Typically, slow steaming is adopted to lower ship’s resistance, which in turn reduces exhaust gas emissions due to lower propulsion power demands. Fine tuning of the underlying hydrodynamic models is key to the reliable forecasting of fuel consumption. Accurate knowledge of the vessel’s speedthrough- water (STW) is paramount to estimate the actual resistance of the vessel. The paper presents a feasibility study about the use of sensor fusion methods for real-time estimation of STW based on inertial measurements of ship motions and measurement of sea current. By combining a purely kinematic model together with linear Kalman filtering, the paper addresses the challenge of designing an optimal STW estimator by detailing the fundamental design choices. The proposed STW estimator is verified on simulated data and tested with measured data from an in-service container vessel.
|Publication status||Published - 2019|
|Event||4th Hull Performance & Insight Conference (HullPIC’19) - Gubbio, Italy|
Duration: 6 May 2019 → 8 May 2019
|Conference||4th Hull Performance & Insight Conference (HullPIC’19)|
|Period||06/05/2019 → 08/05/2019|
Ikonomakis, A., Galeazzi, R., Dietz, J., Kähler Holst, K., & Nielsen, U. D. (2019). Application of Sensor Fusion to Drive Vessel Performance. 229-241. Paper presented at 4th Hull Performance & Insight Conference (HullPIC’19), Gubbio, Italy.