A semi-empirical ship performance model for wind-assisted propulsion under dynamic metocean conditions
Paper i proceeding, 2026

Wind-assisted propulsion systems (WAPS) are increasingly recognized as an effective solution for reducing fuel consumption and greenhouse gas emissions in maritime transportation. However, the energy performance of WAPS ships is strongly influenced by complex interactions among ship subsystems and the metocean environment. Existing WAPS performance models often assume that the aerodynamic lift and drag coefficients of sails depend solely on the angle of attack, neglecting apparent wind speed dependent Reynolds number effects, and typically consider a fixed propeller pitch, which limits their applicability for integrated performance assessment and control optimization. In this study, a semi-empirical 4-DOF ship performance model is developed to evaluate and optimize the energy performance of ships equipped with WAPS under dynamic metocean conditions. The model explicitly accounts for surge, sway, yaw, and roll force and moment equilibria, and incorporates Reynolds number dependent aerodynamic coefficients obtained from the NeuralFoil framework for rigid wingsails. A controllable pitch propeller model based on the boundary element method is coupled with the ship and WAPS models to enable joint optimization of WAPS orientation and propeller pitch. The proposed framework is applied to three case study voyages of a reference 82000 DWT bulk carrier. Simulation results show that WAPS-only optimization reduces fuel consumption by 4.14–7.13%, while joint WAPS–CPP optimization yields an additional 1% savings, with a maximum total reduction of 8.02%.

Författare

Xiao Lang

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Muye Ge

Berg Propulsion AB

Wengang Mao

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE

45th International Conference on Ocean, Offshore & Arctic Engineering
Tokyo, Japan,

PIANO - Physics Informed Machine Learning Architecture for Optimal Auxiliary Wind Propulsion

Trafikverket (2023/98101), 2024-10-01 -- 2027-09-30.

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Chalmers styrkeområde Transport, 2025-01-01 -- 2026-12-31.

Drivkrafter

Hållbar utveckling

Styrkeområden

Transport

Ämneskategorier (SSIF 2025)

Transportteknik och logistik

Marinteknik

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2026-09-02