails, Wind Propulsion Technology (WPT), can replace a part of the need for engine power and thereby reduce fuel consumption and emissions; combined with alternative CO2-neutral fuels there is a possibility to reach fossil free shipping.
One relatively common type of WPT is wing sails. An important operational issue, that is e.g. studied at RISE, KTH, and Chalmers, is how these sails should be sheeted to achieve optimal thrust, considering interaction effects between the sails and between the sails and the ship. The sails will then operate close to stall, i.e. when massive separation occurs around the sails and they suddenly and strongly loose thrust. Studies at KTH have now also started looking at how the atmospheric boundary layer affects the sails, where slower wind speeds close to the surface leads to another optimal sheeting angle than the higher speeds at the sail tip. One further complication is an effect of hysteresis: when stall has occurred it is required to strongly reduce the sheeting angle to regain thrust which is a slow and costly process.
In this project, we will develop a system of active flow control (AFC) for wing sails. This means that by installing sensors and systems to blow and suck air over a wing one can increase thrust and delay stall. One can also expect that if the wing anyway reaches stall, the AFC system will be more effective and restoring thrust than by only changing sheeting angle.