Next Article in Journal
Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System
Next Article in Special Issue
Analysis of Thermodynamic Models for Simulation and Optimisation of Organic Rankine Cycles
Previous Article in Journal
Estimation of the Biot Number Using Genetic Algorithms: Application for the Drying Process
Open AccessArticle

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

1
Department of Mechanical Engineering, Graduate School, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea
2
Department of Mechanical Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea
*
Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2823; https://doi.org/10.3390/en12142823
Received: 27 May 2019 / Revised: 12 July 2019 / Accepted: 18 July 2019 / Published: 22 July 2019
(This article belongs to the Special Issue Modelling of Thermal and Energy Systems)
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. View Full-Text
Keywords: diesel engine; mean value model; real time simulation; artificial neural network; exhaust gas recirculation diesel engine; mean value model; real time simulation; artificial neural network; exhaust gas recirculation
Show Figures

Figure 1

MDPI and ACS Style

Ko, E.; Park, J. Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network. Energies 2019, 12, 2823.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop