2D CFD Simulation of Water Injection Strategies in a Large Marine Engine

Senčić, Tomislav; Mrzljak, Vedran; Blecich, Paolo; Bonefačić, Igor

doi:10.3390/jmse7090296

Cited by 36 publications

(26 citation statements)

References 20 publications

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“…Marine propulsion systems in worldwide fleet nowadays are primarily based on diesel engines of any type [9,10]. The dominant presence of diesel engines enables the development of different numerical models for research and analysis of their operating parameters [11], for optimization of their processes [12] and for investigation of reducing its emissions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

et al. 2019

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In this paper multilayer perceptron (MLP) approach to condition-based maintenance of combined diesel-electric and gas (CODLAG) marine propulsion system is presented. By using data available in UCI, online machine learning repository, MLPs for prediction of gas turbine (GT) and GT compressor decay state coefficients are designed. Aforementioned MLPs are trained and tested by using 11 934 samples, of which 9 548 samples are used for training and 2 386 samples are used testing. In the case of GT decay state coefficient prediction, the lowest mean relative error of 0.622 % is achieved if MLP with one hidden layer of 50 artificial neurons (AN) designed with Tanh activation function is utilized. This configuration achieves the best results if it is trained by using L-BFGS solver. In the case of GT compressor decay state coefficient, the best results are achieved if MLP is designed with four hidden layers of 100, 50, 50 and 20 ANs, respectively. This configuration is designed by using Logistic sigmoid activation function. The lowest mean relative error of 1.094 % is achieved if MLP is trained by using L-BFGS solver.

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Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

et al. 2019

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“…Today, by taking into account the entire world fleet, the dominant power producers for ship propulsion are marine slow speed two-stroke diesel engines [1]. Several authors have proposed improvement of such engines by using various additives in heavy fuel oil [2], by using alternative fuel mixtures with heavy fuel oil [3,4] or by using several water injection techniques [5]. Middle speed and fast speed fourstroke diesel engines are also used in marine propulsion plants, usually for the electricity generator drive or for other plant needs [6].…”

Section: Introductionmentioning

confidence: 99%

Energy Loss Analysis at the Gland Seals of a Marine Turbo-Generator Steam Turbine

Kocijel

Poljak

Mrzljak

et al. 2020

Teh. glas. (Online)

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The paper presents an analysis of marine Turbo-Generator Steam Turbine (TGST) energy losses at turbine gland seals. The analyzed TGST is one of two identical Turbo-Generator Steam Turbines mounted in the steam propulsion plant of a commercial LNG carrier. Research is based on the TGST measurement data obtained during exploitation at three different loads. The turbine front gland seal is the most important element which defines TGST operating parameters, energy losses and energy efficiencies. The front gland seal should have as many chambers as possible in order to minimize the leaked steam mass flow rate, which will result in a turbine energy losses' decrease and in an increase in energy efficiency. The steam mass flow rate leakage through the TGST rear gland seal has a low or negligible influence on turbine operating parameters, energy losses and energy efficiencies. The highest turbine energy efficiencies are noted at a high load -on which TGST operation is preferable.

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“…These models require a large amount of input data in order to increase the accuracy of the results. As the model becomes complex, more time is consumed to perform the simulation while more details can be exported from the computed results [10][11][12]. These simulation models allow the computation of the engine performance based on its type, the fuel Figure 2 presents a general overview of the model's structure to simulate the performance of different ICEs.…”

Section: Introductionmentioning

confidence: 99%

Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Tadros

Ventura

Soares

2020

JMSE

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An engine optimization model is developed to fit the calculated in-cylinder pressure diagram to the experimental data by finding the optimal values of the start angle of injection and the amount of injected fuel for different engine loads. Firstly, the engine model is built in Ricardo Wave software and some parts are calibrated using data collected from the manufacturer. Then, an optimization process is performed based on the fitness function that includes the objective of the study and the penalty functions to express constraints. This optimization environment simulates the performance of a marine generator system for three different loads by minimizing the mean absolute percentage error (MAPE) between the in-cylinder pressure simulated data and the measured data along 40 degrees of the combustion process and by verifying the firing pressure and the engine brake power. The percentage of error between the calculated and the real thermodynamic data does not exceed 3.4% and the MAPE between the calculated and the real in-cylinder pressure diagram along the combustion process does not exceed 5.7% for the different loads. The proposed method can be further used to find the optimal value of different input parameters during the calibration process of different engine numerical models.used and the required details in thermodynamics, combustion, heat transfer, fluid and chemical parts. Figure 2 presents a general overview of the model's structure to simulate the performance of different ICEs.

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2D CFD Simulation of Water Injection Strategies in a Large Marine Engine

Cited by 36 publications

References 20 publications

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

Energy Loss Analysis at the Gland Seals of a Marine Turbo-Generator Steam Turbine

Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

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