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Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.

The recovery of high temperature thermal energy released by propulsion engines in order to cover thermal loads is commonplace in contemporary ships. However, the medium-and low-temperature thermal energy is only partially exploited or not exploited at all. In the present work, an organic Rankine cycle system driving an electric generator is considered, in addition to the exhaust gas boiler, in order to recover available heat and produce electrical energy. The specifications of the system are determined by an optimization procedure taking economic criteria into consideration, apart from the technical criteria usually used in this kind of studies. More specifically, with the net present value as the objective function and by application of optimization algorithms, the optimal synthesis, design and operation of the organic Rankine cycle system are determined. For the particular vessel considered, the installation of the organic Rankine cycle is technically feasible and economically profitable, with a dynamic payback period of 4 years. The solution of the optimization problem is supplemented with a sensitivity analysis with respect to important parameters.

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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 require-

In offshore maritime operations, automated systems capable to maintain the vessel's position and heading using its own propellers and thrusters to compensate exogenous disturbances, like wind, waves and currents, are referred to as Marine Dynamic Positioning (DP) Systems. DP systems play a central role in assuring the mission of the vessels, such as for drilling, pipe-laying, coring, and ocean observation operations. At the same time, vessels operations are the primary cause of fuel consumption, having a strong impact on the overall footprint of the vessel. For this reason, in this paper, we will face the problem of optimising the propellers thrust allocation, namely determining thrust and direction of each propeller and thruster in an overactuated vessel, to maintain its position and heading, while minimising the fuel consumption. State of the art approaches simplify this problem by roughly approximating it and obtain a simple, mostly convex, optimisation problem. This allows to solve it in near-real time allowing its exploitation on-board during operation by simply integrating it in the automation system. In this paper, we deal with the problem of improving the current approaches with a twofold contribution. On one hand, we will exploit a detailed modelling approach of the physical system, resulting in an high fidelity representation of the optimisation problem. On the other hand, we will study and manipulate the resulting optimisation problem in such a way that it is still possible to solve it in near-real time on conventional on-board computing platform. Authors will leverage on a Platform Supply Vessel, equipped with 6 thrusters, as case study to evaluate the quality of the proposal. Results will show that, leveraging on the proposed approach, it is possible to achieve up to 5% of fuel savings with respect to conventional approaches.Note to Practitioners-This paper was motivated by the problem of minimising the fuel consumption in thrust allocation for marine dynamic positioning systems. Current approaches commonly exploit a simplified approach where simpler, yet related, optimisation problems are exploited as surrogates to keep the problem and the computational requirements at a level suited for a near-real time control. We propose, instead, to face the original problem with a state-of-the-art modelisation of the physical system and exploit reasonable and theoretical proprietaries for the purpose of achieving optimal solutions in near-real time. Results on a Platform Supply Vessel will show savings in fuel consumption of up to 5% with respect to using state-of-the-art alternative approaches.

Adaptability, stealth, damage sustainability, extended range and reliability are key factors to every successful naval mission. The shipbuilding industry conceptualized and deployed a wide variety of power and propulsion architectures over the decades: from mechanical, to electrical and hybrid propulsion. The tendency towards increasingly complex propulsion and power generation systems calls for the development of intelligent control strategies, Energy Management Systems (EMSs), that can handle the complexity and exploit the increased degrees-of-freedom (DOFs) of hybrid systems, while conforming to all operational constraints. In current EMSs, the aim is to save fuel costs. However, the ability to adapt to a wide variety of missions in an ever changing world is important for naval vessels. Hence, this raises the question: Can further operational gains be achieved through the use of more sophisticated integrated control algorithm, with multiple optimization goals? The present work aims to address this issue, by developing such a control system for a naval platform. The proposed EMS can modulate shipboard energy production of a hybrid propulsion plant with hybrid power supply, considering the trade-off between multiple conflicting operating goals: fuel savings, maintenance costs of on-board assets, noise and infrared signature. A validated model of a Holland class Patrol Vessel has been utilized to test the proposed EMS. Simulation results under varying operational profiles demonstrate the applicability, validity and the advantages of the approach.

This paper proposes an advanced shipboard energy management strategy (EMS) based on model predictive control (MPC). This EMS aims to reduce mission-scale fuel consumption of ship hybrid power plants, taking into account constraints introduced by the shipboard battery system. Such constraints are present due to the boundaries on the battery capacity and state of charge (SoC) values, aiming to ensure safe seagoing operation and long-lasting battery life. The proposed EMS can be used earlier in the propulsion design process and requires no tuning of parameters for a specific operating profile. The novelties of the study reside in (i) studying the impact of mission-scale effects and integral constraints on optimal fuel consumption and controller robustness, (ii) benchmarking the performance of the proposed MPC framework. A case study carried out on a naval vessel demonstrates near-optimal and robust behaviour of the controller for several loading sequences. The application of the proposed MPC framework can lead to up to 3.5% consumption reduction due to utilisation of long term information, considering specific loading sequences and charge depleting (CD) battery operation.