Energy Performance of Ships

Abstract

This dissertation addresses the increasing global demand for reducing greenhouse gas emissions in the maritime industry. It provides methods and results on ship energy performance assessment and enhancement using high-frequency operational data. These methods can be used to inform operator decisions to increase operational performance, to assess modifications to power and propulsion systems and its control strategies and to evaluate hybrid propulsion and power generation systems for future ship design for ships with similar operating profiles and conditions. The developed methodologies can be implemented on a wide range of ship types and missions, particularly on vessels with highly uncertain mission profiles and operating conditions. The case in this work is the Holland class Ocean-going Patrol Vessels (OPV) of the Royal Netherlands Navy, which are multi-function ships, equipped with hybrid propulsion, that operate a very diverse operating profile worldwide.

First, this study examines the energy performance assessment of ships, discussing the limitations of existing energy efficiency measures such as the EEDI, EEXI, SEEMP, and CII, which do not fully account for operational and environmental uncertainties. It suggests a methodology to enrich datasets of operational data in case certain parameters are not logged, and it provides a number of qualitative and quantitative tools in the assessment of operational and environmental uncertainty, and energy performance, at a ship and component level. In this way, this methodology provides conclusions on design and operational decisions, such as the decision to equip vessels with hybrid propulsion.

Secondly, this research introduces a digital twin modelling approach for energy performance prediction using high-frequency operational data. This steady state approach combines statistical and well established first-principle techniques to model system components and compensate for the accuracy of sensors and uncertainties linked to information provided by the manufacturers and shipbuilder. Results demonstrate the effectiveness of the adopted approach to predict carbon intensity over more than seventy different and diverse actual sailing intervals with high accuracy. The model shows not only a mean absolute percentage error of less than 5% on predicting instant fuel consumption on both mechanical and electrical modes, but also a carbon intensity prediction accuracy within 2.5% with a 95% confidence interval, which justifies a significant improvement over traditional models.

Finally, this study examines the design optimisation of ship energy systems. Building on the conclusions of the previous chapters, it examines the topology selection and sizing problem for the case study class of vessels. This chapter proposes a robust multi-objective optimisation framework using actual sailing profiles. It proves its robustness using actual sailing profiles of different vessels of the same class, and it examines new designs with environmental, financial and technical objectives. Results highlight the importance of accounting for realistic operational and environmental conditions in the design of ship energy systems, but also the environmental and financial benefits of design by optimisation methods.

As a final note and recommendation, this dissertation encourages the collection and use of operational data in design and operational decisions, and it offers tools and directions in which carbon emissions of ship operations can be reduced in a financially and technically viable manner.