Simulation of the dynamics of a marine diesel engine

Maftei, Carmen; Moreira, L.; Soares, C. Guedes

doi:10.1080/20464177.2009.11020225

Cited by 13 publications

(13 citation statements)

References 4 publications

(3 reference statements)

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“…Simulation has been used in engineering for many years as support of design and manufacturing (Altosole et al 2012;Tadros et al 2020a). Marine propulsion system simulations can be used for many purposes, such as ship performance analysis (Campora and Figari 2003;Figari and Soares 2009; Acanfora and Cirillo 2017), maneuvering analysis (Benvenuto et al 2000;Altosole et al 2013;Piaggio et al 2019;Ircani et al 2016), machinery control system development (Altosole et al 2007;Michetti et al 2010), and machinery performance analysis (Maftei et al 2009;Zaccone et al 2015;Altosole et al 2019;Tadros et al 2020c).…”

Section: Introductionmentioning

confidence: 99%

Validation of an Emission Model for a Marine Diesel Engine with Data from Sea Operations

Mocerino

Soares

Rizzuto

et al. 2021

J. Marine. Sci. Appl.

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In this study, a model is developed to simulate the dynamics of an internal combustion engine, and it is calibrated and validated against reliable experimental data, making it a tool that can effectively be adopted to conduct emission predictions. In this work, the Ricardo WAVE software is applied to the simulation of a particular marine diesel engine, a four-stroke engine used in the maritime field. Results from the bench tests are used for the calibration of the model. Finally, the calibration of the model and its validation with full-scale data measured at sea are presented. The prediction includes not only the classic engine operating parameters for a comparison with surveys but also an estimate of nitrogen oxide emissions, which are compared with similar results obtained with emission factors. The calibration of the model made it possible to obtain an overlap between the simulation results and real data with an average error of approximately 7% on power, torque, and consumption. The model provides encouraging results, suggesting further applications, such as in the study on transient conditions, coupling of the engine model with the ship model for a complete simulation of the operating conditions, and optimization studies on consumption and emissions. The availability of the emission data during the sea trial and validated simulation results are the strengths and novelties of this work.

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Section: Introductionmentioning

confidence: 99%

Validation of an Emission Model for a Marine Diesel Engine with Data from Sea Operations

Mocerino

Soares

Rizzuto

et al. 2021

J. Marine. Sci. Appl.

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“…Lars Erikson and Lars Nielsen presented a method of building a complete model of an automotive turbocharged engine, including main components: turbocharger, inlet and exhaust manifolds, cylinder and their relationships [7]. Maftei et al [8] built a model of the dynamics of a marine diesel engine based on the cylinders sequence and the help of the State Flow toolbox in Matlab computing environment, the results were used to predict the behaviour of the propulsion plant.…”

Section: Introductionmentioning

confidence: 99%

A Simulation of Four Stroke Marine Diesel Engines to Predict Internal Cylinder Characteristics

2021

JST: ETSD

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This paper introduces a simulation of four-stroke marine diesel engines. The submodel of a particular cylinder was carried out, based on the first law of thermodynamics, programmed by Matlab/Simulink program, which describes the relations among internal characteristics, including cylinder performance parameters, heat release, heat loss, and pressure. The heat release is based on the Wiebe function and the heat loss is based on the Woschni function to build submodels. From the result of the model, the indicated pressure of a single cylinder was taken, the brake power of the engine could be estimated through this pressure. The object of the simulation is a new engine, hence the technical documents and test records provided by the manufacturer are sufficient. The model got the input parameters from this and the key outputs of the model (for example the brake power, peak combustion pressure, specific fuel consumption) were compared with the test records to adjust and make it more accurate. These gaps were not over 5%, therefore, this model can be used to predict key complicated internal cylinder characteristics, for example, the pressure, temperature, and thermal efficiency of engines.

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“…The modelling of excess temperature development or overheating has traditionally been based on physical models of the system utilising thermodynamics or electrical parameters (see e.g. Gnacinski 2008;Maftei et al 2009;Lystianingrum, Hredzak, and Agelidis 2016;Pawlus et al 2017), but these model-based approaches may be difficult to develop.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of propulsion motor overheating using machine learning

Hellton

Tveten

Stakkeland

et al. 2021

Journal of Marine Engineering & Technology

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Thermal protection in marine electrical propulsion motors is commonly implemented by installing temperature sensors on the windings of the motor. An alarm is issued once the temperature reaches the alarm limit, while the motor shuts down once the trip limit is reached. Field experience shows that this protection scheme in some cases is insufficient, as the motor may already be damaged before reaching the trip limit. In this paper, we develop a machine learning algorithm to predict overheating, based on past data collected from a class of identical vessels. All methods were implemented to comply with real-time requirements of the on-board protective systems with minimal need for memory and computational power. Our two-stage overheating detection algorithm first predicts the temperature in a normal state using linear regression fitted to regular operation motor performance measurements, with exponentially smoothed predictors accounting for time dynamics. Then it identifies and monitors temperature deviations between the observed and predicted temperatures using an adaptive cumulative sum (CUSUM) procedure. Using data from a real fault case, the monitor alerts between 60 to 90 minutes before failure occurs, and it is able to detect the emerging fault at temperatures below the current alarm limits.

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Simulation of the dynamics of a marine diesel engine

Cited by 13 publications

References 4 publications

Validation of an Emission Model for a Marine Diesel Engine with Data from Sea Operations

Validation of an Emission Model for a Marine Diesel Engine with Data from Sea Operations

A Simulation of Four Stroke Marine Diesel Engines to Predict Internal Cylinder Characteristics

Real-time prediction of propulsion motor overheating using machine learning

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