Multi-regime Turbulent Combustion Modeling using Large Eddy Simulation/ Probability Density Function
KashyapShashank Satyanarayana
2019
Combustion research is at the forefront of development of clean and efficient IC engines, gas turbines, rocket propulsion systems etc. With the advent of faster computers and parallel programming, computational studies of turbulent combustion is increasing rapidly. Many turbulent combustion models have been previously developed based on certain underlying assumptions. One of the major assumptions of the models is the regime it can be used for: either premixed or non-premixed combustion. However in reality, combustion systems are multi-regime in nature, i.e.,\ co-existence of premixed and non-premixed modes. Thus, there is a need for development of multi-regime combustion models which closely follows the physics of combustion phenomena. Much of previous modeling efforts for multi-regime combustion was done using flamelet-type models. As a first, the current study uses the highly robust transported Probability Density Function (PDF) method coupled with Large Eddy Simulation (LES) to develop a multi-regime model. The model performance is tested for Sydney Flame L, a piloted methane-air turbulent flame. The concept of flame index is used to detect the extent of premixed and non-premixed combustion modes. The drawbacks of using the traditional flame index definition in the context of PDF method are identified. Necessary refinements to this definition, which are based on the species gradient magnitudes, are proposed for the multi-regime model development. This results in identifying a new model parameter beta which defines a gradient threshold for the calculation of flame index. A parametric study is done to determine a suitable value for beta, using which the multi-regime model performance is assessed for Flame L by comparing it against the widely used non-premixed PDF model for three mixing models: Modified Curl (MCurl), Interaction by Exchange with Mean (IEM) and Euclidean Minimum Spanning Trees (EMST). The multi-regime model shows a significant improvement in prediction of mean scalar quantities compared to the non-premixed PDF model when MCurl mixing model is used. Similar improvements are observed in the multi-regime model when IEM and EMST mixing models are used. The results show potential foundation for further multi-regime model development using PDF model.