Development of an extended mean value engine model for predicting the marine two-stroke engine operation at varying settings

Theotokatos, Gerasimos; Guan, Cong; Chen, Hui; Lazakis, Iraklis

doi:10.1016/j.energy.2017.10.138

Cited by 33 publications

(34 citation statements)

References 35 publications

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“…e maximum sustained power is 16520 kW, and the rated speed is 114 rpm. is paragraph established the mean value model of the diesel engine [25,26]. e details are as follows:…”

Section: Mathematical Modelmentioning

confidence: 99%

Finite-Time Speed Control of Marine Diesel Engine Based on ADRC

Wang

Shi²,

Wang³

et al. 2020

Mathematical Problems in Engineering

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In this paper, in order to handle the nonlinear system and the sophisticated disturbance in the marine engine, a finite-time convergence control method is proposed for the diesel engine rotating speed control. First, the mean value model is established for the diesel engine, which can represent response of engine fuel injection to engine speed. Then, in order to deal with parameter perturbation and load disturbance of the marine diesel engine, a finite-time convergence active disturbance rejection control (ADRC) is proposed. At the last, simulation experiments are conducted to verify the effectiveness of the proposed controller under the different load disturbances for the 7RT-Flex60C marine diesel engine. The simulation results demonstrate that the proposed control scheme has better control effect and stronger anti-interference ability than the linear ADRC.

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“…e maximum sustained power is 16520 kW, and the rated speed is 114 rpm. is paragraph established the mean value model of the diesel engine [25,26]. e details are as follows:…”

Section: Mathematical Modelmentioning

confidence: 99%

Finite-Time Speed Control of Marine Diesel Engine Based on ADRC

Wang

Shi²,

Wang³

et al. 2020

Mathematical Problems in Engineering

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“…To save time and cost, a computer environment verification process is required at the early stage of control system design. The need for real-time numerical analysis is increasing because it can save time and money compared with traditional engine test procedures [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. A hardware-in-the-loop (HiL) system simulates the engine test environment by combining engine components into hardware and plant models into a virtual engine system.…”

Section: Introductionmentioning

confidence: 99%

“…The mean value model can be constructed through a neural network function based on the detailed model with high accuracy; it reduces the model analysis speed through the model reduction process. In an engine study using an artificial neural network, numerical analysis capable of predicting the heat release inside the cylinder, the intake manifold flow rate, and the engine performance was studied [5][6][7][8][9][10][11][12][13]. Some studies use an artificial neural network to predict the dynamic response of the engine and control system in transient conditions by using the mean value model and to balance the trade-off between the model accuracy and execution speed [5].…”

Section: Introductionmentioning

confidence: 99%

Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network

Park

2019

Energies

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This study aims to construct a reduced thermodynamic cycle model with high accuracy and high model execution speed based on artificial neural network training for real-time numerical analysis. This paper proposes a method of constructing a fast average-value model by combining a 1D plant model and exhaust gas recirculation (EGR) control logic. The combustion model of the detailed model uses a direct-injection diesel multi-pulse (DI-pulse) method similar to diesel combustion characteristics. The DI-pulse combustion method divides the volume of the cylinder into three zones, predicting combustion- and emission-related variables, and each combustion step comprises different correction variables. This detailed model is estimated to be within 5% of the reference engine test results. To reduce the analysis time while maintaining the accuracy of engine performance prediction, the cylinder volumetric efficiency and the exhaust gas temperature were predicted using an artificial neural network. Owing to the lack of input variables in the training of artificial neural networks, it was not possible to predict the 0.6–0.7 range for volumetric efficiency and the 1000–1200 K range for exhaust gas temperature. This is because the mean value model changes the fuel injection method from the common rail fuel injection mode to the single injection mode in the model reduction process and changes the in-cylinder combustion according to the injection timing of the fuel amount injected. In addition, the mean value model combined with EGR logic, i.e., the single-input single-output (SISO) coupled mean value model, verifies the accuracy and responsiveness of the EGR control logic model through a step-transient process. By comparing the engine performance results of the SISO coupled mean value model with those of the mean value model, it is observed that the SISO coupled mean value model achieves the desired target EGR rate within 10 s. The EGR rate is predicted to be similar to the response of volumetric efficiency. This process intuitively predicted the main performance parameters of the engine model through artificial neural networks.

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“…Baldi et al combined the zero-dimension and mean value models to calculate the engine performance parameters including the in-cylinder ones in relatively short simulation time [27]. Theotokatos et al developed an extended engine mean value model to predict the thermodynamic parameters of two-stroke marine engine at different injection times [28]. Meanwhile, plenty of "black-box" methods such as multi-level hierarchical, neural networks, fuzzy logic, and wavelet networks are also applied in the modeling engine combustion process [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Investigation into Combustion Fitting in a Direct Injection Marine Diesel Engine

Ding

Sui

2018

Applied Sciences

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The marine diesel engine combustion process is discontinuous and unsteady, resulting in complicated simulations and applications. When the diesel engine is used in the system integration simulation and investigation, a suitable combustion model has to be developed due to compatibility to the other components in the system. The Seiliger process model uses finite combustion stages to perform the main engine combustion characteristics and using the cycle time scale instead of the crank angle shortens the simulation time. Obtaining the defined Seiliger parameters used to calculate the engine performance such as peak pressure, temperature and work is significant and fitting process has to be carried out to get the parameters based on experimental investigation. During the combustion fitting, an appropriate mathematics approach is selected for root finding of non-linear multi-variable functions since there is a large amount of used experimental data. A direct injection marine engine test bed is applied for the experimental investigation based on the combustion fitting approach. The results of each cylinder and four-cylinder averaged pressure signals are fitted with the Seiliger process that is shown separately to obtain the Seiliger parameters, and are varied together with these parameters and with engine operating conditions to provide the basis for engine combustion modeling.

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Development of an extended mean value engine model for predicting the marine two-stroke engine operation at varying settings

Cited by 33 publications

References 35 publications

Finite-Time Speed Control of Marine Diesel Engine Based on ADRC

Finite-Time Speed Control of Marine Diesel Engine Based on ADRC

Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network

An Experimental Investigation into Combustion Fitting in a Direct Injection Marine Diesel Engine

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