Development of speed-power performance models for ship voyage optimization
For the semi-empirical model, formulas to estimate a ship’s added resistance in head waves are developed to effectively describe a ship’s hull forms and other main characteristics. The formulas are then extended to estimate the impacts of wave headings from different angles, and these are verified by experimental model tests. A significant wave height-based correction factor is proposed to consider the nonlinear effect on a ship’s resistance and power increase due to irregular waves. For the machine learning-based model, the XGBoost algorithm is used to establish the model based on full-scale measurements of a PCTC. The input features include parameters related to ship operation profiles, metocean conditions, and motion responses.
For the three case study ships, the discrepancy between power predictions and the actual values is reduced from more than 40% using today’s well-recognized methods to approximately 5% using the semi-empirical model proposed in this thesis. The machine learning model can further reduce the discrepancy to less than 1%. It is also demonstrated that the improved models can help to effectively optimize a ship’s voyage planning to reduce fuel consumption.
added resistance due to waves
Chalmers, Mekanik och maritima vetenskaper, Marin teknik
A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements
Ocean Engineering,; Vol. 209(2020)
Artikel i vetenskaplig tidskrift
A practical speed loss prediction model at arbitrary wave heading for ship voyage optimization
XGBoost method to model a ship’s propulsion power in seaways
EcoSail - Miljövänlig och kunddriven Sailplan optimeringstjänst
Europeiska kommissionen (EU), 2018-11-01 -- 2021-04-30.
Thesis for the degree of Licentiate – Department of Mechanics and Maritime Sciences: 2021:04
Chalmers tekniska högskola
Radion (meeting room), SB building, Hörsalsvägen 11, Göteborg.
Opponent: Associate Professor Ulrik Dam Nielsen, Technical University of Denmark, Lyngby, Denmark.