Combustion Modeling in SI Engines with a Peninsula-Fractal Combustion Model

1996 ◽  
Author(s):  
Ronald D. Matthews ◽  
Matthew J. Hall ◽  
Wen Dai ◽  
George C. Davis
Keyword(s):  
Combustion Model ◽  
Combustion Modeling ◽  
Si Engines

Diluents and Lean Mixture Combustion Modeling for SI Engines with a Quasi-Dimensional Model

1995 ◽  
Author(s):  
Wen Dai ◽  
George C. Davis ◽  
Matthew J. Hall ◽  
Ronald D. Matthews
Keyword(s):  
Dimensional Model ◽  
Lean Mixture ◽  
Combustion Modeling ◽  
Si Engines

Comparison of Temperature Fields and Emissions Predictions Using Both an FGM Combustion Model, With Detailed Chemistry, and a Simple Eddy Dissipation Combustion Model With Simple Global Chemistry

2017 ◽  
Author(s):  
Pierre Q. Gauthier
Keyword(s):  
Mixture Fraction ◽  
Temperature Fields ◽  
Combustion Model ◽  
Detailed Chemistry ◽  
Combustion Modeling ◽  
Flamelet Generated Manifold ◽  
Wide Range ◽  
Turbulence Effects ◽  
Air Chemistry ◽  
Dissipation Model

The detailed modeling of the turbulence-chemistry interactions occurring in industrial flames has always been the leading challenge in combustion Computational Fluid Dynamics (CFD). The wide range of flame types found in Industrial Gas Turbine Combustion systems has exacerbated these difficulties greatly, since the combustion modeling approach must be able to predict the flames behavior from regions of fast chemistry, where turbulence has no significant impact on the reactions, to regions where turbulence effects play a significant role within the flame. One of these combustion models, that is being used more and more in industry today, is the Flamelet Generated Manifold (FGM) model, in which the flame properties are parametrized and tabulated based on mixture fraction and flame progress variables. This paper compares the results obtained using an FGM model, with a GRI-3.0 methane-air chemistry mechanism, against the more traditional Industrial work-horse, Finite-Rate Eddy Dissipation Model (FREDM), with a global 2-step Westbrook and Dryer methane-air mechanism. Both models were used to predict the temperature distributions, as well as emissions (NOx and CO) for a conventional, non-premixed, Industrial RB211 combustion system. The object of this work is to: (i) identify any significant differences in the predictive capabilities of each model and (ii) discuss the strengths and weakness of both approaches.

Development and Validation of an Ignition Model for SI Engines

2006 ◽  
Author(s):  
Stefania Falfari ◽  
Gian Marco Bianchi
Keyword(s):  
Cfd Simulation ◽  
Combustion Process ◽  
Three Dimensional ◽  
Electrical Energy ◽  
Combustion Model ◽  
Test Case ◽  
Ignition Process ◽  
Mean Flow ◽  
Flame Kernel ◽  
Si Engines

In SI engines the ignition process strongly affects the combustion process. Its accurate modelling becomes a key issue for a design-oriented CFD simulation of the combustion process. Different approaches to simulate ignition have been proposed. The common base is decoupling the physics related to the very first ignition phase when a plasma is formed from that of the development of the flame kernel. The critical point of ignition models is related to the capability of representing the effect of ignition system characteristics, the criterion used for flame deposit and the initialisation of the combustion model. This paper aims to present and validates extensively an ignition model suited for CFD calculation of premixed combustion. The ignition model implemented in a customized version of the Kiva 3 code is coupled with ECFM Flamelet combustion model. The ignition model simulates the plasma/kernel expansion based on a lump evaluation of main ignition processes (i.e., breakdown, arc-phase and glow phase). A double switch criterion based on physical and numerical consideration is used to switch to the main combustion model. The Herweg and Maly experimental test case has been used to check the model capability. In particular, two different ignition systems having different amount of electrical energy released during spark discharge are considered. Comparisons with experimental results allowed testing the model with respect to its capability to reproduce the effects of mixture equivalence ratio, mean flow, turbulence and spark energy on flame kernel development as never done before in three-dimensional RANS CFD combustion modelling of premixed flames.

Model-Based Combustion Duration and Ignition Timing Prediction for Combustion Phasing Control of a Spark-Ignition Engine Using In-Cylinder Pressure Sensors

2019 ◽  
Author(s):  
Xin Wang ◽  
Amir Khameneian ◽  
Paul Dice ◽  
Bo Chen ◽  
Mahdi Shahbakhti ◽  
...  
Keyword(s):  
Pressure Sensors ◽  
Spark Ignition ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Combustion Model ◽  
Ignition Timing ◽  
Model Based ◽  
Crank Angle ◽  
Si Engines ◽  
Phasing Control

Abstract Combustion phasing, which can be defined as the crank angle of fifty percent mass fraction burned (CA50), is one of the most important parameters affecting engine efficiency, torque output, and emissions. In homogeneous spark-ignition (SI) engines, ignition timing control algorithms are typically map-based with several multipliers, which requires significant calibration efforts. This work presents a framework of model-based ignition timing prediction using a computationally efficient control-oriented combustion model for the purpose of real-time combustion phasing control. Burn duration from ignition timing to CA50 (ΔθIGN-CA50) on an individual cylinder cycle-by-cycle basis is predicted by the combustion model developed in this work. The model is based on the physics of turbulent flame propagation in SI engines and contains the most important control parameters, including ignition timing, variable valve timing, air-fuel ratio, and engine load mostly affected by combination of the throttle opening position and the previous three parameters. With 64 test points used for model calibration, the developed combustion model is shown to cover wide engine operating conditions, thereby significantly reducing the calibration effort. A Root Mean Square Error (RMSE) of 1.7 Crank Angle Degrees (CAD) and correlation coefficient (R2) of 0.95 illustrates the accuracy of the calibrated model. On-road vehicle testing data is used to evaluate the performance of the developed model-based burn duration and ignition timing algorithm. When comparing the model predicted burn duration and ignition timing with experimental data, 83% of the prediction error falls within ±3 CAD.

Performance Analysis and Valve Event Optimization for SI Engines Using Fractal Combustion Model

2006 ◽  
Author(s):  
Gerhard Regner ◽  
Ho Teng ◽  
Peter Van Wieren ◽  
Jae In Park ◽  
Soo Youl Park ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Combustion Model ◽  
Si Engines ◽  
Valve Event

A Two-Step Combustion Model of Iso-Octane for 3D CFD Combustion Simulation in SI Engines

2019 ◽  
Author(s):  
Xingyuan Su ◽  
Brian Chang ◽  
Haiwen Ge ◽  
Lurun Zhong
Keyword(s):  
Combustion Model ◽  
Combustion Simulation ◽  
Si Engines

scholarly journals Development and Experimental Validation of an Adaptive, Piston-Damage-Based Combustion Control System for SI Engines: Part 2—Implementation of Adaptive Strategies

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5342
Author(s):  
Alessandro Brusa ◽  
Nicolò Cavina ◽  
Nahuel Rojo ◽  
Jacopo Mecagni ◽  
Enrico Corti ◽  
...  
Keyword(s):  
Closed Loop ◽  
Adaptive Strategies ◽  
Open Loop ◽  
Combustion Model ◽  
Phase Index ◽  
Spark Timing ◽  
Combustion Phase ◽  
Rapid Control Prototyping ◽  
Si Engines ◽  
Loop Chain

This work focuses on the implementation of innovative adaptive strategies and a closed-loop chain in a piston-damage-based combustion controller. In the previous paper (Part 1), implemented models and the open loop algorithm are described and validated by reproducing some vehicle maneuvers at the engine test cell. Such controller is further improved by implementing self-learning algorithms based on the analytical formulations of knock and the combustion model, to update the fuel Research Octane Number (RON) and the relationship between the combustion phase and the spark timing in real-time. These strategies are based on the availability of an on-board indicating system for the estimation of both the knock intensity and the combustion phase index. The equations used to develop the adaptive strategies are described in detail. A closed-loop chain is then added, and the complete controller is finally implemented in a Rapid Control Prototyping (RCP) device. The controller is validated with specific tests defined to verify the robustness and the accuracy of the adaptive strategies. Results of the online validation process are presented in the last part of the paper and the accuracy of the complete controller is finally demonstrated. Indeed, error between the cyclic and the target combustion phase index is within the range ±0.5 Crank Angle degrees (°CA), while the error between the measured and the calculated maximum in-cylinder pressure is included in the range ±5 bar, even when fuel RON or spark advance map is changing.

A Quasi-dimensional Model of the Ignition Delay for Combustion Modeling in Spark-Ignition Engines

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Sebastian Grasreiner ◽  
Jens Neumann ◽  
Michael Wensing ◽  
Christian Hasse
Keyword(s):  
Ignition Delay ◽  
Direct Injection ◽  
Control Unit ◽  
Spark Ignition ◽  
Spark Ignition Engines ◽  
Combustion Modeling ◽  
Crank Angle ◽  
Si Engines ◽  
Engine Map ◽  
Start Of Combustion

Quasi-dimensional (QD) modeling of combustion in spark-ignition (SI) engines allows to describe the most relevant processes of heat release. Here, a submodel for the ignition delay is introduced and applied. The start of combustion is considered from ignition to the crank angle of 5% burned gas fraction. The introduced physical approach identifies the turbulent propagation velocity of the initiated kernel by taking into account early flame expansion and geometric restrictions of the flame propagation. The model is applied to stationary operation within an entire engine map of a turbocharged direct injection SI engine with fully variable valvetrain. Based on provided cycle-averaged input data, the model delivers good results within the margins of measured cycle-to-cycle fluctuations. Thus, it contributes to the assessment of the interplay between engine, engine control unit, drivetrain, and vehicle dynamics, hence making a step toward optimization and virtual engine calibration.

Sign in / Sign up

Export Citation Format

Share Document