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.@en

Maritime industry has set ambitious goals to drastically reduce its greenhouse gas emissions through stipulating and enforcing a number of energy assessment measures. Unfortunately, measures like the EEDI, EEXI, SEEMP and CII do not account for the operational and environmental uncertainty of operations at sea, even though they do provide a first means of evaluating the carbon footprint of ships. The increasing availability of high-frequency operational data offers the opportunity to quantify and account for this uncertainty in energy performance predictions. Current methods to evaluate and predict energy performance at a whole energy system level do not sufficiently account for operational and environmental uncertainty. In this work, we propose a digital twin that accurately predicts the fuel consumption and carbon footprint of the hybrid propulsion system of an Ocean-going Patrol Vessel (OPV) of the Royal Netherlands Navy under the aggregate effect of operational and environmental uncertainty. It combines first-principle steady-state models with machine learning algorithms to reach an accuracy of less than 5% MAPE on both mechanical and electrical propulsion, while bringing a 40% to 50% improvement over a model that does not utilise machine learning algorithms. Results over actual voyage intervals indicate a prediction accuracy of consumed fuel and carbon intensity within 2.5% accounting for a confidence interval of 95%. Finally, the direct comparison between mechanical and electrical propulsion showed no clear energy-saving benefits and a strong dependency of the results on each voyage's specific operational and environmental conditions.

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Ship designers hardly ever receive feedback from the actual operation of their designs apart from sea acceptance trials. Similarly, crews operating the vessels do not receive a clear picture of the energy performance and environmental footprint of different options. This paper proposes a methodology based on operational data from continuous monitoring, and applies it to an ocean patrol vessel of the Royal Netherlands Navy in order to identify the impact of diverse operational conditions on energy performance over the whole operating range, but also to examine the decision to equip the vessel with hybrid propulsion. Specifically, it introduces mean energy effectiveness indicator and mean total energy efficiency over discretised vessel speed, as the main tool in quantifying the energy gains and losses to assist in making better-advised design and operational decisions. Moreover, it demonstrates a dataset enrichment procedure, using manufacturers' information, in case not all needed sensors are available. Results suggest that electrical propulsion was 15–25% less efficient than the best mechanical propulsion mode, and on the overall energy performance of the vessel, increasing speed by 1 knot caused a 7% and 14% increase over the minimum (Formula presented.) /mile emissions between 8 and 14, and above 14 knots respectively.

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