Establishing an energy monitoring and management methodology is a quintessential milestone for informed energy savings decision making as well as for effectively reducing the cost and the environmental impact of shipping operations. In this study, a novel systematic methodology is proposed for the energy management of the ship propulsion engine, which is the largest ship energy producer. The methodology employs a combination of tools including statistical analysis, predicting the engine air flow via compressor modelling and energy and exergy analyses, whereas its output includes the engine operating profile, the most frequently occurring propeller curves and the engine most frequent operating points, the key performance indicators for quantitatively assessing the recorded parameters quality as well as the energy and exergy flows and exergy destruction of the engine components. The methodology is implemented for three case studies for a very large crude carrier, a container ship and a bulk carrier, for which recorded data were available by using different monitoring techniques from either noon reports of automatic data acquisition systems. The derived results provide the engine operating profile demonstrating that the investigated vessels were operating in slow steaming conditions with a lower engine efficiency associated with a greater exhaust gas wasted energy. The measured data analysis demonstrates the better quality of the data recorded by automated monitoring systems and pinpoint maintenance issues of the engine components. Lastly, the exergy analysis results demonstrate that the exhaust gas and jacket cooling water provide the greater exergy flows rendering them attractive for energy saving initiatives, whereas the engine block, compressor and turbine are the engine components with the greater exergy destruction, thus requiring closing monitoring for timely identifying mitigating measures.

The crankshaft dynamics model is of vital importance to a multitude of aspects on engine diagnostics; however, systematic investigations of such models performance (especially for large two-stroke diesel engines that are widely used in the power generation and shipping industries) have not been reported in the literature. This study aims to cover this gap by systematically investigating the parameters that affect the performance of a two-stroke diesel engine crankshaft dynamics model, such as the numerical scheme as well as the engine components inertia and friction. Specifically, the following alternatives are analysed: (a) two optimal performing numerical schemes, in particular, a stiff ordinary differential equations (ODE) solver and a fast solver based on a piecewise Linear Time-Invariant (LTI) scheme method, (b) the linear and the non-linear inertia-speed approaches, and (c) three engine friction submodels of varying complexity. All the potential combinations of the alternatives are investigated, and the crankshaft dynamics model performance is evaluated by employing Key Performance Indicators (KPIs), which consider the results accuracy compared to the measured data, the computational time, and the energy balance error. The results demonstrate that the best performing combination includes the stiff ODE solver, the constant inertia-speed approach and the most simplistic engine friction submodel. However, the LTI numerical scheme is recommended for applications that require fast response due to the significant savings in computational time with an acceptable compromise in the model results accuracy.

In this study, a coupled thermodynamics and crankshaft dynamics model of a large two-stroke diesel engine was utilised,to map the relationship of the engine Instantaneous Crankshaft Torque (ICT) with the following frequently occurring malfunctioning conditions: (a) Change in Start of Injection (SOI), (b) change in Rate of Heat Release (RHR), (c) change in scavenge air pressure, and (d) blowby. This was performed using frequency analysis on the engine ICT, which was obtained through a series of parametric runs of the coupled engine model, under the various malfunctioning and healthy operating conditions. This process demonstrated that engine ICT can be successfully utilised to identify the distinct effects of malfunctions (c) or (d), as they occur individually in any cylinder. Furthermore, by using the same process, malfunctions (a) and (b) can be identified as they occur individually for any cylinder, however, there is no distinct effect on the engine ICT among these malfunctions, since their effect on the in-cylinder pressure is similar. As a result, this study demonstrates the usefulness of the engine ICT as a non-intrusive diagnostic measurement, as well as the benefits of malfunctioning conditions mapping, which allows for quick and less resource intensive identification of engine malfunctions.

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.

Current practices of condition assessment in large marine engines are largely based on the measurement of cylinder pressure using external kits, which poses challenges due to sensors synchronisation and durability issues, as well as the inability to perform continuous monitoring. For addressing these challenges, this study aims at developing a novel method to solve the inverse problem of predicting the pressure variations in all engine cylinders, by using the Instantaneous Crankshaft Torque (ICT) measurement for large internal combustion engines. This method is developed by considering the Initial Value Problem (IVP) technique along with the integration of a direct crankshaft dynamics model incorporating the sensitivity parameters and stability criteria calculation based on the Lyapunov Exponent (LE) as well as a state-of-the-art Nonmonotone Self-Adaptive Levenberg-Marquardt (NSALMN) optimisation algorithm. The method is tested for a number of case studies using different combustion models based on the Weibe and sigmoid functions, as well as for healthy, degraded and faulty engine conditions. The derived results demonstrate adequate accuracy exhibiting a maximum error of 0.3% in the prediction of the mean peak in-cylinder pressure. The analysis of the calculated sensitivity parameters resulted in the identification of the parameters that significantly impact the solution, thus providing improved insights for selecting the developed method settings. The developed method renders the continuous and non-intrusive in-cylinder pressures monitoring feasible, by using a permanently installed shaft power metre sensor with higher sample rates.