The continuous stringent requirements of the environmental regulations along with the LNG fuel 12 penetration and the development of port and bunkering facilities, render the use of the dual fuel 13 engines an attractive alternative of the traditional ship propulsion plants based on Diesel engines 14 running with HFO for reducing both the plant operating cost and environmental footprint. The present 15 study deals with the computational investigation of a large marine dual fuel (DF) engine of the four-16 stroke type for comparing its performance and emissions, in both diesel and gas mode operation by 17 using the commercial software GT-ISE. The engine diesel model was initially set up and calibrated to 18 adequately represent the engine operation. Subsequently, the engine dual fuel model was further 19 developed by considering the injection of two different fuels; methane in the cylinder inlet ports and 20 pilot diesel fuel into the engine cylinders. The derived results were analysed for revealing the 21 differences of the engine performance and emissions at each operating mode. In addition, the 22 turbocharger matching was investigated and discussed to enlighten the turbocharging system 23 challenges due to the completely different air fuel ratio requirements in diesel and gas modes, 24 respectively. Finally, parametric simulations were performed for gas mode operation at different loads 25 by varying pilot fuel injection timing, inlet valve closing and inlet manifold boost pressure, aiming to 26 identify the engine settings that simultaneously reduce CO 2 and NOx emissions considering the 27 ratio operation window limitations. The parametric study results are discussed to infer the 28 engine optimal settings.

This study aims at developing an integrated model that combines detailed engine thermodynamic modelling and the control system functional modelling paving the way towards the development of high-fidelity digital twins. To sufficiently represent the combustion process, a multi-Wiebe function approach was employed, whereas a database for storing the combustion model parameters was developed. The developed model was employed for the systematic investigation of a marine four-stroke dual fuel engine response during demanding transient operation with mode switching and load changes. The derived results were analysed to identify the critical engine components and their effect on the engine operational limitations. The results demonstrate that the developed model can sufficiently represent the engine and its subsystems/components behaviour and effectively capture the engine control system’s functionality. The appropriate turbocharger matching along with the sufficient design of the exhaust gas waste gate valve and fuel control systems are crucial for ensuring the smooth engine operation of dual fuel engines.

His research focuses on the development of scientific approaches to holistically capture the safety, energy and sustainability interplay of the complex marine systems including cyber-physical and autonomous systems by employing advanced model-based methods and tools for their design and optimisation pursuing life-cycle risk and energy management, efficiency improvement, and safety and sustainability enhancement. He is a member of the IMarEST Scottish branch committee responsible for the young members early career professionals.

The sensors abnormalities, faults, failure detection and diagnosis for marine engines are considered crucial for ensuring the engine safe and smooth operation. The development of such system(s) is typically based on the manufacturers experience on sensors and actuators faults and failure events. This study aims to introduce a novel methodology for the sensors diagnostics and health management in marine dual fuel engines by employing a combination of thermodynamic, functional control and data-driven models. The concept of an Engine Diagnostics System (EDS) is developed to provide intelligent engine monitoring, advanced sensors’ faults detection as well as timely and profound corrective actions. This system employs a neural networks (NN) Data-Driven (DD) model along with appropriate logic controls. The DD model is set up based on the derived steady state data from a thermodynamic model of high fidelity and is capable of real-time prediction of the engine health condition behaviour. The concept of a novel Unified Digital System (UDS) is proposed that combines the engine’s existing control and diagnostic systems with the EDS. The functionality of the UDS system is validated by employing a digital twin of the considered marine dual fuel engine by investigating scenarios for assessing the engine performance that entail abnormalities in the engine’s speed and boost pressure sensors. The simulation results demonstrate that the developed UDS is capable of sufficiently capturing the engine’s sensors abnormalities and applying appropriate corrective actions to restore the engine operation in its original state. This study benefits the development future systems facilitating the engines condition assessment and self-correction of the engine sensors’ abnormalities, which will be required for smart and autonomous shipping.

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