Numerical simulation of ship propulsion transients and full-scale validation

Campora, Ugo; Figari, Massimo

doi:10.1243/147509003321623130

Cited by 22 publications

(28 citation statements)

References 4 publications

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“…Finally, it is worth noting that model validation for a ferry, a naval corvette and an aircraft carrier can be found in various works. [29][30][31] Asset degradation model…”

Section: Model Descriptionmentioning

confidence: 99%

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Coraddu

Oneto

Ghio

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part M:

123

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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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“…Finally, it is worth noting that model validation for a ferry, a naval corvette and an aircraft carrier can be found in various works. [29][30][31] Asset degradation model…”

Section: Model Descriptionmentioning

confidence: 99%

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Coraddu

Oneto

Ghio

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part M:

123

View full text Add to dashboard Cite

show abstract

“…The latter authors did a similar study with more focus on emission from the engine. Campora and Figari (2003) used a phenomenological engine model with two-zone description in the cylinder for the coupled simulation. Their simulation result was validated by full-scale measurement.…”

Section: Figure 1 the Effects Of Waves On Ship Propulsionmentioning

confidence: 99%

The effect of waves on engine-propeller dynamics and propulsion performance of ships

et al. 2016

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This paper investigates the effect of waves on the propulsion system of a ship. In order to study the propulsion in different wave conditions, a procedure for wake estimation in waves has been implemented. A clear drop in the propulsion performance was observed in waves when engine propeller dynamics, wake variation and thrust and torque losses were taken into account. This can explain the drop in vessel performance often experienced in presence of waves in addition to the effect of added resistance. Therefore, performance prediction of ships in rough weather can be improved if the effects of waves on the propulsion system are considered. Specific problems causing drop in performance have also been identified. System response in case of extreme events like propeller emergence has been simulated for analyzing the performance and safety of the propulsion system. The framework of enginepropeller coupling demonstrated in this paper can also be used to analyze different components of propulsion system (e.g. propeller shaft, control system) in higher detail with realistic inputs. This paper is a step towards optimizing the propulsion of ships for realistic operating conditions rather than calm water condition for energy efficient and economic ships.

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“…Detailed information about the entire structure of the ship simulation model can be found in [7,8], while in [9] a first modification of the model in order to consider independent or cross connected shaftlines was described. Such type of lumped parameters simulation models have proven their technical validity in previous works, including validation at sea [5,8]. The model is able to consider also configurations with cross connected shaftlines, resulting in one unique differential equation of motion with two driving torques and two propeller torques (plus frictional losses due to shaftlines mechanical couplings and bearings).…”

Section: Simulation : Propulsion and Manoeuvrability Modelmentioning

confidence: 99%

Ship control system wide integration and the use of dynamic simulation techniques in the Fremm project

Michetti¹,

Rättö²,

Spadoni³

et al. 2010

Electrical Systems for Aircraft, Railway and Ship Propulsion

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Numerical simulation of ship propulsion transients and full-scale validation

Cited by 22 publications

References 4 publications

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

The effect of waves on engine-propeller dynamics and propulsion performance of ships

Ship control system wide integration and the use of dynamic simulation techniques in the Fremm project

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