Optimising General Configuration of Wing-Sailed Autonomous Sailing Monohulls Using Bayesian Optimisation and Knowledge Transfer
Published in Journal of Marine Science and Engineering, 2023
This research addresses the computational inefficiency in designing wing-sailed autonomous monohulls by introducing a novel dual-surrogate framework combining Bayesian optimisation (BO) and knowledge transfer (KT). Traditional methods require prohibitive CFD simulations due to lack of prior performance data, while the proposed approach dynamically focuses sampling near critical operating points using BO and reuses historical CFD data through KT surrogates. Validated on the “Seagull” prototype, the framework achieved 56% upwind and 106% downwind speed improvements under safety constraints (heel angle ≤26°), demonstrating 60% reduction in simulation costs compared to offline methods. The work provides a generalised, efficient design methodology for mission-specific autonomous sailboats, with potential applications in hydrofoil-coupled systems.
Recommended citation: Yang An*, Feng Hu, Kuo Chen, and Jiancheng Yu*. (2023). "Optimising General Configuration of Wing-Sailed Autonomous Sailing Monohulls Using Bayesian Optimisation and Knowledge Transfer." Journal of Marine Science and Engineering. 11(4), 703.
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