The advancements in digital twins when combined with the use of the machine learning tools can facilitate the effective health assessment and diagnostics of safety critical systems. This study aims at developing a framework to address the health assessment of marine engines utilising digital twins based on first-principles. This framework follows four distinct stages, with the former two including the marine engine digital-twin set up by customising the required thermodynamic models, as well as its calibration using tests trials data representing the engine healthy conditions. In the third stage, measurements from actual operating conditions are corrected and subsequently employed to develop the digital twin representing the prevailing conditions. The fourth stage deals with the engine health assessment by assessing health metrics derived from the developed digital twins. This framework is demonstrated in a case study of a large marine four-stroke nine-cylinder propulsion engine. The results demonstrate that three cylinders are identified to be underperforming leading to an average increase of the engine Brake Specific Fuel Consumption (BSFC) by 2.1%, whereas an average decreases of 6.8% in Indicated Mean Effective Pressure (IMEP) and 6.1% in the Exhaust Gas Temperature (EGT) are exhibited for the underperforming cylinders across the entire operating envelope. The developed digital twins facilitate the effective mapping of the engine performance for the entire operating envelope under several health conditions, providing enhanced insights for the current engine health status. The advantages of the proposed framework include the use of easily obtained data, and its application to several engine types including two and four-stroke engines for both propulsion and auxiliary use.

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Hybrid turbochargers can become an attractive solution for new built and retrofitted ship power plants, as their use can result in increasing the plant efficiency and reducing emissions. This study aims at computationally investigating the hybrid turbocharger effects on a large marine dual-fuel four-stroke engine performance and emissions characteristics as well as determining its electrical generator optimal size for the case of a ship power plant considering an actual operating profile. An existing model of a large marine four-stroke dual-fuel engine of the zero/one-dimensional type, which was developed in the commercial software GT-Power, is extended to include the hybrid turbocharger sub-model. This model is subsequently employed to carry out a parametric investigation considering a wide range for the hybrid turbocharger electric motor power. The derived results are analysed to identify the variations of the investigated dual fuel engine performance and emissions parameters in the whole engine operating envelope at both the diesel and gas modes, whilst taking into account the engine and its components operational limits. For the considered annual load profile, the results demonstrate that the optimal nominal size of the hybrid turbocharger electric motor power is 300 kW and leads to an annual energy surplus between 2% and 3% of the annually delivered engine mechanical energy. This study benefits the quantification of the hybrid turbocharger impact on large marine dual fuel four-stroke engines as well as the ship energy efficiency, thus providing useful decision support to facilitate the shipboard implementation of this technology.

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Accurate, reliable, and computationally inexpensive models of the dynamic state of combustion engines are a fundamental tool to investigate new engine designs, develop optimal control strategies, and monitor their performance. The use of those models would allow to improve the engine cost-efficiency trade-off, operational robustness, and environmental impact. To address this challenge, two state-of-the-art alternatives in literature exist. The first one is to develop high fidelity physical models (e.g., mean value engine, zero-dimensional, and one-dimensional models) exploiting the physical principles that regulate engine behaviour. The second one is to exploit historical data produced by the modern engine control and automation systems or by high-fidelity simulators to feed data-driven models (e.g., shallow and deep machine learning models) able to learn an accurate digital twin of the system without any prior knowledge. The main issues of the former approach are its complexity and the high (in some case prohibitive) computational requirements. While the main issues of the latter approach are the unpredictability of their behaviour (guarantees can be proved only for their average behaviour) and the need for large amount of historical data. In this work, following a recent promising line of research, we describe a modelling framework that is able to hybridise physical and data driven models, delivering a solution able to take the best of the two approaches, resulting in accurate, reliable, and computationally inexpensive models. In particular, we will focus on modelling the dynamic state of a four-stroke diesel engine testing the performance (both in terms of accuracy, reliability, and computational requirements) of this solution against state-of-the-art physical modelling approaches on real-world operational data.

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The immense pressure to decarbonise the maritime industry has led to the Liquefied Natural Gas (LNG) uptake as a marine fuel and the LNG fuelled ships design. As LNG is stored in cryogenic conditions, the heat ingress from the ambient causes the boil-off gas (BOG) production, which, if not controlled, results in the tank overpressure with implications on the fuel storage system safe operation. This study aims at investigating an LNG storage tank behaviour for realistic operating conditions of an LNG fuelled ocean-going ship, targeting to identify the recommended control actions for avoiding tank overpressure. A dynamic model is developed by considering the mass and energy conservation in the liquid and vapour subsystems, the energy conservation in the tank walls, the vapour to liquid equilibrium (VLE), and real gas properties. Following the model validation for a holding test, simulation runs were performed for various operating conditions corresponding to typical long and short voyages of the investigated ship. The simulation results demonstrate that a boil-off gas (BOG) compressor capacity of 450 kg/h along with its on/off control setting the upper and lower limits of the tank absolute pressure at 5.5 and 4 bar leads to tank overpressure avoidance and the minimum number of BOG compressor activations.

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Background: Maritime transportation accounts for around 80% of the world freight movements, remarkably contributing to the global environmental footprint. Dual fuel engines, running on both gaseous and liquid fuels, represent a viable way toward the reduction of emissions at the cost of additional complexity in monitoring activities. Motivation: Data-driven methods represent the frontier in research and in maritime industrial applications, and they usually require a large amount of labelled data, i.e., sensor measurements plus the associated engine status usually annotated by human operators, which are costly and seldomly available in the wild. Unlabelled samples, instead, are commonly, cheaply, and readily available. Hypothesis: The enabling technology for data-driven methods is the availability of a network of sensors and an automation system able to capture and store the associated stream of data. Methods: In this paper, we design and propose multiple alternatives toward the weakly supervised marine dual fuel engines data-driven monitoring. To this aim, we will rely on a Digital Twin of the dual fuel engine or on novelty detection algorithms and we will compare them against state-of-the-art fully supervised approaches. Results: Results on data generated from a real-data validated simulator of a marine dual fuel engine demonstrate that the proposed weakly supervised monitoring approaches lead to a negligible loss in accuracy compared to costly and often unfeasible fully supervised ones supporting the validity of the proposal for its application in the wild. Conclusion: The main outcome is a guideline for selecting the best data-driven dual fuel engine monitoring method according to the available data.

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Modern optimisation methodologies have revolutionised the engineering sector and pave the way for innovation. In ship design, this has been spearheaded by the introduction of the Holistic Design approach that allows more attributes and performances of the end product to be assessed accurately and concurrently even at the early design phase. In this respect, the authors present herein a methodology based on the optimisation of the functionalities that the general arrangement needs to provide (in this case; adjacency, noise and evacuation flow). The methodology allows for a large optimisation space, several objectives and intrinsic control by the user at all the stages. By re-arranging the location of the spaces onboard, in relation to their position on the hull as well as between themselves, significant improvements can be achieved. More functional objectives can be incorporated through modularity and penalty functions, keeping the overall process simple and flexible to adapt to fluid early design requirements.@en