Physics-Based Control Oriented Mean Value Model for Diesel Combustion Process With EGR Sensitivity

2011 ◽  
Author(s):  
Byungchan Lee ◽  
Dohoy Jung ◽  
Yong-Wha Kim
Keyword(s):  
Heat Transfer ◽  
Combustion Process ◽  
Mean Value ◽  
Diesel Combustion ◽  
Design Development ◽  
Combustion Heat ◽  
Mean Value Model ◽  
Pressure Cycle ◽  
The Mean ◽  
Value Model

A Physics-based mean value model that predicts engine combustion, heat transfer, gas exchange, and friction loss has been developed. The model is developed starting from the thermodynamic principles of an ideal gas standard limited pressure cycle concept. The idealized assumptions that are typically used in a limited pressure cycle concept are relieved to enhance the fidelity of the model by introducing variables that account for the in-cylinder heat transfer and the combustion characteristics that change under varying EGR rate as well as the engine speed and load while minimal number of empirical correlations are used to ensure the compactness and flexibility of the physics-based mean value model. The model is calibrated and validated with the simulation results from a detailed GT-Power® engine model previously calibrated with experimental results from a Ford 6.7 liter Diesel engine. The comparison shows good agreement between the results from the mean value model and the GT-Power® model. The mean value model developed in this study is a flexible simulation tool that provides excellent computational efficiency without sacrificing critical physical details of the Diesel combustion process required by the control design development.

Thermodynamics-Based Mean Value Model for Diesel Combustion

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Byungchan Lee ◽  
Dohoy Jung ◽  
Yong-Wha Kim ◽  
Michiel van Nieuwstadt
Keyword(s):  
Combustion Process ◽  
Mean Value ◽  
Ideal Gas ◽  
Operating Conditions ◽  
Strategy Development ◽  
Diesel Combustion ◽  
Engine Model ◽  
Exhaust Temperature ◽  
Mean Value Model ◽  
Value Model

A thermodynamics-based computationally efficient mean value engine model that computes ignition delay, combustion phases, exhaust temperature, and indicated mean effective pressure has been developed for the use of control strategy development. The model is derived from the thermodynamic principles of ideal gas standard limited pressure cycle. In order to improve the fidelity of the model, assumptions that are typically used to idealize the cycle are modified or replaced with ones that more realistically replicate the physical process such as exhaust valve timing, in-cylinder heat transfer, and the combustion characteristics that change under varying engine operating conditions. The model is calibrated and validated with the test data from a Ford 6.7 liter diesel engine. The mean value model developed in this study is a flexible simulation tool that provides excellent computational efficiency without sacrificing critical details of the underlying physics of the diesel combustion process.

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

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2823 ◽  
Author(s):  
Eunhee Ko ◽  
Jungsoo Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Value ◽  
Volumetric Efficiency ◽  
Exhaust Gas ◽  
Control Logic ◽  
Mean Value Model ◽  
The Mean ◽  
Artificial Neural ◽  
Value Model

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.

A Control-Oriented Mean-Value Model of HCCI Diesel Engines With External Mixture Formation

2005 ◽  
Author(s):  
Marcello Canova ◽  
Luca Garzarella ◽  
Marco Ghisolfi ◽  
Shawn Midlam-Mohler ◽  
Yann Guezennec ◽  
...  
Keyword(s):  
Diesel Engines ◽  
Combustion Process ◽  
Mean Value ◽  
Ignition Process ◽  
Fuel Injector ◽  
Mixture Formation ◽  
Calculation Algorithm ◽  
Hcci Combustion ◽  
Mean Value Model ◽  
Value Model

Homogeneous Charge Compression Ignition (HCCI) is considered a promising concepts to achieve low NOx and Particulate Matter emissions in traditional Spark Ignition and Diesel engines. However, understanding and controlling the complex mechanisms which govern the combustion process is still extremely difficult. A viable method to obtain HCCI combustion in DI Diesel engines consists of premixing the charge by applying an additional fuel injector in the intake port, thus decoupling the HCCI mixture formation from the traditional in-cylinder injection. The system allows high load operation in DI mode without compromising performance, low to mid-load operation in HCCI mode, and a region in between where both systems operate together. To manage HCCI combustion with external mixture formation it is essential to identify the most important control parameters and understand their influence on the auto-ignition process. The proposed paper deals with the analysis of HCCI combustion with external mixture formation through experimental investigation and a Control-Oriented mean-value model. The model provides the data required by a combustion calculation algorithm to perform a first-law analysis that estimates the in-cylinder heat release and pressure. The tool developed was then validated on data provided by an extensive experimental activity on a 4-cylinder Diesel engine equipped with an external fuel atomizer to operate in HCCI mode.

A Mean-Value Model of a Turbocharged HCCI Diesel Engine with External Mixture Formation

2005 ◽  
Author(s):  
M. Canova ◽  
L. Garzarella ◽  
M. Ghisolfi ◽  
S. Midlam-Mohler ◽  
Y. Guezennec ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Mean Value ◽  
Mixture Formation ◽  
Mean Value Model ◽  
Value Model

The Use of a Mean Value Model to Achieve the Best Efficiency of a Variable Speed Generator Coupled to a Diesel Engine

2012 ◽  
Author(s):  
Augusto F. Pacheco ◽  
Jonas R. Tibola ◽  
Mario E. S. Martins ◽  
Paulo R. M. Machado ◽  
Humberto Pinheiro ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Mean Value ◽  
Variable Speed ◽  
Mean Value Model ◽  
Value Model
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