A Zero-Dimensional Mixing Controlled Combustion Model for Real Time Performance Simulation of Marine Two-Stroke Diesel Engines

Feng, Yongming; Wang, Haiyan; Gao, Ruifeng; Zhu, Yuanqing

doi:10.3390/en12102000

Cited by 18 publications

(8 citation statements)

References 33 publications

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“…From literature overview, it can be noticed that the majority of modern ship propulsion systems are based on various types of diesel engines [17] [18]. The data obtained from the aforementioned propulsion systems is then used for design of various numerical models that are used in simulations of diesel engines.…”

Section: Introduction / Uvodmentioning

confidence: 99%

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

et al. 2020

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Authors present a Multilayer Perceptron (MLP) artifi cial neural network (ANN) method for the purpose of estimating a speed of a frigate using a combined diesel-electric and gas (CODLAG) propulsion system. Dataset used is publicly available, as conditionbased maintenance of naval propulsion plants dataset, out of which GT Compressor decay state coeffi cient and GT Turbine decay state coeffi cient are unused, while 15 features are used as input and ship speed is used as dataset output. Data set consists of 11934 data points out of which 8950 (75%) are used as a training set and 2984 (25%) are used as a testing set. 26880 MLPs, with 8960 diff erent parameter combinations are trained using a grid search algorithm, quality of each solution being estimated with coeffi cient of determination (R2) and mean absolute error (MAE). Results show that a high-quality estimation can be made using an MLP, with best result having an error of just 3.4485x10 -5 knots (absolute error of 0.00014% of the range). This result was achieved with a MLP with three hidden layers containing 100 neurons each, logistic activation function, LBFGS solver, constant learning rate of 0.1 and no L2 regularization. Sažetak Autori predstavljaju metodu višeslojnog perceptrona (MLP) umjetne neuronske mreže (ANN) u svrhu procjene brzine fregate koristeći se kombiniranim dizel-električnim i plinskim pogonskim sustavom (CODLAG). Korišteni skup podataka javno je dostupan, poput skupa podataka o održavanju po stanju brodskih pogonskih postrojenja, od kojih se ne koriste koefi cijent raspadanja GT kompresora i koefi cijent stanja raspada GT Turbine, dok se 15 značajki koriste kao ulazni, a brzina broda kao izlazni podatak. Skup podataka sastoji se od 11934 podatkovne točke, od čega se 8950 (75 %) koristi kao skup za vježbu, a 2984 (25 %) kao testni skup. 26880 MLP-ova s 8960 različitih kombinacija parametara uvježbava se algoritmom pretraživanja energetske mreže, a kvaliteta svakog rješenja procjenjuje se koefi cijentom određivanja (R2) i prosječnom apsolutnom pogreškom (MAE). Rezultati pokazuju da se visokokvalitetna procjena može napraviti uz pomoć MLP-a, pri čemu će najbolji rezultat imati pogrešku od samo 3,4485x10 -5 čvorova (apsolutna pogreška raspona 0,00014 %). Za postizanje rezultata korišten je MLP s tri skrivena sloja koji sadrže po 100 neurona, logistička funkcija aktivacije, LBFGS rješavač, konstantna brzina učenja od 0,1 bez L2 regulacija. KEY WORDS artifi cial intelligence artifi cial neural networks CODLAG propulsion system multilayer perceptron speed estimation KLJUČNE RIJEČI umjetna inteligencija umjetne neuronske mreže CODLAG pogonski sustav višeslojni perceptron procjena brzine

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Section: Introduction / Uvodmentioning

confidence: 99%

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

et al. 2020

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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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“…Gatowski et al [38], Feng et al [40] offer a correlation with a joint temperature and fuel-air ratio dependency coherent with Equations (A5) and (A6).…”

Section: Data Availability Statementmentioning

confidence: 65%

Open Dual Cycle with Composition Change and Limited Pressure for Prediction of Miller Engines Performance and Its Turbine Temperature

2021

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An improved thermodynamic open Dual cycle is proposed to simulate the working of internal combustion engines. It covers both spark ignition and Diesel types through a sequential heat release. This study proposes a procedure that includes (i) the composition change caused by internal combustion, (ii) the temperature excursions, (iii) the combustion efficiency, (iv) heat and pressure losses, and (v) the intake valve timing, following well-established methodologies. The result leads to simple analytical expressions, valid for portable models, optimization studies, engine transformations, and teaching. The proposed simplified model also provides the working gas properties and the amount of trapped mass in the cylinder resulting from the exhaust and intake processes. This allows us to yield explicit equations for cycle work and efficiency, as well as exhaust temperature for turbocharging. The model covers Atkinson and Miller cycles as particular cases and can include irreversibilities in compression, expansion, intake, and exhaust. Results are consistent with the real influence of the fuel-air ratio, overcoming limitations of standard air cycles without the complex calculation of fuel-air cycles. It includes Exhaust Gas Recirculation, EGR, external irreversibilities, and contemporary high-efficiency and low-polluting technologies. Correlations for heat ratio γ are given, including renewable fuels.

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A Zero-Dimensional Mixing Controlled Combustion Model for Real Time Performance Simulation of Marine Two-Stroke Diesel Engines

Cited by 18 publications

References 33 publications

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

Frigate Speed Estimation Using CODLAG Propulsion System Parameters and Multilayer Perceptron

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

Open Dual Cycle with Composition Change and Limited Pressure for Prediction of Miller Engines Performance and Its Turbine Temperature

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