OS3-6 Multi-Fuel and Mixed-Mode IC Engine Combustion Simulation with a Detailed Chemistry based Progress Variable Library Approach(OS3 Application of chemical kinetics to combustion modeling,Organized Session Papers)

Author(s):  
Tao Bo ◽  
Rajesh Rawat ◽  
Richard Johns ◽  
Fabian Mauss
2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Hai-Wen Ge ◽  
Harmit Juneja ◽  
Yu Shi ◽  
Shiyou Yang ◽  
Rolf D. Reitz

An efficient multigrid (MG) model was implemented for spark-ignited (SI) engine combustion modeling using detailed chemistry. The model is designed to be coupled with a level-set-G-equation model for flame propagation (GAMUT combustion model) for highly efficient engine simulation. The model was explored for a gasoline direct-injection SI engine with knocking combustion. The numerical results using the MG model were compared with the results of the original GAMUT combustion model. A simpler one-zone MG model was found to be unable to reproduce the results of the original GAMUT model. However, a two-zone MG model, which treats the burned and unburned regions separately, was found to provide much better accuracy and efficiency than the one-zone MG model. Without loss in accuracy, an order of magnitude speedup was achieved in terms of CPU and wall times. To reproduce the results of the original GAMUT combustion model, either a low searching level or a procedure to exclude high-temperature computational cells from the grouping should be applied to the unburned region, which was found to be more sensitive to the combustion model details.


2005 ◽  
Vol 6 (5) ◽  
pp. 475-486 ◽  
Author(s):  
S-C Kong ◽  
Y Ra ◽  
R D Reitz

An engine CFD model has been developed to simulate premixed charge compression ignition (PCCI) combustion using detailed chemistry. The numerical model is based on the KIVA code that is modified to use CHEMKIN as the chemistry solver. The model was applied to simulate ignition, combustion, and emissions processes in diesel engines operated to achieve PCCI conditions. Diesel PCCI experiments using both low- and high-pressure injectors were simulated. For the low-pressure injector with early injection (close to intake valve closure), the model shows that wall wetting can be minimized by using a pressure-swirl atomizer with a variable spray angle. In the case of using a high-pressure injector, it is found that late injection (SOI = 5 ° ATDC) benefits soot emissions as a result of low-temperature combustion at highly premixed conditions. The model was also used to validate the emission reduction potential of an HSDI diesel engine using a double injection strategy that favours PCCI conditions. It is concluded that the present model is useful to assess future engine combustion concepts, such as PCCI and low-temperature combustion (LTC).


Author(s):  
Pierre Q. Gauthier

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.


Author(s):  
Manuel Alejandro Echeverri Marquez ◽  
Ponnya Hlaing ◽  
Priybrat Sharma ◽  
Emre Cenker ◽  
Jihad Badra ◽  
...  
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