Sensitivity of Flamelet Combustion Model to Flame Curvature for IC Engine Application

3D CFD IC engine simulations that use a simplified combustion model based on the flamelets concept can provide acceptable results with minimum computational costs and reasonable running times. More, the simulation can neglect small combustion chamber details such as valve crevices, valve recesses and piston crevices volume. The missing volumes are usually compensated by changes in the squish volume (i.e., by increasing the clearance height of the model compared to the real engine). This paper documents some of the effects that such an approach would have on the simulated results of the combustion phenomena inside a conventional heavy-duty direct-injection CI engine, which was converted to port-fuel injection SI operation. 3D IC engine simulations with or without crevice volumes were run using the G-equation combustion model. A proper parameter choice ensured that the simulation results agreed well with the experimental pressure trace. The results show that including the crevice volume affected the mass of unburned mixture inside the squish region, which in turn influenced the flame behavior and heat release during late-combustion stages.

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A combustion model for IC engine combustion simulations with multi-component fuels

Combustion and Flame ◽

10.1016/j.combustflame.2010.07.019 ◽

2011 ◽

Vol 158 (1) ◽

pp. 69-90 ◽

Cited By ~ 169

Author(s):

Youngchul Ra ◽

Rolf D. Reitz

Keyword(s):

Combustion Model ◽

Ic Engine ◽

Engine Combustion

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Control Oriented Modeling and Nonlinear Model Predictive Control of Advanced SI Engine System

ASME 2010 Dynamic Systems and Control Conference, Volume 1 ◽

10.1115/dscc2010-4024 ◽

2010 ◽

Author(s):

Tae-Kyung Lee ◽

Zoran S. Filipi

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Combustion Process ◽

Nonlinear Model Predictive Control ◽

Combustion Model ◽

Ic Engine ◽

Actuator Dynamics ◽

Engine Simulation ◽

Prediction Horizon

Control oriented model (COM) using crank-angle resolved flame propagation simulation and nonlinear model predictive control (NMPC) methodology for the purpose of transient control of HDOF engines are proposed in this paper. The nonlinear nature of the combustion process has been a challenge in building a reliable COM and engine simulation. Artificial neural networks (ANNs) are subsequently trained on the data generated with a quasi-D combustion model to create fast surrogate combustion models. System dynamics are augmented by manifold and actuator dynamics models. Then, NMPC for an internal combustion (IC) engine with a dual-independent variable valve timing (VVT) system is designed to achieve fast torque responses, to eliminate exhaust emissions penalty, and to track the optimal actuator response closely. The NMPC significantly improves engine dynamics and minimizes excursions of in-cylinder variables under highly transient operation. Dead-beat like control is achieved with selected prediction horizon and control horizon in the NMPC.

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Application of Random Forest Machine Learning Models to Forecast Combustion Profile Parameters of a Natural Gas Spark Ignition Engine

Volume 6: Design, Systems, and Complexity ◽

10.1115/imece2020-23973 ◽

2020 ◽

Author(s):

Jinlong Liu ◽

Christopher Ulishney ◽

Cosmin E. Dumitrescu

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Spark Ignition ◽

Spark Ignition Engine ◽

Combustion Model ◽

Learning Models ◽

Ic Engine ◽

Spark Timing ◽

Machine Learning Models

Abstract Predicting internal combustion (IC) engine variables such as the combustion phasing and duration are essential to zero-dimensional (0D) single-zone engine simulations (e.g., for the Wiebe function combustion model). This paper investigated the use of random forest machine learning models to predict these engine combustion parameters as a modality to reduce expensive engine dynamometer tests. A single-cylinder four-stroke heavy-duty spark-ignition engine fueled with methane was operated at different engine speeds and loads to provide the data for training, validation, and testing the proposed correlated model. Key engine operating variables such as spark timing, mixture equivalence ratio, and engine speed were the model inputs. The performance of the models was validated by comparing the prediction dataset with the experimentally measured results. Results showed that the prediction error of the random forest machine learning algorithm was acceptable, suggesting that it can be used to predict the combustion parameters of interest with acceptable accuracy.

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0D/1D Thermo-Fluid Dynamic Modeling Tools for the Simulation of Driving Cycles and the Optimization of IC Engine Performances and Emissions

Applied Sciences ◽

10.3390/app11178125 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8125

Author(s):

Andrea Marinoni ◽

Matteo Tamborski ◽

Tarcisio Cerri ◽

Gianluca Montenegro ◽

Gianluca D’Errico ◽

...

Keyword(s):

Vehicle Dynamics ◽

Fluid Dynamic ◽

Driving Cycle ◽

Computational Time ◽

Combustion Model ◽

Modeling Tools ◽

Ic Engine ◽

Vehicle Simulation ◽

Engine Model ◽

Simulation Performance

The prediction of internal combustion engine performance and emissions in real driving conditions is getting more and more important due to the upcoming stricter regulations. This work aims at introducing and validating a new transient simulation methodology of an ICE coupled to a hybrid architecture vehicle, getting closer to real-time calculations. A one-dimensional computational fluid dynamic software has been used and suitably coupled to a vehicle dynamics model in a user function framework integrated within a Simulink® environment. A six-cylinder diesel engine has been modeled by means of the 1D tool and cylinder-out emissions have been compared to experimental data. The measurements available have been used also to calibrate the combustion model. The developed 1D engine model has been then used to perform driving cycle simulations considering the vehicle dynamics and the coupling with the energy storage unit in the hybrid mode. The map-based approach along with the vehicle simulation tool has also been used to perform the same simulation and the two results are compared to evaluate the accuracy of each approach. In this framework, to achieve the best simulation performance in terms of computational time over simulated time ratio, the 1D engine model has been used in a configuration with a very coarse mesh. Results have shown that despite the high mesh spacing used the accuracy of the wave dynamics prediction was not affected in a significant way, whereas a remarkable speed-up factor was achieved. This means that a crank angle resolution approach to the vehicle simulation is a viable and accurate strategy to predict the engine emission during any driving cycle with a computation effort compatible with the tight schedule of a design process.

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