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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Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal.

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In this paper the authors investigate the problems of predicting the fuel consumption and of providing the best value for the trim of a vessel in real operations based on data measured by the onboard automation systems. Three different approaches for the prediction of the fuel consumption are compared: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Finally, the authors propose two different Gray Box Model (GBM) which are able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Based on these predictive models of the fuel consumption a new strategy for the optimisation of the trim of a vessel is proposed. Results on real world operational data show that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data. Moreover, results show that the GBM can be used as an effective tool for optimising the trim of a vessel in real operational conditions.

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The main purpose of thiswork is to build a data driven model to create realistic operating profiles in order to assess and compare different design solutions. The proposed approach takes advantage on the new generation of automation systems which allow gathering a large amount of data from on-board machinery. Data driven models are built upon statistical inference procedures based on the historical data collection. The advantage of these methods is that there is no need of any a-priory knowledge of the underlying physical system. Furthermore, thanks to the nature of these approaches, it is possible to exploit even data from sensors that could contain some kind of hidden information that cannot be easily extracted with a parametric approach. The use of these tools is nowadays made possible since such information is digitally available from different sources: (i) data stored on board of vessels; (ii) Automatic Identification System (AIS) data available through the internet. A data driven modelling of the operational profiles of the vessel (and in general of the fleet) could provide a tool both to diagnose and predict the vessel’s state (e.g. for condition based maintenance purposes), for improving the performance and the efficiency of the vessel, and for improving design solutions. The diagnosis and prognosis of the ship’s performance can be used as decision support in determining when actions to improve performance should be taken. The developed model will be tested on a real DAMEN vessel where on-board sensors data acquisitions are available from the automation system.@en

The main purpose of this work is to build a data driven model to create realistic operating profiles in order to assess and compare different design solutions. The proposed approach takes advantage on the new generation of automation systems which allow gathering a large amount of data from on-board machinery. A data driven modeling of the operational profiles of the vessel (and in general of the fleet) could provide a tool both to diagnose and predict the vessel's state (e.g. for condition based maintenance purposes), for improving the performance and the efficiency of the vessel, and for improving design solutions. The diagnosis and prognosis of the ship's performance can be used as decision support in determining when actions to improve performance should be taken. The developed model will be tested on a real DAMEN vessel where on-board sensors data acquisitions are available from the automation system.

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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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In this paper authors investigate the problem of predicting the fuel consumption of a vessel in real scenario based on data measured by the onboard automation systems. The goal is achieved by exploiting three different approaches: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Author proposal is a Gray Box Model (GBM) which is able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Results on real world data shows that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data.

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In this work an integration of high-resolution meteo-marine forecast data with detailed ship powering and seakeeping computational algorithms is investigated in order to asses the potential for the attainable fuel savings by weather routing algorithms along relatively short routes (w.r.t. oceanic ones) as those in the Mediterranean Sea. The capability of high resolution models to predict the detailed space-time evolution of meteo-marine conditions at the Mediterranean basin scale is exploited in order to reliably forecast ship performances along Mediterranean routes. Based on this a computational framework applicable to weather routing algorithms is implemented and tested with the main goals of improving operational efficiency, by fuel savings, and of reducing the navigational risks connected to heavy weather at sea. The main elements of the proposed approach are: i) the construction of a detailed numerical model of a real ship; ii) the exploitation of in-service performance data recorded during real voyages of such a ship to tune its numerical model; iii) the use of high-resolution meteo-marine forecast and hindcast data for winds and complete directional wave spectra to assess the fuel saving potentialities of the implemented computational framework cited above. This last task is performed by exploiting forecast data coming from Ensemble Prediction Systems (EPS) applied to high resolution wind and wave forecasts. The obtained results are very interesting and encouraging to further develop the presented approaches. In particular a fuel saving potential ranging from few points of percentage till nearly 10% emerged from the two studied cases. Moreover the approach based on EPS reveled itself to be very useful as an investigation tool for the assessment of the reliability and stability of the fuel minimization with respect to forecast uncertainties.@en

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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Sustainability and energy efficiency issues, connected to the search for fuel consumption reductions and to the more and more mandatory international regulations regarding the emissions of atmospheric pollutants, are acquiring a growing relevance in shipping industry. In this context, the integration of forecasting resources for meteo-marine conditions with the capability of performing detailed ship performances simulations may give relevant contributions to improve the in service energy efficiency of ships through the use of weather routing systems. These systems are today widely used, almost in transoceanic navigation, and their effectiveness may be improved if they are utilized in integration with other energy efficiency measures and with observing systems for ship responses and encountered meteo-marine conditions. The work here reported has been developed in the context of the PROFUMO project, financed by the European Space Agency, in the framework of the IAP Artes 20 Programme for Feasibility Studies. Part of the project activities are devoted to perform numerical simulations of ship seakeeping and powering performances along Mediterranean routes, by the use of high resolution wind wave forecasts data produced by the operational service of Consorzio LaMMA. With this aim, in particular a numerical model of a real Ro-Ro ship has been constructed and the results of the numerical simulations are exploited to evaluate the potential impact of the variability of meteo-marine forecasting skills on weather routing and to estimate the potential contribution to fuel savings of meteorological route optimization at the Mediterranean scale.@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

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

Twin screw ships may experience considerably asymmetric propeller functioning during manoeuvres. This phenomenon may result in large power fluctuations during tight manoeuvres, with increases of shaft torque up to and over 100% of the steady values in straight course and considerable unbalances; this, in its turn, may be potentially dangerous, especially in case of particularly complex propulsion plant configurations, such as those with coupled shaftlines. A joint research project supported by the Italian Navy 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, the extensive experimental campaign results on a free running model of a twin-screw ship are presented, allowing to obtain a deeper insight of the problem. In particular, tests have been carried out simulating different simplified control schemes, starting from the most common constant rate of revolution tests and including different control strategies (constant torque and power). Usual standard manoeuvres (turning circle, zigzag and spiral) have been carried out, providing results for asymmetric shaft functioning and ship manoeuvrability behaviour. Results from the present analysis allow to obtain the complete model for the time domain simulation of asymmetric shaft functioning.

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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

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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The influence of wave profile on ship righting arm is among the possible stability failure issues addressed by the recent activity at IMO. The present paper is mainly focused on the so called "pure loss of stability", a dangerous phenomenon that can occur for specific conditions in following seas. In the paper, the development of a computational tool is described, able to evaluate the influence of wave profile on ship metacentric height and righting arms, giving the possibility to investigate different ship geometries and loading conditions in relation with wave profiles in terms of different lengths and steepness. The tool is applied to several ship typologies and a special attention in the investigations is given to transversal metacentric height GM and to the maximum righting arm GZmax since, in some proposals, these are the parameters assumed as indicative while performing first and second level vulnerability analysis for pure loss of stability.

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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

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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Although the appraisal of economical and commercial parameters relating to ship business is never trivial, it is rather intuitive that goods transportation by larger ships is more profitable, given of course the appropriate logistic boundary conditions. As far as oil tankers are concerned, it may be of common perception that a bigger ship represents a greater hazard. In order to gain a possible practical insight about the specific aspect of ship size influence on the relevant environmental impact, a Product, a Panamax, an Aframax and a VLCC have been investigated and compared in terms of oil outflow performance. As measurement tools, the three parameters defined in the IMO probabilistic methodology for the approval of alternative oil tanker designs have been exploited. As it can be expected, not only the size but also the internal subdivision has a significant influence on the ship outflow performance.@en