Data-Driven Optimization of Ship Propulsion Efficiency and Emissions Considering Relative Wind

The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of relative wind on ship propulsion efficiency and pollutant emissions. The collected navigational, engine performance, and emission data—including parameters such as shaft power, engine load, specific fuel oil consumption (SFOC), and NOx and SOx concentrations—were synchronized and then analyzed using statistical methods and a generalized additive model (GAM). Statistical correlation analysis and a GAM were applied to capture nonlinear relationships between variables. Compared with linear models, the GAM achieved higher predictive accuracy (R2 = 0.98) and effectively identified threshold and interaction effects. The results showed that headwind conditions increased the engine load by ~12% and SFOC by 8.4 g/kWh while tailwind conditions reduced SFOC by up to 6.7 g/kWh. NOx emissions peaked under headwind conditions and exhibited nonlinear escalation beyond a relative wind speed of 12 kn. An operational window was identified for simultaneous improvement of the propulsion efficiency and reduction in pollutant emissions under beam wind and tailwind conditions at moderate relative wind speeds of 6–10 kn and an engine load of 30–40%. These findings can serve as a guide for incorporating relative wind into operational strategies for maritime autonomous surface ships.