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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Dynamic positioning systems are most commonly used in offshore operations. They provide an automated controlling of position and heading of the vessel using its own thrusters to compensate environmental disturbances. The allocation of total required force over the available actuators is a complex task, as DP-systems are over-actuated. Therefore, one of the main challenges faced by the industry is constantly seeking to improve the systems efficiency for both sustainability and economic reasons. Furthermore, it is important to evaluate the performance of a DP vessel under critical conditions. In this paper, the authors aim to compare different thrust allocation logics based on the optimisation of different objective functions. Using a simple validation tool, the authors were able to investigate the overall efficiency of a dynamic positioning propulsion system and its ability to operate when a failure occurs.

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Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or preventive maintenance up to more effective condition-based maintenance approaches. The central idea in condition-based maintenance is to monitor the propulsion equipment by exploiting heterogeneous sensors, enabling diagnosis and, most of all, prognosis of the propulsion system's components and of their potential future failures. The success of condition-based maintenance clearly hinges on the capability of developing effective predictive models; for this purpose, effective use of machine learning methods is proposed in this article. In particular, authors take into consideration an application of condition-based maintenance to gas turbines used for vessel propulsion, where the performance and advantages of exploiting machine learning methods in modeling the degradation of the propulsion plant over time are tested. Experiments, conducted on data generated from a sophisticated simulator of a gas turbine, mounted on a Frigate characterized by a COmbined Diesel eLectric And Gas propulsion plant type, will allow to show the effectiveness of the proposed machine learning approaches and to benchmark them in a realistic maritime application.

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Economic sustainability of running Naval Propulsion Plants is a key element to cope with, and maintenance costs represent a large slice of total operational expenses: last decades' approaches, based on a repairing-replacing methodology, are being trespassed by more effective approaches, relying on effective continuous monitoring of assets wear. In this framework, Condition-Based Maintenance (CBM) is becoming key thanks to the enhancing capabilities of monitoring the propulsion equipment by exploiting heterogeneous sensors: this enables diagnosis and prognosis of the propulsion system's components and of their potential future failures. The success of CBM is based on the capability of developing effective predictive models, for which purpose state-of-the-art Machine Learning (ML) methods must be developed. Nevertheless, testing the performance of ML models for CBM purposes is not straightforward, mostly due to the lack of publicly available datasets for benchmarking purposes: thus, we present in this work a new dataset, that will be freely distributed to the community working on ML models for CBM, generated from an accurate simulator of a naval vessel Gas Turbine propulsion plant. The latter is then used for benchmarking the effectiveness of two state-of-the-art ML techniques in the considered maritime domain.

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In this article, the authors present a procedure to predict the energy efficiency operational indicator by Monte Carlo simulations, estimating the total ship fuel consumption as a function of displacement and speed considered as random variables. To characterize the probability density function of displacement and speed, a complete series of operating data concerning 2 years of navigation, of a RoPax engaged in a commercial trade in the Mediterranean Sea, were collected and used.@en

The integrated use of modern seakeeping and powering computational techniques and state of the art meteo-marine forecasting and hindcasting resources has the potential of disclosing new ways to approach some relevant issues, such as for instance operations planning at sea, weather routing, in-service ship energy management and pollution control. Such an integrated approach could be profitably used during ship design for optimizing both hydrodynamic and powering characteristics too. In this paper the authors describe preliminary results of a study aimed at estimating the in-service optimal efficiency propulsion plant setting for a ship in real seaways conditions. To this aim numerical simulations of seakeeping and powering conditions of a ship along a real route in the Mediterranean sea have been performed. In the simulations detailed meteo-marine hindcasting data, consisting of wind data and complete directional waves spectra along the route, have been used. Strip theory has been applied for the seakeeping computation in order to evaluate the contribution of waves added resistance in realistic seaways. Such results have been then used in powering computation, to evaluate the dynamically evolving engine conditions in terms of fuel consumption. The results of such computations have been analyzed to evaluate the in-service optimization potentialities for the available setting of propulsion plant. Finally, heuristic hints are preliminarily suggested by the authors as a support for the use of scenario simulations with realistic meteo-marine data in the powering matching phases of the ship design process.@en

Traditionally, the environmental performance of marine systems in terms of exhaust emissions has never been among the primary concerns of the maritime industry. However, this situation is going to quickly change, as in the shipping sector Energy Efficiency Design and Operation IMO Indexes testify. In such a context it is worth mentioning that the greening of shipping operations can be effectively achieved by a suitable system design and energy management. Ship Energy Efficiency Management Plan (SEEMP) should be a tool to monitor and to optimize ship and fleet efficiency performance; as a consequence it will have a deep impact on the reduction not only of the exhaust gas emissions but also of the operational costs. To reach this goal energy modelling represents the keyword. In particular, the estimate by simulation, of onboard power generation, consumption and losses plays a fundamental role for driving, for instance, a decision support tool toward the optimal choices as far as energy management is concerned. To this purpose, in the paper the authors present the outcomes of the activities carried out to develop a simulation tool able to represent an overall ship system from the energetic point of view. Modelling approach and the results of numerical simulations performed by the authors to estimate vessel fuel consumption in a real case study are shown and discussed.@en

IMO is introducing two different emission indexes for a vessel: the Energy Efficiency Design Index (EEDI) and the Energy Efficiency Operational Indicator (EEOI). Both EEDI and EEOI will be probably used as part of a levy scheme to force the maritime sector to significantly reduce the carbon footprint. At present do exist few ports and/or states applying fees based on ship emissions. The reliability of a regular shipping service, or service reliability, defined as the probability to complete the link within schedule, is an important parameter that greatly influences commercial aspects, as well as the ship carbon footprint. In this paper the authors present a procedure to evaluate the EEOI indicator by Monte Carlo simulation, estimating the total ship fuel consumption as a function of the random variables displacement and speed. A complete series of operating data concerning two years of navigation, for a commercial trade in the Mediterranean Sea, were collected and used to validate the code.@en

Traditional engine-propeller matching techniques are mainly based on nondimensional parameters analysis: Thrust/advance coefficient or torque/advance coefficient ratio are the most used variables to assess the ship propulsion point or to match, at each selected ship speed, the selected propeller with the ship engine. The advantages of this robust and well established procedure (standard propeller open water measures only at some pitch ratios around the design configuration, availability of large databases and extrapolation laws) however, turn into the drawbacks for the inclusion of different constraints and objectives, further than the minimum fuel consumption, in the matching algorithm. On the other hand modern numerical tools and available hardware resources let to partially substitute, in the design stage, experimental campaigns and to collect large amount of information on propeller performances, including cavitation. In this sense numerical computations make out of date approaches just developed to overcome the deficiency of experimental measures. On the basis of these numerical data new algorithms for the engine-propeller matching can be developed capable of investigate different objectives and the influence of different constraints on the traditional optimum points.

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This document provides a method of modelling and simulating synchronous machine for naval application.@en

Marine propulsion plants can experience large power fluctuations during tight maneuvers, with increases of shaft torque up to and over 100% of the steady values in straight course and considerable asymmetry between internal and external shafts during turning circle. This phenomenon (studied in Viviani et al 2007a and 2007b can be of particular interest for twin screw ships propulsion systems with coupled shaftlines, in which asymmetrical loads can represent a challenge for the whole propulsion system (e.g. unique reduction gear, shaftlines, automation). A joint research has been set up in order to deeply investigate the phenomenon, by means of large scale model testing and related numerical simulations. In the present work, preliminary simulation results with different simplified automation systems and with an automation system more similar to the real one are reported, allowing to get a better insight into this complex problem.

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